Understanding the Developmental Trajectory of Behavioral Problems & Subcortical Structure Morphometry in Healthy Children at 6 years old and Long-Term Impact of Early Nutrition: The COGNIS Study

Understanding the Developmental Trajectory of Behavioral Problems & Subcortical Structure Morphometry in Healthy Children at 6 years old and Long-Term Impact of Early Nutrition: The COGNIS Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Understanding the Developmental Trajectory of Behavioral Problems & Subcortical Structure Morphometry in Healthy Children at 6 years old and Long-Term Impact of Early Nutrition: The COGNIS Study Elvira Catena-Verdejo, Ana Nieto-Ruiz, José Antonio García-Santos, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7142478/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Feb, 2026 Read the published version in Child and Adolescent Psychiatry and Mental Health → Version 1 posted 9 You are reading this latest preprint version Abstract Background early behavioral problems may influence adult psychopathology, and early-life nutrition plays a critical role in shaping behavioral outcomes during childhood. Objective this study investigated whether subcortical brain volumetry at age six is associated with early behavioral trajectories and the potential influence of early nutrition on this relationship. Methods data from 82 children participants in the COGNIS study were included in the present analysis. During the first 2 months of life, 50 infants were randomized to receive up to 18 months of life, either a standard infant formula (SF, n = 26) or an experimental formula enriched with supplemented with several bioactive compounds (EF, n = 24). A reference group of breastfed infants (BF, n = 32) was also included. Behavioral assessments were conducted using the Child Behavior Checklist (CBCL) at 18 months, 2.5 years, and 4 years. Structural magnetic resonance imaging (MRI) was performed at 6 years to assess volumes of bilateral subcortical nuclei, brainstem, cerebellum, and total intracranial volume. Complete behavioral and imaging data were available for 37 participants. Weights for linear, quadratic, and mixed linear/quadratic growth curves were computed for CBCL total, internalizing, externalizing, and DSM-oriented scales. Non-parametric correlations between CBCL growth curves and subcortical brain volumetry were computed after adjusting for relevant confounding factors. Generalized linear mixed model for repeated measures was performed. Results no significant effects of early nutrition on behavioral trajectories were found; in fact, EF and BF groups exhibited similar patterns across internalizing, externalizing, total problems and DSM-oriented scales. CBCL domains followed distinct developmental trajectories, and interestingly, children’s subcortical volumetry of specific brain area at 6 years old, were primarily associated with non-linear behavioral growth curves. Amygdala volume correlated with total problems scores and DSM-oriented scales, while hippocampal volume was linked to internalizing, oppositional defiant, and ADHD-related behaviors. Cerebellar cortex volume correlated with ADHD and externalizing problems, the latter also associating with putamen. Pallidum volume was correlated with internalizing and anxiety symptoms. Conclusions these findings suggest that non-linear behavioral growth models more effectively reflect brain–behavior associations. Futhermore, subcortical brain morphometry, particularly of the hippocampus, may be shaped by behavioral patterns during critical developmental windows—most notably around 2.5 years of age. Behavior problems subcortical morphometry brain neuroimaging early nutrition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Behavioral problems in early childhood are a potential indicator of psychopathological problems at later ages [ 1 ]. The global worldwide prevalence rate of mental disorders in children and adolescents is around 13.4%, similar to those reported for other chronic health problems, such as obesity (16.8%) [ 2 ]. Consequently, optimal clinical and practical methodologies must be crucial to design policies that address mental health needs of children and adolescents. It is well known that early nutrition is of vital importance for optimal brain development and subsequent prevention of behavioral problems in children. Adequate intake of essential nutrients, including but not limited to proteins, long-chain polyunsaturated fatty acids (LC-PUFAs), nucleotides, sialic acid, milk fat globule membrane (MFGM) and synbiotics, plays a critical role in shaping both brain structure and function [ 3 – 5 ]. In fact, recent studies have demonstrated that early-life nutritional supplementation can have long-lasting positive effects on cognitive function, brain structure and behavior development in later stages of development [ 6 , 7 ]. Therefore, promoting and ensuring a well-balanced and nutrient-rich diet during this critical period is necessary for supporting healthy brain development, thus reducing the risk of behavioral problems in children. In addition to environmental and genetic factors, both anatomical and functional bases of cortical and subcortical structures, as well as their maturation, also play a key role in the transition from simple signals to symptoms and subsequent mental disorders development [ 8 ]. In this line, externalizing spectrum disorders in children, including oppositional defiant disorder (ODD), attention deficit/hyperactivity disorder (ADHD) and behavioral disorders, were traditionally considered as separate entities; however, based on their comorbidity patterns, trait impulsivity has been currently identified as a common factor [ 9 , 10 ]. Trait impulsivity is controlled by a series of dopaminergic subcortical mechanisms, including the nucleus accumbens, ventral tegmentum, and ventral areas of caudate and putamen [ 10 , 11 ]. Interestingly, these areas are not only part of the reward circuit of the brain, but also are associated with irritability and anhedonia, which might explain the poor response to incentives observed in externalizing disorders (ADHD, ODD, behavior problems and substance use disorder) [ 12 ]. Additionally, above-mentioned structures also receive control inputs during maturation from cortical structures, mainly prefrontal cortex involved in emotional regulation and posterior cingulate cortex, as well as other subcortical structures, including amygdala, hippocampus, periaqueductal gray substance and medial hypothalamus[ 13 ] [ 14 ]. In fact, current research supports that severity of externalizing symptoms is associated with a reduction in the activity of the amygdala and its connectivity with the prefrontal ventromedial cortex before frightening stimuli [ 15 ]. Furthermore, this severity is also negatively correlated with volume of amygdala, thalamus and parahippocampal gyrus [ 16 , 17 ]. Taken together, these findings suggest that both cortical and subcortical structures play a fundamental role in explaining externalizing and internalizing disorders. The current study was aimed to determine the associations between longitudinal trajectories of early childhood behavioral problems evaluated at 18 months, 2.5 and 4 years old, and the volumes of the subcortical structures most frequently associated with these problems, including accumbens, amygdala, caudate, hippocampus, putamen, pale, thalamus and brainstem (which contains ventral tegmentum, substantia nigra and periaqueductal gray matter, among other areas) assessed at 6y. A secondary objective was to analyze the potential long-term effects of a bioactive nutrients-enriched infant formula on behavioral developmental trajectories up to 4 years old and its association with brain morphology. 2. Methods 2.1. Participants The initial study sample was based on the 220 (99 girls) healthy full-term infants participating in the COGNIS study, a prospective, double-blind, randomized clinical trial (RCT) designed to evaluate the effects of a novel infant formula on immunological and neurocognitive development in healthy infants (registered at www.ClinicalTrials.gov, Identifier NCT02094547). Detailed information on this project, including study design, subject recruitment and population characteristics, have been previously described [7]. Briefly, a total of 170 healthy Spanish infants aged between 0–2 months old were randomized (ratio 1:1) to receive, during their first 18 months of life, either a standard infant formula (SF, n = 85) or an experimental infant formula (EF, n = 85) enriched with MFGM components [10% of total protein content (wt:wt)], synbiotics [Fructooligosaccharides (FOS): Inulin proportion 1:1; Bifidobacterium longum subsp infantis CECT7210 ( Bifidobacterium infantis IM1) and Lactobacillus rhamnosus LCS-74], LC-PUFAs, gangliosides, nucleotides and sialic acid. Additionally, 50 exclusively breastfed infants were enrolled as a control group (BF, n = 50). Maternal educational level was determined (0: primary, 1: secondary, 2: vocational training, 3: university) on the first visit. Maternal IQ was evaluated using Cattell’s intelligence test (G Factor) [18]. Emotional/behavioral evaluation using CBCL (Child Behavior Checklist ) test was conducted in COGNIS children at 18 months, 2.5 and 4 years old; however, in current analysis, only participants whose evaluation was completed at the three mentioned time points were considered (CBCL sample, n = 82, 35 girls / SF, n = 26; EF, n = 24; BF, n = 32). Structural Magnetic Resonance Imaging (MRI) was performed on 67 (24 girls) volunteering participants from the COGNIS study at 6 years old (mean age = 6.13; range [6.02–6.57 years old]). However, only the images of 37 participants (10 girls) were of adequate quality for further analysis. In this regard, 46 participants (25 girls) completed CBCL questionnaire at the three time points, but they had no MRI results or images obtained were deficient in quality (“Missing” group). Another set of participants was defined as those who had quality resonances but had not completed all three CBCL measures (“MRI only”, n = 19, 14 girls). Both “Missing” and “MRI only” groups were used in the sensitivity analysis. The COGNIS study was conducted in accordance with the revised Declaration of Helsinki II Principles [19]. Ethical approval was obtained from the Research Bioethical Committee of the University of Granada, Spain, as well as the Bioethical Committees for Clinical Research of the San Cecilio University Clinical and University Mother-Infant Hospitals of Granada. Prior to their participation in the study, all families were thoroughly informed about the procedures, and written informed consent was obtained from each parent or legal guardian. 2.2. Assessment of child behavior problems: CBCL test (Child Behavior Checklist) The Child Behavior Checklist (CBCL)/1.5-5 inventory for preschoolers is designed to assess the child´s emotional and behavioral problems from 1.5 to 5 years old, and it is used internationally for clinical evaluation and research in early stages of development [20, 21]. The Spanish version of the CBCL/1.5-5 (Unitat d'Epidemiologia i de Disgnòstic en Psicopatologia del Desenvolupament, Barcelona) consists in 101 items divided into three scales (internalizing, externalizing and global). The internalizing scale includes four categories of problems: emotionally reactive, anxious/depressed, somatic complaints and withdrawn. The externalizing scale encompasses two types of problems: attention problems and aggressive behavior. CBCL/1.5-5 questionnaire also includes the DSM-oriented scales used in this analysis: affective problems, anxiety problems, pervasive developmental problems (autistic spectrum), ADHD, and oppositional defiant problems. For closed items, a Likert scale is used with answers ranging from 0 ("not true"), 1 ("something or sometimes true") or 2 ("very true or often true"), considering only the two previous months to the evaluation. The total direct score on a scale was obtained automatically. Direct scores on the total, externalizing, internalizing and DSM-oriented scales were used to perform the longitudinally study and to compute the developmental trajectories of the participants. Typical scores (T) were used only to facilitate categorization into clinical and borderline clinical problems (for example, in the summary scales T ≥ 64 is usually used for clinical problems, while 60 ≤ T ≤ 63 is used for clinical borderline) [22]. Parents or legal guardians received the CBCL/1.5-5 inventory and appropriate instructions to complete it. CBCL questionnaire was filled out by the mother (81.67%) or the father (18.33%). However, given that there was no difference from answers depending on who completed it (all p > 0.10), this factor was ignored in current analysis. In current study, the direct scores of internalizing, externalizing or total behavioral problems and DSM-oriented scales (affective problems, anxiety, pervasive developmental problems, ADHD, and oppositional-defiant problems) were used. Externalizing and internalizing scales were considered as substitutes for syndromic scales. Although the total scale is the sum of the externalizing and internalizing, its use provides information relative to the total sum of problems that is not contained in either of two scales separately. Likewise, the direct score is considered instead of the T-score or percentiles to prevent taking normative samples as a reference, which could hide each participant’s trajectory over time [23]. 2.3. Magnetic Resonance Imaging (MRI) procedure Brain imaging was performed when participants were 6.13 years old on average, range = [6.02, 6.57]. Finally, as previously indicated, brain images were evaluated by an expert radiologist to determine their quality and the presence of any brain abnormality, in accordance with the ethical standards of the University of Granada. 2.3.1. Imaging data acquisition Brain structure scanning was performed using the Siemens Tim Trio 3T MRI system (Siemens, Erlangen, GE) equipped with a 32-channel head antenna, installed in the Mind, Brain and Behavior Research Centre (CIMCYC) (Granada, ES). Head movements were minimized using foam positioners around the participant's head. A T1w 3D MPRAGE (magnetization-prepared rapid acquisition with gradient echo) sequence was used with the following parameters: RT = 2300 ms, RT = 3.10 ms, IT = 900, flip angle = 9 °. In each volume, 208 slides of 0.8 mm thickness were acquired, so that the voxel size was 0.8x0.8x0.8 with a matrix size of 320x320. 2.3.2. Subcortical Structures Morphometry The structural volumes were processed using Freesurfer's (v. 6) recon-all script, a brain image analysis suite (available at http://surfer.nmr.mgh.harvard.edu/) running on the UNIX platform of the Alhambra supercomputer from the University of Granada (http://alhambra.ugr.es/). Preprocessing and segmentation of subcortical structures (including cerebellum, thalamus, caudate, putamen, pallidum hippocampus, amygdala, accumbens and brainstem) are fully automated. Processing details have been described elsewhere [24, 25]. Briefly, the automated protocol includes motion correction, non-cerebral tissue elimination, and correction of inhomogeneities in the magnetic field, linear and non-linear registration to the Talairach atlas as well as label dissemination for each voxel according to Freesurfer’s subcortical atlas. In subcortical segmentation, Freesurfer combines both intensity of the voxels with the probability distribution of the different types of brain tissue, as well as the spatial relationships of each voxel in relation to neighboring structures defined in labeled atlases [25]. After segmentation, volumes of each subcortical structure were obtained. The validity and reliability of this automatic segmentation is well established [26, 27] with intraclass correlation coefficients greater than 0.80 [27]. However, there is a tendency to overestimate the volume of the structures by around 10%, which can lead to an increase in the false-negatives rate [28]. In paediatric population, correspondence between the volumes of structures such as the amygdala and the hippocampus estimated by Freesurfer or manually has been found to be superior to other automatic methods such as FSL-First. Consequently, this method still needs improvement [29] and careful visual inspection is necessary to ensure segmentation quality. For this reason, an expert radiologist assessed the segmentation process, and 12 participants were excluded from this analysis due to low quality images or defective segmentation. 2.4. Statistical analysis To avoid any bias in the selection of the final sample, comparisons in the confounding variables were performed between final “MRI” sample and “Missing” sample using non-parametric Mann-Withney’s test. Welch’s robust t-test was used on both CBCL scales and volumes of subcortical structures, thus controlling the inequality of groups in size and variance. Longitudinal differences between COGNIS groups were estimated using a generalized linear mixed model (GLMM) for repeated measures, controlling for sex, gestational age, maternal IQ and maternal educational level. Bonferroni-corrected post-hoc comparisons were used to identify significant pair-wise group differences (adjusted p value < 0.05). Potential associations between behavioral problems trajectories and subcortical structures volumetry were evaluated in two phases. First, the trajectory of each participant was computed in each of the evaluation’s scales using a polynomial approximation. To this effect, the time lag between evaluations was considered as well as the fact that two polynomials of first or second degree can be only defined given that there were only 3 CBCL measures. Therefore, ideal trajectories can be classified as: a) linear (the direct score grows/decreases steadily); b) quadratic (up/down peak on the direct score in the second measure); and c) mixed, in which the direct score shows an increase/decrease followed by a plateau, Type I, or vice versa, Type II (Supplementary Fig. 1) . The coefficients of each participant’s trajectory were next computed by linear correlation. Consequently, a linear trajectory with a positive coefficient indicates that direct score has increased steadily over time, while those with a negative coefficient indicates that score has decreased consistently with age. A quadratic trajectory with positive coefficient indicates that score was reduced at 2.5 years, but at 4 years returned at levels similar to the initial ones, while a negative coefficient indicates that score achieved a maximum peak at 2.5 years. Finally, a Type I trajectory describes an increasing/decreasing score (positive/negative coefficient, respectively) in the first time period that then remains constant during the second. Note that the coefficients so obtained are equivalent to standardized regression coefficients. Spearman's nonparametric partial correlation coefficient was used to determine the association between the volume of subcortical structures and behavioral trajectory assessed by CBCL, considering sex, gestational age (months), maternal age (years), maternal IQ, maternal educational level (primary, secondary, second grade, University) and total intracranial volume (TIV). The latter factor is included since the volume of subcortical structures is expected to depend on the total brain volume. The criterion of statistical significance was p < 0.025. All statistical analyses were performed using the SPSS statistical software package (version 28.0; IBM SPSS Inc., Chicago, IL, USA) and Matlab software (version 20.0; Mathworks, US). 3. Results 3.1. General characteristics of the COGNIS study participants. Table 1 shows general characteristics of the children classified into COGNIS groups (type of early nutritional intervention) and with CBCL measurements obtained at three time-points (18 months, 2.5 and 4 years). Significant differences between study groups were found in terms of sex distribution and maternal educational level. In fact, a higher proportion of girls was observed in BF group compared to EF (p=0.010). Furthermore, mothers of BF infants showed a higher educational level (p=0.001) compared to mothers of both formula-fed groups (SF and EF). Differences found in maternal age disappeared after Bonferroni post hoc test. Additionally, we also evaluated general characteristics of the participant's complete sample, regardless of type of early nutrition (Table 1) . In this case, data are presented into three groups: i) the participants who completed the three evaluations at mentioned time-points (“CBCL” group, n = 82); ii) participants who also presented adequate MRI at the age of 6 years (“MRI” group, n = 36); and iii) the participants with missing data due to absence or low quality MRI (“Missing” group, n = 46). As shown in Table 1 , significant differences were only observed in maternal educational level (p <0.001) between “MRI” and “Missing” groups. These data suggest that maternal educational level could be an important factor in explaining the observed loss of data in current analysis. Table 1. General characteristics of the COGNIS study participants with the three inventory measurements (CBCL), participant´s with high quality of the brain’s magnetic resonance imaging (MRI) and Missing (missing values in the MRI). SF (n=26) EF (n=24) BF (n=32) P-value CBCL (n=82) MRI (n=36) Missing (n=46) P-value Maternal age (years) 31.6 (6.30) a 31.2 (5.02) a 34.5 (4.87) a 0.042 32.6 (5.55) a,b 30.4 (5.80) a 34.3 (4.80) b 0.005 Maternal IQ 107.5 (14.46) 104.1 (14.15) 109.2 (14.17) 0.420 107.1 (14.24) 104.9 (14.76) 108.9 (13.71) 0.442 Maternal educational level Primary Secondary VT University 11.5 34.6 a 15.4 38.5 a,b 20.8 16.7 a,b 45.8 16.7 b 3.1 6.3 b 25.0 65.6 a 0.001 11.0 18.3 a,b 28.0 42.7 a,b 16.7 30.6 b 30.6 22.2 b 6.5 8.7 a 26.1 58.7 a 0.034 Gestational Age (weeks) 39.8 (1.27) 39.7 (1.35) 39.4 (1.36) 0.462 39.6 (1.33) 40.0 (1.30) 39.4 (1.32) 0.124 Sex (boys) 65.4 a,b 75.0 a 37.5 b 0.010 57.3 69.4 47.8 0.145 Birth weight (kg) 3.37 (0.47) 3.35 (0.41) 3.37 (0.40) 0.976 3.36 (0.42) 3.39 (0.44) 3.34 (0.41) 0.600 Birth length (cm) 50.92 (2.34) 50.96 (2.47) 50.59 (2.29) 0.806 50.80 (2.29) 51.07 (2.30) 50.60 (2.28) 0.360 HC at birth (cm) 34.93 (1.42) 34.21 (1.17) 34.56 (1.34) 0.237 34.58 (1.33) 34.79 (1.31) 34.40 (1.34) 0.200 Data are expressed as means (standard deviation); Numbers for maternal educational level and Sex are in percentage. Notes. BF: breastfed infants; CBCL: Check Behavior Checklist; EF: experimental infant formula; IQ: intelligent quotient; MRI: Magnetic Resonance Imaging; n= number of cases; SF: standard infant formula; VT: Vocational Training; HC: head circumference. Values not sharing the same suffix (ab) were significantly different in the Bonferroni post hoc test. P-values < 0.05 are highlighted in bold. <> 3.2. CBCL scales We first analyzed whether type of early nutrition might have long-term effects on behavioral development from 18 months to 4 years old. For this purpose, a GLMM of repeated measures was used, which included a within-subjects time factor with three levels (18 months, 2.5, and 4 years) as well as a group factor with three levels (SF, EF and BF). Additionally, analysis was adjusted by sex, gestational age, maternal IQ and educational level. In the present analysis no statistical differences were found regarding CBCL scores between COGNIS groups (Supplementary Figure 2) . However, both EF and BF groups seem to show a more similar pattern regarding internalizing, externalizing and total problems (Supplementary Figure 2 A-C) . Finally, patterns obtained for DSM-oriented scales (affective problems, anxiety problems, pervasive developmental, ADHD and oppositional defiant) seem to be more similar