Hierarchical Neurocognitive Model of Externalizing and Internalizing Comorbidity

Hierarchical Neurocognitive Model of Externalizing and Internalizing Comorbidity | 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 Hierarchical Neurocognitive Model of Externalizing and Internalizing Comorbidity Tianye Jia, Chao Xie, Shitong Xiang, Yueyuan Zheng, Chun Shen, and 51 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5397195/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Feb, 2026 Read the published version in Nature Mental Health → Version 1 posted You are reading this latest preprint version Abstract Mounting evidence suggests hierarchical psychopathology factors underlying psychiatric comorbidity. However, the exact neurobiological characterizations of these multilevel factors remain elusive. In this study, leveraging the brain-behavior predictive framework with a 10-year longitudinal imaging-genetic cohort (IMAGEN, ages 14, 19 and 23, N = 1,750), we constructed two neural factors underlying externalizing and internalizing symptoms, which were reproducible across six clinical and population-based datasets (ABCD, STRATIFY/ ESTRA, ABIDE II, ADHD-200 and XiNan, from age 10 to age 36, N = 3,765). These two neural factors exhibit distinct neural configurations: hyperconnectivity in impulsivity-related circuits for the externalizing symptoms and hypoconnectivity in goal-directed circuits for the internalizing symptoms. Both factors also differ in their cognitive-behavior relevance, genetic substrates and developmental profiles. Together with previous studies, these findings propose a hierarchical neurocognitive spectral model of comorbid mental illnesses from preadolescence to adulthood: a general neuropsychopathological (NP) factor (manifested as inefficient executive control) and two stratified factors for externalizing (deficient inhibition control) and internalizing (impaired goal-directed function) symptoms, respectively. These holistic insights are crucial for the development of stratified therapeutic interventions for mental disorders. Health sciences/Diseases/Psychiatric disorders Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Psychiatric comorbidity is prevalent and often leads to more severe prognoses 1 , posing a major challenge to the current mental health diagnostic system 2 . In response, the Hierarchical Taxonomy of Psychopathology (HiTOP) was proposed to categorize the complex psychiatric comorbidities into a general factor alongside multiple stratified transdiagnostic spectra, for instance, the externalizing (aggressive and hyperactive-impulsive) vs. internalizing (anxious and depressive) spectrum 3 , 4 . Recently, our research team identified a prefrontal-related general Neuropsychopathological (NP) factor underlying both externalizing and internalizing symptoms from preadolescence to early adulthood 5 . However, the neurobiological mechanisms of externalizing and internalizing disorders and the interaction of general-stratified factors during development remain elusive. Another dimensional framework, the Research Domain Criteria (RDoC) 6 , was developed to advance the investigation of neurobiological foundations of dimensional psychopathology. However, the research progress in uncovering the stratified neural bases of externalizing and internalizing disorders has remained slow 7 . This sluggishness is partly due to the historical emphasis on localizing brain abnormalities at the regional level in psychiatric disorders 8 – 10 . It is crucial to recognize that different brain regions do not function or develop independently; instead, they work in distributed and anatomically interconnected systems 11 , 12 . The above evidence hence suggests that distinct regionalized brain markers of psychiatric disorders might be located within a common psychopathological brain network. This hypothesis has recently gained support from normative network mapping and connectivity-based transdiagnostic studies 13 , 14 , emphasizing the importance of network-based approaches in unifying region-level heterogeneous neural underpinnings of psychiatric disorders 15 , 16 . Furthermore, previous transdiagnostic neuroimaging studies have predominantly employed a cross-sectional approach 17 – 19 , thereby overlooking the developmental perspective on how the general and stratified neural substrates manifest and evolve longitudinally 3 , especially during critical developmental periods like adolescence. Utilizing a longitudinal large-scale imaging dataset, we can further elucidate the nuanced interplay between the enduring and phasic neural mechanisms of psychiatric comorbidity 20 . This approach will significantly advance our understanding of the onset and progression of psychiatric comorbidity. The present study addresses three major questions regarding the specific transdiagnostic neural bases of externalizing and internalizing symptoms: (1) Can we identify stratified cross-disorder neural factors for externalizing and internalizing symptoms, respectively? (2) Do the two stratified neural factors exhibit distinct characterisations regarding neurobiological risk factors and clinical conditions? (3) How can we synthesize the general and stratified neural factors into a hierarchical neurocognitive model of comorbid psychopathology? Result Stratified neural factors of externalizing and internalizing symptoms Our previous study found significant predictive effects of task-based connectomes on eight psychiatric symptoms in 14-year-old participants from the IMAGEN study (Fig. 1 a and Supplementary Tables 1 and 2 , N = 1,750) 5 . For each psychiatric symptom, we generated a brain-predicted measure. Interestingly, we observed that there were significantly higher similarities between brain-predicted symptoms than the observed psychiatric symptoms (externalizing symptoms: brain-predicted r mean = 0.91, observed r mean = 0.37, P perm < 0.001 for the difference; internalizing symptoms: brain-predicted r mean = 0.52, observed r mean = 0.28, P perm < 0.001 for the difference) (Fig. 1 b and c ). The results thus suggested substantial shared neural bases within externalizing and internalizing symptoms. We next aimed to identify specific neural