Increased Default Mode Network Connectivity and Temperament Predict Internalizing Symptoms in Early Childhood

Increased Default Mode Network Connectivity and Temperament Predict Internalizing Symptoms in Early Childhood | 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 Article Increased Default Mode Network Connectivity and Temperament Predict Internalizing Symptoms in Early Childhood Halle Miller, Johanna Chajes, Tobias Grossmann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7528043/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the early neural and temperament precursors to internalizing psychopathology. Increased default mode network (DMN) functional connectivity and decreased frontoparietal network (FPN) connectivity are established neural signatures of internalizing disorders in adults, but it is unknown if these patterns represent early-emerging vulnerability. In a community sample of typically developing children aged 4 to 6 years, we examined the concurrent predictive power of resting-state functional connectivity and temperament for internalizing symptoms. We hypothesized that increased DMN connectivity, decreased FPN connectivity, and temperament traits of high negative affect and low surgency would predict higher internalizing problems. Functional connectivity was measured using functional near-infrared spectroscopy (fNIRS), a child-friendly neuroimaging technique. Internalizing symptoms and temperament were assessed via parent report using the Child Behavior Checklist and Child Behavior Questionnaire, respectively. A multiple linear regression revealed that greater DMN connectivity, higher negative affect, and lower surgency were significant, independent predictors of internalizing symptoms. FPN connectivity was not a significant predictor. These findings suggest that a key neural signature of adult depression—DMN hyperconnectivity—is already associated with subclinical symptoms in early childhood, supporting its role as a primary vulnerability marker. The absence of an FPN association likely reflects the protracted maturation of this cognitive control network, pointing to a developmental lag in the emergence of distinct neural risk factors for internalizing disorders. Biological sciences/Psychology/Human behaviour Biological sciences/Neuroscience/Molecular neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Internalizing disorders, primarily encompassing depression and anxiety, constitute a profound and escalating public health challenge 1 , 2 . Now recognized as the leading cause of morbidity and disability among adolescents, these conditions impose immense social, academic, and personal costs 1 . A critical feature of internalizing psychopathology is its early developmental origin. Symptoms can be reliably identified in children as young as toddlers, and these early difficulties demonstrate significant stability over time 3 . For instance, preschoolers identified with an internalizing disorder are three times more likely than their peers to exhibit similar psychopathology eight years later, highlighting early childhood as a crucial period for understanding etiological pathways 3 . Despite their prevalence and long-term impact, these disorders are often characterized by quiet, internal distress—such as sadness, worry, and social withdrawal—which can make them difficult for parents, caregivers, and educators to detect 3 , 4 . Unlike externalizing problems, which are disruptive and demand attention, the "quiet" suffering of an internalizing child may go unnoticed, delaying or preventing access to necessary support 4 . This diagnostic challenge, coupled with the high rates of treatment non-response in adolescence 4 , underscores an urgent need for research that can translate fundamental discoveries about risk into more effective, early-stage prevention and intervention strategies. In the search for biological markers of risk, neuroimaging research in adults with major depressive disorder (MDD) has consistently converged on a model of dysregulated large-scale brain networks 5 , 6 . Two networks are central to this model: the Default Mode Network (DMN) and the Frontoparietal Network (FPN). The DMN, comprising regions such as the medial prefrontal cortex and posterior cingulate cortex, is most active during periods of rest and is implicated in internally oriented, self-referential cognitive processes 5 , 7 . In adults with MDD, the DMN is frequently found to be hyperconnected , a state of excessive functional integration that is thought to be the neural substrate for rumination—the persistent, repetitive focus on negative thoughts and feelings that is a core symptom of depression 7 , 8 . Conversely, the FPN (also known as the central executive network), which includes the dorsolateral prefrontal cortex and posterior parietal cortex, is critical for "top-down" cognitive control, goal-directed attention, and the regulation of emotion 9 , 10 . This network is often found to be hypoconnected in MDD, reflecting a deficit in the neural systems that support adaptive, flexible behavior and emotional modulation 5 , 7 . A landmark meta-analysis by Kaiser et al. (2015) solidified this model, demonstrating a pattern of DMN hyperconnectivity, FPN hypoconnectivity, and an overall imbalance between networks dedicated to internal versus external attention in MDD. This neural profile is theorized to reflect a depressive bias toward maladaptive internal thought at the expense of effective engagement with the external world and cognitive control 7 . The remarkable consistency of this DMN/FPN pattern in adults raises a fundamental developmental question: When do these neural signatures of risk emerge? Are they present as a pre-existing vulnerability before the onset of a formal disorder, or do they develop as a "neural scar" resulting from chronic illness? Investigating these network dynamics in young, typically developing children provides a direct test of the hypothesis that these neural patterns are developmental precursors to psychopathology rather than its consequence. Parallel to neurobiological investigations, developmental psychopathology has long established temperament as a primary risk factor for mental illness 4 , 11 , 12 . Temperament refers to early-appearing, biologically-based individual differences in emotional reactivity and self-regulation that form the foundation of later personality 11 , 12 . Two core temperament dimensions are robustly implicated in the developmental pathways to internalizing disorders. First, high negative affectivity (also termed emotionality or neuroticism), a constitutional predisposition to experience negative emotions such as fear, sadness, and frustration, is a powerful predictor of both anxiety and depression 11 , 12 . Second, deficits in regulatory capacity, particularly low effortful control, are strongly linked to psychopathology 3 , 12 . Effortful control is the ability to inhibit a dominant or prepotent response in favor of a subdominant one, a capacity essential for managing emotional impulses and focusing attention 3 , 12 . The related construct of Surgency, which encompasses traits of positive emotionality, activity level, and approach behaviors, is also relevant; low surgency often manifests as shyness and behavioral inhibition, key features of an internalizing profile 13 . It is widely theorized that the combination of high reactivity (negative affect) and low regulation (effortful control) confers the greatest vulnerability to psychopathology 12 . Despite robust, parallel lines of evidence from neuroscience and developmental psychology, few studies have integrated these perspectives by examining neural and temperament risk factors concurrently, especially during the preschool years. This developmental period is marked by the rapid maturation of brain circuits and the consolidation of temperament traits, making it a critical window for understanding the origins of psychopathology 14 – 16 . Furthermore, most of the neuroimaging research on internalizing disorders has utilized functional magnetic resonance imaging (fMRI) in adolescent and adult populations, leaving a significant gap in our knowledge of the neural underpinnings of risk in early childhood 1 , 2 , 5 . Functional near-infrared spectroscopy (fNIRS) is an optical neuroimaging modality particularly well-suited for bridging this gap. As a non-invasive, portable, and relatively motion-tolerant technique, fNIRS makes it feasible to measure cortical brain function in young, active children who cannot easily tolerate the confines of an fMRI scanner 17 – 19 . The present study leverages this technology to investigate the concurrent predictive power of resting-state functional connectivity within the DMN and FPN, alongside core temperament traits, for internalizing symptoms in a community sample of 4- to 6-year-old children. Based on the adult and developmental literature, we formed the following hypotheses: Higher internalizing symptoms will be predicted by increased within-network DMN functional connectivity. Higher internalizing symptoms will be predicted by decreased within-network FPN functional connectivity. Higher internalizing symptoms will be predicted by higher temperament negative affect and lower surgency and effortful control. Method Participants The final sample consisted of 78 children (44 female) between 4 and 6 years of age ( M age = 65.4 months, SD = 11.3, range = 48-83 months). Participants were recruited from the local community through flyers and online advertisements. An initial 100 children were enrolled, but data from 22 children were excluded from the final analysis due to excessive motion artifacts or poor signal quality in the fNIRS data [n=14], a common challenge in pediatric neuroimaging 18 , the scan cut short due to experimenter error (n = 1), being in physical contact with the parent during the fNIRS scan (n = 2), poor fNIRS cap placement (n = 2), refusal to wear the fNIRS cap (n = 1), issues with the video recording of the structured play session (n = 1), or having an autism spectrum disorder diagnosis (n = 1). The sample was predominantly White and from families with high socioeconomic status (see Table 1). Parents provided written informed consent, and children provided verbal assent prior to participation. The study protocol was approved by the local Institutional Review Board. Table 1 Sociodemographic Characteristics of Included Participants Sociodemographic Characteristic Mean ( M ) or Count ( n ) SD or % Age in months, M 65.4 11.3 Gender, n Male 34 44% Female 