in experimental formula-fed infants respect to those who were breastfed (Supplementary Figure 2 D-H). We next performed descriptive statistics for each of the inventory scales according to the set of participants “CBCL”, “MRI” and “Missing”. As shown in Table 2 , there were no significant differences between “MRI” and “Missing” groups in any of the evaluations, although there was a trend in externalizing problems at 2.5 years [t (80) = 1.90, p = 0.061]. Therefore, these results indicate that infant’s behavior evaluation in both “Missing” and “MRI” groups was very similar. Table 2. Descriptive statistics of the direct CBCL scores on the DSM-oriented, total, internalizing and externalizing scales. CBCL (n=82) MRI (n=37) Missing (n=46) CBCL Scales 18 (m) 2.5 (y) 4 (y) 18 (m) 2.5 (y) 4 (y) 18 (m) 2.5 (y) 4 (y) Internalizing Problems 7.96 (0.94) 9.3 (1.17) 9.89 (1.09) 8.43 (1.03) 10.27 (0.98) 10.65 (1.12) 7.59 (0.86) 8.52 (1.31) 9.28 (1.08) Externalizing Problems 13.58 (1.1) 13.07 (1.27) 12.3 (1.14) 14.51 (1.2) 14.49 (1.12) 12.35 (1.09) 12.83 (1.01) 11.93 (1.37) 12.26 (1.19) Total Problems 35.18 (2.93) 36.2 (3.47) 34.23 (2.91) 36.86 (3.32) 38.97 (2.83) 34.68 (2.92) 33.83 (2.6) 33.98 (3.9) 33.87 (2.94) Affective Problems 2.51 (0.37) 2.86 (0.37) 2.46 (0.32) 2.76 (0.46) 2.78 (0.32) 2.59 (0.31) 2.3 (0.28) 2.91 (0.41) 2.35 (0.33) Anxiety Problems 3.49 (0.34) 4.06 (0.46) 4.02 (0.45) 3.49 (0.33) 4.19 (0.41) 3.97 (0.45) 3.5 (0.35) 3.96 (0.5) 4.07 (0.46) Perv. Develop. Problems 3.29 (0.44) 3.52 (0.48) 3.77 (0.47) 3.24 (0.48) 3.84 (0.46) 4.05 (0.54) 3.33 (0.42) 3.26 (0.5) 3.54 (0.4) ADHD 5.42 (0.41) 4.86 (0.46) 4.58 (0.39) 5.62 (0.42) 5.35 (0.42) 4.49 (0.41) 5.26 (0.41) 4.46 (0.49) 4.65 (0.38) Oppositional defiant Problems 3.14 (0.32) 3.54 (0.39) 3.65 (0.4) 3.38 (0.33) 4.08 (0.36) 3.62 (0.35) 2.96 (0.3) 3.11 (0.4) 3.67 (0.44) Data are expressed as means (typical errors) Note. Perv. Develop. Problems: Pervasive Developmental Problems; ADHD: attention deficit hyperactivity disorder; m: months; y: years <> The percentages of participants with clinical problems (T≥64) and clinical borderline (60≤T≤63) were calculated in relation to the preliminary 82 participants for each of the scales (Table 3) . Our results show that the most frequent clinical problems were the developmental ones, which reached a maximum at 4 years (20.7%). Considering clinical borderline, the most frequent were the affective and anxious problems at 2.5 years (22% and 18.3%, respectively). Overall, internalizing problems were more common than externalizing, both at clinical and subclinical levels. The internalizing problems showed a growing trend with a maximum at 4 years (12.2% and 17.1% clinical and subclinical, respectively), while the externalizing problems did not show this same tendency. Further analysis was aimed to evaluate the percentage of participants whose T-scores increased or decreased between the three-evaluation time-points on a subject-by-subject basis (Table 3) . Overall, a major increase was observed between 18 months and 2.5 years compared to 2.5-4 years period, supporting a non-linear overall trajectory. More specifically, anxiety problems grow by 19.5% between 18 months and 2.5 years, and by 14.6% between 2.5 and 4 years. Similar pattern was observed in affective (18.3 vs. 9.8%), ADHD (9.8 vs. 4.9%), and oppositional defiant (11.0 vs. 7.3%) problems. However, the developmental problems showed a different increase pattern, with a lower percentage in the early period than in the later one (9.8 vs 18.3%). On the other hand, the decrease pattern is more diffuse since no general trend was observed. In fact, externalizing, ADHD and developmental problems decreased more in the first-time frame than in the second, while affective and, to a greater extent, anxiety problems showed a larger decrease in the later period (8.5 vs 14.6% and 7.3 vs. 17.1%, respectively). Table 3. Prevalence of clinical pathological and clinical borderline problems in the total participants’ sample (n = 82), with the percentage of participants whose T scores increase / decrease from one measure to the next (2.5 y-18 m, 4-2.5 y). Clinical pathology Clinical borderline Increase Decrease CBCL Scales 18m 2.5y 4y 18m 2.5y 4y 2.5y-18m 4-2.5y 2.5y-18m 4-2.5y Internalizing Problems 4.9 9.8 12.2 8.5 15.9 17.1 19.5 9.8 7.3 6.1 Externalizing Problems 6.1 8.5 3.7 9.8 7.3 8.5 12.2 6.1 12.2 9.8 Total Problems 6.1 11.0 8.5 12.2 12.2 9.8 12.2 6.1 7.3 11.0 Affective Problems 8.5 11.0 9.8 14.6 22.0 18.3 18.3 9.8 8.5 14.6 Anxiety Problems 4.9 11.0 11.0 12.2 18.3 15.9 19.5 14.6 7.3 17.1 Perv. Develop. Problems 11.0 13.4 20.7 4.9 2.4 7.3 9.8 18.3 9.8 6.1 ADHD 11.0 11.0 3.7 12.2 6.1 7.3 9.8 4.9 15.9 11.0 Oppositional Defiant Problems 4.9 11.0 12.2 0.0 0.0 0.0 11.0 7.3 4.9 6.1 Data are expressed as percentages of the total population. Note. Perv. Develop. Problems: Pervasive Developmental Problems; ADHD: attention deficit hyperactivity disorder; m: months; y: years We next evaluated the ability to predict participants’ borderline or clinical scores at 4 years old considering previous borderline or clinical scores obtained at earlier ages. For this purpose, the odds of a positive or normal classification, considering a positive at the 18-months-old or 2.5-years-old evaluation, were calculated (Table 4) . Overall, our data suggest that the risk of obtaining a borderline or clinical score at 4 years old was significantly higher if it has been obtained previously, except for anxiety problems (OR=2.16, p=0.131). Table 4. Conditional probabilities and odds ratio (OR) for the CBCL scales obtained in total sample (n = 82). CBCL Scales p (O=1/(A=1| B=1) p (O=0/(A=1 | B=1) OR SE P-value Internalizing Problems 0.75 0.16 16.33 1.813 0.000 Externalizing Problems 0.70 0.22 8.17 2.109 0.005 Total Problems 0.80 0.19 16.62 2.045 0.000 Affective Problems 0.78 0.27 9.68 1.794 0.000 Anxiety Problems 0.50 0.32 2.16 1.663 0.131 Perv. Dev. Problems 0.52 0.15 6.06 1.738 0.001 ADHD 0.78 0.27 9.28 2.325 0.008 Oppositional Defiant Problems 0.50 0.11 2.086 2.828 0.005 Note. O: evaluation at 4 years; A: evaluation at 18 months; B: evaluation at 2.5 years; OR: Odds-Ratio; SE: standard error of the OR; 0: normal; 1: borderline or clinical pathology. Perv. Develop. Problems: Pervasive Developmental Problems; P-value: Level of significance. Finally, non-parametric Spearman's correlations between the three CBCL evaluations time-points in the total sample, controlling for confounding variables, were obtained (Table 5) . All correlations were positive and statistically significant (p <0.01). Overall, our results suggest that the scores are serial dependent and, therefore, the percentiles at a specific time point (t) might be partly predictable from the percentiles at previous time points (t-1 and t-2). Using the algorithm described by Lee and Preacher (2013), which contrasts the differences between correlated correlation coefficients, no significant differences were observed in any of the scales (data not shown). According to these results, the scores are consistent over time, thus supporting the use of trajectory computation for determining potential effects of direct scores on subcortical structures volume, compared to classical approximation ways based on general linear model. Table 5. Spearman's partial correlations between the three evaluations of CBCL (n=82), controlling by sex, gestational age, maternal IQ and educational level. CBCL Scales 18m-2.5y 18m-4y 2.5-4y Internalizing Problems 0.352* 0.334* 0.493* Externalizing Problems 0.562* 0.514* 0.547* Total Problems 0.482* 0.491* 0.607* Affective Problems 0.300* 0.356* 0.504* Anxiety Problems 0.417* 0.203 0.439* Pervasive Developmental Problems 0.290* 0.315* 0.377* ADHD 0.423* 0.397* 0.427* Oppositional defiant Problems 0.340* 0.334* 0.499* Note. ADHD: attention deficit hyperactivity disorder; m: months; y: years. * p <0.05. 3.3. Volumetry of subcortical structures. The volume of subcortical brain structures for MRI participants and for those with quality MRI but had not completed all three CBCL measurements (“MRI only”) are described in Table 6 . As can be seen, no significant differences in brain structure volumetry were found, indicating there is no selection bias in the final sample (“MRI” group). Table 6. Volume (mm 3 ) of subcortical brain structures in “MRI” and “MRI Only” participants. Structure MRI (n=37) MRI Only (n=18) P-value L. Cerebellum white matter 13298 (277.2) 12824 (400.9) 0.196 L. Cerebellum (cortex) 58108 (973.6) 56400 (1409) 0.186 L. Thalamus 7366 (99.2) 7139 (155.8) 0.102 L. Caudate 3893 (66.2) 3828 (99.5) 0.472 L. Putamen 5284 (96.3) 5138 (137.5) 0.249 L Pallidum 1959 (36.2) 1901 (52.5) 0.228 Brain stem 17812 (335.9) 17101 (498.7) 0.117 L. Hippocampus 3716 (64.6) 3661 (83.4) 0.493 L. Amygdala 1462 (48.3) 1443 (57.7) 0.747 L. Accumbens 576 (19.2) 564 (24.2) 0.622 R. Cerebellum white matter 12418 (238.9) 11956 (344.3) 0.144 R. Cerebellum (cortex) 57455 (962.1) 55628 (1413.9) 0.156 R. Thalamus 7350 (99.1) 7130 (152.6) 0.108 R. Caudate 4000 (65.2) 3911 (99.7) 0.318 R. Putamen 5387 (94) 5244 (134.2) 0.246 R. Pallidum 1808 (32.3) 1760 (46.7) 0.256 R. Hippocampus 3873 (60.6) 3800 (78.6) 0.326 R. Amygdala 1652 (26.8) 1619 (38.9) 0.355 R. Accumbens 644 (12.5) 629 (18.2) 0.368 Data are expressed as means (standard error of the mean); P-value: nivel de significancia; L: left; R: right. 3.4. Developmental Patterns of the CBCL scales. The proportion of participants whose trajectory can be described mainly by one of the four defined patterns {linear (L), quadratic (Q), Type I and Type II} are shown in Table 7 . Each proportion has been calculated from the correlation coefficient between the three CBCL measurements and the ideal trajectory coefficients. A value of 0 was defined as cut-off point in the magnitude of the correlation, so that correlations less than or equal to 0 are considered negative trajectories, while those greater than 0 are considered positive. For example, to properly interpret the results obtained, it should be taken into account that a value of 0.61 in linear internalization means that 61% of the participants tended to increase their percentile over time, while the remaining 39% tended to decrease it. Likewise, a value of 0.43 in the quadratic internalization means that 43% of the participants showed a minimum in the measurement obtained at 30 months, while 57% showed a maximum at that same age. A value of 0.58 in Type I internalization means that 58% of the participants increased their percentile between 18 months and 2.5 years and remained at values similar to those of 2.5 years when they were evaluated at 4 years; consequently, 42% of the participants decreased their percentile at 2.5 years and remained approximately at that value at 4 years. Since the classification is not exclusive, each participant has a weight in each of the trajectories. Interestingly, using Mann-Whitney’s test, there is only a significant difference between the “MRI” and “Missing” samples in total quadratic trajectory score (p=0.03), which once again confirms that the final sample (“MRI”) is not biased. Table 7. Proportion of participants in each group that show the different trajectory patterns over time (linear, quadratic, Type I and Type II) for each inventory scale. CBCL group MRI group Missing group CBCL Scales L Q Type I Type II L Q Type I Type II L Q Type I Type II Internalizing Problems 0.61 0.43 0.58 0.36 0.54 0.46 0.51 0.43 0.57 0.46 0.54 0.37 Externalizing Problems 0.43 0.58 0.39 0.57 0.49 0.49 0.51 0.46 0.52 0.65 0.37 0.50 Total Problems 0.47 0.53 0.51 0.55 0.57 0.41* 0.59 0.46 0.52 0.65 0.46 0.46 Affective Problems 0.41 0.37 0.48 0.48 0.41 0.35 0.51 0.49 0.41 0.39 0.48 0.48 Anxiety Problems 0.54 0.43 0.53 0.40 0.49 0.49 0.46 0.46 0.54 0.48 0.54 0.37 Perv. Develop. Problems 0.52 0.49 0.49 0.37 0.41 0.43 0.51 0.51 0.57 0.59 0.43 0.37 ADHD 0.34 0.54 0.31 0.52 0.41 0.46 0.38 0.46 0.43 0.63 0.35 0.46 Oppositional defiant Problems 0.48 0.48 0.54 0.45 0.46 0.49 0.54 0.46 0.50 0.54 0.54 0.43 Note: L: linear trajectory pattern; Q: quadratic trajectory pattern; Perv. Develop. Problems: Pervasive Developmental Problems; ADHD: attention deficit hyperactivity disorder; *: p-value < 0.05 <> 3.5. Relationship between the volumes of subcortical brain structures and CBCL trajectories. The partial correlation analysis of the evolution trajectories of direct CBCL scores and the volumes of subcortical brain structures is summarized in Table 8 . As shown in Supplementary Figure 3 , the volume-CBCL-trajectory relationship was both linear and non-linear, and 9 brain structures whose volume is related to CBCL trajectories were identified: bilateral hippocampus, accumbens, amygdala (right), pallidum (left), bilateral cerebellar cortex and bilateral putamen. Table 8. Non-parametric correlations between developmental trajectories determined by CBCL scales and the volume of brain subcortical structures. Only significant correlations are presented. Path Scale Structure Rho P-Value Linear Internalizing Problems L. Hippocampus -0.413 0.021 Linear Internalizing Problems R. Hippocampus -0.418 0.019 Quadratic Internalizing Problems L. Pallidum 0.429 0.016 Quadratic Internalizing Problems R. Hippocampus 0.434 0.015 Type I Internalizing Problems L. Pallidum -0.488 0.005 Type I Internalizing Problems L. Hippocampus -0.460 0.009 Type I Internalizing Problems R. Hippocampus -0.500 0.004 Linear Externalizing Problems L. Putamen -0.451 0.011 Linear Externalizing Problems R. Putamen -0.407 0.023 Type I Externalizing Problems R. Cerebellum (cortex) -0.407 0.023 Type II Externalizing Problems L. Putamen 0.484 0.006 Type II Externalizing Problems R. Putamen 0.455 0.010 Linear Total Problems R. Amygdala -0.488 0.005 Linear Anxiety Problems L. Pallidum -0.477 0.007 Type I Perv. Develop. Problems R. Amygdala -0.474 0.007 Type II Perv. Develop. Problems L. Accumbens 0.408 0.023 Linear ADHD L. Cerebellum (cortex) -0.585 0.001 Linear ADHD R. Cerebellum (cortex) -0.639 0.000 Linear ADHD R. Amygdala -0.483 0.006 Quadratic ADHD R. Hippocampus 0.459 0.009 Quadratic Oppositional Defiant Problems R. Hippocampus 0.448 0.011 Type I Oppositional Defiant Problems R. Amygdala -0.504 0.004 L: left; R: right; Perv. Develop. Problems: Pervasive Developmental Problems; ADHD: Attention Deficit Hyperactivity Disorder. Spearman’s correlations. P-value: level of significance. A negative correlation between linear trajectory in internalizing problems and both right and left hippocampus volume were found; however, when the trajectory is quadratic, with a minimum at 2.5 years, an increase in volume of the left pallidum and right hippocampus was observed. Likewise, when the trajectory is Type I with a maximum at 2.5 years that continues at 4 years, both left pallidum and both hippocampus decrease in volume (Figure 1, upper row) . In addition, a Type II trajectory (with a plateau at the minimum reached at 2.5 years) in externalizing problems is associated with greater volumes of left and right putamen (Figure 1, lower row) . <> Regarding total problems, a negative correlation was observed between the linear trajectory and the volume of the right amygdala (Figure 2) , indicating that an incremental trajectory of these problems associated with a reduction in right amygdala volume. Interestingly, no other trajectory correlated significantly with the volumes of this subcortical structure. <> On the other hand, the linear trajectory of anxiety problems correlated negatively with the volume of the left pallidum (r = -0.477, p = 0.007, Figure 3 ), then left pallidum volumetry seems to be smaller when there is a steady increase in the trajectory of anxiety problems. <> Likewise, in terms of developmental problems, Type I trajectory negatively correlated with the volume of the right amygdala (r = -0.474, p = 0.007, Figure 4 left ); however, a positive correlation was found between Type II trajectory and right nucleus accumbens volumetry (r = 0.408, p = 0.023, Figure 4 right ). The first result implies an increase in the size of the amygdala when development problems decrease and stabilize at 2.5 years. The second result indicates that the volume of right nucleus accumbens tends to be higher the greater the reduction of developmental problems at 4 years. <> Regarding ADHD problems, significant correlations were observed with both left and right cerebellar cortex, right amygdala and right hippocampus (Figure 5) . Thus, the volumes of both left and right cerebellum, as well as right amygdala, negatively correlated with a linear trajectory (r = -0.585, p = 0.001; r = -0.639, p <0.001; r = -0.483, p = 0.006, respectively). Therefore, both cerebellar and right amygdala volumes are smaller the higher the level of problems with increasing age. Conversely, the right hippocampus volume positively correlated with a quadratic trajectory of ADHD problems (r = 0.459, p = 0.009), which suggests that the volume of the hippocampus could be positively affected by a reduction in the level of problems at a minimum peak around the age of 2.5 years. <> Finally, the Type I trajectories of oppositional-defiant problems were negatively associated with the right hippocampus and right amygdala (r = -0.476, p = 0.007; r = -0.504, p = 0.004, respectively) (Figure 6) . Therefore, the volume of both subcortical brain structures is higher the greater the reduction of these problems at 2.5 and 4 years with respect to the level observed at 18 months. <> 4. Discussion This research was primarily aimed to understand the developmental trajectories of behavioral problems during the first 4 years of life and determine their potential associations with children’s subcortical brain structures volumes. Moreover, potential long-term effects of early nutrition on such trajectories and their potential associations with brain morphology at 6 years old were also evaluated. Within COGNIS framework, we have previously reported that bioactive compounds-enriched infant formula, containing MFGM components, synbiotics, LC-PUFAs, gangliosides, sialic acid and nucleotides, provide a protective role against behavioural problems development at 2.5 years old (affective problems) compared to standard formula-fed infants, and similar to those effects found in breastfed infants. In fact, these results showed significant higher increase of internalizing, externalizing and total problems, as well as ADHD and Oppositional Defiant problems from 18 months to 2.5 years in the SF group, compared to EF and BF infants [ 7 ]. Nevertheless, in the current longitudinal study, a similar effect on behavioural development could not be identified, which can be mostly explained by the smaller sample size included in the present analysis. Interestingly, as previously reported, our results also suggest that maternal factors, including maternal IQ and educational level, lead to better mental health and behavioural development in childhood [ 7 ]. In other previous publication, we demonstrated the importance of early nutrition up to 18 months of life, on later brain structure at 6 years old and neurocognitive outcomes, similar in EF children to BF ones, and better than those fed SF [ 7 ]. Therefore, our results highlight the inclusion of both early nutrition and maternal factors into designed policies that address mental health needs of children and adolescents. However, it is noteworthy that important sets of results in the present analysis of COGNIS study data has yielded two key findings: the first relates to behavioral developmental trajectories during the first four years of life, and the second to the association between these trajectories and the volume of children’s subcortical brain structures at 6 years of age. Overall, results obtained here suggest that behavioral trajectories lead to different relationship between behavioral problems up to 4 years and subcortical brain structures morphometry. More specifically, these behavioral trajectories seem to have a profound effect on volumes of hippocampus, amygdala, pallidum, cerebellum, putamen and accumbens throughout the first four years of life, in which higher levels of problems over time were associated with reduced volume of these structures. Interestingly, results obtained from non-linear trajectories also suggest that subcortical nuclei morphometry, particularly the hippocampus, could depend on behavioral problems in a critical period around the age of 2.5 years old. Taken together, our results emphasize the need for early and effective prevention and detection of behavioral problems during the first years of life, thus reducing the risk of psychopathological disorders later in life. First, in relation to the developmental trajectories, our results indicate that the CBCL evaluations are consistent over time, showing a high degree of autocorrelation. Interestingly, except for the anxiety scale, these trajectories might allow a significant prediction of possible diagnostic categorization (borderline or clinical) from measurements taken up to 2.5 years earlier. Moreover, from an analytical point of view, these data also suggest that, when repeated measurements are performed, an analysis approach that does not take into account the autocorrelation of the measurements (for example: analysis of variance with repeated measures) will produce unreliable results [ 23 ], with incorrect statistical significances [ 30 ]. In contrast to other development curve analysis strategies, such as linear mixed models or mixed growth models [ 31 ], the one used here offers the advantage of modelling the set of possible curves in a concrete way, as well as assigning a weight for each participant in each of these curves. Therefore, it can be used regardless of the total number of participants in the sample. Based on this developed methodology, our results suggest that CBCL scales in children with typical development do not seem to follow the same development curves. In fact, in the case of both anxiety and developmental internalizing problems, the greatest weight corresponds to a linear trajectory, while total, externalizing and ADHD-type problems follow a quadratic scheme, with a peak at the age of 2.5 years. Likewise, affective and oppositional-defiant problems follow a Type I pattern, with a plateau starting at 2.5 years. Finally, the total scale most often follows a Type II pattern with a plateau up to 2.5 years old. Once analyzed the developmental trajectories in our study population, we next evaluated their potential relationships with the volume of subcortical brain structures. A total of 9 structures, 3 bilateral (cerebellum cortex, hippocampus and putamen), 2 in the left hemisphere (accumbens and pallidum), and one in the right hemisphere (amygdala), showed significant correlations with developmental trajectories. Specifically, regardless of the category of problems, a linear trajectory is significantly associated with all mentioned brain structures. In all cases, the volume of these structures decreases as the negative slope of the curve becomes steeper, thereby exacerbating the level of problems over time. On the other hand, quadratic trajectory is positively associated with right hippocampus and pallidum, so that the volume of these structures seems to increase when there is a minimal peak at 2.5 years old. Thirdly, Type I curve is negatively associated with different brain structures, including pallidum, bilateral hippocampus, right cerebellar cortex and right amygdala. These data indicate that volume of mentioned brain structures is smaller the greater the trajectory weight, supporting that low score at 18 months, although followed by a subsequent increase, are associated with higher structural volume. Finally, Type II trajectory is positively associated with bilateral putamen and left accumbens, which implies that their volume is