factors, termed as ‘stratified neural factors’, comprising cross-disorder edges that predicted two or more symptoms from a single psychiatric domain (externalizing or internalizing), while not predicting any symptoms from the other domain (Fig. 1 d). We found that stratified cross-disorder edges were consistently and reliably identified from task conditions relating to inhibitory control and reward sensitivity (i.e. stop success, stop failure, positive reward feedback and reward anticipation; all P perm < 0.01, Supplementary Table 3 ). We further ascertained the neural mechanisms of the stratified cross-disorder edges in terms of their predictive effects (Fig. 1 d). Intriguingly, we observed a double dissociation effect: externalizing symptoms were reliably associated with positive-positive cross-disorder edges ( n edge = 1,268, P perm < 0.001, showing positive correlations with externalizing symptoms), whereas internalizing symptoms were reliably associated with negative-negative cross-disorder edges ( n edge = 469, P perm < 0.001, showing negative correlations with internalizing symptoms) ( Supplementary Table 4 ). Therefore, in the following analyses, the summed functional connectivity (FC) strength of positive-positive cross-disorder edges will be referred to as the externalizing neural factor, and the summed strength of negative-negative cross-disorder edges as the internalizing neural factor. Longitudinal analysis of stratified neural factors As previous research has highlighted the stability of internalizing and externalizing behaviors over time 21 , we next examined the longitudinal predictive performance of the two stratified neural factors from adolescence to early adulthood over a 10-year span (Fig. 1 e). The externalizing factor demonstrated consistent performance for longitudinal prediction across ages 14, 19 and 23. To elaborate, the externalizing neural factor estimated at age 14 could significantly predict externalizing symptoms at ages 19 (N = 1,045, r = 0.095, t = 3.09, P one−tailed = 0.001, Supplementary Table 5A ) and 23 (N = 1,043, r = 0.117, t = 3.79, P one−tailed = 7.98E-05, Supplementary Table 5A ). Also, the externalizing neural factors estimated at ages 19 and 23 could predict their corresponding externalizing symptoms at the same age or later age (brain and symptoms both at age 19, N = 1,095, r = 0.096, t = 3.20, P one−tailed =7.05E-04; brain at age 19 and symptoms at age 23, N = 1,029, r = 0.055, t = 1.77, P one−tailed =0.038; brain and symptoms both at age 23, N = 937, r = 0.078, t = 2.388, P one−tailed =0.009, Supplementary Table 5C and 5E ). In contrast, only the internalizing neural factor estimated at age 14 could significantly predict future internalizing symptoms measured at ages 19 and 23 (age 19, N = 1,045, r = -0.080, t = -2.59, P one−tailed = 0.005; age 23, N = 1,043, r = -0.088, t = -2.84, P one−tailed = 0.002, Supplementary Table 5B ), but not for the internalizing neural factors estimates at ages 19 and 23 ( Supplementary Table 5D and 5F ). We next examined the longitudinal changes of the two stratified neural factors across ages 14, 19 and 23 (Fig. 1 e). While both the externalizing and internalizing neural factors maintained highly significant positive FC strengths throughout ages 14, 19 and 23 (All t > 100, Cohen ‘ d > 3.1, P < 0.001), steady decreases in FC strength from age 14 to age 23 were also observed for both neural factors (externalizing slop β mean = -9.44, t = -17.64, Cohen’s d = -0.68, P two−tailed = 1.95E-57; internalizing slop β mean = -2.33, t = − 18.89, Cohen’s d = -0.73, P two−tailed = 4.83E-64). During this critical developmental period, the normative decrease in connectivity strength could be explained by the neural pruning for a more efficient brain information process 22 . Lastly, we investigated the associations between psychiatric symptoms at age 14 and the rate of decline in the stratified neural factors from age 14 to 23. We observed that individuals with higher baseline externalizing symptoms may have undergone an under-pruning process of the externalizing neural factor from adolescence to early adulthood (N = 575, r = -0.20, t = -4.92, P two−tailed = 1.12E-06); conversely, individuals with higher baseline internalizing symptoms experienced an over-pruning process of the internalizing neural factor from adolescence to early adulthood (N = 575, r = 0.12, t = 2.96, P two−tailed = 0.003). These results indicated that while both the externalizing and internalizing behavioral domains showed strong within-domain intra-correlations for both observed and neural predicted symptoms, each behavior domain may be represented by a distinct cross-disorder neural substrate. Neuroanatomical characterization of stratified neural factors We characterized the above two stratified neural factors at multiple neuroanatomical levels. In terms of the regional network degree, the externalizing neural factor was mainly located in brain regions such as the middle cingulate cortex (MCC), precentral gyrus (PreCG), precuneus (PreCun), supramarginal gyrus (SMG), and putamen (Fig. 2 a and Supplementary table 6a ), which were commonly implicated in the habitual control process 23 . In contrast, the internalizing neural factor was primarily enriched in regions such as the precuneus (PreCun), ventromedial prefrontal cortex (vmPFC), medial orbitofrontal cortex (mOFC), and caudate (Fig. 2 b and Supplementary table 6b ), all known to play crucial roles in goal-directed processing 24 . Next, while the two stratified neural factors did not share overlapping edges by definition, they did share similar network configurations at a higher neuroanatomical level. For instance, the externalizing and internalizing neural factors exhibited similar network-level configurations, primarily in the motor, frontoparietal, and salience networks (Fig. 2 c, d and Supplementary table 7 ). Notably, the salience network plays a pivotal role in attending to motivational stimuli and recruiting appropriate functional brain-behavior networks to modulate behavior 25 . Therefore, the hyperconnectivity of the externalizing neural factor (e.g. in high-risk individuals) might be associated with excessive perception of external