44 56% Race/Ethnicity, n Black or African American 1 1% Hispanic, Latino, or Spanish Origin 2 3% Asian 1 1% White 61 78% Multiracial 13 17% Annual household income, n Less than $15,000 1 1% $15,001 to $30,000 1 1% $30,001 to $45,000 1 1% $45,001 to $60,000 2 3% $60,001 to $75,000 4 5% $75,001 to $90,000 4 5% $90,001 to $110,000 10 13% $110,001 to $125,000 7 9% $125,001 to $175,000 13 17% $175,001 to $225,000 19 25% $225,001 to $275,000 5 7% More than $275,000 10 13% Parent with a 4-year college degree, n Neither parent 5 6% One parent 14 18% Both parents 59 76% Note. SD , standard deviation. N = 78. Procedure Each child and a parent attended a single 1.5-hour laboratory visit. Upon arrival, parents completed a battery of online questionnaires while the child was familiarized with the lab environment and the fNIRS equipment. Participants were brought to a quiet, dimly lit testing room and seated at a child-sized table with the presentation computer. The participants' head circumference was measured before they were fitted with an appropriately sized fNIRS fabric cap (52-cm EasyCap, n = 78). The fNIRS caps were organized by 10-20 coordinates and placed on the head so that Fpz was centered above the nasion and equidistant between the nasion and AFz. The initial signal quality was checked for all channels, and any channels with poor quality were manually adjusted to ensure that all channels had acceptable signal quality before the recording. The child then completed a resting-state brain imaging session, during which they were instructed to sit still for a passive viewing task. Following the imaging session, the child participated in a structured play session (data not reported here). Families were compensated for their time. Measures Internalizing Symptoms. Parent-reported internalizing problems were measured using the Child Behavior Checklist for Ages 1.5-5 20 . The CBCL is a widely used, psychometrically robust measure of childhood emotional and behavioral problems. The Internalizing Problems scale T-score was used as the primary outcome variable. Temperament. Child temperament was assessed using the parent-report Child Behavior Questionnaire (CBQ) – Very Short Form 21 . This measure provides scores on three broad, well-validated temperament dimensions: Negative Affect, Surgency/Extraversion, and Effortful Control. These scales demonstrate good internal consistency and validity. Socio-Economic Status (SES). SES was operationalized using parent-reported total annual household income, a standard proxy in developmental research. fNIRS Data Acquisition and Processing Resting-state fNIRS data were acquired using a continuous-wave fNIRS system. A standardized optode cap was placed on the child's head, with emitters and detectors arranged to provide coverage over cortical regions associated with the DMN and FPN (see Figure 1). Data were collected for a continuous 12-minute period while the child sat at rest. Raw light intensity data were converted to changes in oxygenated (Δ[HbO]) and deoxygenated (Δ) hemoglobin concentration using the modified Beer-Lambert law 18 . Channels with a signal-to-noise ratio (calculated by the mean signal intensity divided by the standard deviation of the signal intensity over time) below our chosen threshold of 12.5 for both wavelengths were marked as poor-quality channels and excluded from further analyses. The processing pipeline, conducted using FC-NIRS software 22 , included several critical steps to ensure high data quality. Age appropriate differential pathlength factor values of 5.5 for 760 nm and 4.5 for 850 nm were used 23 . A wavelet-based motion correction algorithm was applied to identify and regress motion artifacts. Data were then band-pass filtered (0.01-0.08 Hz) to isolate the low-frequency fluctuations characteristic of resting-state neural activity. Systemic physiological artifacts, such as heart rate and respiration, were removed using principal component analysis. Functional Connectivity (FC) Analysis A seed-based correlation analysis was performed on the preprocessed Δ[HbO] time-series data. Seeds were defined based on the channel locations corresponding to key nodes of the DMN and FPN. For each network, the time-series from all channels within that network were averaged to create a representative network time-series. The functional connectivity for each network was then calculated as the average Pearson correlation coefficient between each individual channel in the network and the representative network time-series. This resulted in a single value for each participant representing the average within-network FC for the DMN and the FPN. Statistical Analysis The primary analysis was a multiple linear regression using the entry method to test the concurrent prediction of internalizing symptoms. The CBCL Internalizing T-score served as the outcome variable. The predictor variables entered into the model were: DMN within-network FC, FPN within-network FC, CBQ Negative Affect score, CBQ Surgency score, CBQ Effortful Control score, household income (SES), and the child's age in months. Results A multiple linear regression was conducted to evaluate how well DMN and FPN functional connectivity, temperament, SES, and age predicted internalizing symptoms. The overall regression model was statistically significant, F (7, 58) = 6.64, p <.001, and accounted for approximately 44.1% of the variance in internalizing problems ( R ² =.441, Adjusted R ² =.374). Examination of the individual predictors revealed that three variables made unique, statistically significant contributions to the model. Default Mode Network connectivity was a significant positive predictor of internalizing symptoms (β =.31, t = 2.94, p =.005) (see Figure 2). Negative Affect was also a significant positive predictor (β =.39, t = 3.25, p =.002) (see Figure 3), while Surgency was a significant negative predictor (β = -.41, t = -3.40, p =.001) (see Figure 4). These results indicate that, when controlling for all other variables in the model, children with higher DMN connectivity, higher negative affect, and lower surgency tended to have more parent-reported internalizing problems. Contrary to our hypothesis, Frontoparietal Network connectivity was not a significant predictor of internalizing symptoms (β = -.05, p =.603). Similarly, Effortful Control (β = -.09, p =.381), household income (β = -.02, p =.824), and age (β = -.19, p =.102) did not significantly predict internalizing symptoms in the final model. Discussion This study provides novel evidence for the concurrent neural and temperament predictors of internalizing symptoms in early childhood. In a community sample of 4- to 6-year-old children, we found that greater within-network DMN functional connectivity, higher negative affect, and lower surgency were significant, independent predictors of higher parent-reported internalizing problems. Contrary to hypotheses derived from the adult literature, FPN connectivity was not associated with internalizing symptoms in this young age group. These findings suggest that the neural and temperament foundations of vulnerability to internalizing disorders are established by the preschool years, but that the specific neural signatures of risk may follow distinct developmental timetables. The positive association between DMN connectivity and internalizing symptoms represents a striking downward extension of findings from adult and adolescent psychopathology 1 , 5 , 6 . This result suggests that hyperconnectivity of the DMN, a neural pattern consistently linked to rumination and maladaptive self-referential thought in adult depression 7 , 8 , is not merely a consequence of chronic illness. Instead, its association with subclinical symptoms in very young children supports the view that it may represent a primary, early emerging biological vulnerability. In early childhood, this increased DMN integration may not manifest as the complex, abstract rumination seen in adults, but rather as a more foundational processing bias toward internal states at the expense of engagement with the external world. This could be a neural mechanism underlying behaviors like social withdrawal and excessive shyness, which are hallmarks of the internalizing spectrum in young children 4 . The presence of this DMN-symptom relationship in a non-clinical sample of young children, where symptoms exist on a continuum, strengthens the argument for DMN hyperconnectivity as a candidate endophenotype for internalizing disorders. An endophenotype is a measurable, heritable trait that lies on the causal pathway between genetic predisposition and the manifest disorder. The logic is as follows: (1) adult clinical studies establish DMN hyperconnectivity as a correlate of MDD 5 , 7 ; (2) the current study demonstrates a linear relationship between DMN connectivity and symptoms in young, non-diagnosed children; (3) this implies the neural pattern exists on a continuum and is present before the typical age of onset for a formal disorder. Therefore, this finding supports a model in which DMN hyperconnectivity is not simply a state marker of being depressed, but rather a stable trait marker of vulnerability that is present from early in development. The lack of an association between FPN connectivity and internalizing symptoms is a critical finding that must be interpreted through a developmental lens. Rather than a failure to replicate adult findings, this null result may provide an accurate snapshot of the FPN's maturational state in early childhood. The FPN is the brain's hub for "top-down" cognitive control and deliberate, effortful emotion regulation strategies like reappraisal 9 , 10 . This network undergoes a particularly protracted developmental course, with its structural and functional architecture continuing to mature throughout childhood and into adolescence 6 , 24 , 25 . Studies consistently show that children exhibit weaker FPN activation during cognitive control tasks compared to adults, and their ability to leverage FPN-mediated strategies to modulate emotional responses improves with age 26 , 27 . The combination of a significant DMN finding and a null FPN finding in our sample points toward a developmental lag between the maturation and pathological involvement of these two key networks. The DMN matures relatively early in development, whereas the FPN matures much later 24 , 28 , 29 . The most parsimonious explanation for our results, therefore, is that vulnerability to internalizing problems first manifests in the brain systems that are functionally