greater when the scores have their minimum after the 2.5 years old. Having in mind these results, it is feasible to think that there could be a critical age around 2.5 years in relation to the development of brain structures, not determined by the score at that age, but its relation to the obtained at the other ages. Regarding internalizing problems, our results indicate a bilateral hippocampal volume reduction when the problem trajectory is linear, but an increase in the left pallidum and right hippocampus volume when this trajectory is quadratic. Our results with the linear trajectory are consistent with previous research on volume of subcortical structures and internalizing problems in the pediatric and adolescent population, including depression, anxiety or phobias, where hippocampal volume reduction and volume increase of the left pallidum globe have been observed [ 32 ]. [ 33 ], Mueller et al. (2013) Merz, He and Noble, (2018) [ 34 , 35 ]. Although the role of hippocampus in psychopathological problems is well known (Macpherson and Hikida, 2019), the potential effects of the increase in the volume of the pallidum are still poorly understood. In this sense, this nucleus is part of three cortico-subcortical circuits (sensory-motor, cognitive and limbic) through which facilitates or suppress behavior via regulation of the subthalamic nucleus (STN) and the substantia nigra pars reticulata (SNr) activities. Interestingly, these two structures are involved in the regulation of the thalamus, one of whose functions is salience attribution to stimuli. Consequently, reduced control by the pallidum on the STN/SNr pair results in the reduction of thalamic activity and the appearance of symptoms characteristic of depression or anxiety [ 11 ]. Although this seems to explain its potential effects on internalizing problems, further studies are still needed. Concerning externalizing problems, our results highlighted bilateral putamen (linear and Type II trajectory) and left cerebellar cortex (Type I trajectory). Due to both linear and Type II trajectories include the difference between the two-time limits (4 years and 18 months) but of opposite sign, we will discuss both together. Furthermore, considering the externalizing DSM scales, our results showed a reduction in the volume of right amygdala and bilateral cerebellar cortex in ADHD with linear trajectory, reduction of right amygdala (Type I trajectory) and increase of left accumbens (Type II trajectory) in developmental problems; as well as a reduction of right hippocampus and amygdala in oppositional defiant problems (Type I trajectory). Thus, except for the right cerebellum, there seems to be no common nuclei between the overall score of the externalizing scale and the specific scales of this type of problem. However, using functional MRI, it has been observed that both putamen and bilateral accumbens are activated in adolescents with externalizing symptoms in response to reward, with respect to non-reward [ 36 ]. Moreover, in cases with clinical levels, both a dysfunction of the amygdala and structures that process the reward led to emotional empathy disorders as well as problems in learning by strengthening and taking of decisions [ 37 ]. On the other hand, both structural integrity and connectivity of the cerebellum seem to be crucial factors explaining the called "p factor" [(general factor of psychopathology [ 38 ]], due to poor efficiency in the operation of information processing systems could be based on externalizing and internalizing disorders. Consequently, both factors have been proposed as risk markers for developing any psychopathology [ 39 ]. Therefore, the cerebellum not only play a key role in movement control and motor coordination but also seems to control visual attention and operational memory [ 40 , 41 ]. Currently, its consideration as a node of the dorsal attentional network (DAN) also allows it to influence on the maintenance of attention over time (sustained attention) [ 42 ]. Taking into account these considerations, and although not as differentiated as the cerebral cortex, the cerebellum seems to have a relevant role in complex cognitive functions such as attention, working memory, and social and emotional processing [ 43 ]. In adolescent patients with ADHD, lower volumes of the hippocampus and amygdala have been observed with respect to the control group with usual development [ 44 ]. In a recent study, conducted under the umbrella of the ENIGMA collaboration [ 45 ], in which morphometric alterations were addressed in 1713 participants diagnosed with ADHD and 1529 controls, at ages between 4 and 63 years, it was observed that, with volume effect sizes is higher in children than in adults, showed a volumetric reduction in accumbens, hippocampus and putamen, in addition to total intracranial volume and caudate. Our results are consistent with those of this study, although they also suggest that this is especially true in children with linear developmental trajectories, but not so much in quadratic trajectories, in which the right hippocampus could benefit if the level of the problem is reduced at 2.5 years old. Unfortunately, there is no published literature on the effect of non-linear trajectories on the development of cortical or subcortical structures, so we can only speculate on the potential benefits of an intervention aimed to reduce the level of problems at an early age. The role of the amygdala and its developmental trajectory at an early age is not yet well known, especially with regard to its potential consequences on behavioral problems [ 46 ]. Although it is a very small structure (approximately 0.3% of the total brain volume), it is attributed a key role in the processing of outgoing environmental information, emotional learning, sensory information and prior knowledge linking as well as in the ability to produce an adaptive response. In this regard, the amygdala is important in social cognition, since it is probably the one that organizes the ocular exploration of space, especially the faces of others, the evaluation of the trust that can be placed on others or the identification facial emotional expression [ 47 ]. Consequently, several psychiatric disorders have been linked to a malfunction of the amygdala, including obsessive-compulsive disorder, anxiety or problems in emotion regulation [ 48 ]. Moreover, this malfunction could also contribute to the development of problems related to autistic spectrum disorders [ 46 ]. Structurally, a greater volume of the bilateral amygdala has been observed in 5-year-old children with ASD [ 49 ]. In line with these findings, our data also suggest that the volume of the amygdala could be a good indicator for the trajectory of total problems. In this sense, the number of neurons in the amygdala grows postnatally, leading to about a 40% increase in volume at a mature stage. However, in patients with ASD, an abnormal developmental trajectory has been found, resulting in a substantially lower final number of mature neurons [ 50 ]. It is also important to note that abnormal development of the amygdala is also characterized by an excessive number of dendritic spines in ASD patients [ 51 ], which can lead to an inappropriate emotional development sequence [ 46 ]. In addition, in healthy patients, a change in the connectivity of the amygdala with the medial prefrontal cortex is observed over time. While in childhood a positive relationship is observed, where the increases in one structure are associated with increases in the other, in maturity a negative relationship is described so that, increases in the one decrease the activity of the other [ 48 , 52 ]. This pattern is altered in ASD patients, thus supporting the potential role of malfunction of the amygdala in this disorder [ 53 ]. The nucleus accumbens also seems to play a crucial role in ASD. In fact, study carried out in animal model showed that that the decrease in the serotonin levels injected into the accumbens causes social behavior deficiencies. These difficulties are reversed when the dorsal raphe is activated [ 54 ]. These findings suggest that the underlying cause could be brainstem-born abnormalities in the serotonergic circuit that spread to the subcortical structures and the cerebral cortex [ 55 ]. Oppositional defiant disorder (ODD) is perhaps the most frequent childhood disorder, with prevalence rates around 5% in the 6–16 age group [ 56 ]. Its characteristics are well known and related to respect for/negativity before authority and norms, animosity and disobedience. A recent meta-analysis suggests that insecure and disorganized attachment is associated with a higher probability of developing ODD but cannot be considered a necessary cause [ 57 ]. In the long term, it is associated with difficulties in accomplishing academic and professional achievements and developing antisocial behaviors [ 58 ], which also associates ODD with "insensitivity traits'' ("callous traits") [ 59 ]. These characteristics, in 8-to-11-year-old children correlate negatively with the total volume of gray and white matter, and modestly with the volume of the right amygdala [ 59 ]. A decrease in hippocampal activation has been observed in tasks that require effort to regulate emotions [ 60 ]. In the current study, no significant association were observed between trajectories of affective problems and subcortical volume. This null finding is surprising because there is abundant literature that suggests that affective problems such as depression are associated with volume reductions or increases in subcortical structures such as the hippocampus [ 35 ] or the amygdala, respectively [ 61 ]. A possible explanation of this difference is that it is possible that this relationship emerges later in time. For example, Jaworska et al. (2016) participants were 12–25 years old, and Albaugh et al, (2017) used an even larger range of age. Similarly, a more recent study has a smaller range of age but start when children were 6 years [ 62 ]. Strengths and limitations The main contribution of the present research is that a longitudinal strategy is used, that is, the trajectories of behavioral problems are evaluated throughout the first years of life (18 months, 2.5 and 4 years of age), instead of a transversal approach, in which each measure of the problem level is considered in isolation. We believe that this longitudinal approach allows us to determine more precisely if the brain structures morphometry anatomy is really related to the evolution of the problem. Furthermore, the analysis of our “Missing cases” indicate that the final sample is not biased neither in terms of the CBCL scales nor regarding the confounding factors. Nor does our final sample differ from the “Missing cases” at same-quality MRI. The results of our research, although consistent with the literature, have some limitations that restrict their generality and interpretation. The first limitation is the smaller size of the final sample compared to the standards in literature. However, very few published studies use a longitudinal strategy like the one used here, which adds extra value to our results. The second limitation is related to the small number of observations available to obtain the developmental trajectories and the temporal distance between them. Unfortunately, it was not possible to obtain cerebral structural images at the ages of 18 months and 2.5 years old, which made it impossible to compare and associate the trajectories of the subcortical structures and the behavioral development. Finally, our results are relative to volume of structures, not function. The relationship between structure and function is not yet entirely clear, although it is common to assume that greater volume implies greater functionality. However, functional studies are necessary to strengthen the interpretations regarding the structure-function relationship. 5. Conclusions The relationship between the morphometry of subcortical brain structures and behavioral problems is influenced by the trajectories of these problems. The hippocampus, amygdala, pallidum, cerebellum, putamen, and accumbens appear to be the most affected structures by the history of behavioral problems during the first four years of life. Generally, trajectories with higher levels of problems over time are associated with reduced structure volumes. However, the observed non-linear trajectories suggest that the morphometry of subcortical nuclei, particularly the hippocampus, may be influenced by behavioral problems during a critical period around 2.5 years of age. Building on our previously published findings at this age, the new data presented here further emphasize the importance of supplementing infant formula with bioactive components naturally present in human milk as strategy for reducing later behavioral problems and changes in brain morphology. Based on our previously published data and the present analysis, it is suggested that such supplementation of infant formulas could help protect children from behavioral problems during their early years, promote optimal brain morphology development in childhood, and reduce the risk of mental illnesses in adulthood. The present study also highlights the need for further adequately powered studies with long-term follow-up. Abbreviations ADHD: Attention Deficit/Hyperactivity Disorder; BF: Breastfed infants; CBCL: Child Behavior Checklist; EF: Bioactive compounds-enriched infant formula; FOS: Fructooligossacharides; GLMM: Generalized linear mixed models; MFGM: Milk fat globule membrane; MRI: Magnetic resonance Imaging; LC-PUFAs: Long-chain polyunsaturated fatty acids; ODD: Oppositional Defiant Disorder; RCT: Randomized Clinical Trial; SF: Standard infant formula; T: Typical Scores. Declarations Ethics approval and consent to participate The COGNIS study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Research Ethics Committee of the University of Granada, the Bioethical Committees for Clinical Research of the Clinical University Hospital San Cecilio and the Mother-Infant University Hospital of Granada, Spain. The project was registered at www.ClinicalTrial.gov no.: NCT01634464. Informed consent was obtained from all subjects involved in the study Consent for publication Not applicable Availability of data and materials The data presented in this study are available on request form the corresponding author. The data are not publicy available due to ethical reasons. Competing interests Dra. Roser De-Castellar and Dra. Mª Teresa Pérez are employees of Ordesa Laboratories S.L., company that have funded in part the COGNIS RCT study. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This project has been funded by Laboratorios Ordesa, S.L. Contract University of Granada General Foundation, No. 3349 and SMARTFOODS (CIEN) Contract University of Granada General Foundation, No. 4003, Spanish Ministry of Economy, Industry and Competitiveness. Furthermore, the project has been partially funded by HORIZON 2020 EU DynaHEALTH Project (GA No. 633595). Authors´ contributions Conceptualization, CC, AC; Study design and methodology, CC; AC ; Formal analysis, EC-V, AN-R, AC; Investigation, EV-C, AN-R, FH ; Data curation, EV-C, AN-R, AC ; Writing-original draft preparation, EC-V, AN-R, JAGS ; Writing-review and editing, JAGS, AC, CC ; Supervision, CC; Project administration, CC; Funding acquisition, Rd-C, MTP-H, CC ; All authors have read and approved the final manuscript. Acknowledgments The authors want to acknowledge the parents and children who participated in the study, the pediatricians and technicians of the EURISTIKOS Excellence Centre for Paediatric Research at the Department of Pediatrics (School of Medicine, University of Granada, Spain), the technicians of the RMN, Jose and Félix, at the CIMCYC (University of Granada, Spain) and also Laboratorios Ordesa, S.L. (Barcelona, Spain ) for providing the infant formulas. References Wakschlag LS, Perlman SB, Blair RJ, Leibenluft E, Briggs-Gowan MJ, Pine DS. The neurodevelopmental basis of early childhood disruptive behavior: Irritable and callous phenotypes as exemplars. American journal of psychiatry. 2018;175:114–30. Polanczyk G V, Salum GA, Sugaya LS, Caye A, Rohde LA. Annual research review: A meta-analysis of the worldwide prevalence of mental disorders in children and adolescents. J Child Psychol Psychiatry. 2015;56:345–65. Koletzko B, Bergmann K, Brenna JT, Calder PC, Campoy C, Clandinin MT, et al. Should formula for infants provide arachidonic acid along with DHA? A position paper of the European Academy of Paediatrics and the Child Health Foundation. Am J Clin Nutr. 2020;111:10–6. Timby N, Domellöf E, Hernell O, Lönnerdal B, Domellöf M. Neurodevelopment, nutrition, and growth until 12 mo of age in infants fed a low-energy, low-protein formula supplemented with bovine milk fat globule membranes: a randomized controlled trial. Am J Clin Nutr. 2014;99:860–8. Carlson AL, Xia K, Azcarate-Peril MA, Goldman BD, Ahn M, Styner MA, et al. Infant Gut Microbiome Associated With Cognitive Development. Biol Psychiatry. 2018;83:148–59. Nieto-Ruiz A, García-Santos JA, Verdejo-Román J, Diéguez E, Sepúlveda-Valbuena N, Herrmann F, et al. Infant Formula Supplemented With Milk Fat Globule Membrane, Long-Chain Polyunsaturated Fatty Acids, and Synbiotics Is Associated With Neurocognitive Function and Brain Structure of Healthy Children Aged 6 Years: The COGNIS Study . Frontiers in Nutrition . 2022. Nieto-Ruiz A, Diéguez E, Sepúlveda-Valbuena N, Herrmann F, Cerdó T, López-Torrecillas F, et al. The Effects of an Infant Formula Enriched with Milk Fat Globule Membrane, Long-Chain Polyunsaturated Fatty Acids and Synbiotics on Child Behavior up to 2.5 Years Old: The COGNIS Study. Nutrients . 2020. Beauchaine TP, Constantino JN, Hayden EP. Psychiatry and developmental psychopathology: Unifying themes and future directions. Compr Psychiatry. 2018;87:143–52. Martel MM, Levinson CA, Lee CA, Smith TE. Impulsivity symptoms as core to the developmental externalizing spectrum. J Abnorm Child Psychol. 2017;45:83–90. Beauchaine TP, Zisner AR, Sauder CL. Trait impulsivity and the externalizing spectrum. Annu Rev Clin Psychol. 2017;13:343–68. Macpherson T, Hikida T. Role of basal ganglia neurocircuitry in the pathology of psychiatric disorders. Psychiatry Clin Neurosci. 2019;73:289–301. Zisner A, Beauchaine TP. Neural substrates of trait impulsivity, anhedonia, and irritability: Mechanisms of heterotypic comorbidity between externalizing disorders and unipolar depression. Dev Psychopathol. 2016;28:1177–208. Casey BJ. Beyond simple models of self-control to circuit-based accounts of adolescent behavior. Annu Rev Psychol. 2015;66:295–319. Corr PF, McNaughton N. Neural mechanisms of low trait anxiety and risk for externalizing behavior. 2016; Marsh AA, Finger EC, Mitchell DG V, Reid ME, Sims C, Kosson DS, et al. Reduced amygdala response to fearful expressions in children and adolescents with callous-unemotional traits and disruptive behavior disorders. American Journal of Psychiatry. 2008;165:712–20. Benegal V, Antony G, Venkatasubramanian G, Jayakumar PN. Imaging study: gray matter volume abnormalities and externalizing symptoms in subjects at high risk for alcohol dependence. Addiction biology. 2007;12:122–32. Cheetham A, Allen NB, Whittle S, Simmons J, Yücel M, Lubman DI. Amygdala volume mediates the relationship between externalizing symptoms and daily smoking in adolescence: a prospective study. Psychiatry Res Neuroimaging. 2018;276:46–52. Cattell, R. B., & Cattell AKS. g Factor Test. Scale 2. Form A. Institute for personality and ability testing. (Spanish adaptation: Madrid, Spain: TEA Ediciones, S.A. 1994. The World Medical Association Inc. Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. Ferney-Voltaire, France. 2008;1–5. Achenbach PT, Rescorla PL. Achenbach System of Empirically Based Assessment. In: Volkmar FR, editor. Encyclopedia of Autism Spectrum Disorders. Springer New York; 2013. p. 31–9. Volkmar FR. Encyclopedia of Autism Spectrum Disorders | Fred R. Volkmar | Springer. 2017. Rescorla LA. Assessment of young children using the Achenbach System of Empirically Based Assessment (ASEBA). Ment Retard Dev Disabil Res Rev. 2005;11:226–37. Hartman RR, Stage SA, Webster‐Stratton C. A growth curve analysis of parent training outcomes: Examining the influence of child risk factors (inattention, impulsivity, and hyperactivity problems), parental and family risk factors. Journal of Child Psychology and Psychiatry. 2003;44:388–98. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179–94. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33:341–55. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968–80. Liem F, Mérillat S, Bezzola L, Hirsiger S, Philipp M, Madhyastha T, et al. Reliability and statistical power analysis of cortical and subcortical FreeSurfer metrics in a large sample of healthy elderly. Neuroimage. 2015;108:95–109. Sánchez-Benavides G, Gómez-Ansón B, Sainz A, Vives Y, Delfino M, Peña-Casanova J. Manual validation of FreeSurfer’s automated hippocampal segmentation in normal aging, mild cognitive impairment, and Alzheimer Disease subjects. Psychiatry Res Neuroimaging. 2010;181:219–25. Schoemaker D, Buss C, Head K, Sandman CA, Davis EP, Chakravarty MM, et al. Hippocampus and amygdala volumes from magnetic resonance images in children: Assessing accuracy of FreeSurfer and FSL against manual segmentation. Neuroimage. 2016;129:1–14. Beauchaine TP, Webster-Stratton C, Reid MJ. Mediators, moderators, and predictors of 1-year outcomes among children treated for early-onset conduct problems: a latent growth curve analysis. J Consult Clin Psychol. 2005;73:371. Ezpeleta L, Granero R, De La Osa N, Navarro JB, Penelo E, Domènech JM. Tracing developmental trajectories of oppositional defiant behaviors in preschool children. PLoS One. 2014;9:e101089. Koolschijn PCMP, van IJzendoorn MH, Bakermans-Kranenburg MJ, Crone EA. Hippocampal volume and internalizing behavior problems in adolescence. European Neuropsychopharmacology. 2013;23:622–8. Blanken LME, White T, Mous SE, Basten M, Muetzel RL, Jaddoe VW V, et al. Cognitive functioning in children with internalising, externalising and dysregulation problems: a population-based study. Eur Child Adolesc Psychiatry. 2017;26:445–56. Blom EH, Han LKM, Connolly CG, Ho TC, Lin J, LeWinn KZ, et al. Peripheral telomere length and hippocampal volume in adolescents with major depressive disorder. Transl Psychiatry. 2015;5:e676–e676. Jaworska N, Yücel K, Courtright A, MacMaster FP, Sembo M, MacQueen G. Subgenual anterior cingulate cortex and hippocampal volumes in depressed youth: the role of comorbidity and age. J Affect Disord. 2016;190:726–32. Bjork JM, Chen G, Smith AR, Hommer DW. Incentive‐elicited mesolimbic activation and externalizing symptomatology in adolescents. Journal of Child Psychology and Psychiatry. 2010;51:827–37. Blair RJR. The neurobiology of psychopathic traits in youths. Nat Rev Neurosci. 2013;14:786–99. Watts AL, Greene AL, Bonifay W, Fried EI. A critical evaluation of the p-factor literature. Nature Reviews Psychology. 2024;3:108–22. Hariri AR. The emerging importance of the cerebellum in broad risk for psychopathology. Neuron. 2019;102:17–20. Brissenden JA, Tobyne SM, Osher DE, Levin EJ, Halko MA, Somers DC. Topographic cortico-cerebellar networks revealed by visual attention and working memory. Current Biology. 2018;28:3364–72. Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BTT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106:2322–45. Brissenden JA, Somers DC. Cortico–cerebellar networks for visual attention and working memory. Curr Opin Psychol. 2019;29:239–47. Guell X, Gabrieli JDE, Schmahmann JD. Triple representation of language, working memory, social and emotion processing in the cerebellum: convergent evidence from task and seed-based resting-state fMRI analyses in a single large cohort. Neuroimage. 