stimuli and a lack of inhibitory control over automatic responses 26 , 27 . Conversely, the hypoconnectivity of the internalizing neural factor might be related to limited salience processing, resulting in difficulties in engaging goal-directed behaviors in individuals with internalizing disorders 28 . Functional and genetic bases of stratified neural factors We then investigated the associations between task performance measures with the two stratified neural factors. Both the externalizing and internalizing neural factors showed significant negative correlations with accuracies in the monetary incentive delay (MID) task (externalizing: N = 1,620, r = -0.14, t = -5.73, P two−tailed = 1.20E-08; internalizing: N = 1,620, r = -0.08, t = -3.22, P two−tailed = 0.001) and the go-trials in the stop-signal task (SST) (externalizing: N = 1,567, r = -0.26, t = -10.62, P two−tailed = 1.81E-25; internalizing: N = 1,567, r = -0.20, t = -8.01, P two−tailed = 2.30E-15), but no significant associations with the reaction time in the MID task (externalizing: N = 1,620, r = -0.05, t = -1.97, P two−tailed = 0.05; internalizing: N = 1,620, r = -0.02, t = -0.84, P two−tailed = 0.40) nor stop signal delay in the SST (externalizing: N = 1,567, r = -0.04, t = -1.51, P two−tailed = 0.13; internalizing: N = 1,567, r = -0.04, t = -1.77, P two−tailed = 0.08). These results were similar to the general neural factor (i.e. the NP -factor) findings in our previous study 5 . Next, we examined the functional specificity of the two stratified neural factors across a wide range of cognitive-behavioral phenotypes. The externalizing neural factor exhibited specific associations with impulsive and substance use behaviors (Fig. 3 a, Supplementary Table 8a ), where impulsivity is a characteristic feature of externalizing disorders and a known risk factor for future substance abuse 29 . In contrast, the internalizing neural factor was primarily correlated with maladaptive traits, such as neuroticism and negative thinking (Fig. 3 a, Supplementary Table 8b ), where neuroticism plays a pivotal role in longitudinally predicting various internalizing disorders, like anxiety and depression 30 . At last, we investigated whether the two stratified neural factors had different genetic substrates by examining their associations with the polygenic risk scores (PRS) of attention-deficit/hyperactivity disorder (ADHD) and major depressive disorder (MDD) as the representations of externalizing and internalizing disorders, respectively. We observed a significant association of the externalizing neural factor with an increased PRS of ADHD (N = 1,594) ( r = 0.082, t = 3.27, P one−tailed = 5.31E-05), but not with the PRS of MDD ( r = 0.024, t = 0.96, P two−tailed = 0.33. Conversely, a lower internalizing neural factor was only correlated with the PRS of MDD (N = 1,594) ( r = -0.04, t = -1.74, P one−tailed = 0.041), but not with that of ADHD ( r = 0.008, t = 0.32, P two−tailed = 0.74). The above results hence suggested that the externalizing and internalizing neural factors have distinct behavioral and genetic implications for their corresponding psychiatric comorbidity. Generalization of stratified neural factors We evaluated the generalization performance of the two stratified neural factors in multiple population and clinical datasets. First, with the MID task and SST in the Adolescent Brain Cognitive Development cohort (ABCD) dataset 31 , we found that the externalizing neural factor was significantly correlated with a wide range of externalizing symptoms at age 10 (N = 1,799), including externalizing ( r = 0.059, t = 2.51, P one−tailed = 0.006), rule break ( r = 0.070, t = 2.97, P one−tailed = 0.002), conduct ( r = 0.063, t = 2.69, P one−tailed = 0.006), aggressive ( r = 0.057, t = 2.41, P one−tailed = 0.008), oppositional defiant ( r = 0.054, t = 2.30, P one−tailed = 0.011), and attention symptoms ( r = 0.050, t = 2.10, P one−tailed = 0.018). In addition, the externalizing neural factor estimated at age 10 could also predict the summary score of all externalizing symptoms at age 11 (N = 1,042, r = 0.052, t = 1.67, P one−tailed = 0.047), as well as the subscores (ADHD: r = 0.078, t = 2.49, P one−tailed = 0.007; opposite: r = 0.062, t = 2.03, P one−tailed = 0.021; rule break: r = 0.062, t = 2.00, P one−tailed = 0.022). In contrast, the internalizing neural factor showed no significant association with the internalizing-related symptom at ages 10 or 11. Next, we compared diagnosed patients (marked as severe or high risk for at least one externalizing or internalizing disorder) with healthy controls for the two stratified neural factors in the IMAGEN and ABCD datasets. For the externalizing neural factor, we found that externalizing participants had significantly higher factor scores than control groups in both IMAGEN (externalizing participants N = 93, controls N = 859, t = 7.10, Cohen’s d = 0.78, P one−tailed = 1.24e-12) and ABCD datasets (externalizing participants N = 206, controls N = 1,596, t = 2.67, Cohen’s d = 0.20, P one−tailed = 0.003). However, the comparison of the internalizing neural factor between diagnosed patients and healthy controls was only significant in IMAGEN (internalizing participants: N = 46, controls N = 859, t = -3.42, Cohen’s d = 0.52, P one−tailed = 3.29E-04), but not in ABCD dataset (internalizing participants N = 32, controls N = 1,596, t = 1.85, Cohen’s d = 0.33, P one−tailed = 0.97). Further, we investigated whether the two stratified neural factors had clinical relevance in the case-control STRATIFY and ESTRA cohort (age = 23) with the SST 32 . We found that the externalizing and internalizing neural factors were differentially associated with psychiatric disorders. To elaborate, the externalizing neural factor of alcohol use disorder (AUD) patients (N = 127) was significantly higher than in the healthy controls (N = 64) (t = 1.82, Cohen’s d = 0.28, P one−tailed = 0.035), but no significant group differences were found for this neural factor between healthy controls (N = 64) and patients with internalizing disorders (Anorexia Nervosa: N = 55, t = -0.54, Cohen’s d = 0.10, P two−tailed = 0.059; Bulimia Nervosa: N = 44, t = 1.47, Cohen’s d = 0.29, P