mature enough to display dysregulation. In early childhood, this is the DMN, via hyperconnectivity that may bias the child toward an internal focus. The role of FPN hypo connectivity, reflecting deficits in cognitive control, may emerge as a significant risk factor only later in development—perhaps during adolescence—when these circuits are expected to be fully engaged for self-regulation and are now failing to do so. This suggests a dynamic, two-stage model of evolving neural risk. Furthermore, some evidence suggests the DMN may act as a "scaffold" for the immature FPN during typical development, with stronger DMN-FPN coupling observed in children than in adults 24 . It is plausible that at this young age, the FPN is not yet sufficiently independent or mature to be a distinct source of variance contributing to psychopathology. Our statistical model demonstrates that DMN connectivity, negative affect, and surgency are all independent, significant predictors of internalizing symptoms. This finding supports a multi-pathway model of risk, where biological predispositions in brain function and early emerging behavioral dispositions both make unique contributions to vulnerability. The results showing that high negative affect and low surgency predict internalizing problems are highly consistent with decades of developmental theory and research 4 , 11 . High negative affect reflects a child's intrinsic reactivity to stress, while low surgency indicates a tendency toward shyness, low positive emotionality, and behavioral inhibition—a classic internalizing profile. The current findings are in favor of an etiological model where multiple, distinct risk factors can converge on the same clinical phenotype. In other words, a child may be at risk due to their brain connectivity, their temperament, or an additive combination of both. This study has several notable strengths. It is among the first to examine concurrent neural and temperament predictors of internalizing symptoms within the critical preschool period. The successful use of fNIRS allowed for the collection of functional neuroimaging data from this challenging-to-scan population, providing a unique window into early brain-behavior associations 17 . The multimodal approach, integrating brain and behavior, provides a more holistic understanding of risk than either modality alone. However, the findings must be considered in light of several limitations. First, the cross-sectional design precludes any causal inferences; while we have identified significant predictors, we cannot determine the direction of these effects over time. Second, we must acknowledge the technical limitations of fNIRS, which include lower spatial resolution compared to fMRI and a sensitivity that is primarily limited to the cortical surface 17 , 19 . Third, the sample was relatively homogenous, consisting of primarily White children from high-SES families, which may limit the generalizability of the findings to more diverse populations. Finally, a notable number of participants (22%) were excluded due to data quality issues, a persistent challenge in pediatric fNIRS that highlights the ongoing need for methodological refinement 18 . Notably, a recent meta-analysis suggests an average attrition rate of 34.23% in infant fNIRS work 30 . Future research should prioritize longitudinal designs. Following this cohort into middle childhood and adolescence would allow for a direct test of the "developmental lag" hypothesis: does FPN connectivity emerge as a significant predictor of internalizing trajectories as children age? Integrating additional levels of analysis, such as genetic data and measures of environmental adversity (e.g., adverse childhood experiences), would provide an even more comprehensive and ecologically valid model of risk and resilience 6 , 31 . The findings of this study hold significant translational potential. The identification of DMN connectivity and specific temperament traits as early, independent risk markers could inform the development of novel, targeted, and timely prevention strategies. For instance, children identified with a temperament profile of high negative affect and low surgency could benefit from established behavioral interventions focused on building emotion regulation skills and fostering social engagement. In parallel, children identified with DMN hyperconnectivity might one day benefit from interventions aimed at modulating self-focused attention, such as mindfulness-based practices that have been adapted for young children. In conclusion, this study demonstrates that the roots of internalizing psychopathology are evident in both brain function and behavior by the preschool years. The early emergence of DMN dysregulation, coupled with well-established temperament vulnerabilities, appears to set the stage for later difficulties. The distinct developmental trajectory of the FPN underscores that the neurobiology of risk is not static but dynamic, evolving as the brain matures. Understanding this developmental timing is a crucial step toward identifying the right children at the right time for the right intervention, ultimately improving long-term mental health outcomes. Declarations Conflict of Interests The authors have no conflicts of interest to declare. Acknowledgements Thank you to all of the families who participated in the study. JRC was partially supported by the Jefferson Scholars Foundation Fellowship award. References Miller, C. H., Hamilton, J. P., Sacchet, M. D. & Gotlib, I. H. Meta-analysis of Functional Neuroimaging of Major Depressive Disorder in Youth. JAMA Psychiatry 72 , 1045–1053 (2015). Solmi, M. et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol. Psychiatry 27 , 281–295 (2022). Tandon, M., Cardeli, E. & Luby, J. Internalizing Disorders in Early Childhood: A Review of Depressive and Anxiety Disorders. Child Adolesc. Psychiatr. Clin. N. Am. 18 , 593–610 (2009). Rubin, K. H. & Mills, R. S. Conceptualizing developmental pathways to internalizing disorders in childhood. Can. J. Behav. Sci. Rev. Can. Sci. Comport. 23 , 300–317 (1991). Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D. & Pizzagalli, D. A. Large-scale network dysfunction in Major Depressive Disorder: Meta-analysis of resting-state functional connectivity. JAMA Psychiatry 72 , 603–611 (2015). Tooley, U. A. et al. The Age of Reason: Functional Brain Network Development during Childhood. J. Neurosci. 42 , 8237–8251 (2022). Yoon, S., Kleinman, M., Mertz, J. & Brannick, M. Is social network site usage related to depression? A meta-analysis of Facebook–depression relations. J. Affect. Disord. 248 , 65–72 (2019). Yan, C.-G. et al. Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc. Natl. Acad. Sci. 116 , 9078–9083 (2019). Menon, V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn. Sci. 15 , 483–506 (2011). Ochsner, K. N. & Gross, J. J. The cognitive control of emotion. Trends Cogn. Sci. 9 , 242–249 (2005). Muris, P. & Ollendick, T. H. The role of temperament in the etiology of child psychopathology. Clin. Child Fam. Psychol. Rev. 8 , 271–289 (2005). Nigg, J. T. Annual Research Review: On the relations among self-regulation, self-control, executive functioning, effortful control, cognitive control, impulsivity, risk-taking, and inhibition for developmental psychopathology. J. Child Psychol. Psychiatry 58 , 361–383 (2017). Gartstein, M. A., Putnam, S. P. & Rothbart, M. K. Etiology of preschool behavior problems: Contributions of temperament attributes in early childhood. Infant Ment. Health J. 33 , 197–211 (2012). Center on the Developing Child at Harvard Universit. Children’s Emotional Development Is Built into the Architecture of Their Brains. (2024). Brown, T. T. & Jernigan, T. L. Brain development during the preschool years. Neuropsychol. Rev. 22 , 313–333 (2012). Campbell, S. B. The State of Young Children in the United States. in The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies 18–34 (John Wiley & Sons, Ltd, 2017). doi:10.1002/9781118937334.ch2. Pinti, P. et al. The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. Ann. N. Y. Acad. Sci. 1464 , 5–29 (2020). Scholkmann, F. et al. A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. NeuroImage 85 , 6–27 (2014). Yücel, M. A. et al. Best practices for fNIRS publications. Neurophotonics 8 , 012101 (2021). Achenbach, T. M. & Rescorla, L. A. Manual for the ASEBA school-age forms and profiles. Burlington, Vermont: University of Vermont. Res. Cent. Child. Youth Fam. (2001). Putnam, S. P. & Rothbart, M. K. Development of short and very short forms of the Children’s Behavior Questionnaire. J. Pers. Assess. 87 , 102–112 (2006). Xu, J. et al. FC-NIRS: A Functional Connectivity Analysis Tool for Near-Infrared Spectroscopy Data. BioMed Res. Int. 2015 , 248724 (2015). Scholkmann, F. & Wolf, M. General equation for the differential pathlength factor of the frontal human head depending on wavelength and age. J. Biomed. Opt. 18 , 105004 (2013). Chen, M. et al. Default mode network scaffolds immature frontoparietal network in cognitive development. Cereb. Cortex 33 , 5251–5263 (2023). Wendelken, C. et al. Frontoparietal Structural Connectivity in Childhood Predicts Development of Functional Connectivity and Reasoning Ability: A Large-Scale Longitudinal Investigation. J. Neurosci. Off. J. Soc. Neurosci. 37 , 8549–8558 (2017). McRae, K. et al. The development of emotion regulation: an fMRI study of cognitive reappraisal in children, adolescents and young adults. Soc. Cogn. Affect. Neurosci. 7 , 11–22 (2012). Silvers, J. A. et al. Age-related differences in emotional reactivity, regulation, and rejection sensitivity in adolescence. Emot. Wash. DC 12 , 1235–1247 (2012). Fair, D. A. et al. The maturing architecture of the brain’s default network. Proc. Natl. Acad. Sci. 105 , 4028–4032 (2008). Gao, W. et al. Functional Network Development During the First Year: Relative Sequence and Socioeconomic Correlations. Cereb. Cortex N. Y. NY 25 , 2919–2928 (2015). Baek, S. et al. Attrition rate in infant fNIRS research: A meta-analysis. Infancy Off. J. Int. Soc. Infant Stud. 28 , 507–531 (2023). Albertina, E. A., Barch, D. M. & Karcher, N. R. Internalizing Symptoms & Adverse Childhood Experiences Associated with Functional Connectivity in A Middle Childhood Sample. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 9 , 50–59 (2024). Additional Declarations The authors have declared there is NO conflict of interest to disclose Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7528043","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":513967369,"identity":"f28ea000-4e28-4e4d-9682-a67037a19bed","order_by":0,"name":"Halle Miller","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3RvQrCMBDA8ZNCs5x1rSjqIwRcRV+lpWCXDplcHNQlLursY/gIgQxdih+b4CIITt1cFBRMBMGpwU0w/yk58oODANhsvxm5AnTAVScBVA8Cs/EB+t8T+Xk3EJrGW3rjm3BRS3eSsS5USEKLSZazcM4PIfcSJpc0guosN5B9EogyP7RdxEAidfTESMLJg6/fZAQ9M4mlU+ai4SIRikigvoFUs9x16utIEQRFUvSzMyskXhqfS/mgi80ZOV3wPmxUptGqkLSEWqOk/xHwtQ8WPtc1x/ER4K6P5Gh8bbPZbP/ZE2GRR1TnxuULAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0002-4900-3459","institution":"University of Virginia","correspondingAuthor":true,"prefix":"","firstName":"Halle","middleName":"","lastName":"Miller","suffix":""},{"id":513967370,"identity":"eb228242-cf29-40e8-bb76-ff30295aebe6","order_by":1,"name":"Johanna Chajes","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Johanna","middleName":"","lastName":"Chajes","suffix":""},{"id":513967371,"identity":"63a5ac67-0e86-44b9-9ec4-8e96999d3145","order_by":2,"name":"Tobias Grossmann","email":"","orcid":"https://orcid.org/0000-0002-1116-6423","institution":"University of Virginia","correspondingAuthor":false,"prefix":"","firstName":"Tobias","middleName":"","lastName":"Grossmann","suffix":""}],"badges":[],"createdAt":"2025-09-03 14:28:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7528043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7528043/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91848061,"identity":"86f80d8d-7ce0-4e5b-ae0e-e107ebb359f8","added_by":"auto","created_at":"2025-09-22 10:35:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5703566,"visible":true,"origin":"","legend":"","description":"","filename":"functionalconnectivityinternalizing08312025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/2d7b3eb00de0606fc429257b.docx"},{"id":91847777,"identity":"da60921a-ae23-4992-ae74-6170f2c5f24d","added_by":"auto","created_at":"2025-09-22 10:27:26","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7020204,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/c0c4c23b0d62b719ec4a7f34.tif"},{"id":91847779,"identity":"d4a95c00-3551-4321-bfd9-b949ff402deb","added_by":"auto","created_at":"2025-09-22 10:27:26","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7020204,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/cbd736a1bcc3ca34e4850935.tif"},{"id":91846490,"identity":"63c5a3c8-4abb-45d1-bbcc-5ace42f97b5f","added_by":"auto","created_at":"2025-09-22 10:19:26","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7020204,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/aad26b9db7a71d9d6215745d.tif"},{"id":91847776,"identity":"0d9148b9-cf89-4e83-8e0b-bedbf094b8d6","added_by":"auto","created_at":"2025-09-22 10:27:26","extension":"json","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5156,"visible":true,"origin":"","legend":"","description":"","filename":"2025TP002081.json","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/aa3b873dcaed9f89d8e33387.json"},{"id":91847782,"identity":"42888dc5-905f-4c66-b69f-449b7947c67c","added_by":"auto","created_at":"2025-09-22 10:27:26","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85543,"visible":true,"origin":"","legend":"","description":"","filename":"2025TP0020810enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/91dde736785034e193aec017.xml"},{"id":91848065,"identity":"d327be5a-4e2f-4214-88bc-b0d551300d71","added_by":"auto","created_at":"2025-09-22 10:35:26","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86623,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/67b4dc3f022557d87def9845.pdf"},{"id":91846507,"identity":"6bea064f-03c8-4646-a18d-d84309599326","added_by":"auto","created_at":"2025-09-22 10:19:26","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7020204,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/d678ccc38ffa9a8038553f3e.tif"},{"id":91846497,"identity":"1964a03e-ba02-4377-915e-b95aa11dd630","added_by":"auto","created_at":"2025-09-22 10:19:26","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7020204,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/6c12d9ae3c8c4e93ac211a92.tif"},{"id":91847789,"identity":"60ebb219-0241-4130-9395-70049d1603a3","added_by":"auto","created_at":"2025-09-22 10:27:26","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7020204,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/53ef7be9070dd25fbb978f71.tif"},{"id":91848756,"identity":"9fb609a3-2992-4e8e-876b-1f15f0a71b83","added_by":"auto","created_at":"2025-09-22 10:51:26","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1041602,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/a5a19b31d860c6f5ad82199b.png"},{"id":91848064,"identity":"0b259f2c-dae0-4433-b9e1-1fa048de5a08","added_by":"auto","created_at":"2025-09-22 10:35:26","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1494928,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/1fafc2588763bab241ae89ac.jpeg"},{"id":91848755,"identity":"3f7635d5-69f2-472d-9790-522bb7a4a39a","added_by":"auto","created_at":"2025-09-22 10:51:26","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1527880,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/21e812abd77543c695400b01.jpeg"},{"id":91846503,"identity":"758cb896-97b2-41c0-a0fc-373a25633cda","added_by":"auto","created_at":"2025-09-22 10:19:26","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1548922,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/7e5d248a08e6265d63e35554.jpeg"},{"id":91846486,"identity":"e62730d7-3749-46dc-8123-e4fbaeebf5c8","added_by":"auto","created_at":"2025-09-22 10:19:26","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":51288,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/5b3e2816a91e5a90d170b413.png"},{"id":91848059,"identity":"04ffec54-3077-43ed-a531-91cf15131a03","added_by":"auto","created_at":"2025-09-22 10:35:26","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45861,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/024c130966d5ee47d2dcc66a.png"},{"id":91846493,"identity":"4cadf132-1bbb-4ee8-b8e9-93f09e76b8ae","added_by":"auto","created_at":"2025-09-22 10:19:26","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48069,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/131409ccc622d55177e580a8.png"},{"id":91847784,"identity":"4dd62747-041f-41f2-b8b7-36a1777619e6","added_by":"auto","created_at":"2025-09-22 10:27:26","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103455,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/3d372b565e2d8b0ec433d307.png"},{"id":91848066,"identity":"bfcdcc7a-9538-49e5-8f2f-2802fb9cb71f","added_by":"auto","created_at":"2025-09-22 10:35:26","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87860,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/c1a3df650159ef5f5b54e19f.png"},{"id":91848487,"identity":"11dce789-7a59-4c22-9cfb-326134575a86","added_by":"auto","created_at":"2025-09-22 10:43:26","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":77434,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/b07435986d10d304a4beec9c.png"},{"id":91847787,"identity":"8f27b846-08dc-445c-84d5-43c2bd54e8fc","added_by":"auto","created_at":"2025-09-22 10:27:26","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79721,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/4875de0df2e35c9d07ad026f.png"},{"id":91846508,"identity":"4ccf5624-9e68-48cf-bcf8-67b279c08b90","added_by":"auto","created_at":"2025-09-22 10:19:26","extension":"xml","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82977,"visible":true,"origin":"","legend":"","description":"","filename":"2025TP0020810structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/451d45d00c2bb5216b1b1d7b.xml"},{"id":91846506,"identity":"283c49d9-e0d9-4d13-8e7e-d19f97aee7c1","added_by":"auto","created_at":"2025-09-22 10:19:26","extension":"html","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93703,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/aa3fa680d3089f5d73e8b1a0.html"},{"id":91846500,"identity":"b7a0c3a8-2be7-4d9f-93cd-a45244212b26","added_by":"auto","created_at":"2025-09-22 10:19:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":187364,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of\u003cstrong\u003e \u003c/strong\u003efNIRS Channels (numbered 1-52) with Short-Separation Channels on 10-20 Landmark Grid. Short-Separation Channels (numbers 4, 11, 16, 26, 30, 41, 45, and 49) shown in purple. Blue channels indicate channels selected to create regions of interest (ROI) for the Default Mode Network (DMN). Green channels indicate channels selected to create regions of interest (ROI) for the Frontoparietal Network (FPN).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/b9669e299ff3ea34dd99d95e.png"},{"id":91846482,"identity":"a0b74b54-004f-4fbb-b682-46c352a93eef","added_by":"auto","created_at":"2025-09-22 10:19:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137460,"visible":true,"origin":"","legend":"\u003cp\u003eMarginal effects plot showing the association between Default Mode Network (DMN) connectivity and internalizing symptoms. DMN connectivity was measured using fNIRS as changes in oxygenated hemoglobin (ΔHbO) during rest. Internalizing symptoms were assessed using the Child Behavior Checklist (CBCL) internalizing score. The solid line represents the fitted regression line, and the shaded region indicates the 95% confidence interval. Vertical ticks along the x-axis represent individual participant data points (n = 78). A positive association was observed, suggesting higher DMN connectivity is linked to greater internalizing symptoms.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/bbce6f50a19d2ff37db2ae27.png"},{"id":91846483,"identity":"f516d2a8-c1ed-4af8-aa8e-0c783e1fb4c4","added_by":"auto","created_at":"2025-09-22 10:19:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126585,"visible":true,"origin":"","legend":"\u003cp\u003eMarginal effects plot showing the association between negative affect and internalizing symptoms in children. Negative affect was measured using the Child Behavior Questionnaire (CBQ) and represents a composite temperament score reflecting a predisposition to experience negative emotions such as fear, sadness, and frustration. The solid line represents the predicted marginal effect from the regression model, and the shaded region shows the 95% confidence interval. Vertical tick marks on the x-axis indicate individual participant data points (n = 78). Higher negative emotionality was associated with more internalizing symptoms.