2018;172:437–49. Bonath B, Tegelbeckers J, Wilke M, Flechtner H-H, Krauel K. Regional gray matter volume differences between adolescents with ADHD and typically developing controls: further evidence for anterior cingulate involvement. J Atten Disord. 2018;22:627–38. Hoogman M, Bralten J, Hibar DP, Mennes M, Zwiers MP, Schweren L, et al. Subcortical brain volume differences of participants with ADHD across the lifespan: an ENIGMA collaboration. Lancet Psychiatry. 2017;4:310. Nordahl CW, Schumann CM. Early variations in amygdala development may signal divergent behavioral outcomes. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4:3–4. Wang S, Yu R, Tyszka JM, Zhen S, Kovach C, Sun S, et al. The human amygdala parametrically encodes the intensity of specific facial emotions and their categorical ambiguity. Nat Commun. 2017;8:1–13. Salzwedel AP, Stephens RL, Goldman BD, Lin W, Gilmore JH, Gao W. Development of amygdala functional connectivity during infancy and its relationship with 4-year behavioral outcomes. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4:62–71. Bellani M, Calderoni S, Muratori F, Brambilla P. Brain anatomy of autism spectrum disorders II. Focus on amygdala. Epidemiol Psychiatr Sci. 2013;22:309–12. Avino TA, Barger N, Vargas M V, Carlson EL, Amaral DG, Bauman MD, et al. Neuron numbers increase in the human amygdala from birth to adulthood, but not in autism. Proceedings of the National Academy of Sciences. 2018;115:3710–5. Weir RK, Bauman MD, Jacobs B, Schumann CM. Protracted dendritic growth in the typically developing human amygdala and increased spine density in young ASD brains. Journal of Comparative Neurology. 2018;526:262–74. Gabard-Durnam LJ, Flannery J, Goff B, Gee DG, Humphreys KL, Telzer E, et al. The development of human amygdala functional connectivity at rest from 4 to 23 years: a cross-sectional study. Neuroimage. 2014;95:193–207. Shen MD, Li DD, Keown CL, Lee A, Johnson RT, Angkustsiri K, et al. Functional connectivity of the amygdala is disrupted in preschool-aged children with autism spectrum disorder. J Am Acad Child Adolesc Psychiatry. 2016;55:817–24. Walsh JJ, Christoffel DJ, Heifets BD, Ben-Dor GA, Selimbeyoglu A, Hung LW, et al. 5-HT release in nucleus accumbens rescues social deficits in mouse autism model. Nature. 2018;560:589–94. Takumi T, Tamada K, Hatanaka F, Nakai N, Bolton PF. Behavioral neuroscience of autism. Neurosci Biobehav Rev. 2020;110:60–76. López-Villalobos JA, Andrés-De Llano JM, Rodríguez-Molinero L, Garrido-Redondo M, Sacristán-Martín AM, Martínez-Rivera MT, et al. Prevalencia del trastorno negativista desafiante en España. Rev Psiquiatr Salud Ment. 2014;7:80–7. Theule J, Germain SM, Cheung K, Hurl KE, Markel C. Conduct disorder/oppositional defiant disorder and attachment: A meta-analysis. J Dev Life Course Criminol. 2016;2:232–55. Waldman ID, Rowe R, Boylan K, Burke JD. External validation of a bifactor model of oppositional defiant disorder. Mol Psychiatry. 2021;26:682–93. Bolhuis K, Viding E, Muetzel RL, El Marroun H, Kocevska D, White T, et al. Neural profile of callous traits in children: A population-based neuroimaging study. Biol Psychiatry. 2019;85:399–407. Raschle NM, Fehlbaum LV, Menks WM, Martinelli A, Prätzlich M, Bernhard A, et al. Atypical Dorsolateral Prefrontal Activity in Female Adolescents With Conduct Disorder During Effortful Emotion Regulation. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4:984–94. Albaugh MD, Nguyen T-V, Ducharme S, Collins DL, Botteron KN, D’Alberto N, et al. Age-related volumetric change of limbic structures and subclinical anxious/depressed symptomatology in typically developing children and adolescents. Biol Psychol. 2017;124:133–40. Blok E, Geenjaar EPT, Geenjaar EAW, Calhoun VD, White T. Neurodevelopmental trajectories in children with internalizing, externalizing and emotion dysregulation symptoms. Front Psychiatry. 2022;13:846201. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.jpg Supplementary Figure 1. Leyend: Supp. Fig. 1. Ideal trajectories of direct scores over time (18, 30 and 48 months). Note that symmetric trajectories would imply path coefficients of the opposite sign. SupplementaryFigure2.jpg Supplementary Figure 2. Leyend: Supp. Fig. 2. Longitudinal study of CBCL Scales from 1.5 to 4 years old in COGNIS study participants. ADHD, attention deficit/hyperactivity disorders; BF, breastfeeding; EF, experimental infant formula; SF, standard infant formula. SupplementaryFigure3.jpg Supplementary Figure 3. Leyend: Supp. Fig. 3. Frontal and lateral view of subcortical structures. Nuclei related with behavioural paths are coloured as indicate Cite Share Download PDF Status: Published Journal Publication published 19 Feb, 2026 Read the published version in Child and Adolescent Psychiatry and Mental Health → Version 1 posted Editorial decision: Revision requested 26 Sep, 2025 Reviews received at journal 25 Sep, 2025 Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers invited by journal 06 Aug, 2025 Editor assigned by journal 21 Jul, 2025 Submission checks completed at journal 20 Jul, 2025 First submitted to journal 16 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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12:46:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":180181,"visible":true,"origin":"","legend":"\u003cp\u003eLineal relationship between the right amygdala volumetry and the total CBCL score path.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142478/v1/ebc6477cda48d121c8f57e4b.jpg"},{"id":88890701,"identity":"f316d6b8-dab6-436f-b326-3ab39a7998f5","added_by":"auto","created_at":"2025-08-12 12:46:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":167086,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between volume of left pallidum and anxiety paths.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142478/v1/ff88a85d445e43fab48be308.jpg"},{"id":88890703,"identity":"d82b9ce9-0539-420f-b439-0b6cc5694bf6","added_by":"auto","created_at":"2025-08-12 12:46:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":235968,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between right amygdala and right accumbens with development paths.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142478/v1/cb7c005705d29a3b7760ec7f.jpg"},{"id":88897984,"identity":"675b59f4-bc2c-488c-9a42-924c6eb49768","added_by":"auto","created_at":"2025-08-12 13:18:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":337993,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between ADHD paths with volumes of cerebellum (left and right), right amygdala and right hippocampus\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142478/v1/84b55c839428ad5cf60e5191.jpg"},{"id":88890710,"identity":"e02f6f37-0a7d-440f-9f17-7f48356fbed1","added_by":"auto","created_at":"2025-08-12 12:46:47","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":191724,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between right hippocampus and right amygdala with oppositional-defiant type I paths.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142478/v1/d02f167d17ef6f5708f9b4c2.jpg"},{"id":103251355,"identity":"906e083f-ed32-4e3a-a623-7fdb61727ade","added_by":"auto","created_at":"2026-02-23 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Note that symmetric trajectories would imply path coefficients of the opposite sign.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142478/v1/7195a8b3b43ffbee4a430fdd.jpg"},{"id":88894568,"identity":"ba331097-fdd6-4b66-975c-fcda1d55c908","added_by":"auto","created_at":"2025-08-12 13:02:47","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1820062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLeyend:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Fig. 2. \u003c/strong\u003eLongitudinal study of CBCL Scales from 1.5 to 4 years old in COGNIS study participants. ADHD, attention deficit/hyperactivity disorders; BF, breastfeeding; EF, experimental infant formula; SF, standard infant formula.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142478/v1/cde55ef65f03507fff56feec.jpg"},{"id":88890711,"identity":"aab06516-c1e6-4ce5-9c00-9e1e0587c8b2","added_by":"auto","created_at":"2025-08-12 12:46:47","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":99732,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLeyend:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupp. Fig. 3. \u003c/strong\u003eFrontal and lateral view of subcortical structures. Nuclei related with behavioural paths are coloured as indicate\u003c/p\u003e","description":"","filename":"SupplementaryFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7142478/v1/c80b9f38751fb6c2879b5442.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding the Developmental Trajectory of Behavioral Problems \u0026 Subcortical Structure Morphometry in Healthy Children at 6 years old and Long-Term Impact of Early Nutrition: The COGNIS Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBehavioral problems in early childhood are a potential indicator of psychopathological problems at later ages [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The global worldwide prevalence rate of mental disorders in children and adolescents is around 13.4%, similar to those reported for other chronic health problems, such as obesity (16.8%) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consequently, optimal clinical and practical methodologies must be crucial to design policies that address mental health needs of children and adolescents.\u003c/p\u003e\u003cp\u003eIt is well known that early nutrition is of vital importance for optimal brain development and subsequent prevention of behavioral problems in children. Adequate intake of essential nutrients, including but not limited to proteins, long-chain polyunsaturated fatty acids (LC-PUFAs), nucleotides, sialic acid, milk fat globule membrane (MFGM) and synbiotics, plays a critical role in shaping both brain structure and function [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In fact, recent studies have demonstrated that early-life nutritional supplementation can have long-lasting positive effects on cognitive function, brain structure and behavior development in later stages of development [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, promoting and ensuring a well-balanced and nutrient-rich diet during this critical period is necessary for supporting healthy brain development, thus reducing the risk of behavioral problems in children.\u003c/p\u003e\u003cp\u003eIn addition to environmental and genetic factors, both anatomical and functional bases of cortical and subcortical structures, as well as their maturation, also play a key role in the transition from simple signals to symptoms and subsequent mental disorders development [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this line, externalizing spectrum disorders in children, including oppositional defiant disorder (ODD), attention deficit/hyperactivity disorder (ADHD) and behavioral disorders, were traditionally considered as separate entities; however, based on their comorbidity patterns, trait impulsivity has been currently identified as a common factor [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Trait impulsivity is controlled by a series of dopaminergic subcortical mechanisms, including the nucleus accumbens, ventral tegmentum, and ventral areas of caudate and putamen [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Interestingly, these areas are not only part of the reward circuit of the brain, but also are associated with irritability and anhedonia, which might explain the poor response to incentives observed in externalizing disorders (ADHD, ODD, behavior problems and substance use disorder) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, above-mentioned structures also receive control inputs during maturation from cortical structures, mainly prefrontal cortex involved in emotional regulation and posterior cingulate cortex, as well as other subcortical structures, including amygdala, hippocampus, periaqueductal gray substance and medial hypothalamus[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In fact, current research supports that severity of externalizing symptoms is associated with a reduction in the activity of the amygdala and its connectivity with the prefrontal ventromedial cortex before frightening stimuli [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, this severity is also negatively correlated with volume of amygdala, thalamus and parahippocampal gyrus [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Taken together, these findings suggest that both cortical and subcortical structures play a fundamental role in explaining externalizing and internalizing disorders.\u003c/p\u003e\u003cp\u003eThe current study was aimed to determine the associations between longitudinal trajectories of early childhood behavioral problems evaluated at 18 months, 2.5 and 4 years old, and the volumes of the subcortical structures most frequently associated with these problems, including accumbens, amygdala, caudate, hippocampus, putamen, pale, thalamus and brainstem (which contains ventral tegmentum, substantia nigra and periaqueductal gray matter, among other areas) assessed at 6y. A secondary objective was to analyze the potential long-term effects of a bioactive nutrients-enriched infant formula on behavioral developmental trajectories up to 4 years old and its association with brain morphology.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv\u003e\n \u003ch2\u003e2.1. Participants\u003c/h2\u003e\n \u003cp\u003eThe initial study sample was based on the 220 (99 girls) healthy full-term infants participating in the COGNIS study, a prospective, double-blind, randomized clinical trial (RCT) designed to evaluate the effects of a novel infant formula on immunological and neurocognitive development in healthy infants (registered at www.ClinicalTrials.gov, Identifier NCT02094547). Detailed information on this project, including study design, subject recruitment and population characteristics, have been previously described [7]. Briefly, a total of 170 healthy Spanish infants aged between 0–2 months old were randomized (ratio 1:1) to receive, during their first 18 months of life, either a standard infant formula (SF, n = 85) or an experimental infant formula (EF, n = 85) enriched with MFGM components [10% of total protein content (wt:wt)], synbiotics [Fructooligosaccharides (FOS): Inulin proportion 1:1; \u003cem\u003eBifidobacterium longum\u003c/em\u003e subsp \u003cem\u003einfantis\u003c/em\u003e CECT7210 (\u003cem\u003eBifidobacterium infantis\u003c/em\u003e IM1) and \u003cem\u003eLactobacillus rhamnosus\u003c/em\u003e LCS-74], LC-PUFAs, gangliosides, nucleotides and sialic acid. Additionally, 50 exclusively breastfed infants were enrolled as a control group (BF, n = 50).\u003c/p\u003e\n \u003cp\u003eMaternal educational level was determined (0: primary, 1: secondary, 2: vocational training, 3: university) on the first visit. Maternal IQ was evaluated using Cattell’s intelligence test (G Factor) [18].\u003c/p\u003e\n \u003cp\u003eEmotional/behavioral evaluation using CBCL (Child Behavior Checklist\u003cem\u003e)\u003c/em\u003e test was conducted in COGNIS children at 18 months, 2.5 and 4 years old; however, in current analysis, only participants whose evaluation was completed at the three mentioned time points were considered (CBCL sample, n = 82, 35 girls / SF, n = 26; EF, n = 24; BF, n = 32). Structural Magnetic Resonance Imaging (MRI) was performed on 67 (24 girls) volunteering participants from the COGNIS study at 6 years old (mean age = 6.13; range [6.02–6.57 years old]). However, only the images of 37 participants (10 girls) were of adequate quality for further analysis. In this regard, 46 participants (25 girls) completed CBCL questionnaire at the three time points, but they had no MRI results or images obtained were deficient in quality (“Missing” group). Another set of participants was defined as those who had quality resonances but had not completed all three CBCL measures (“MRI only”, n = 19, 14 girls). Both “Missing” and “MRI only” groups were used in the sensitivity analysis.\u003c/p\u003e\n \u003cp\u003eThe COGNIS study was conducted in accordance with the revised Declaration of Helsinki II Principles [19]. Ethical approval was obtained from the Research Bioethical Committee of the University of Granada, Spain, as well as the Bioethical Committees for Clinical Research of the San Cecilio University Clinical and University Mother-Infant Hospitals of Granada. Prior to their participation in the study, all families were thoroughly informed about the procedures, and written informed consent was obtained from each parent or legal guardian.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003e2.2. Assessment of child behavior problems: CBCL test (Child Behavior Checklist)\u003c/h2\u003e\n \u003cp\u003eThe Child Behavior Checklist (CBCL)/1.5-5 inventory for preschoolers is designed to assess the child´s emotional and behavioral problems from 1.5 to 5 years old, and it is used internationally for clinical evaluation and research in early stages of development [20, 21]. The Spanish version of the CBCL/1.5-5 (Unitat d'Epidemiologia i de Disgnòstic en Psicopatologia del Desenvolupament, Barcelona) consists in 101 items divided into three scales (internalizing, externalizing and global). The internalizing scale includes four categories of problems: emotionally reactive, anxious/depressed, somatic complaints and withdrawn. The externalizing scale encompasses two types of problems: attention problems and aggressive behavior. CBCL/1.5-5 questionnaire also includes the DSM-oriented scales used in this analysis: affective problems, anxiety problems, pervasive developmental problems (autistic spectrum), ADHD, and oppositional defiant problems. For closed items, a Likert scale is used with answers ranging from 0 (\"not true\"), 1 (\"something or sometimes true\") or 2 (\"very true or often true\"), considering only the two previous months to the evaluation. The total direct score on a scale was obtained automatically. Direct scores on the total, externalizing, internalizing and DSM-oriented scales were used to perform the longitudinally study and to compute the developmental trajectories of the participants. Typical scores (T) were used only to facilitate categorization into clinical and borderline clinical problems (for example, in the summary scales T ≥ 64 is usually used for clinical problems, while 60 ≤ T ≤ 63 is used for clinical borderline) [22]. Parents or legal guardians received the CBCL/1.5-5 inventory and appropriate instructions to complete it. CBCL questionnaire was filled out by the mother (81.67%) or the father (18.33%). However, given that there was no difference from answers depending on who completed it (all p \u0026gt; 0.10), this factor was ignored in current analysis.\u003c/p\u003e\n \u003cp\u003eIn current study, the direct scores of internalizing, externalizing or total behavioral problems and DSM-oriented scales \u003cem\u003e(affective problems, anxiety, pervasive developmental problems, ADHD, and oppositional-defiant problems)\u003c/em\u003e were used. Externalizing and internalizing scales were considered as substitutes for syndromic scales. Although the total scale is the sum of the externalizing and internalizing, its use provides information relative to the total sum of problems that is not contained in either of two scales separately. Likewise, the direct score is considered instead of the T-score or percentiles to prevent taking normative samples as a reference, which could hide each participant’s trajectory over time [23].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003e2.3. Magnetic Resonance Imaging (MRI) procedure\u003c/h2\u003e\n \u003cp\u003eBrain imaging was performed when participants were 6.13 years old on average, range = [6.02, 6.57]. Finally, as previously indicated, brain images were evaluated by an expert radiologist to determine their quality and the presence of any brain abnormality, in accordance with the ethical standards of the University of Granada.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ch2\u003e2.3.1. Imaging data acquisition\u003c/h2\u003e\n \u003cp\u003eBrain structure scanning was performed using the Siemens Tim Trio 3T MRI system (Siemens, Erlangen, GE) equipped with a 32-channel head antenna, installed in the Mind, Brain and Behavior Research Centre (CIMCYC) (Granada, ES). Head movements were minimized using foam positioners around the participant's head. A T1w 3D MPRAGE (magnetization-prepared rapid acquisition with gradient echo) sequence was used with the following parameters: RT = 2300 ms, RT = 3.10 ms, IT = 900, flip angle = 9 °. In each volume, 208 slides of 0.8 mm thickness were acquired, so that the voxel size was 0.8x0.8x0.8 with a matrix size of 320x320.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ch2\u003e2.3.2. Subcortical Structures Morphometry\u003c/h2\u003e\n \u003cp\u003eThe structural volumes were processed using Freesurfer's (v. 6) recon-all script, a brain image analysis suite (available at http://surfer.nmr.mgh.harvard.edu/) running on the UNIX platform of the Alhambra supercomputer from the University of Granada (http://alhambra.ugr.es/). Preprocessing and segmentation of subcortical structures (including cerebellum, thalamus, caudate, putamen, pallidum hippocampus, amygdala, accumbens and brainstem) are fully automated. Processing details have been described elsewhere [24, 25]. Briefly, the automated protocol includes motion correction, non-cerebral tissue elimination, and correction of inhomogeneities in the magnetic field, linear and non-linear registration to the Talairach atlas as well as label dissemination for each voxel according to Freesurfer’s subcortical atlas. In subcortical segmentation, Freesurfer combines both intensity of the voxels with the probability distribution of the different types of brain tissue, as well as the spatial relationships of each voxel in relation to neighboring structures defined in labeled atlases [25]. After segmentation, volumes of each subcortical structure were obtained.\u003c/p\u003e\n \u003cp\u003eThe validity and reliability of this automatic segmentation is well established [26, 27] with intraclass correlation coefficients greater than 0.80 [27]. However, there is a tendency to overestimate the volume of the structures by around 10%, which can lead to an increase in the false-negatives rate [28]. In paediatric population, correspondence between the volumes of structures such as the amygdala and the hippocampus estimated by Freesurfer or manually has been found to be superior to other automatic methods such as FSL-First. Consequently, this method still needs improvement [29] and careful visual inspection is necessary to ensure segmentation quality. For this reason, an expert radiologist assessed the segmentation process, and 12 participants were excluded from this analysis due to low quality images or defective segmentation.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e\n \u003cp\u003eTo avoid any bias in the selection of the final sample, comparisons in the confounding variables were performed between final “MRI” sample and “Missing” sample using non-parametric Mann-Withney’s test. Welch’s robust t-test was used on both CBCL scales and volumes of subcortical structures, thus controlling the inequality of groups in size and variance.