two−tailed = 0.15; Major Depression: N = 143, t = 1.44, Cohen’s d = 0.22, P two−tailed = 0.15). On the other hand, the internalizing neural factor was significantly lower in patients with internalizing disorders than in healthy controls (N = 64) (internalizing patients: N = 242, t = -2.31, Cohen’s d = 0.32, P one−tailed = 0.011; Anorexia Nervosa: N = 55, t = -3.04, Cohen’s d = 0.56, P one−tailed = 0.002; Bulimia Nervosa: N = 44, t = -0.83, Cohen’s d = 0.16, P one−tailed = 0.20; Major Depression: N = 143, t = -2.04, Cohen’s d = 0.31, P one−tailed = 0.021), but no significant group difference was found for the alcohol use disorder (N = 127, t = -0.92, Cohen’s d = 0.14, P two−tailed = 0.36). Finally, we found that the externalizing factor generated using resting-state fMRI (with the same set of FC as defined in the IMAGEN cohort) was significantly higher in the ASD patients (N = 233) than in healthy controls (N = 331) (ABIDEII, Mean age = 10.5, t = 1.90, Cohen’s d = 0.08, P one−tailed = 0.01). A significant difference were also obersved in the group comparison between ADHD patients (N = 292) and healthy controls (N = 228) (ADHD-200, Mean age = 11, t = 2.06, Cohen’s d = 0.18, P one−tailed = 0.020). Furthermore, the internalizing factor generated using resting-state fMRI (with the same set of FC as defined in the IMAGEN cohort) of depressive patients (N = 277) was significantly lower than the control group (N = 172) (XiNan dataset, Mean age = 36.1 t = -3.11, Cohen’s d = -0.30, P one−tailed = 9.67e-04). These results further supported the distinct contribution of externalizing and internalizing neural factors to externalizing and internalizing comorbidity, respectively. Neural specificities of the three cross-disorder networks Our previous study identified a general neural factor ( NP factor) across the externalizing and internalizing symptoms. Here, we would like to closely examine the specific configurations of the three cross-disorder neural factors (one general and two stratified neural factors) based on the Specificity Score, i.e., the contribution of a factor after controlling the other two factors (Fig. 4 a, with further details available in the Method). We first estimated the Specificity Score of each brain region for the three cross-disorder neural factors respectively (Fig. 4 b, total 268 regions). We found that 79 regions exhibited predominant associations (i.e. with the highest Specificity Score) with the general NP factor, with the most prominent regions including the ventral precuneus, middle occipital cortex, and inferior frontal cortex ( Supplementary Table S9 A ). Additionally, 97 regions were associated predominantly with the externalizing neural factor, most notably the primary sensorimotor cortex areas such as the precentral and middle cingulate cortex ( Supplementary Table S9B ). Finally, 92 regions showed predominant associations with the internalizing neural factor, with notable regions including the medial prefrontal cortex and orbitofrontal cortex ( Supplementary Table S9C ). The Specificity Scores of the three cross-disorder neural factors were significantly different ( F (2,265) = 15.80, P = 3.31E-07), with the general NP factor demonstrating significantly higher scores compared to either the externalizing or internalizing counterparts ( NP vs externalizing: t (174) = 4.70, P = 5.17E-06; NP vs internalizing: t (169) = 3.85, P = 1.63E-04). As previous findings have suggested that heterogeneous regions of the same psychiatric disorder might be linked in a common brain network 13 , we next investigated whether the three cross-disorder factors may also share common network configurations (Fig. 4 b, 55 networks in total). We found that 20 networks were predominantly associated with the general neural factor, primarily consisting of connections with the superior medial frontal (SMF) network ( Supplementary Table S10A ). Additionally, 18 networks were predominantly associated with the externalizing neural factor, mainly encompassing connections with the motor areas networks (Mot) ( Supplementary Table S10B ). Moreover, 17 networks were predominantly associated with the internalizing neural factor, primarily connected to the default mode network (DMN) ( Supplementary Table S10C ). However, at the network level, we observed indifferentiable Specificity Scores among the three cross-disorder networks ( F (2,54) = 0.18, P = 0.83). In summary, our findings indicated that the general and stratified factors exhibited increased neural specificity along the neuroanatomical coarse-fine gradient from the network level to the region level. Discussion In the present study, based on a large longitudinal cohort from adolescence to early young adulthood, we identified two stratified neural factors, respectively, underlying the externalizing and internalizing symptoms, each characterized by unique neurobiological configurations, genetic underpinnings, and clinical relevance. These two stratified neural factors, along with the previously identified general neuropsychopathological factor ( NP factor), collectively constitute a hierarchical neurocognitive model that characterizes neural mechanisms underlying psychiatric comorbidity, with implications for early prevention and therapeutics in psychiatry (Fig. 5 ). The externalizing neural factor is characterized by hyperconnectivity of primary sensory and motor regions, which has specific associations with higher impulsivity and inhibitory deficits compared to other behavioral phenotypes. This neural factor may serve as a neural mechanism underlying poor impulse control, a core dimension of externalizing psychopathology 33 , 34 . Additionally, the externalizing neural factor was longitudinally predictive of externalizing symptoms across developmental stages, from preadolescence to adulthood, elucidating neural mechanisms behind the enduring impact of impulsivity on the externalizing spectrum 35 . In contrast, the internalizing factor is characterized by hypoconnectivity of the ventromedial prefrontal cortex and medial orbitofrontal cortex, and both are crucial components of the goal-directed circuitry 36 , 37 . Notably, the internalizing neural factor was specifically linked to