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/c6d0674a82b8f02436d26655.png"},{"id":91846484,"identity":"bbf1236a-4b47-4f06-9945-1b9ad92856de","added_by":"auto","created_at":"2025-09-22 10:19:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":138878,"visible":true,"origin":"","legend":"\u003cp\u003eMarginal effects plot showing the association between surgency and internalizing symptoms in children. Surgency was measured using the Child Behavior Questionnaire (CBQ) and represents a composite temperament score reflecting positive emotionality, activity level, and approach behaviors. The solid line represents the predicted marginal effect from the regression model, and the shaded region shows the 95% confidence interval. Vertical tick marks on the x-axis indicate individual participant data points (n = 78). Higher Surgency was associated with fewer internalizing symptoms.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/c2b1d59d63fcf4fa0f5cfbff.png"},{"id":98426448,"identity":"e0731bca-c8f7-4e35-91fc-066544ac6c46","added_by":"auto","created_at":"2025-12-17 16:36:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":989512,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7528043/v1/490df368-dbd1-4705-a07f-e85c8c2e7b21.pdf"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Increased Default Mode Network Connectivity and Temperament Predict Internalizing Symptoms in Early Childhood","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInternalizing disorders, primarily encompassing depression and anxiety, constitute a profound and escalating public health challenge\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Now recognized as the leading cause of morbidity and disability among adolescents, these conditions impose immense social, academic, and personal costs\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. A critical feature of internalizing psychopathology is its early developmental origin. Symptoms can be reliably identified in children as young as toddlers, and these early difficulties demonstrate significant stability over time\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. For instance, preschoolers identified with an internalizing disorder are three times more likely than their peers to exhibit similar psychopathology eight years later, highlighting early childhood as a crucial period for understanding etiological pathways\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite their prevalence and long-term impact, these disorders are often characterized by quiet, internal distress\u0026mdash;such as sadness, worry, and social withdrawal\u0026mdash;which can make them difficult for parents, caregivers, and educators to detect\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Unlike externalizing problems, which are disruptive and demand attention, the \"quiet\" suffering of an internalizing child may go unnoticed, delaying or preventing access to necessary support\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This diagnostic challenge, coupled with the high rates of treatment non-response in adolescence\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, underscores an urgent need for research that can translate fundamental discoveries about risk into more effective, early-stage prevention and intervention strategies.\u003c/p\u003e\u003cp\u003eIn the search for biological markers of risk, neuroimaging research in adults with major depressive disorder (MDD) has consistently converged on a model of dysregulated large-scale brain networks\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Two networks are central to this model: the Default Mode Network (DMN) and the Frontoparietal Network (FPN). The DMN, comprising regions such as the medial prefrontal cortex and posterior cingulate cortex, is most active during periods of rest and is implicated in internally oriented, self-referential cognitive processes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In adults with MDD, the DMN is frequently found to be \u003cem\u003ehyperconnected\u003c/em\u003e, a state of excessive functional integration that is thought to be the neural substrate for rumination\u0026mdash;the persistent, repetitive focus on negative thoughts and feelings that is a core symptom of depression\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eConversely, the FPN (also known as the central executive network), which includes the dorsolateral prefrontal cortex and posterior parietal cortex, is critical for \"top-down\" cognitive control, goal-directed attention, and the regulation of emotion\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This network is often found to be \u003cem\u003ehypoconnected\u003c/em\u003e in MDD, reflecting a deficit in the neural systems that support adaptive, flexible behavior and emotional modulation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. A landmark meta-analysis by Kaiser et al. (2015) solidified this model, demonstrating a pattern of DMN hyperconnectivity, FPN hypoconnectivity, and an overall imbalance between networks dedicated to internal versus external attention in MDD. This neural profile is theorized to reflect a depressive bias toward maladaptive internal thought at the expense of effective engagement with the external world and cognitive control\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe remarkable consistency of this DMN/FPN pattern in adults raises a fundamental developmental question: When do these neural signatures of risk emerge? Are they present as a pre-existing vulnerability before the onset of a formal disorder, or do they develop as a \"neural scar\" resulting from chronic illness? Investigating these network dynamics in young, typically developing children provides a direct test of the hypothesis that these neural patterns are developmental precursors to psychopathology rather than its consequence.\u003c/p\u003e\u003cp\u003eParallel to neurobiological investigations, developmental psychopathology has long established temperament as a primary risk factor for mental illness\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Temperament refers to early-appearing, biologically-based individual differences in emotional reactivity and self-regulation that form the foundation of later personality \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Two core temperament dimensions are robustly implicated in the developmental pathways to internalizing disorders. First, high negative affectivity (also termed emotionality or neuroticism), a constitutional predisposition to experience negative emotions such as fear, sadness, and frustration, is a powerful predictor of both anxiety and depression \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Second, deficits in regulatory capacity, particularly low effortful control, are strongly linked to psychopathology\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Effortful control is the ability to inhibit a dominant or prepotent response in favor of a subdominant one, a capacity essential for managing emotional impulses and focusing attention \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The related construct of Surgency, which encompasses traits of positive emotionality, activity level, and approach behaviors, is also relevant; low surgency often manifests as shyness and behavioral inhibition, key features of an internalizing profile\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. It is widely theorized that the combination of high reactivity (negative affect) and low regulation (effortful control) confers the greatest vulnerability to psychopathology\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite robust, parallel lines of evidence from neuroscience and developmental psychology, few studies have integrated these perspectives by examining neural and temperament risk factors concurrently, especially during the preschool years. This developmental period is marked by the rapid maturation of brain circuits and the consolidation of temperament traits, making it a critical window for understanding the origins of psychopathology\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Furthermore, most of the neuroimaging research on internalizing disorders has utilized functional magnetic resonance imaging (fMRI) in adolescent and adult populations, leaving a significant gap in our knowledge of the neural underpinnings of risk in early childhood \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFunctional near-infrared spectroscopy (fNIRS) is an optical neuroimaging modality particularly well-suited for bridging this gap. As a non-invasive, portable, and relatively motion-tolerant technique, fNIRS makes it feasible to measure cortical brain function in young, active children who cannot easily tolerate the confines of an fMRI scanner \u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The present study leverages this technology to investigate the concurrent predictive power of resting-state functional connectivity within the DMN and FPN, alongside core temperament traits, for internalizing symptoms in a community sample of 4- to 6-year-old children. Based on the adult and developmental literature, we formed the following hypotheses:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHigher internalizing symptoms will be predicted by increased within-network DMN functional connectivity.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHigher internalizing symptoms will be predicted by decreased within-network FPN functional connectivity.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHigher internalizing symptoms will be predicted by higher temperament negative affect and lower surgency and effortful control.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final sample consisted of 78 children (44 female) between 4 and 6 years of age (\u003cem\u003eM\u003c/em\u003eage = 65.4 months, \u003cem\u003eSD\u003c/em\u003e = 11.3, range = 48-83 months). Participants were recruited from the local community through flyers and online advertisements. An initial 100 children were enrolled, but data from 22 children were excluded from the final analysis due to excessive motion artifacts or poor signal quality in the fNIRS data [n=14], a common challenge in pediatric neuroimaging \u003csup\u003e18\u003c/sup\u003e, the scan cut short due to experimenter error (n = 1), being in physical contact with the parent during the fNIRS scan (n = 2), poor fNIRS cap placement (n = 2), refusal to wear the fNIRS cap (n = 1), issues with the video recording of the structured play session (n = 1), or having an autism spectrum disorder diagnosis (n = 1). The sample was predominantly White and from families with high socioeconomic status (see Table 1). Parents provided written informed consent, and children provided verbal assent prior to participation. The study protocol was approved by the local Institutional Review Board.