\u003c/p\u003e\n \u003cp\u003eLongitudinal differences between COGNIS groups were estimated using a generalized linear mixed model (GLMM) for repeated measures, controlling for sex, gestational age, maternal IQ and maternal educational level. Bonferroni-corrected post-hoc comparisons were used to identify significant pair-wise group differences (adjusted \u003cem\u003ep\u003c/em\u003e value \u0026lt; 0.05).\u003c/p\u003e\n \u003cp\u003ePotential associations between behavioral problems trajectories and subcortical structures volumetry were evaluated in two phases. First, the trajectory of each participant was computed in each of the evaluation’s scales using a polynomial approximation. To this effect, the time lag between evaluations was considered as well as the fact that two polynomials of first or second degree can be only defined given that there were only 3 CBCL measures. Therefore, ideal trajectories can be classified as: a) linear (the direct score grows/decreases steadily); b) quadratic (up/down peak on the direct score in the second measure); and c) mixed, in which the direct score shows an increase/decrease followed by a plateau, Type I, or vice versa, Type II \u003cstrong\u003e(Supplementary Fig.\u0026nbsp;1)\u003c/strong\u003e. The coefficients of each participant’s trajectory were next computed by linear correlation. Consequently, a linear trajectory with a positive coefficient indicates that direct score has increased steadily over time, while those with a negative coefficient indicates that score has decreased consistently with age. A quadratic trajectory with positive coefficient indicates that score was reduced at 2.5 years, but at 4 years returned at levels similar to the initial ones, while a negative coefficient indicates that score achieved a maximum peak at 2.5 years. Finally, a Type I trajectory describes an increasing/decreasing score (positive/negative coefficient, respectively) in the first time period that then remains constant during the second. Note that the coefficients so obtained are equivalent to standardized regression coefficients.\u003c/p\u003e\n \u003cp\u003eSpearman's nonparametric partial correlation coefficient was used to determine the association between the volume of subcortical structures and behavioral trajectory assessed by CBCL, considering sex, gestational age (months), maternal age (years), maternal IQ, maternal educational level (primary, secondary, second grade, University) and total intracranial volume (TIV). The latter factor is included since the volume of subcortical structures is expected to depend on the total brain volume. The criterion of statistical significance was p \u0026lt; 0.025.\u003c/p\u003e\n \u003cp\u003eAll statistical analyses were performed using the SPSS statistical software package (version 28.0; IBM SPSS Inc., Chicago, IL, USA) and Matlab software (version 20.0; Mathworks, US).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003ch2\u003e\u003cstrong\u003e\u003cem\u003e3.1. General characteristics of the COGNIS study participants.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e shows general characteristics of the children classified into COGNIS groups (type of early nutritional intervention) and with CBCL measurements obtained at three time-points (18 months, 2.5 and 4 years). Significant differences between study groups were found in terms of sex distribution and maternal educational level. In fact, a higher proportion of girls was observed in BF group compared to EF (p=0.010). Furthermore, mothers of BF infants showed a higher educational level (p=0.001) compared to mothers of both formula-fed groups (SF and EF). Differences found in maternal age disappeared after Bonferroni post hoc test.\u003c/p\u003e\n\u003cp\u003eAdditionally, we also evaluated general characteristics of the participant\u0026apos;s complete sample, regardless of type of early nutrition \u003cstrong\u003e(Table 1)\u003c/strong\u003e. In this case, data are presented into three groups: i) the participants who completed the three evaluations at mentioned time-points (\u0026ldquo;CBCL\u0026rdquo; group, n = 82); ii) participants who also presented adequate MRI at the age of 6 years (\u0026ldquo;MRI\u0026rdquo; group, n = 36); and iii) the participants with missing data due to absence or low quality MRI (\u0026ldquo;Missing\u0026rdquo; group, n = 46). As shown in \u003cstrong\u003eTable 1\u003c/strong\u003e, significant differences were only observed in maternal educational level (p \u0026lt;0.001) between \u0026ldquo;MRI\u0026rdquo; and \u0026ldquo;Missing\u0026rdquo; groups. These data suggest that maternal educational level could be an important factor in explaining the observed loss of data in current analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGeneral characteristics of the COGNIS study participants with the three inventory measurements (CBCL), participant\u0026acute;s with high quality of the brain\u0026rsquo;s magnetic resonance imaging (MRI) and Missing (missing values in the MRI).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"967\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSF (n=26)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEF (n=24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBF (n=32)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBCL (n=82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRI (n=36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissing (n=46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal age (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e31.6 (6.30)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e31.2 (5.02)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e34.5 (4.87)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e32.6 (5.55)\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e30.4 (5.80)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e34.3 (4.80)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal IQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e107.5 (14.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e104.1 (14.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e109.2 (14.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e107.1 (14.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e104.9 (14.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e108.9 (13.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal educational level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003ePrimary\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003cp\u003eVT\u003c/p\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003cp\u003e34.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003cp\u003e38.5\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003cp\u003e16.7\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e45.8\u003c/p\u003e\n \u003cp\u003e16.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003cp\u003e6.3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003cp\u003e65.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003cp\u003e18.3\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003cp\u003e42.7\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003cp\u003e30.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003cp\u003e22.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003cp\u003e8.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003cp\u003e58.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGestational Age (weeks)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e39.8 (1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e39.7 (1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e39.4 (1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e39.6 (1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e40.0 (1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e39.4 (1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (boys)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;65.4\u003csup\u003ea,b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e75.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e37.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e57.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e69.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e47.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth weight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e3.37 (0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.35 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3.37 (0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3.36 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3.39 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3.34 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth length (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e50.92 (2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e50.96 (2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e50.59 (2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e50.80 (2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e51.07 (2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e50.60 (2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC at birth (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e34.93 (1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e34.21 (1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e34.56 (1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e34.58 (1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e34.79 (1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e34.40 (1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are expressed as means (standard deviation); Numbers for maternal educational level and Sex are in percentage.\u003c/p\u003e\n\u003cp\u003eNotes. BF: breastfed infants; CBCL: Check Behavior Checklist; EF: experimental infant formula; IQ: intelligent quotient; MRI: Magnetic Resonance Imaging; n= number of cases; SF: standard infant formula; VT: Vocational Training; HC: head circumference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eValues not sharing the same suffix (ab) were significantly different in the Bonferroni post hoc test. \u0026nbsp;P-values \u0026lt; 0.05 are highlighted in bold.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;Insert Table 1 somewhere here\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003e3.2. CBCL scales\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe first analyzed whether type of early nutrition might have long-term effects on behavioral development from 18 months to 4 years old. For this purpose, a GLMM of repeated measures was used, which included a within-subjects time factor with three levels (18 months, 2.5, and 4 years) as well as a group factor with three levels (SF, EF and BF). Additionally, analysis was adjusted by sex, gestational age, maternal IQ and educational level. In the present analysis no statistical differences were found regarding CBCL scores between COGNIS groups \u003cstrong\u003e(Supplementary Figure 2)\u003c/strong\u003e. However, both EF and BF groups seem to show a more similar pattern regarding internalizing, externalizing and total problems \u003cstrong\u003e(Supplementary Figure 2 A-C)\u003c/strong\u003e. Finally, patterns obtained for DSM-oriented scales (affective problems, anxiety problems, pervasive developmental, ADHD and oppositional defiant) seem to be more similar in experimental formula-fed infants respect to those who were breastfed \u003cstrong\u003e(Supplementary Figure 2 D-H).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe next performed descriptive statistics for each of the inventory scales according to the set of participants \u0026ldquo;CBCL\u0026rdquo;, \u0026ldquo;MRI\u0026rdquo; and \u0026ldquo;Missing\u0026rdquo;. As shown in \u003cstrong\u003eTable 2\u003c/strong\u003e, there were no significant differences between \u0026ldquo;MRI\u0026rdquo; and \u0026ldquo;Missing\u0026rdquo; groups in any of the evaluations, although there was a trend in externalizing problems at 2.5 years [t (80) = 1.90, p = 0.061]. Therefore, these results indicate that infant\u0026rsquo;s behavior evaluation in both \u0026ldquo;Missing\u0026rdquo; and \u0026ldquo;MRI\u0026rdquo; groups was very similar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Descriptive statistics of the direct CBCL scores on the DSM-oriented, total, internalizing and externalizing scales.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"989\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 261px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBCL (n=82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRI (n=37)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissing (n=46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBCL Scales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18 (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5 (y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4 (y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18 (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5 (y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4 (y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18 (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5 (y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4 (y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternalizing Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7.96 (0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e9.3 (1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9.89 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8.43 (1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e10.27 (0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e10.65 (1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7.59 (0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8.52 (1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9.28 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExternalizing Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e13.58 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e13.07 (1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e12.3 (1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e14.51 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e14.49 (1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e12.35 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e12.83 (1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e11.93 (1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e12.26 (1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e35.18 (2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e36.2 (3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e34.23 (2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e36.86 (3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e38.97 (2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e34.68 (2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e33.83 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e33.98 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e33.87 (2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffective Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.51 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e2.86 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.46 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.76 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.78 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.59 (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.3 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.91 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.35 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.49 (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e4.06 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.02 (0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.49 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.19 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.97 (0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.5 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.96 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.07 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerv. Develop. Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.29 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e3.52 (0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.77 (0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.24 (0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.84 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.05 (0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.33 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.26 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.54 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADHD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5.42 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e4.86 (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.58 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5.62 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5.35 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.49 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5.26 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.46 (0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.65 (0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOppositional defiant Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.14 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e3.54 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.65 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.38 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4.08 (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.62 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.96 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.11 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.67 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are expressed as means (typical errors)\u003c/p\u003e\n\u003cp\u003eNote. Perv. Develop. Problems: Pervasive Developmental Problems; ADHD: attention deficit hyperactivity disorder; m: months; y: years\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;Insert Table 2 somewhere here\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe percentages of participants with clinical problems (T\u0026ge;64) and clinical borderline (60\u0026le;T\u0026le;63) were calculated in relation to the preliminary 82 participants for each of the scales \u003cstrong\u003e(Table 3)\u003c/strong\u003e. Our results show that the most frequent clinical problems were the developmental ones, which reached a maximum at 4 years (20.7%). Considering clinical borderline, the most frequent were the affective and anxious problems at 2.5 years (22% and 18.3%, respectively). Overall, internalizing problems were more common than externalizing, both at clinical and subclinical levels. The internalizing problems showed a growing trend with a maximum at 4 years (12.2% and 17.1% clinical and subclinical, respectively), while the externalizing problems did not show this same tendency. Further analysis was aimed to evaluate the percentage of participants whose T-scores increased or decreased between the three-evaluation time-points on a subject-by-subject basis \u003cstrong\u003e(Table 3)\u003c/strong\u003e. Overall, a major increase was observed between 18 months and 2.5 years compared to 2.5-4 years period, supporting a non-linear overall trajectory. More specifically, anxiety problems grow by 19.5% between 18 months and 2.5 years, and by 14.6% between 2.5 and 4 years. Similar pattern was observed in affective (18.3 vs. 9.8%), ADHD (9.8 vs. 4.9%), and oppositional defiant (11.0 vs. 7.3%) problems. However, the developmental problems showed a different increase pattern, with a lower percentage in the early period than in the later one (9.8 vs 18.3%). On the other hand, the decrease pattern is more diffuse since no general trend was observed. In fact, externalizing, ADHD and developmental problems decreased more in the first-time frame than in the second, while affective and, to a greater extent, anxiety problems showed a larger decrease in the later period (8.5 vs 14.6% and 7.3 vs. 17.1%, respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;Prevalence of clinical pathological and clinical borderline problems in the total participants\u0026rsquo; sample (n = 82), with the percentage of participants whose T scores increase / decrease from one measure to the next (2.5 y-18 m, 4-2.5 y).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.357%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 21.4826%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical pathology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 19.9697%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical borderline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 17.0953%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncrease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 17.0953%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDecrease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.357%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBCL Scales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.26172%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.74887%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5y-18m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4-2.5y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5y-18m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4-2.5y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.357%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternalizing Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.26172%;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.74887%;\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.357%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExternalizing Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.26172%;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.74887%;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.357%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.26172%;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.74887%;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.357%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffective Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.26172%;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.74887%;\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.357%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.26172%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.74887%;\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.357%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerv. Develop. Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.26172%;\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.74887%;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.357%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.26172%;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.74887%;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.357%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOppositional Defiant Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.26172%;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.74887%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.98487%;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.11044%;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are expressed as percentages of the total population.\u003c/p\u003e\n\u003cp\u003eNote. Perv. Develop. Problems: Pervasive Developmental Problems; ADHD: attention deficit hyperactivity disorder; m: months; y: years\u003c/p\u003e\n\u003cp\u003eWe next evaluated the ability to predict participants\u0026rsquo; borderline or clinical scores at 4 years old considering previous borderline or clinical scores obtained at earlier ages. For this purpose, the odds of a positive or normal classification, considering a positive at the 18-months-old or 2.5-years-old evaluation, were calculated \u003cstrong\u003e(Table 4)\u003c/strong\u003e. Overall, our data suggest that the risk of obtaining a borderline or clinical score at 4 years old was significantly higher if it has been obtained previously, except for anxiety problems (OR=2.16, p=0.131).