neuroticism/negative affect traits, which predisposed individuals to experience negative emotional states and life events and was proposed as a common vulnerability factor for internalizing psychopathology 38 . Hypoconnectivity of the internalizing neural factor might lead to challenges in responding adaptively to negative events, which perpetuate negative emotional states and result in the development of mood disorders 39 – 41 . Notably, we previously identified a general cross-disorder neural factor ( NP factor), characterized by hyperconnectivity of executive control networks, which inefficiently regulate/support other neural networks 42 . Our studies help to clarify how the three cross-disorder networks interact in the manifestation of comorbid neuropsychopathology (summarized as a hierarchical neurocognitive spectra model in Fig. 5 ): externalizing comorbid symptoms may result from the combination of hyperconnectivity in the impulsivity circuit and the executive control network's failure to inhibit impulsive behaviors, whereas internalizing comorbid symptoms may stem from the combination of the hypoconnectivity of the goal-directed circuit and the executive control network’s failure to initiate adaptive behavior. Nevertheless, several limitations necessitate further investigation in future research endeavors. First, this study mainly focused on delineating the cross-disorder neural foundations associated with internalizing and externalizing symptoms from preadolescence to adulthood, overlooking the dimension of psychotic experiences, which typically manifest in late developmental stages 44 . Future research could investigate whether early internalizing and externalizing neural factors (the shared and stratified ones) are risk factors for subsequent thought disorders, which, such as bipolar disorder, also demonstrate impaired behavior that falls into externalizing and internalizing domains. Second, we failed to identify a stable cross-disorder neural basis in the emotional face task, in which the standard emotional face was used to induce basic emotion. Future investigations could capitalize on more ecologically naturalistic paradigms, such as movie-watching, to illuminate the shared emotion-related neural substrates across psychiatric disorders 45 . Last, this study focused on identifying comorbidity networks at age 14 (wherein functional connections are linked to at least two psychiatric disorders at the same time). Nonetheless, previous longitudinal clinical studies had reported prevalent temporal comorbidity between psychiatric disorders 46 , implying the presence of a transition neural network. For instance, this network was initially associated with externalizing symptoms but not internalizing symptoms, later transitioning to associate with internalizing symptoms while not externalizing symptoms. Integrating persistent and transition comorbidity networks in future research will offer a more comprehensive understanding of the evolution and interaction mechanisms underlying psychiatric comorbidity. In conclusion, we identified two cross-disorder neural factors for internalizing and externalizing symptoms that persist longitudinally from adolescence to early adulthood. The two stratified neural factors demonstrated neuroanatomical specificity and are further delineated by cognitive, behavioral and genetic risk factors. Combining with the previously identified general NP factor, we present a hierarchical neurocognitive spectra model for psychiatric comorbidity. These findings might help provide a unified neurobehavioral-based psychiatric nosology that could improve diagnostic precision and treatment efficacy 43 . Methods Study overview. In the present study, we aimed to identify a stratified cross-disorder neural factor for externalizing and internalizing symptoms with multivariate associations between psychiatric symptoms and task-based functional connectivity, which were estimated at age 14. These multivariate associations have also been used in the identification of general neural factor in the previous study 5 . Briefly, we employed a mutually exclusive approach to identify specific externalizing and internalizing edges. For example, externalizing edges, composed of functional connectivity, were predictive only of externalizing symptoms and not internalizing symptoms, and vice versa for internalizing edges. Only those stratified edges surviving permutation-based reliability analysis were identified as the externalizing or internalizing neural factor. Then, we checked the longitudinal persistence of the externalizing and internalizing neural factors across age 19 and age 23. Last, we conducted multilevel specificity characterisations of the externalizing and internalizing neural factors, such as anatomical, cognitive-behavioral, genetic and generalisation analyses. Detailed information about the psychiatric questionnaire (Development and Well-Being Assessment (DAWBA) and the Strengths and Difficulties Questionnaire (SDQ)), task design (Monetary incentive delay (MID) task, Stop-signal task (SST) and Emotional face task (EFT)), and brain-behaviour modelling were provided in the Supplementary Method. Externalizing and internalizing factor. The stratified neural factor was constructed to represent specific brain signatures to externalizing or internalizing symptoms. Specifically, we first removed the general cross-disorder edges that were associated with both externalizing and internalizing symptoms simultaneously 5 . Next, we identified two types of stratified cross-disorder edges: (1) the externalizing edges that predict at least two externalizing symptoms and (2) the internalizing edges that predict at least two internalizing symptoms. Then, for each task condition, we investigated if the number of stratified edges identified was significantly higher than a random observation using a permutation test (see the supplementary methods for more details). Only the significant task conditions and their stratified edges were kept in the following analyses. Next, to improve interpretability of results, the stratified edges were split into four different groups according to the predictive effect directions: positive-positive (or