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eSociodemographic Characteristics of Included Participants\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSociodemographic Characteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (\u003cem\u003eM\u003c/em\u003e) or Count (\u003cem\u003en\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cem\u003eSD\u003c/em\u003e or %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003eAge in months, \u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e65.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026nbsp;11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003eGender, \u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e44%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e56%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003eRace/Ethnicity, \u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Black or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hispanic, Latino, or Spanish Origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e78%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Multiracial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e17%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003eAnnual household income, \u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Less than $15,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;$15,001 to $30,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;$30,001 to $45,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;$45,001 to $60,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;$60,001 to $75,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;$75,001 to $90,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;$90,001 to $110,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;$110,001 to $125,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;$125,001 to $175,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e17%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;$175,001 to $225,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;$225,001 to $275,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;More than $275,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003eParent with a 4-year college degree, \u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Neither parent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;One parent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e18%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Both parents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003e76%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSD\u003c/em\u003e, standard deviation. \u003cem\u003eN\u003c/em\u003e = 78.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProcedure\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach child and a parent attended a single 1.5-hour laboratory visit. Upon arrival, parents completed a battery of online questionnaires while the child was familiarized with the lab environment and the fNIRS equipment. Participants were brought to a quiet, dimly lit testing room and seated at a child-sized table with the presentation computer. The participants\u0026apos; head circumference was measured before they were fitted with an appropriately sized fNIRS fabric cap (52-cm EasyCap, n = 78).\u0026nbsp;The fNIRS caps were organized by 10-20 coordinates and placed on the head so that Fpz was centered above the nasion and equidistant between the nasion and AFz. The initial signal quality was checked for all channels, and any channels with poor quality were manually adjusted to ensure that all channels had acceptable signal quality before the recording. The child then completed a resting-state brain imaging session, during which they were instructed to sit still for a passive viewing task. Following the imaging session, the child participated in a structured play session (data not reported here). Families were compensated for their time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInternalizing Symptoms.\u003c/em\u003e\u003c/strong\u003e Parent-reported internalizing problems were measured using the Child Behavior Checklist for Ages 1.5-5\u003csup\u003e20\u003c/sup\u003e. The CBCL is a widely used, psychometrically robust measure of childhood emotional and behavioral problems. The Internalizing Problems scale T-score was used as the primary outcome variable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTemperament.\u003c/em\u003e\u003c/strong\u003e Child temperament was assessed using the parent-report Child Behavior Questionnaire (CBQ) \u0026ndash; Very Short Form\u003csup\u003e21\u003c/sup\u003e. This measure provides scores on three broad, well-validated temperament dimensions: Negative Affect, Surgency/Extraversion, and Effortful Control. These scales demonstrate good internal consistency and validity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSocio-Economic Status (SES).\u003c/em\u003e\u003c/strong\u003e SES was operationalized using parent-reported total annual household income, a standard proxy in developmental research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003efNIRS Data Acquisition and Processing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResting-state fNIRS data were acquired using a continuous-wave fNIRS system. A standardized optode cap was placed on the child\u0026apos;s head, with emitters and detectors arranged to provide coverage over cortical regions associated with the DMN and FPN (see Figure 1). Data were collected for a continuous 12-minute period while the child sat at rest. Raw light intensity data were converted to changes in oxygenated (\u0026Delta;[HbO]) and deoxygenated (\u0026Delta;) hemoglobin concentration using the modified Beer-Lambert law \u003csup\u003e18\u003c/sup\u003e. Channels with a signal-to-noise ratio (calculated by the mean signal intensity divided by the standard deviation of the signal intensity over time) below our chosen threshold of 12.5 for both wavelengths were marked as poor-quality channels and excluded from further analyses. The processing pipeline, conducted using FC-NIRS software\u003csup\u003e22\u003c/sup\u003e, included several critical steps to ensure high data quality. Age appropriate differential pathlength factor values of 5.5 for 760 nm and 4.5 for 850 nm were used \u003csup\u003e23\u003c/sup\u003e. A wavelet-based motion correction algorithm was applied to identify and regress motion artifacts. Data were then band-pass filtered (0.01-0.08 Hz) to isolate the low-frequency fluctuations characteristic of resting-state neural activity. Systemic physiological artifacts, such as heart rate and respiration, were removed using principal component analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunctional Connectivity (FC) Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA seed-based correlation analysis was performed on the preprocessed\u0026nbsp;\u0026Delta;[HbO]\u0026nbsp;time-series data. Seeds were defined based on the channel locations corresponding to key nodes of the DMN and FPN. For each network, the time-series from all channels within that network were averaged to create a representative network time-series. The functional connectivity for each network was then calculated as the average Pearson correlation coefficient between each individual channel in the network and the representative network time-series. This resulted in a single value for each participant representing the average within-network FC for the DMN and the FPN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary analysis was a multiple linear regression using the entry method to test the concurrent prediction of internalizing symptoms. The CBCL Internalizing T-score served as the outcome variable. The predictor variables entered into the model were: DMN within-network FC, FPN within-network FC, CBQ Negative Affect score, CBQ Surgency score, CBQ Effortful Control score, household income (SES), and the child\u0026apos;s age in months.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA multiple linear regression was conducted to evaluate how well DMN and FPN functional connectivity, temperament, SES, and age predicted internalizing symptoms. The overall regression model was statistically significant, \u003cem\u003eF\u003c/em\u003e(7, 58) = 6.64, \u003cem\u003ep\u003c/em\u003e \u0026lt;.001, and accounted for approximately 44.1% of the variance in internalizing problems (\u003cem\u003eR\u003c/em\u003e\u0026sup2; =.441, Adjusted \u003cem\u003eR\u003c/em\u003e\u0026sup2; =.374).\u003c/p\u003e\n\u003cp\u003eExamination of the individual predictors revealed that three variables made unique, statistically significant contributions to the model. Default Mode Network connectivity was a significant positive predictor of internalizing symptoms (\u0026beta; =.31, \u003cem\u003et\u003c/em\u003e = 2.94, \u003cem\u003ep\u003c/em\u003e =.005) (see Figure 2). Negative Affect was also a significant positive predictor (\u0026beta; =.39, \u003cem\u003et\u003c/em\u003e = 3.25, \u003cem\u003ep\u003c/em\u003e =.002) (see Figure 3), while Surgency was a significant negative predictor (\u0026beta; = -.41, \u003cem\u003et\u003c/em\u003e = -3.40, \u003cem\u003ep\u003c/em\u003e =.001) (see Figure 4). These results indicate that, when controlling for all other variables in the model, children with higher DMN connectivity, higher negative affect, and lower surgency tended to have more parent-reported internalizing problems.\u003c/p\u003e\n\u003cp\u003eContrary to our hypothesis, Frontoparietal Network connectivity was not a significant predictor of internalizing symptoms (\u0026beta; = -.05, \u003cem\u003ep\u003c/em\u003e =.603). Similarly, Effortful Control (\u0026beta; = -.09, \u003cem\u003ep\u003c/em\u003e =.381), household income (\u0026beta; = -.02, \u003cem\u003ep\u003c/em\u003e =.824), and age (\u0026beta; = -.19, \u003cem\u003ep\u003c/em\u003e =.102) did not significantly predict internalizing symptoms in the final model.