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Conditional probabilities and odds ratio (OR) for the CBCL scales obtained in total sample (n = 82).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBCL Scales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4158%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep (O=1/(A=1| B=1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9313%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep (O=0/(A=1 | B=1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.543%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3402%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3711%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternalizing Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4158%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9313%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.543%;\"\u003e\n \u003cp\u003e16.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3402%;\"\u003e\n \u003cp\u003e1.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3711%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExternalizing Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4158%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9313%;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.543%;\"\u003e\n \u003cp\u003e8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3402%;\"\u003e\n \u003cp\u003e2.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3711%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4158%;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9313%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.543%;\"\u003e\n \u003cp\u003e16.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3402%;\"\u003e\n \u003cp\u003e2.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3711%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffective Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4158%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9313%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.543%;\"\u003e\n \u003cp\u003e9.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3402%;\"\u003e\n \u003cp\u003e1.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3711%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4158%;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9313%;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.543%;\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3402%;\"\u003e\n \u003cp\u003e1.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3711%;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerv. Dev. Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4158%;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9313%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.543%;\"\u003e\n \u003cp\u003e6.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3402%;\"\u003e\n \u003cp\u003e1.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3711%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4158%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9313%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.543%;\"\u003e\n \u003cp\u003e9.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3402%;\"\u003e\n \u003cp\u003e2.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3711%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3986%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOppositional Defiant Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4158%;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9313%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.543%;\"\u003e\n \u003cp\u003e2.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3402%;\"\u003e\n \u003cp\u003e2.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3711%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. O: evaluation at 4 years; A: evaluation at 18 months; B: evaluation at 2.5 years; OR: Odds-Ratio; SE: standard error of the OR; 0: normal; 1: borderline or clinical pathology. Perv. Develop. Problems: Pervasive Developmental Problems;\u0026nbsp;P-value: Level of significance.\u003c/p\u003e\n\u003cp\u003eFinally, non-parametric Spearman\u0026apos;s correlations between the three CBCL evaluations time-points in the total sample, controlling for confounding variables, were obtained \u003cstrong\u003e(Table 5)\u003c/strong\u003e. All correlations were positive and statistically significant (p \u0026lt;0.01). Overall, our results suggest that the scores are serial dependent and, therefore, the percentiles at a specific time point (t) might be partly predictable from the percentiles at previous time points (t-1 and t-2). Using the algorithm described by Lee and Preacher (2013), which contrasts the differences between correlated correlation coefficients, no significant differences were observed in any of the scales (data not shown). According to these results, the scores are consistent over time, thus supporting the use of trajectory computation for determining potential effects of direct scores on subcortical structures volume, compared to classical approximation ways based on general linear model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Spearman\u0026apos;s partial correlations between the three evaluations of CBCL (n=82), controlling by sex, gestational age, maternal IQ and educational level.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7401%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBCL Scales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3971%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18m-2.5y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1047%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18m-4y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5-4y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7401%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternalizing Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3971%;\"\u003e\n \u003cp\u003e0.352*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1047%;\"\u003e\n \u003cp\u003e0.334*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7581%;\"\u003e\n \u003cp\u003e0.493*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7401%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExternalizing Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3971%;\"\u003e\n \u003cp\u003e0.562*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1047%;\"\u003e\n \u003cp\u003e0.514*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7581%;\"\u003e\n \u003cp\u003e0.547*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7401%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3971%;\"\u003e\n \u003cp\u003e0.482*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1047%;\"\u003e\n \u003cp\u003e0.491*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7581%;\"\u003e\n \u003cp\u003e0.607*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7401%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffective Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3971%;\"\u003e\n \u003cp\u003e0.300*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1047%;\"\u003e\n \u003cp\u003e0.356*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7581%;\"\u003e\n \u003cp\u003e0.504*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7401%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3971%;\"\u003e\n \u003cp\u003e0.417*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1047%;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7581%;\"\u003e\n \u003cp\u003e0.439*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7401%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePervasive Developmental Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3971%;\"\u003e\n \u003cp\u003e0.290*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1047%;\"\u003e\n \u003cp\u003e0.315*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7581%;\"\u003e\n \u003cp\u003e0.377*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7401%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3971%;\"\u003e\n \u003cp\u003e0.423*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1047%;\"\u003e\n \u003cp\u003e0.397*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7581%;\"\u003e\n \u003cp\u003e0.427*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.7401%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOppositional defiant Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3971%;\"\u003e\n \u003cp\u003e0.340*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1047%;\"\u003e\n \u003cp\u003e0.334*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7581%;\"\u003e\n \u003cp\u003e0.499*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. ADHD: attention deficit hyperactivity disorder; m: months; y: years. * p \u0026lt;0.05.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003e3.3. Volumetry of subcortical structures.\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe volume of subcortical brain structures for MRI participants and for those with quality MRI but had not completed all three CBCL measurements (\u0026ldquo;MRI only\u0026rdquo;) are described in \u003cstrong\u003eTable 6\u003c/strong\u003e. As can be seen, no significant differences in brain structure volumetry were found, indicating there is no selection bias in the final sample (\u0026ldquo;MRI\u0026rdquo; group).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u003c/strong\u003e \u003cstrong\u003eVolume (mm\u003csup\u003e3\u003c/sup\u003e) of subcortical brain structures in \u0026ldquo;MRI\u0026rdquo; and \u0026ldquo;MRI Only\u0026rdquo; participants.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStructure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRI (n=37)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRI Only (n=18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL. Cerebellum white matter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e13298 (277.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e12824 (400.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL. Cerebellum (cortex)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e58108 (973.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e56400 (1409)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL. Thalamus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e7366 (99.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e7139 (155.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL. Caudate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e3893 (66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e3828 (99.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL. Putamen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e5284 (96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e5138 (137.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL Pallidum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e1959 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e1901 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrain stem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e17812 (335.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e17101 (498.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL. Hippocampus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e3716 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e3661 (83.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL. Amygdala\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e1462 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e1443 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL. Accumbens\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e576 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e564 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR. Cerebellum white matter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e12418 (238.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e11956 (344.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR. Cerebellum (cortex)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e57455 (962.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e55628 (1413.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR. Thalamus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e7350 (99.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e7130 (152.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR. Caudate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e4000 (65.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e3911 (99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR. Putamen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e5387 (94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e5244 (134.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR. Pallidum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e1808 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e1760 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR. Hippocampus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e3873 (60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e3800 (78.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR. Amygdala\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e1652 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e1619 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.3777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR. Accumbens\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e644 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.597%;\"\u003e\n \u003cp\u003e629 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4283%;\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are expressed as means (standard error of the mean); P-value: nivel de significancia; L: left; R: right.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003e3.4. Developmental Patterns of the CBCL scales.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe proportion of participants whose trajectory can be described mainly by one of the four defined patterns {linear (L), quadratic (Q), Type I and Type II} are shown in \u003cstrong\u003eTable 7\u003c/strong\u003e.\u0026nbsp;Each proportion has been calculated from the correlation coefficient between the three CBCL measurements and the ideal trajectory coefficients. A value of 0 was defined as cut-off point in the magnitude of the correlation, so that correlations less than or equal to 0 are considered negative trajectories, while those greater than 0 are considered positive. For example, to properly interpret the results obtained, it should be taken into account that a value of 0.61 in linear internalization means that 61% of the participants tended to increase their percentile over time, while the remaining 39% tended to decrease it. Likewise, a value of 0.43 in the quadratic internalization means that 43% of the participants showed a minimum in the measurement obtained at 30 months, while 57% showed a maximum at that same age. A value of 0.58 in Type I internalization means that 58% of the participants increased their percentile between 18 months and 2.5 years and remained at values similar to those of 2.5 years when they were evaluated at 4 years; consequently, 42% of the participants decreased their percentile at 2.5 years and remained approximately at that value at 4 years. Since the classification is not exclusive, each participant has a weight in each of the trajectories. Interestingly, using Mann-Whitney\u0026rsquo;s test, there is only a significant difference between the \u0026ldquo;MRI\u0026rdquo; and \u0026ldquo;Missing\u0026rdquo; samples in total quadratic trajectory score (p=0.03), which once again confirms that the final sample (\u0026ldquo;MRI\u0026rdquo;) is not biased.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7.\u003c/strong\u003e \u003cstrong\u003eProportion of participants in each group that show the different trajectory patterns over time (linear, quadratic, Type I and Type II) for each inventory scale.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"870\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBCL group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRI group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissing group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBCL Scales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternalizing Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExternalizing Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.41*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffective Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerv. Develop. Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOppositional defiant Problems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: L: linear trajectory pattern; Q: quadratic trajectory pattern; Perv. Develop. Problems: Pervasive Developmental Problems; ADHD: attention deficit hyperactivity disorder; *: p-value \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;Insert Table 7 somewhere here\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003e3.5. Relationship between the volumes of subcortical brain structures and CBCL trajectories.\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe partial correlation analysis of the evolution trajectories of direct CBCL scores and the volumes of subcortical brain structures is summarized in \u003cstrong\u003eTable 8\u003c/strong\u003e. As shown in \u003cstrong\u003eSupplementary Figure 3\u003c/strong\u003e, the volume-CBCL-trajectory relationship was both linear and non-linear, and 9 brain structures whose volume is related to CBCL trajectories were identified: bilateral hippocampus, accumbens, amygdala (right), pallidum (left), bilateral cerebellar cortex and bilateral putamen.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8.\u003c/strong\u003e \u003cstrong\u003eNon-parametric correlations between developmental trajectories determined by CBCL scales and the volume of brain subcortical structures. Only significant correlations are presented.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStructure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRho\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eInternalizing Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eL. Hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eInternalizing Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eQuadratic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eInternalizing Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eL. Pallidum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eQuadratic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eInternalizing Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eType I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eInternalizing Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eL. Pallidum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eType I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eInternalizing Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eL. Hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eType I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eInternalizing Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eExternalizing Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eL. Putamen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eExternalizing Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Putamen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eType I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eExternalizing Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Cerebellum (cortex)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eType II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eExternalizing Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eL. Putamen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eType II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eExternalizing Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Putamen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eTotal Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Amygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eAnxiety Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eL. Pallidum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eType I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003ePerv. Develop. Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Amygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eType II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003ePerv. Develop. Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eL. Accumbens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eL. Cerebellum (cortex)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Cerebellum (cortex)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Amygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eQuadratic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eQuadratic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eOppositional Defiant Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8211%;\"\u003e\n \u003cp\u003eType I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.0976%;\"\u003e\n \u003cp\u003eOppositional Defiant Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.3659%;\"\u003e\n \u003cp\u003eR. Amygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e-0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3577%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eL: left; R: right; Perv. Develop. Problems: Pervasive Developmental Problems; ADHD: Attention Deficit Hyperactivity Disorder. \u0026nbsp; Spearman\u0026rsquo;s correlations. P-value: level of significance.