negative-negative) edges that have the same predictive effect to externalizing/internalizing symptoms positively (or negatively); positive-negative (or negative-positive) edges that have different predictive directions to externalizing/internalizing symptoms. We found that only the positive-positive externalizing edges (i.e. positively associated with externalizing symptoms) and the negative-negative internalizing edges (i.e. negatively associated with internalizing symptoms) were significantly higher than a random observation. Therefore, the two groups of stratified edges were termed the externalizing factor (positive-positive externalizing edges) and internalizing factor (negative-negative internalizing edges), respectively, and used in the following analyses. Specificity score of cross-disorder neural factors. In delineating the specific underpinnings of each cross-disorder neural factor, we devised a specificity score for the brain measurements, assessing their relative contribution. At the brain region level, we initially normalized the region degree by dividing the total sum of degrees for each cross-disorder factor respectively, then plus 1 to prevent potential singularities in subsequent calculations. This normalized region degree ranged from 1 to 2, indicating the relative importance of each brain region to the cross-disorder neural factor. Last, for each brain region, the normalized brain degree was divided by that of the other two cross-disorder neural factors, and the sum of these two ratios is then calculated as the specificity score. This score reflects the importance of this brain region to the cross-disorder factor and controls for its influence on the other two cross-disorder factors. The same computational steps were applied at the network level. Generalization datasets. To investigate whether the two cross-disorder factors identified with the adolescent population-based IMAGEN dataset could be generalized into other developmental periods and clinical conditions, we utilised multiple large-scale population-based datasets (the Adolescent Brain Cognitive Development cohort 31 , ABCD ) and clinical case-control datasets ( The STRATIFY and ESTRA 32 , ADHD-200 48 , ABIDE II; XiNan ). The details of these datasets are provided in the supplementary method. Declarations Data availability. IMAGEN data are available from a dedicated database: https://imagen2.cea.fr; Stratify data are also available from the IMAGEN database: https://imagen2.cea.fr; ABCD data are available from a dedicated database: https://abcdstudy.org/; HCP data are available from a dedicated database: https://www.humanconnectome.org/; ADHD-200 data are available from a dedicated database: http://fcon_1000.projects.nitrc.org/indi/adhd200. ABIDEII data are available from a dedicated database: http://fcon_1000.projects.nitrc.org/indi/abide/abide_II.html. XiNan and STRATIFY/ESTRA datasets are available from the principal investigator of the study and are subject to local ethics committee requirements. Shen 268 parcellation is available from https://www.nitrc.org/frs/?group_id=51. Code availability. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Information. Custom code will be uploaded to GitHub before publication. Acknowledgements. We thank R. Kotov for comments on a previous draft. This work received support from the following sources: the National Natural Science Foundation of China (T2122005, 82150710554,82302288 and 81801773), National Key R and D Program of China (2019YFA0709501, 2022CSJGG1000, 2021YFC2501402, 2018YFC1312900, 2019YFA0709502 and 2018YFC1312904),the China Postdoctoral Science Foundation (2023M740659 and BX20230075), the Shanghai Pujiang Project (18PJ1400900), the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313), Horizon Europe ‘environMENTAL’, grant no: 101057429, UK Research and Innovation (UKRI) Horizon Europe funding guarantee (10041392 and 10038599), Human Brain Project (HBP SGA 2, 785907, and HBP SGA 3, 945539), The German Center for Mental Health (DZPG), the Bundesministerium für Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC-Mind 01GL1745B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940, TRR 265, NE 1383/14-1), the Medical Research Foundation (MRF-058-0014-F-ZHAN-C0866) and Medical Research Council (grants MR/R00465X/1, MR/S020306/1 and MRF-058-0004-RG-DESRI: ‘ESTRA: Neurobiological underpinning of eating disorders: integrative biopsychosocial longitudinal analyses in adolescents’; MR/S020306/1 and MRF-058-0009-RG-DESR-C0759: ‘Establishing causal relationships between biopsychosocial predictors and correlates of eating disorders and their mediation by neural pathways’).the National Institutes of Health (NIH) funded ENIGMA-grants 5U54EB020403-05, 1R56AG058854-01 and U54 EB020403 as well as NIH R01DA049238, the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797). NSFC grant 82150710554. Further support was provided by grants from: - the ANR (ANR-12-SAMA-0004, AAPG2019 - GeBra), the Eranet Neuron (AF12-NEUR0008-01 - WM2NA; and ANR-18-NEUR00002-01 - ADORe), the region lle de France (QIM-VEAVE project, convention n°23002745-230002747), the Fondation de France (00081242), the Fondation pour la Recherche Médicale (DPA20140629802), the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l’Avenir (grant AP-RM-17-013 ), the Fédération pour la Recherche sur le Cerveau. ImagenPathways "Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways" is a collaborative project supported by the European Research Area Network on Illicit Drugs (ERANID), the Medical Research Council/Arts and Humanities Research Council/Economic and Social Research Council Adolescence, Mental Health and the Developing Mind initiative as part of the EDIFY programme (grant number MR/W002418/1). US receives salary support from the National Institute of Health Research (NIHR) Biomedical Research Centre (BRC) at the South London and Maudsley (SLaM) NHS Foundation Trust and King’s College London (KCL). This paper is based on independent research commissioned and funded in England by the National Institute for Health Research (NIHR) Policy Research Programme (project ref. PR-ST-0416-10001). The views expressed in this article are those of the authors and not necessarily those of the national funding agencies or ERANID. Author contributions T.J., G.S., T.W.R. and J.F. conceptualized the study. C.X. and T.J. designed the analytic approach. C.X. analyzed the data. C.X. and T.J. wrote the manuscript. S.X. preprocessed the neuroimaging data. Y.L. and S.X. helped with visualization. J.K. calculated the PRS. T.W.R., G.S., B.J.S. and J.F. revised the first draft. T.B., G.J.B., A.L.W.B., C.B., S.D., J.F., H.F., A.G., H.G., P.G., A.H., B.I., J.