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides novel evidence for the concurrent neural and temperament predictors of internalizing symptoms in early childhood. In a community sample of 4- to 6-year-old children, we found that greater within-network DMN functional connectivity, higher negative affect, and lower surgency were significant, independent predictors of higher parent-reported internalizing problems. Contrary to hypotheses derived from the adult literature, FPN connectivity was not associated with internalizing symptoms in this young age group. These findings suggest that the neural and temperament foundations of vulnerability to internalizing disorders are established by the preschool years, but that the specific neural signatures of risk may follow distinct developmental timetables.\u003c/p\u003e\u003cp\u003eThe positive association between DMN connectivity and internalizing symptoms represents a striking downward extension of findings from adult and adolescent psychopathology \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This result suggests that hyperconnectivity of the DMN, a neural pattern consistently linked to rumination and maladaptive self-referential thought in adult depression \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, is not merely a consequence of chronic illness. Instead, its association with subclinical symptoms in very young children supports the view that it may represent a primary, early emerging biological vulnerability. In early childhood, this increased DMN integration may not manifest as the complex, abstract rumination seen in adults, but rather as a more foundational processing bias toward internal states at the expense of engagement with the external world. This could be a neural mechanism underlying behaviors like social withdrawal and excessive shyness, which are hallmarks of the internalizing spectrum in young children\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe presence of this DMN-symptom relationship in a non-clinical sample of young children, where symptoms exist on a continuum, strengthens the argument for DMN hyperconnectivity as a candidate endophenotype for internalizing disorders. An endophenotype is a measurable, heritable trait that lies on the causal pathway between genetic predisposition and the manifest disorder. The logic is as follows: (1) adult clinical studies establish DMN hyperconnectivity as a correlate of MDD \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e; (2) the current study demonstrates a linear relationship between DMN connectivity and symptoms in young, non-diagnosed children; (3) this implies the neural pattern exists on a continuum and is present \u003cem\u003ebefore\u003c/em\u003e the typical age of onset for a formal disorder. Therefore, this finding supports a model in which DMN hyperconnectivity is not simply a state marker of being depressed, but rather a stable trait marker of vulnerability that is present from early in development.\u003c/p\u003e\u003cp\u003eThe lack of an association between FPN connectivity and internalizing symptoms is a critical finding that must be interpreted through a developmental lens. Rather than a failure to replicate adult findings, this null result may provide an accurate snapshot of the FPN's maturational state in early childhood. The FPN is the brain's hub for \"top-down\" cognitive control and deliberate, effortful emotion regulation strategies like reappraisal \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This network undergoes a particularly protracted developmental course, with its structural and functional architecture continuing to mature throughout childhood and into adolescence\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Studies consistently show that children exhibit weaker FPN activation during cognitive control tasks compared to adults, and their ability to leverage FPN-mediated strategies to modulate emotional responses improves with age\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe combination of a significant DMN finding and a null FPN finding in our sample points toward a developmental lag between the maturation and pathological involvement of these two key networks. The DMN matures relatively early in development, whereas the FPN matures much later\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The most parsimonious explanation for our results, therefore, is that vulnerability to internalizing problems first manifests in the brain systems that are functionally mature enough to display dysregulation. In early childhood, this is the DMN, via hyperconnectivity that may bias the child toward an internal focus. The role of FPN \u003cem\u003ehypo\u003c/em\u003econnectivity, reflecting deficits in cognitive control, may emerge as a significant risk factor only later in development\u0026mdash;perhaps during adolescence\u0026mdash;when these circuits are expected to be fully engaged for self-regulation and are now failing to do so. This suggests a dynamic, two-stage model of evolving neural risk. Furthermore, some evidence suggests the DMN may act as a \"scaffold\" for the immature FPN during typical development, with stronger DMN-FPN coupling observed in children than in adults\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. It is plausible that at this young age, the FPN is not yet sufficiently independent or mature to be a distinct source of variance contributing to psychopathology.\u003c/p\u003e\u003cp\u003eOur statistical model demonstrates that DMN connectivity, negative affect, and surgency are all independent, significant predictors of internalizing symptoms. This finding supports a multi-pathway model of risk, where biological predispositions in brain function and early emerging behavioral dispositions both make unique contributions to vulnerability. The results showing that high negative affect and low surgency predict internalizing problems are highly consistent with decades of developmental theory and research \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. High negative affect reflects a child's intrinsic reactivity to stress, while low surgency indicates a tendency toward shyness, low positive emotionality, and behavioral inhibition\u0026mdash;a classic internalizing profile. The current findings are in favor of an etiological model where multiple, distinct risk factors can converge on the same clinical phenotype. In other words, a child may be at risk due to their brain connectivity, their temperament, or an additive combination of both.\u003c/p\u003e\u003cp\u003eThis study has several notable strengths. It is among the first to examine concurrent neural and temperament predictors of internalizing symptoms within the critical preschool period. The successful use of fNIRS allowed for the collection of functional neuroimaging data from this challenging-to-scan population, providing a unique window into early brain-behavior associations \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The multimodal approach, integrating brain and behavior, provides a more holistic understanding of risk than either modality alone. However, the findings must be considered in light of several limitations. First, the cross-sectional design precludes any causal inferences; while we have identified significant predictors, we cannot determine the direction of these effects over time. Second, we must acknowledge the technical limitations of fNIRS, which include lower spatial resolution compared to fMRI and a sensitivity that is primarily limited to the cortical surface\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Third, the sample was relatively homogenous, consisting of primarily White children from high-SES families, which may limit the generalizability of the findings to more diverse populations. Finally, a notable number of participants (22%) were excluded due to data quality issues, a persistent challenge in pediatric fNIRS that highlights the ongoing need for methodological refinement\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Notably, a recent meta-analysis suggests an average attrition rate of 34.23% in infant fNIRS work\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Future research should prioritize longitudinal designs. Following this cohort into middle childhood and adolescence would allow for a direct test of the \"developmental lag\" hypothesis: does FPN connectivity emerge as a significant predictor of internalizing trajectories as children age? Integrating additional levels of analysis, such as genetic data and measures of environmental adversity (e.g., adverse childhood experiences), would provide an even more comprehensive and ecologically valid model of risk and resilience\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe findings of this study hold significant translational potential. The identification of DMN connectivity and specific temperament traits as early, independent risk markers could inform the development of novel, targeted, and timely prevention strategies. For instance, children identified with a temperament profile of high negative affect and low surgency could benefit from established behavioral interventions focused on building emotion regulation skills and fostering social engagement. In parallel, children identified with DMN hyperconnectivity might one day benefit from interventions aimed at modulating self-focused attention, such as mindfulness-based practices that have been adapted for young children.\u003c/p\u003e\u003cp\u003eIn conclusion, this study demonstrates that the roots of internalizing psychopathology are evident in both brain function and behavior by the preschool years. The early emergence of DMN dysregulation, coupled with well-established temperament vulnerabilities, appears to set the stage for later difficulties. The distinct developmental trajectory of the FPN underscores that the neurobiology of risk is not static but dynamic, evolving as the brain matures. Understanding this developmental timing is a crucial step toward identifying the right children at the right time for the right intervention, ultimately improving long-term mental health outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interests\u003c/h2\u003e\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThank you to all of the families who participated in the study. JRC was partially supported by the Jefferson Scholars Foundation Fellowship award.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMiller, C. H., Hamilton, J. P., Sacchet, M. D. \u0026amp; Gotlib, I. H. Meta-analysis of Functional Neuroimaging of Major Depressive Disorder in Youth. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, 1045\u0026ndash;1053 (2015).\u003c/li\u003e\n\u003cli\u003eSolmi, M. \u003cem\u003eet al.\u003c/em\u003e Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. \u003cem\u003eMol. Psychiatry\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 281\u0026ndash;295 (2022).