\u003c/p\u003e\n\u003cp\u003eA negative correlation between linear trajectory in internalizing problems and both right and left hippocampus volume were found; however, when the trajectory is quadratic, with a minimum at 2.5 years, an increase in volume of the left pallidum and right hippocampus was observed. Likewise, when the trajectory is Type I with a maximum at 2.5 years that continues at 4 years, both left pallidum and both hippocampus decrease in volume \u003cstrong\u003e(Figure 1, upper row)\u003c/strong\u003e. In addition, a Type II trajectory (with a plateau at the minimum reached at 2.5 years) in externalizing problems is associated with greater volumes of left and right putamen \u003cstrong\u003e(Figure 1, lower row)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;Insert Figure 1 somewhere here\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding total problems, a negative correlation was observed between the linear trajectory and the volume of the right amygdala \u003cstrong\u003e(Figure 2)\u003c/strong\u003e, indicating that an incremental trajectory of these problems associated with a reduction in right amygdala volume. Interestingly, no other trajectory correlated significantly with the volumes of this subcortical structure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;Insert Figure 2 somewhere here\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn the other hand, the linear trajectory of anxiety problems correlated negatively with the volume of the left pallidum (r = -0.477, p = 0.007, \u003cstrong\u003eFigure 3\u003c/strong\u003e), then left pallidum volumetry seems to be smaller when there is a steady increase in the trajectory of anxiety problems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;Insert Figure 3 somewhere here\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLikewise, in terms of developmental problems, Type I trajectory negatively correlated with the volume of the right amygdala (r = -0.474, p = 0.007, \u003cstrong\u003eFigure 4 left\u003c/strong\u003e); however, a positive correlation was found between Type II trajectory and right nucleus accumbens volumetry (r = 0.408, p = 0.023, \u003cstrong\u003eFigure 4 right\u003c/strong\u003e). The first result implies an increase in the size of the amygdala when development problems decrease and stabilize at 2.5 years. The second result indicates that the volume of right nucleus accumbens tends to be higher the greater the reduction of developmental problems at 4 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;Insert Figure 4 somewhere here\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding ADHD problems, significant correlations were observed with both left and right cerebellar cortex, right amygdala and right hippocampus \u003cstrong\u003e(Figure 5)\u003c/strong\u003e. Thus, the volumes of both left and right cerebellum, as well as right amygdala, negatively correlated with a linear trajectory (r = -0.585, p = 0.001; r = -0.639, p \u0026lt;0.001; r = -0.483, p = 0.006, respectively). Therefore, both cerebellar and right amygdala volumes are smaller the higher the level of problems with increasing age. Conversely, the right hippocampus volume positively correlated with a quadratic trajectory of ADHD problems (r = 0.459, p = 0.009), which suggests that the volume of the hippocampus could be positively affected by a reduction in the level of problems at a minimum peak around the age of 2.5 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;Insert Figure 5 somewhere here\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, the Type I trajectories of oppositional-defiant problems were negatively associated with the right hippocampus and right amygdala (r = -0.476, p = 0.007; r = -0.504, p = 0.004, respectively) \u003cstrong\u003e(Figure 6)\u003c/strong\u003e. Therefore, the volume of both subcortical brain structures is higher the greater the reduction of these problems at 2.5 and 4 years with respect to the level observed at 18 months.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;Insert Figure 6 somewhere here\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis research was primarily aimed to understand the developmental trajectories of behavioral problems during the first 4 years of life and determine their potential associations with children\u0026rsquo;s subcortical brain structures volumes. Moreover, potential long-term effects of early nutrition on such trajectories and their potential associations with brain morphology at 6 years old were also evaluated. Within COGNIS framework, we have previously reported that bioactive compounds-enriched infant formula, containing MFGM components, synbiotics, LC-PUFAs, gangliosides, sialic acid and nucleotides, provide a protective role against behavioural problems development at 2.5 years old \u003cem\u003e(affective problems)\u003c/em\u003e compared to standard formula-fed infants, and similar to those effects found in breastfed infants. In fact, these results showed significant higher increase of internalizing, externalizing and total problems, as well as ADHD and Oppositional Defiant problems from 18 months to 2.5 years in the SF group, compared to EF and BF infants [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nevertheless, in the current longitudinal study, a similar effect on behavioural development could not be identified, which can be mostly explained by the smaller sample size included in the present analysis. Interestingly, as previously reported, our results also suggest that maternal factors, including maternal IQ and educational level, lead to better mental health and behavioural development in childhood [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In other previous publication, we demonstrated the importance of early nutrition up to 18 months of life, on later brain structure at 6 years old and neurocognitive outcomes, similar in EF children to BF ones, and better than those fed SF [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, our results highlight the inclusion of both early nutrition and maternal factors into designed policies that address mental health needs of children and adolescents. However, it is noteworthy that important sets of results in the present analysis of COGNIS study data has yielded two key findings: the first relates to behavioral developmental trajectories during the first four years of life, and the second to the association between these trajectories and the volume of children\u0026rsquo;s subcortical brain structures at 6 years of age. Overall, results obtained here suggest that behavioral trajectories lead to different relationship between behavioral problems up to 4 years and subcortical brain structures morphometry. More specifically, these behavioral trajectories seem to have a profound effect on volumes of hippocampus, amygdala, pallidum, cerebellum, putamen and accumbens throughout the first four years of life, in which higher levels of problems over time were associated with reduced volume of these structures. Interestingly, results obtained from non-linear trajectories also suggest that subcortical nuclei morphometry, particularly the hippocampus, could depend on behavioral problems in a critical period around the age of 2.5 years old. Taken together, our results emphasize the need for early and effective prevention and detection of behavioral problems during the first years of life, thus reducing the risk of psychopathological disorders later in life.\u003c/p\u003e\u003cp\u003eFirst, in relation to the developmental trajectories, our results indicate that the CBCL evaluations are consistent over time, showing a high degree of autocorrelation. Interestingly, except for the anxiety scale, these trajectories might allow a significant prediction of possible diagnostic categorization (borderline or clinical) from measurements taken up to 2.5 years earlier. Moreover, from an analytical point of view, these data also suggest that, when repeated measurements are performed, an analysis approach that does not take into account the autocorrelation of the measurements (for example: analysis of variance with repeated measures) will produce unreliable results [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], with incorrect statistical significances [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In contrast to other development curve analysis strategies, such as linear mixed models or mixed growth models [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], the one used here offers the advantage of modelling the set of possible curves in a concrete way, as well as assigning a weight for each participant in each of these curves. Therefore, it can be used regardless of the total number of participants in the sample. Based on this developed methodology, our results suggest that CBCL scales in children with typical development do not seem to follow the same development curves. In fact, in the case of both anxiety and developmental internalizing problems, the greatest weight corresponds to a linear trajectory, while total, externalizing and ADHD-type problems follow a quadratic scheme, with a peak at the age of 2.5 years. Likewise, affective and oppositional-defiant problems follow a Type I pattern, with a plateau starting at 2.5 years. Finally, the total scale most often follows a Type II pattern with a plateau up to 2.5 years old.\u003c/p\u003e\u003cp\u003eOnce analyzed the developmental trajectories in our study population, we next evaluated their potential relationships with the volume of subcortical brain structures. A total of 9 structures, 3 bilateral (cerebellum cortex, hippocampus and putamen), 2 in the left hemisphere (accumbens and pallidum), and one in the right hemisphere (amygdala), showed significant correlations with developmental trajectories. Specifically, regardless of the category of problems, a linear trajectory is significantly associated with all mentioned brain structures. In all cases, the volume of these structures decreases as the negative slope of the curve becomes steeper, thereby exacerbating the level of problems over time. On the other hand, quadratic trajectory is positively associated with right hippocampus and pallidum, so that the volume of these structures seems to increase when there is a minimal peak at 2.5 years old. Thirdly, Type I curve is negatively associated with different brain structures, including pallidum, bilateral hippocampus, right cerebellar cortex and right amygdala. These data indicate that volume of mentioned brain structures is smaller the greater the trajectory weight, supporting that low score at 18 months, although followed by a subsequent increase, are associated with higher structural volume. Finally, Type II trajectory is positively associated with bilateral putamen and left accumbens, which implies that their volume is greater when the scores have their minimum after the 2.5 years old. Having in mind these results, it is feasible to think that there could be a critical age around 2.5 years in relation to the development of brain structures, not determined by the score at that age, but its relation to the obtained at the other ages.\u003c/p\u003e\u003cp\u003eRegarding internalizing problems, our results indicate a bilateral hippocampal volume reduction when the problem trajectory is linear, but an increase in the left pallidum and right hippocampus volume when this trajectory is quadratic. Our results with the linear trajectory are consistent with previous research on volume of subcortical structures and internalizing problems in the pediatric and adolescent population, including depression, anxiety or phobias, where hippocampal volume reduction and volume increase of the left pallidum globe have been observed [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], Mueller et al. (2013) Merz, He and Noble, (2018) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Although the role of hippocampus in psychopathological problems is well known (Macpherson and Hikida, 2019), the potential effects of the increase in the volume of the pallidum are still poorly understood. In this sense, this nucleus is part of three cortico-subcortical circuits (sensory-motor, cognitive and limbic) through which facilitates or suppress behavior via regulation of the subthalamic nucleus (STN) and the substantia nigra pars reticulata (SNr) activities. Interestingly, these two structures are involved in the regulation of the thalamus, one of whose functions is salience attribution to stimuli. Consequently, reduced control by the pallidum on the STN/SNr pair results in the reduction of thalamic activity and the appearance of symptoms characteristic of depression or anxiety [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Although this seems to explain its potential effects on internalizing problems, further studies are still needed.\u003c/p\u003e\u003cp\u003eConcerning externalizing problems, our results highlighted bilateral putamen (linear and Type II trajectory) and left cerebellar cortex (Type I trajectory). Due to both linear and Type II trajectories include the difference between the two-time limits (4 years and 18 months) but of opposite sign, we will discuss both together. Furthermore, considering the externalizing DSM scales, our results showed a reduction in the volume of right amygdala and bilateral cerebellar cortex in ADHD with linear trajectory, reduction of right amygdala (Type I trajectory) and increase of left accumbens (Type II trajectory) in developmental problems; as well as a reduction of right hippocampus and amygdala in oppositional defiant problems (Type I trajectory). Thus, except for the right cerebellum, there seems to be no common nuclei between the overall score of the externalizing scale and the specific scales of this type of problem. However, using functional MRI, it has been observed that both putamen and bilateral accumbens are activated in adolescents with externalizing symptoms in response to reward, with respect to non-reward [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Moreover, in cases with clinical levels, both a dysfunction of the amygdala and structures that process the reward led to emotional empathy disorders as well as problems in learning by strengthening and taking of decisions [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. On the other hand, both structural integrity and connectivity of the cerebellum seem to be crucial factors explaining the called \"p factor\" [(general factor of psychopathology [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]], due to poor efficiency in the operation of information processing systems could be based on externalizing and internalizing disorders. Consequently, both factors have been proposed as risk markers for developing any psychopathology [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, the cerebellum not only play a key role in movement control and motor coordination but also seems to control visual attention and operational memory [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Currently, its consideration as a node of the dorsal attentional network (DAN) also allows it to influence on the maintenance of attention over time (sustained attention) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Taking into account these considerations, and although not as differentiated as the cerebral cortex, the cerebellum seems to have a relevant role in complex cognitive functions such as attention, working memory, and social and emotional processing [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn adolescent patients with ADHD, lower volumes of the hippocampus and amygdala have been observed with respect to the control group with usual development [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In a recent study, conducted under the umbrella of the ENIGMA collaboration [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], in which morphometric alterations were addressed in 1713 participants diagnosed with ADHD and 1529 controls, at ages between 4 and 63 years, it was observed that, with volume effect sizes is higher in children than in adults, showed a volumetric reduction in accumbens, hippocampus and putamen, in addition to total intracranial volume and caudate. Our results are consistent with those of this study, although they also suggest that this is especially true in children with linear developmental trajectories, but not so much in quadratic trajectories, in which the right hippocampus could benefit if the level of the problem is reduced at 2.5 years old. Unfortunately, there is no published literature on the effect of non-linear trajectories on the development of cortical or subcortical structures, so we can only speculate on the potential benefits of an intervention aimed to reduce the level of problems at an early age.\u003c/p\u003e\u003cp\u003eThe role of the amygdala and its developmental trajectory at an early age is not yet well known, especially with regard to its potential consequences on behavioral problems [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Although it is a very small structure (approximately 0.3% of the total brain volume), it is attributed a key role in the processing of outgoing environmental information, emotional learning, sensory information and prior knowledge linking as well as in the ability to produce an adaptive response. In this regard, the amygdala is important in social cognition, since it is probably the one that organizes the ocular exploration of space, especially the faces of others, the evaluation of the trust that can be placed on others or the identification facial emotional expression [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Consequently, several psychiatric disorders have been linked to a malfunction of the amygdala, including obsessive-compulsive disorder, anxiety or problems in emotion regulation [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Moreover, this malfunction could also contribute to the development of problems related to autistic spectrum disorders [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Structurally, a greater volume of the bilateral amygdala has been observed in 5-year-old children with ASD [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In line with these findings, our data also suggest that the volume of the amygdala could be a good indicator for the trajectory of total problems. In this sense, the number of neurons in the amygdala grows postnatally, leading to about a 40% increase in volume at a mature stage. However, in patients with ASD, an abnormal developmental trajectory has been found, resulting in a substantially lower final number of mature neurons [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. It is also important to note that abnormal development of the amygdala is also characterized by an excessive number of dendritic spines in ASD patients [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], which can lead to an inappropriate emotional development sequence [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In addition, in healthy patients, a change in the connectivity of the amygdala with the medial prefrontal cortex is observed over time. While in childhood a positive relationship is observed, where the increases in one structure are associated with increases in the other, in maturity a negative relationship is described so that, increases in the one decrease the activity of the other [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This pattern is altered in ASD patients, thus supporting the potential role of malfunction of the amygdala in this disorder [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The nucleus accumbens also seems to play a crucial role in ASD. In fact, study carried out in animal model showed that that the decrease in the serotonin levels injected into the accumbens causes social behavior deficiencies. These difficulties are reversed when the dorsal raphe is activated [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These findings suggest that the underlying cause could be brainstem-born abnormalities in the serotonergic circuit that spread to the subcortical structures and the cerebral cortex [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOppositional defiant disorder (ODD) is perhaps the most frequent childhood disorder, with prevalence rates around 5% in the 6\u0026ndash;16 age group [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Its characteristics are well known and related to respect for/negativity before authority and norms, animosity and disobedience. A recent meta-analysis suggests that insecure and disorganized attachment is associated with a higher probability of developing ODD but cannot be considered a necessary cause [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In the long term, it is associated with difficulties in accomplishing academic and professional achievements and developing antisocial behaviors [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], which also associates ODD with \"insensitivity traits'' (\"callous traits\") [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. These characteristics, in 8-to-11-year-old children correlate negatively with the total volume of gray and white matter, and modestly with the volume of the right amygdala [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. A decrease in hippocampal activation has been observed in tasks that require effort to regulate emotions [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the current study, no significant association were observed between trajectories of affective problems and subcortical volume. This null finding is surprising because there is abundant literature that suggests that affective problems such as depression are associated with volume reductions or increases in subcortical structures such as the hippocampus [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] or the amygdala, respectively [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. A possible explanation of this difference is that it is possible that this relationship emerges later in time. For example, Jaworska et al. (2016) participants were 12\u0026ndash;25 years old, and Albaugh et al, (2017) used an even larger range of age. Similarly, a more recent study has a smaller range of age but start when children were 6 years [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe main contribution of the present research is that a longitudinal strategy is used, that is, the trajectories of behavioral problems are evaluated throughout the first years of life (18 months, 2.5 and 4 years of age), instead of a transversal approach, in which each measure of the problem level is considered in isolation. We believe that this longitudinal approach allows us to determine more precisely if the brain structures morphometry anatomy is really related to the evolution of the problem. Furthermore, the analysis of our \u0026ldquo;Missing cases\u0026rdquo; indicate that the final sample is not biased neither in terms of the CBCL scales nor regarding the confounding factors. Nor does our final sample differ from the \u0026ldquo;Missing cases\u0026rdquo; at same-quality MRI.\u003c/p\u003e\u003cp\u003eThe results of our research, although consistent with the literature, have some limitations that restrict their generality and interpretation. The first limitation is the smaller size of the final sample compared to the standards in literature. However, very few published studies use a longitudinal strategy like the one used here, which adds extra value to our results. The second limitation is related to the small number of observations available to obtain the developmental trajectories and the temporal distance between them. Unfortunately, it was not possible to obtain cerebral structural images at the ages of 18 months and 2.5 years old, which made it impossible to compare and associate the trajectories of the subcortical structures and the behavioral development. Finally, our results are relative to volume of structures, not function. The relationship between structure and function is not yet entirely clear, although it is common to assume that greater volume implies greater functionality. However, functional studies are necessary to strengthen the interpretations regarding the structure-function relationship.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe relationship between the morphometry of subcortical brain structures and behavioral problems is influenced by the trajectories of these problems. The hippocampus, amygdala, pallidum, cerebellum, putamen, and accumbens appear to be the most affected structures by the history of behavioral problems during the first four years of life. Generally, trajectories with higher levels of problems over time are associated with reduced structure volumes. However, the observed non-linear trajectories suggest that the morphometry of subcortical nuclei, particularly the hippocampus, may be influenced by behavioral problems during a critical period around 2.5 years of age. Building on our previously published findings at this age, the new data presented here further emphasize the importance of supplementing infant formula with bioactive components naturally present in human milk as strategy for reducing later behavioral problems and changes in brain morphology. Based on our previously published data and the present analysis, it is suggested that such supplementation of infant formulas could help protect children from behavioral problems during their early years, promote optimal brain morphology development in childhood, and reduce the risk of mental illnesses in adulthood. The present study also highlights the need for further adequately powered studies with long-term follow-up.