-L.M., M.-L.P.M., F.N., L.P., J.H.F., M.N.S., H.W., R.W. and G.S. were the principal investigators of IMAGEN. T.B., G.J.B., A.L.W.B., C.B., H.F., A.G., H.G., P.G., A.H., B.I., J.-L.M., M.-L.P.M., F.N., D.P.O., L.P., J.H.F., M.N.S., H.W., R.W. and G.S. acquired the data. All authors critically revised the manuscript. IMAGEN Consortium Eric Artiges 16,17 , Marina Bobou 4,18 , M. John Broulidakis 19 , Tobias Banaschewski 20 , Andreas Becker 21 , Christian Büchel 22 , Patricia Conrod 23 , Tahmine Fadai 24 , Herta Flor 25,26 , Antoine Grigis 27 , Yvonne Grimmer 20 , Hugh Garavan 28 , Penny Gowland 29 , Andreas Heinz 30 , Corinna Insensee 31 , Viola Kappel 32 , Hervé Lemaître 33 , Jean-Luc Martinot 16 , Marie-Laure Paillère Martinot 16,34 , Betteke Maria van Noort 35 , Frauke Nees 20,25,36 , Dimitri Papadopoulos Orfanos 27 , Jani Penttilä 37 , Juliane H. Fröhner 39 , Ulrike Schmidt 40,41 , Julia Sinclair 42 , Michael N.Smolka 22 , Maren Struve 32 , Henrik Walter 30 , Robert Whelan 43 , Barbara J. Sahakian 1,45 , Trevor W. Robbins 1,45 , Sylvane Desrivières 5 , Gunter Schumann 1,4,46,47,48 , Tianye Jia 1,2,5,10 STRATIFY Consortium Nilakshi Vaidya 4 , Zuo Zhang 5,6 , Lauren Robinson 5,7 , Jeanne Winterer 8,9 , Yuning Zhang 10 , Sinead King 5 , Gareth J. Barker 11 , Arun L. Bokde 12 , Rüdiger Brühl 13 , Hedi Kebir 4 , Hervé Lemaître 33 , Frauke Nees 20,25,36 , Dimitri Papadopoulos Orfanos 27 , Ulrike Schmidt 40,41 , Julia Sinclair 42 , Robert Whelan 43 , Henrik Walter 30 , Sylvane Desrivières 5 , Gunter Schumann 1,4,46,47,48 ESTRA Consortium Zuo Zhang 5,6 , Marina Bobou 4,18 , Lauren Robinson 5,7 , Arun L. Bokde 12 , Hervé Lemaître 33 , Dimitri Papadopoulos Orfanos 27 , Henrik Walter 30 , Ulrike Schmidt 40,41 , Trevor W. Robbins 1,45 , Sylvane Desrivières 5 , Gunter Schumann 1,4,46,47,48 ZIB Consortium Chao Xie 1,2 , Shitong Xiang 1,2 , Wei Cheng 1,2 , Gunter Schumann 1,4,46,47,48 , Jianfeng Feng 1,2,49,50,51 , Tianye Jia 1,2,5,10 References Fusar-Poli, P., et al. Transdiagnostic psychiatry: a systematic review. World psychiatry: official journal of the World Psychiatric Association (WPA) 18, 192–207 (2019). Regier, D.A., Kuhl, E.A. & Kupfer, D.J. The DSM-5: Classification and criteria changes. World psychiatry 12, 92–98 (2013). Caspi, A. & Moffitt, T.E. 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Emotion representations in context: maturation and convergence pathways. Trends Cogn Sci 27, 883–885 (2023). Plana-Ripoll, O., et al. Exploring Comorbidity Within Mental Disorders Among a Danish National Population. JAMA Psychiatry (2019). Dalley, J.W. & Robbins, T.W. Fractionating impulsivity: neuropsychiatric implications. Nat Rev Neurosci 18, 158–171 (2017). Consortium, H.D. The ADHD-200 Consortium: A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience. Front Syst Neurosci 6, 62–62 (2012). Additional Declarations Yes there is potential Competing Interest. T.B. served in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH and Shire. He received conference support or speaker’s fee from Lilly, Medice, Novartis and Shire. He has been involved in clinical trials conducted by Shire and Viforpharma. He received royalties from Hogrefe, Kohlhammer, CIP Medien and Oxford University Press. The present work is unrelated to the above grants and relationships. G.J.B. received honoraria from General Electric Healthcare for teaching scanner programming courses. All other authors declare no competing interests. GJB received honoraria for teaching from GE Healthcare Supplementary Files SupplementaryresultsExterInter.docx nreditorialpolicychecklistEIP.pdf nrreportingsummaryEIP.pdf Cite Share Download PDF Status: Published Journal Publication published 13 Feb, 2026 Read the published version in Nature Mental Health → 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. 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Germany","correspondingAuthor":false,"prefix":"","firstName":"Tahmine","middleName":"","lastName":"Fadai","suffix":""},{"id":377260038,"identity":"c1b4e63b-3b47-4ef8-936b-a9477f85d44e","order_by":26,"name":"Herta Flor","email":"","orcid":"https://orcid.org/0000-0003-4809-5398","institution":"Central Institute of Mental Health Medical Faculty Mannheim Heidelberg University","correspondingAuthor":false,"prefix":"","firstName":"Herta","middleName":"","lastName":"Flor","suffix":""},{"id":377260039,"identity":"2208392e-cdeb-465a-bdab-54facd0d7c5b","order_by":27,"name":"Antoine Grigis","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Antoine","middleName":"","lastName":"Grigis","suffix":""},{"id":377260040,"identity":"a8b44753-2967-48de-8081-c6a236f630e9","order_by":28,"name":"Yvonne Grimmer","email":"","orcid":"","institution":"Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany","correspondingAuthor":false,"prefix":"","firstName":"Yvonne","middleName":"","lastName":"Grimmer","suffix":""},{"id":377260041,"identity":"8af507e7-8394-41a8-a121-c6486d0db7b1","order_by":29,"name":"Hugh Garavan","email":"","orcid":"https://orcid.org/0000-0002-8939-1014","institution":"University of Vermont","correspondingAuthor":false,"prefix":"","firstName":"Hugh","middleName":"","lastName":"Garavan","suffix":""},{"id":377260042,"identity":"be22e77f-1207-4823-8c5a-fb7ebb945ac9","order_by":30,"name":"Penny Gowland","email":"","orcid":"https://orcid.org/0000-0002-4900-4817","institution":"University of Nottingham","correspondingAuthor":false,"prefix":"","firstName":"Penny","middleName":"","lastName":"Gowland","suffix":""},{"id":377260043,"identity":"46f116f5-2270-4368-abd6-7e3f33b91087","order_by":31,"name":"Andreas Heinz","email":"","orcid":"https://orcid.org/0000-0001-5405-9065","institution":"Charité