\u003c/li\u003e\n\u003cli\u003eTandon, M., Cardeli, E. \u0026amp; Luby, J. Internalizing Disorders in Early Childhood: A Review of Depressive and Anxiety Disorders. \u003cem\u003eChild Adolesc. Psychiatr. Clin. N. Am.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 593\u0026ndash;610 (2009).\u003c/li\u003e\n\u003cli\u003eRubin, K. H. \u0026amp; Mills, R. S. Conceptualizing developmental pathways to internalizing disorders in childhood. \u003cem\u003eCan. J. Behav. Sci. Rev. Can. Sci. Comport.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 300\u0026ndash;317 (1991).\u003c/li\u003e\n\u003cli\u003eKaiser, R. H., Andrews-Hanna, J. R., Wager, T. D. \u0026amp; Pizzagalli, D. A. Large-scale network dysfunction in Major Depressive Disorder: Meta-analysis of resting-state functional connectivity. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, 603\u0026ndash;611 (2015).\u003c/li\u003e\n\u003cli\u003eTooley, U. A. \u003cem\u003eet al.\u003c/em\u003e The Age of Reason: Functional Brain Network Development during Childhood. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 8237\u0026ndash;8251 (2022).\u003c/li\u003e\n\u003cli\u003eYoon, S., Kleinman, M., Mertz, J. \u0026amp; Brannick, M. Is social network site usage related to depression? A meta-analysis of Facebook\u0026ndash;depression relations. \u003cem\u003eJ. Affect. Disord.\u003c/em\u003e \u003cstrong\u003e248\u003c/strong\u003e, 65\u0026ndash;72 (2019).\u003c/li\u003e\n\u003cli\u003eYan, C.-G. \u003cem\u003eet al.\u003c/em\u003e Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 9078\u0026ndash;9083 (2019).\u003c/li\u003e\n\u003cli\u003eMenon, V. Large-scale brain networks and psychopathology: a unifying triple network model. \u003cem\u003eTrends Cogn. Sci.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 483\u0026ndash;506 (2011).\u003c/li\u003e\n\u003cli\u003eOchsner, K. N. \u0026amp; Gross, J. J. The cognitive control of emotion. \u003cem\u003eTrends Cogn. Sci.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 242\u0026ndash;249 (2005).\u003c/li\u003e\n\u003cli\u003eMuris, P. \u0026amp; Ollendick, T. H. The role of temperament in the etiology of child psychopathology. \u003cem\u003eClin. Child Fam. Psychol. Rev.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 271\u0026ndash;289 (2005).\u003c/li\u003e\n\u003cli\u003eNigg, J. T. Annual Research Review: On the relations among self-regulation, self-control, executive functioning, effortful control, cognitive control, impulsivity, risk-taking, and inhibition for developmental psychopathology. \u003cem\u003eJ. Child Psychol. Psychiatry\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e, 361\u0026ndash;383 (2017).\u003c/li\u003e\n\u003cli\u003eGartstein, M. A., Putnam, S. P. \u0026amp; Rothbart, M. K. Etiology of preschool behavior problems: Contributions of temperament attributes in early childhood. \u003cem\u003eInfant Ment. Health J.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 197\u0026ndash;211 (2012).\u003c/li\u003e\n\u003cli\u003eCenter on the Developing Child at Harvard Universit. Children\u0026rsquo;s Emotional Development Is Built into the Architecture of Their Brains. (2024).\u003c/li\u003e\n\u003cli\u003eBrown, T. T. \u0026amp; Jernigan, T. L. Brain development during the preschool years. \u003cem\u003eNeuropsychol. Rev.\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 313\u0026ndash;333 (2012).\u003c/li\u003e\n\u003cli\u003eCampbell, S. B. The State of Young Children in the United States. in \u003cem\u003eThe Wiley Handbook of Early Childhood Development Programs, Practices, and Policies\u003c/em\u003e 18\u0026ndash;34 (John Wiley \u0026amp; Sons, Ltd, 2017). doi:10.1002/9781118937334.ch2.\u003c/li\u003e\n\u003cli\u003ePinti, P. \u003cem\u003eet al.\u003c/em\u003e The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience. \u003cem\u003eAnn. N. Y. Acad. Sci.\u003c/em\u003e \u003cstrong\u003e1464\u003c/strong\u003e, 5\u0026ndash;29 (2020).\u003c/li\u003e\n\u003cli\u003eScholkmann, F. \u003cem\u003eet al.\u003c/em\u003e A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e85\u003c/strong\u003e, 6\u0026ndash;27 (2014).\u003c/li\u003e\n\u003cli\u003eY\u0026uuml;cel, M. A. \u003cem\u003eet al.\u003c/em\u003e Best practices for fNIRS publications. \u003cem\u003eNeurophotonics\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 012101 (2021).\u003c/li\u003e\n\u003cli\u003eAchenbach, T. M. \u0026amp; Rescorla, L. A. Manual for the ASEBA school-age forms and profiles. Burlington, Vermont: University of Vermont. \u003cem\u003eRes. Cent. Child. Youth Fam.\u003c/em\u003e (2001).\u003c/li\u003e\n\u003cli\u003ePutnam, S. P. \u0026amp; Rothbart, M. K. Development of short and very short forms of the Children\u0026rsquo;s Behavior Questionnaire. \u003cem\u003eJ. Pers. Assess.\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, 102\u0026ndash;112 (2006).\u003c/li\u003e\n\u003cli\u003eXu, J. \u003cem\u003eet al.\u003c/em\u003e FC-NIRS: A Functional Connectivity Analysis Tool for Near-Infrared Spectroscopy Data. \u003cem\u003eBioMed Res. Int.\u003c/em\u003e \u003cstrong\u003e2015\u003c/strong\u003e, 248724 (2015).\u003c/li\u003e\n\u003cli\u003eScholkmann, F. \u0026amp; Wolf, M. General equation for the differential pathlength factor of the frontal human head depending on wavelength and age. \u003cem\u003eJ. Biomed. Opt.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 105004 (2013).\u003c/li\u003e\n\u003cli\u003eChen, M. \u003cem\u003eet al.\u003c/em\u003e Default mode network scaffolds immature frontoparietal network in cognitive development. \u003cem\u003eCereb. Cortex\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 5251\u0026ndash;5263 (2023).\u003c/li\u003e\n\u003cli\u003eWendelken, C. \u003cem\u003eet al.\u003c/em\u003e Frontoparietal Structural Connectivity in Childhood Predicts Development of Functional Connectivity and Reasoning Ability: A Large-Scale Longitudinal Investigation. \u003cem\u003eJ. Neurosci. Off. J. Soc. Neurosci.\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 8549\u0026ndash;8558 (2017).\u003c/li\u003e\n\u003cli\u003eMcRae, K. \u003cem\u003eet al.\u003c/em\u003e The development of emotion regulation: an fMRI study of cognitive reappraisal in children, adolescents and young adults. \u003cem\u003eSoc. Cogn. Affect. Neurosci.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 11\u0026ndash;22 (2012).\u003c/li\u003e\n\u003cli\u003eSilvers, J. A. \u003cem\u003eet al.\u003c/em\u003e Age-related differences in emotional reactivity, regulation, and rejection sensitivity in adolescence. \u003cem\u003eEmot. Wash. DC\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1235\u0026ndash;1247 (2012).\u003c/li\u003e\n\u003cli\u003eFair, D. A. \u003cem\u003eet al.\u003c/em\u003e The maturing architecture of the brain\u0026rsquo;s default network. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e, 4028\u0026ndash;4032 (2008).\u003c/li\u003e\n\u003cli\u003eGao, W. \u003cem\u003eet al.\u003c/em\u003e Functional Network Development During the First Year: Relative Sequence and Socioeconomic Correlations. \u003cem\u003eCereb. Cortex N. Y. NY\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 2919\u0026ndash;2928 (2015).\u003c/li\u003e\n\u003cli\u003eBaek, S. \u003cem\u003eet al.\u003c/em\u003e Attrition rate in infant fNIRS research: A meta-analysis. \u003cem\u003eInfancy Off. J. Int. Soc. Infant Stud.\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 507\u0026ndash;531 (2023).\u003c/li\u003e\n\u003cli\u003eAlbertina, E. A., Barch, D. M. \u0026amp; Karcher, N. R. Internalizing Symptoms \u0026amp; Adverse Childhood Experiences Associated with Functional Connectivity in A Middle Childhood Sample. \u003cem\u003eBiol. Psychiatry Cogn. Neurosci. Neuroimaging\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 50\u0026ndash;59 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7528043/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7528043/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the early neural and temperament precursors to internalizing psychopathology. Increased default mode network (DMN) functional connectivity and decreased frontoparietal network (FPN) connectivity are established neural signatures of internalizing disorders in adults, but it is unknown if these patterns represent early-emerging vulnerability. In a community sample of typically developing children aged 4 to 6 years, we examined the concurrent predictive power of resting-state functional connectivity and temperament for internalizing symptoms. We hypothesized that increased DMN connectivity, decreased FPN connectivity, and temperament traits of high negative affect and low surgency would predict higher internalizing problems. Functional connectivity was measured using functional near-infrared spectroscopy (fNIRS), a child-friendly neuroimaging technique. Internalizing symptoms and temperament were assessed via parent report using the Child Behavior Checklist and Child Behavior Questionnaire, respectively. A multiple linear regression revealed that greater DMN connectivity, higher negative affect, and lower surgency were significant, independent predictors of internalizing symptoms. FPN connectivity was not a significant predictor. These findings suggest that a key neural signature of adult depression\u0026mdash;DMN hyperconnectivity\u0026mdash;is already associated with subclinical symptoms in early childhood, supporting its role as a primary vulnerability marker. The absence of an FPN association likely reflects the protracted maturation of this cognitive control network, pointing to a developmental lag in the emergence of distinct neural risk factors for internalizing disorders.\u003c/p\u003e","manuscriptTitle":"Increased Default Mode Network Connectivity and Temperament Predict Internalizing Symptoms in Early Childhood","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 10:19:21","doi":"10.21203/rs.3.rs-7528043/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9090a5f6-7b2e-48c5-a2d0-918a6ea3d386","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54602357,"name":"Biological sciences/Psychology/Human behaviour"},{"id":54602358,"name":"Biological sciences/Neuroscience/Molecular neuroscience"}],"tags":[],"updatedAt":"2025-12-11T22:10:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 10:19:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7528043","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7528043","identity":"rs-7528043","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}