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADHD: Attention Deficit/Hyperactivity Disorder; BF: Breastfed infants; CBCL: Child Behavior Checklist; EF: Bioactive compounds-enriched infant formula; FOS: Fructooligossacharides; GLMM: Generalized linear mixed models; MFGM: Milk fat globule membrane; MRI: Magnetic resonance Imaging; LC-PUFAs: Long-chain polyunsaturated fatty acids; ODD: Oppositional Defiant Disorder; RCT: Randomized Clinical Trial; SF: Standard infant formula; T: Typical Scores.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe COGNIS study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Research Ethics Committee of the University of Granada, the Bioethical Committees for Clinical Research of the Clinical University Hospital San Cecilio and the Mother-Infant University Hospital of Granada, Spain. The project was registered at www.ClinicalTrial.gov no.: NCT01634464. Informed consent was obtained from all subjects involved in the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available on request form the corresponding author. The data are not publicy available due to ethical reasons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDra. Roser De-Castellar and Dra. Mª Teresa Pérez are employees of Ordesa Laboratories S.L., company that have funded in part the COGNIS RCT study.\u003c/p\u003e\n\u003cp\u003eThe remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project has been funded by Laboratorios Ordesa, S.L. Contract University of Granada General Foundation, No. 3349 and SMARTFOODS (CIEN) Contract University of Granada General Foundation, No. 4003, Spanish Ministry of Economy, Industry and Competitiveness. Furthermore, the project has been partially funded by HORIZON 2020 EU DynaHEALTH Project (GA No. 633595).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors´ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, \u0026nbsp;CC, AC; Study design and methodology, \u0026nbsp;CC; AC ; Formal analysis, EC-V, AN-R, AC; Investigation, EV-C, AN-R, FH \u0026nbsp;; Data curation, EV-C, AN-R, AC ; Writing-original draft preparation, EC-V, AN-R, JAGS ; Writing-review and editing, JAGS, AC, CC ; Supervision, CC; Project administration, CC; Funding acquisition, \u0026nbsp; Rd-C, MTP-H, CC ; All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors want to acknowledge the parents and children who participated in the study, the pediatricians and technicians of the EURISTIKOS Excellence Centre for Paediatric Research at the Department of Pediatrics (School of Medicine, University of Granada, Spain), \u0026nbsp;the technicians of the RMN, Jose and Félix, at the CIMCYC (University of Granada, Spain) and also Laboratorios Ordesa, S.L. (Barcelona, Spain ) for providing the infant formulas.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWakschlag LS, Perlman SB, Blair RJ, Leibenluft E, Briggs-Gowan MJ, Pine DS. The neurodevelopmental basis of early childhood disruptive behavior: Irritable and callous phenotypes as exemplars. American journal of psychiatry. 2018;175:114\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003ePolanczyk G V, Salum GA, Sugaya LS, Caye A, Rohde LA. Annual research review: A meta-analysis of the worldwide prevalence of mental disorders in children and adolescents. J Child Psychol Psychiatry. 2015;56:345\u0026ndash;65. \u003c/li\u003e\n\u003cli\u003eKoletzko B, Bergmann K, Brenna JT, Calder PC, Campoy C, Clandinin MT, et al. Should formula for infants provide arachidonic acid along with DHA? A position paper of the European Academy of Paediatrics and the Child Health Foundation. Am J Clin Nutr. 2020;111:10\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eTimby N, Domell\u0026ouml;f E, Hernell O, L\u0026ouml;nnerdal B, Domell\u0026ouml;f M. Neurodevelopment, nutrition, and growth until 12 mo of age in infants fed a low-energy, low-protein formula supplemented with bovine milk fat globule membranes: a randomized controlled trial. Am J Clin Nutr. 2014;99:860\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eCarlson AL, Xia K, Azcarate-Peril MA, Goldman BD, Ahn M, Styner MA, et al. Infant Gut Microbiome Associated With Cognitive Development. Biol Psychiatry. 2018;83:148\u0026ndash;59. \u003c/li\u003e\n\u003cli\u003eNieto-Ruiz A, Garc\u0026iacute;a-Santos JA, Verdejo-Rom\u0026aacute;n J, Di\u0026eacute;guez E, Sep\u0026uacute;lveda-Valbuena N, Herrmann F, et al. Infant Formula Supplemented With Milk Fat Globule Membrane, Long-Chain Polyunsaturated Fatty Acids, and Synbiotics Is Associated With Neurocognitive Function and Brain Structure of Healthy Children Aged 6 Years: The COGNIS Study . Frontiers in Nutrition . 2022. \u003c/li\u003e\n\u003cli\u003eNieto-Ruiz A, Di\u0026eacute;guez E, Sep\u0026uacute;lveda-Valbuena N, Herrmann F, Cerd\u0026oacute; T, L\u0026oacute;pez-Torrecillas F, et al. The Effects of an Infant Formula Enriched with Milk Fat Globule Membrane, Long-Chain Polyunsaturated Fatty Acids and Synbiotics on Child Behavior up to 2.5 Years Old: The COGNIS Study. Nutrients . 2020. \u003c/li\u003e\n\u003cli\u003eBeauchaine TP, Constantino JN, Hayden EP. Psychiatry and developmental psychopathology: Unifying themes and future directions. Compr Psychiatry. 2018;87:143\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eMartel MM, Levinson CA, Lee CA, Smith TE. Impulsivity symptoms as core to the developmental externalizing spectrum. J Abnorm Child Psychol. 2017;45:83\u0026ndash;90. \u003c/li\u003e\n\u003cli\u003eBeauchaine TP, Zisner AR, Sauder CL. Trait impulsivity and the externalizing spectrum. Annu Rev Clin Psychol. 2017;13:343\u0026ndash;68. \u003c/li\u003e\n\u003cli\u003eMacpherson T, Hikida T. Role of basal ganglia neurocircuitry in the pathology of psychiatric disorders. Psychiatry Clin Neurosci. 2019;73:289\u0026ndash;301. \u003c/li\u003e\n\u003cli\u003eZisner A, Beauchaine TP. Neural substrates of trait impulsivity, anhedonia, and irritability: Mechanisms of heterotypic comorbidity between externalizing disorders and unipolar depression. Dev Psychopathol. 2016;28:1177\u0026ndash;208. \u003c/li\u003e\n\u003cli\u003eCasey BJ. Beyond simple models of self-control to circuit-based accounts of adolescent behavior. Annu Rev Psychol. 2015;66:295\u0026ndash;319. \u003c/li\u003e\n\u003cli\u003eCorr PF, McNaughton N. Neural mechanisms of low trait anxiety and risk for externalizing behavior. 2016; \u003c/li\u003e\n\u003cli\u003eMarsh AA, Finger EC, Mitchell DG V, Reid ME, Sims C, Kosson DS, et al. Reduced amygdala response to fearful expressions in children and adolescents with callous-unemotional traits and disruptive behavior disorders. American Journal of Psychiatry. 2008;165:712\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eBenegal V, Antony G, Venkatasubramanian G, Jayakumar PN. Imaging study: gray matter volume abnormalities and externalizing symptoms in subjects at high risk for alcohol dependence. Addiction biology. 2007;12:122\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eCheetham A, Allen NB, Whittle S, Simmons J, Y\u0026uuml;cel M, Lubman DI. Amygdala volume mediates the relationship between externalizing symptoms and daily smoking in adolescence: a prospective study. Psychiatry Res Neuroimaging. 2018;276:46\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eCattell, R. B., \u0026amp; Cattell AKS. g Factor Test. Scale 2. Form A. Institute for personality and ability testing. (Spanish adaptation: Madrid, Spain: TEA Ediciones, S.A. 1994. \u003c/li\u003e\n\u003cli\u003eThe World Medical Association Inc. Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. Ferney-Voltaire, France. 2008;1\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eAchenbach PT, Rescorla PL. Achenbach System of Empirically Based Assessment. In: Volkmar FR, editor. Encyclopedia of Autism Spectrum Disorders. Springer New York; 2013. p. 31\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eVolkmar FR. Encyclopedia of Autism Spectrum Disorders | Fred R. Volkmar | Springer. 2017. \u003c/li\u003e\n\u003cli\u003eRescorla LA. Assessment of young children using the Achenbach System of Empirically Based Assessment (ASEBA). Ment Retard Dev Disabil Res Rev. 2005;11:226\u0026ndash;37. \u003c/li\u003e\n\u003cli\u003eHartman RR, Stage SA, Webster‐Stratton C. A growth curve analysis of parent training outcomes: Examining the influence of child risk factors (inattention, impulsivity, and hyperactivity problems), parental and family risk factors. Journal of Child Psychology and Psychiatry. 2003;44:388\u0026ndash;98. \u003c/li\u003e\n\u003cli\u003eDale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179\u0026ndash;94. \u003c/li\u003e\n\u003cli\u003eFischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33:341\u0026ndash;55. \u003c/li\u003e\n\u003cli\u003eDesikan RS, S\u0026eacute;gonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eLiem F, M\u0026eacute;rillat S, Bezzola L, Hirsiger S, Philipp M, Madhyastha T, et al. Reliability and statistical power analysis of cortical and subcortical FreeSurfer metrics in a large sample of healthy elderly. Neuroimage. 2015;108:95\u0026ndash;109. \u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Benavides G, G\u0026oacute;mez-Ans\u0026oacute;n B, Sainz A, Vives Y, Delfino M, Pe\u0026ntilde;a-Casanova J. Manual validation of FreeSurfer\u0026rsquo;s automated hippocampal segmentation in normal aging, mild cognitive impairment, and Alzheimer Disease subjects. Psychiatry Res Neuroimaging. 2010;181:219\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eSchoemaker D, Buss C, Head K, Sandman CA, Davis EP, Chakravarty MM, et al. Hippocampus and amygdala volumes from magnetic resonance images in children: Assessing accuracy of FreeSurfer and FSL against manual segmentation. Neuroimage. 2016;129:1\u0026ndash;14. \u003c/li\u003e\n\u003cli\u003eBeauchaine TP, Webster-Stratton C, Reid MJ. Mediators, moderators, and predictors of 1-year outcomes among children treated for early-onset conduct problems: a latent growth curve analysis. J Consult Clin Psychol. 2005;73:371. \u003c/li\u003e\n\u003cli\u003eEzpeleta L, Granero R, De La Osa N, Navarro JB, Penelo E, Dom\u0026egrave;nech JM. Tracing developmental trajectories of oppositional defiant behaviors in preschool children. PLoS One. 2014;9:e101089. \u003c/li\u003e\n\u003cli\u003eKoolschijn PCMP, van IJzendoorn MH, Bakermans-Kranenburg MJ, Crone EA. Hippocampal volume and internalizing behavior problems in adolescence. European Neuropsychopharmacology. 2013;23:622\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eBlanken LME, White T, Mous SE, Basten M, Muetzel RL, Jaddoe VW V, et al. Cognitive functioning in children with internalising, externalising and dysregulation problems: a population-based study. Eur Child Adolesc Psychiatry. 2017;26:445\u0026ndash;56. \u003c/li\u003e\n\u003cli\u003eBlom EH, Han LKM, Connolly CG, Ho TC, Lin J, LeWinn KZ, et al. Peripheral telomere length and hippocampal volume in adolescents with major depressive disorder. Transl Psychiatry. 2015;5:e676\u0026ndash;e676. \u003c/li\u003e\n\u003cli\u003eJaworska N, Y\u0026uuml;cel K, Courtright A, MacMaster FP, Sembo M, MacQueen G. Subgenual anterior cingulate cortex and hippocampal volumes in depressed youth: the role of comorbidity and age. J Affect Disord. 2016;190:726\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eBjork JM, Chen G, Smith AR, Hommer DW. Incentive‐elicited mesolimbic activation and externalizing symptomatology in adolescents. Journal of Child Psychology and Psychiatry. 2010;51:827\u0026ndash;37. \u003c/li\u003e\n\u003cli\u003eBlair RJR. The neurobiology of psychopathic traits in youths. Nat Rev Neurosci. 2013;14:786\u0026ndash;99. \u003c/li\u003e\n\u003cli\u003eWatts AL, Greene AL, Bonifay W, Fried EI. A critical evaluation of the p-factor literature. Nature Reviews Psychology. 2024;3:108\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eHariri AR. The emerging importance of the cerebellum in broad risk for psychopathology. Neuron. 2019;102:17\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eBrissenden JA, Tobyne SM, Osher DE, Levin EJ, Halko MA, Somers DC. Topographic cortico-cerebellar networks revealed by visual attention and working memory. Current Biology. 2018;28:3364\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eBuckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BTT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106:2322\u0026ndash;45. \u003c/li\u003e\n\u003cli\u003eBrissenden JA, Somers DC. Cortico\u0026ndash;cerebellar networks for visual attention and working memory. Curr Opin Psychol. 2019;29:239\u0026ndash;47. \u003c/li\u003e\n\u003cli\u003eGuell X, Gabrieli JDE, Schmahmann JD. Triple representation of language, working memory, social and emotion processing in the cerebellum: convergent evidence from task and seed-based resting-state fMRI analyses in a single large cohort. Neuroimage. 2018;172:437\u0026ndash;49. \u003c/li\u003e\n\u003cli\u003eBonath B, Tegelbeckers J, Wilke M, Flechtner H-H, Krauel K. Regional gray matter volume differences between adolescents with ADHD and typically developing controls: further evidence for anterior cingulate involvement. J Atten Disord. 2018;22:627\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eHoogman M, Bralten J, Hibar DP, Mennes M, Zwiers MP, Schweren L, et al. Subcortical brain volume differences of participants with ADHD across the lifespan: an ENIGMA collaboration. Lancet Psychiatry. 2017;4:310. \u003c/li\u003e\n\u003cli\u003eNordahl CW, Schumann CM. Early variations in amygdala development may signal divergent behavioral outcomes. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4:3\u0026ndash;4. \u003c/li\u003e\n\u003cli\u003eWang S, Yu R, Tyszka JM, Zhen S, Kovach C, Sun S, et al. The human amygdala parametrically encodes the intensity of specific facial emotions and their categorical ambiguity. Nat Commun. 2017;8:1\u0026ndash;13. \u003c/li\u003e\n\u003cli\u003eSalzwedel AP, Stephens RL, Goldman BD, Lin W, Gilmore JH, Gao W. Development of amygdala functional connectivity during infancy and its relationship with 4-year behavioral outcomes. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4:62\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003eBellani M, Calderoni S, Muratori F, Brambilla P. Brain anatomy of autism spectrum disorders II. Focus on amygdala. Epidemiol Psychiatr Sci. 2013;22:309\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eAvino TA, Barger N, Vargas M V, Carlson EL, Amaral DG, Bauman MD, et al. Neuron numbers increase in the human amygdala from birth to adulthood, but not in autism. Proceedings of the National Academy of Sciences. 2018;115:3710\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eWeir RK, Bauman MD, Jacobs B, Schumann CM. Protracted dendritic growth in the typically developing human amygdala and increased spine density in young ASD brains. Journal of Comparative Neurology. 2018;526:262\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eGabard-Durnam LJ, Flannery J, Goff B, Gee DG, Humphreys KL, Telzer E, et al. The development of human amygdala functional connectivity at rest from 4 to 23 years: a cross-sectional study. Neuroimage. 2014;95:193\u0026ndash;207. \u003c/li\u003e\n\u003cli\u003eShen MD, Li DD, Keown CL, Lee A, Johnson RT, Angkustsiri K, et al. Functional connectivity of the amygdala is disrupted in preschool-aged children with autism spectrum disorder. J Am Acad Child Adolesc Psychiatry. 2016;55:817\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eWalsh JJ, Christoffel DJ, Heifets BD, Ben-Dor GA, Selimbeyoglu A, Hung LW, et al. 5-HT release in nucleus accumbens rescues social deficits in mouse autism model. Nature. 2018;560:589\u0026ndash;94. \u003c/li\u003e\n\u003cli\u003eTakumi T, Tamada K, Hatanaka F, Nakai N, Bolton PF. Behavioral neuroscience of autism. Neurosci Biobehav Rev. 2020;110:60\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Villalobos JA, Andr\u0026eacute;s-De Llano JM, Rodr\u0026iacute;guez-Molinero L, Garrido-Redondo M, Sacrist\u0026aacute;n-Mart\u0026iacute;n AM, Mart\u0026iacute;nez-Rivera MT, et al. Prevalencia del trastorno negativista desafiante en Espa\u0026ntilde;a. Rev Psiquiatr Salud Ment. 2014;7:80\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eTheule J, Germain SM, Cheung K, Hurl KE, Markel C. Conduct disorder/oppositional defiant disorder and attachment: A meta-analysis. J Dev Life Course Criminol. 2016;2:232\u0026ndash;55. \u003c/li\u003e\n\u003cli\u003eWaldman ID, Rowe R, Boylan K, Burke JD. External validation of a bifactor model of oppositional defiant disorder. Mol Psychiatry. 2021;26:682\u0026ndash;93. \u003c/li\u003e\n\u003cli\u003eBolhuis K, Viding E, Muetzel RL, El Marroun H, Kocevska D, White T, et al. Neural profile of callous traits in children: A population-based neuroimaging study. Biol Psychiatry. 2019;85:399\u0026ndash;407. \u003c/li\u003e\n\u003cli\u003eRaschle NM, Fehlbaum LV, Menks WM, Martinelli A, Pr\u0026auml;tzlich M, Bernhard A, et al. Atypical Dorsolateral Prefrontal Activity in Female Adolescents With Conduct Disorder During Effortful Emotion Regulation. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4:984\u0026ndash;94. \u003c/li\u003e\n\u003cli\u003eAlbaugh MD, Nguyen T-V, Ducharme S, Collins DL, Botteron KN, D\u0026rsquo;Alberto N, et al. Age-related volumetric change of limbic structures and subclinical anxious/depressed symptomatology in typically developing children and adolescents. Biol Psychol. 2017;124:133\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eBlok E, Geenjaar EPT, Geenjaar EAW, Calhoun VD, White T. Neurodevelopmental trajectories in children with internalizing, externalizing and emotion dysregulation symptoms. Front Psychiatry. 2022;13:846201. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"child-and-adolescent-psychiatry-and-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caph","sideBox":"Learn more about [Child and Adolescent Psychiatry and Mental Health](http://capmh.biomedcentral.com)","snPcode":"13034","submissionUrl":"https://submission.nature.com/new-submission/13034/3","title":"Child and Adolescent Psychiatry and Mental Health","twitterHandle":"@IACAPAP","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Behavior problems, subcortical morphometry, brain, neuroimaging, early nutrition","lastPublishedDoi":"10.21203/rs.3.rs-7142478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7142478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eearly behavioral problems may influence adult psychopathology, and early-life nutrition plays a critical role in shaping behavioral outcomes during childhood.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003ethis study investigated whether subcortical brain volumetry at age six is associated with early behavioral trajectories and the potential influence of early nutrition on this relationship.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003edata from 82 children participants in the COGNIS study were included in the present analysis. During the first 2 months of life, 50 infants were randomized to receive up to 18 months of life, either a standard infant formula (SF, n\u0026thinsp;=\u0026thinsp;26) or an experimental formula enriched with supplemented with several bioactive compounds (EF, n\u0026thinsp;=\u0026thinsp;24). A reference group of breastfed infants (BF, n\u0026thinsp;=\u0026thinsp;32) was also included. Behavioral assessments were conducted using the Child Behavior Checklist (CBCL) at 18 months, 2.5 years, and 4 years. Structural magnetic resonance imaging (MRI) was performed at 6 years to assess volumes of bilateral subcortical nuclei, brainstem, cerebellum, and total intracranial volume. Complete behavioral and imaging data were available for 37 participants. Weights for linear, quadratic, and mixed linear/quadratic growth curves were computed for CBCL total, internalizing, externalizing, and DSM-oriented scales. Non-parametric correlations between CBCL growth curves and subcortical brain volumetry were computed after adjusting for relevant confounding factors. Generalized linear mixed model for repeated measures was performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eno significant effects of early nutrition on behavioral trajectories were found; in fact, EF and BF groups exhibited similar patterns across internalizing, externalizing, total problems and DSM-oriented scales. CBCL domains followed distinct developmental trajectories, and interestingly, children\u0026rsquo;s subcortical volumetry of specific brain area at 6 years old, were primarily associated with non-linear behavioral growth curves. Amygdala volume correlated with total problems scores and DSM-oriented scales, while hippocampal volume was linked to internalizing, oppositional defiant, and ADHD-related behaviors. Cerebellar cortex volume correlated with ADHD and externalizing problems, the latter also associating with putamen. Pallidum volume was correlated with internalizing and anxiety symptoms.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ethese findings suggest that non-linear behavioral growth models more effectively reflect brain\u0026ndash;behavior associations. Futhermore, subcortical brain morphometry, particularly of the hippocampus, may be shaped by behavioral patterns during critical developmental windows\u0026mdash;most notably around 2.5 years of age.\u003c/p\u003e","manuscriptTitle":"Understanding the Developmental Trajectory of Behavioral Problems \u0026amp; Subcortical Structure Morphometry in Healthy Children at 6 years old and Long-Term Impact of Early Nutrition: The COGNIS Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 12:46:42","doi":"10.21203/rs.3.rs-7142478/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-27T02:30:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T15:12:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T14:06:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333485887560053018693293559534493127113","date":"2025-09-15T11:51:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148033450854046697944359984919120195406","date":"2025-09-03T15:24:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-06T15:21:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-21T06:37:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-20T04:08:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Child and Adolescent Psychiatry and Mental Health","date":"2025-07-16T17:30:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"child-and-adolescent-psychiatry-and-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caph","sideBox":"Learn more about [Child and Adolescent Psychiatry and Mental Health](http://capmh.biomedcentral.com)","snPcode":"13034","submissionUrl":"https://submission.nature.com/new-submission/13034/3","title":"Child and Adolescent Psychiatry and Mental Health","twitterHandle":"@IACAPAP","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"357ff864-9a5b-4765-9ef2-7f8da5b036f0","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:05:22+00:00","versionOfRecord":{"articleIdentity":"rs-7142478","link":"https://doi.org/10.1186/s13034-026-01030-7","journal":{"identity":"child-and-adolescent-psychiatry-and-mental-health","isVorOnly":false,"title":"Child and Adolescent Psychiatry and Mental Health"},"publishedOn":"2026-02-19 15:57:35","publishedOnDateReadable":"February 19th, 2026"},"versionCreatedAt":"2025-08-12 12:46:42","video":"","vorDoi":"10.1186/s13034-026-01030-7","vorDoiUrl":"https://doi.org/10.1186/s13034-026-01030-7","workflowStages":[]},"version":"v1","identity":"rs-7142478","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7142478","identity":"rs-7142478","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}