Universitätsmedizin, Berlin","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Heinz","suffix":""},{"id":377260044,"identity":"44ece3ed-f402-4dcc-9691-7678e2ae47f3","order_by":32,"name":"Corinna Insensee","email":"","orcid":"","institution":"Georg-Elias-Müller-Institute of Psychology, Department of Clinical Psychology and Psychotherapy, University of Göttingen, Gosslerstraße 14, 37073 Göttingen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Corinna","middleName":"","lastName":"Insensee","suffix":""},{"id":377260045,"identity":"616e5fda-fb00-4ab0-a665-997728da0bff","order_by":33,"name":"Viola Kappel","email":"","orcid":"","institution":"Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Mannheim, Germany","correspondingAuthor":false,"prefix":"","firstName":"Viola","middleName":"","lastName":"Kappel","suffix":""},{"id":377260046,"identity":"71379229-9299-4b7a-9734-e6541d1d294c","order_by":34,"name":"Hervé Lemaître","email":"","orcid":"https://orcid.org/0000-0002-5952-076X","institution":"Groupe d'Imagerie Neurofonctionnelle","correspondingAuthor":false,"prefix":"","firstName":"Hervé","middleName":"","lastName":"Lemaître","suffix":""},{"id":377260047,"identity":"ec226349-976c-4e21-b3a9-89ea869223f0","order_by":35,"name":"Jean-Luc Martinot","email":"","orcid":"https://orcid.org/0000-0002-0136-0388","institution":"Institut National de la Santé et de la Recherche Médicale","correspondingAuthor":false,"prefix":"","firstName":"Jean-Luc","middleName":"","lastName":"Martinot","suffix":""},{"id":377260048,"identity":"550d260a-e558-46e2-9776-857b859bd9df","order_by":36,"name":"Marie-Laure Martinot","email":"","orcid":"","institution":"Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 \"Trajectoires développementales \u0026 psychiatrie\", University Paris-Saclay, CNRS","correspondingAuthor":false,"prefix":"","firstName":"Marie-Laure","middleName":"","lastName":"Martinot","suffix":""},{"id":377260049,"identity":"ba6ad1ce-4fb5-4dec-900a-41bb241aaa5e","order_by":37,"name":"Betteke Noort","email":"","orcid":"","institution":"Department of Psychology, MSB Medical School Berlin, Rüdesheimer Str. 50, 14197 Berlin, Germany","correspondingAuthor":false,"prefix":"","firstName":"Betteke","middleName":"","lastName":"Noort","suffix":""},{"id":377260050,"identity":"a1114859-f41b-41be-80c9-c3a5abc61d2e","order_by":38,"name":"Frauke Nees","email":"","orcid":"https://orcid.org/0000-0002-7796-8234","institution":"University Medical Center Schleswig-Holstei,n Kiel University","correspondingAuthor":false,"prefix":"","firstName":"Frauke","middleName":"","lastName":"Nees","suffix":""},{"id":377260051,"identity":"caee9852-bfcd-476b-b78b-a8f61092f9dd","order_by":39,"name":"Dimitri Papadopoulos Orfanos","email":"","orcid":"https://orcid.org/0000-0002-1242-8990","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Dimitri","middleName":"Papadopoulos","lastName":"Orfanos","suffix":""},{"id":377260052,"identity":"774b9518-af8e-47d1-b93a-851c583a90f2","order_by":40,"name":"Jani Penttilä","email":"","orcid":"","institution":"Department of Social and Health Care, Psychosocial Services Adolescent Outpatient Clinic Kauppakatu 14, Lahti, Finland","correspondingAuthor":false,"prefix":"","firstName":"Jani","middleName":"","lastName":"Penttilä","suffix":""},{"id":377260053,"identity":"de7247c2-b68f-4119-9049-5b4c8aa1aad3","order_by":41,"name":"Luise Poustka","email":"","orcid":"","institution":"Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Gottingen","correspondingAuthor":false,"prefix":"","firstName":"Luise","middleName":"","lastName":"Poustka","suffix":""},{"id":377260054,"identity":"b02b92bc-72ae-445b-8af5-89aa67deb84d","order_by":42,"name":"Juliane Frohner","email":"","orcid":"https://orcid.org/0000-0002-8493-6396","institution":"Department of Psychiatry and Psychotherapy, Technische Universitat Dresden, Dresden, Germany","correspondingAuthor":false,"prefix":"","firstName":"Juliane","middleName":"","lastName":"Frohner","suffix":""},{"id":377260055,"identity":"e702df22-222f-481f-8127-4882602f9049","order_by":43,"name":"Ulrike Schmidt","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Ulrike","middleName":"","lastName":"Schmidt","suffix":""},{"id":377260056,"identity":"3ebb3899-dacd-4b10-a01a-8f1efe55fadd","order_by":44,"name":"Julia Sinclair","email":"","orcid":"https://orcid.org/0000-0002-1905-2025","institution":"University of Southampton","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Sinclair","suffix":""},{"id":377260057,"identity":"2dd69ec7-0983-4618-a2c0-f2b23c5cb2a5","order_by":45,"name":"Michael Smolka","email":"","orcid":"https://orcid.org/0000-0001-5398-5569","institution":"Technische Universität Dresden","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Smolka","suffix":""},{"id":377260058,"identity":"b5d1cd82-8eb2-4ea7-8c71-e1ff36b31d20","order_by":46,"name":"Maren Struve","email":"","orcid":"","institution":"Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Mannheim, Germany","correspondingAuthor":false,"prefix":"","firstName":"Maren","middleName":"","lastName":"Struve","suffix":""},{"id":377260059,"identity":"12941531-8e84-4710-a54e-c286820da885","order_by":47,"name":"Henrik Walter","email":"","orcid":"https://orcid.org/0000-0002-9403-6121","institution":"Department of Psychiatry and Psychotherapy CCM, Charite - Universitatsmedizin Berlin, corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health, Be","correspondingAuthor":false,"prefix":"","firstName":"Henrik","middleName":"","lastName":"Walter","suffix":""},{"id":377260060,"identity":"7817d815-0988-410c-822e-2507a8b4abe5","order_by":48,"name":"Robert Whelan","email":"","orcid":"","institution":"School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Whelan","suffix":""},{"id":377260061,"identity":"0616f178-f8a0-484b-81b7-b23da183857b","order_by":49,"name":"Jiang Qiu","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Qiu","suffix":""},{"id":377260062,"identity":"c3a23be2-ca1b-4400-88db-7b068e07d789","order_by":50,"name":"Peng Xie","email":"","orcid":"https://orcid.org/0000-0002-0081-6048","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Xie","suffix":""},{"id":377260063,"identity":"0521eab5-841d-4e84-91fe-581567efc0da","order_by":51,"name":"Barbara Sahakian","email":"","orcid":"https://orcid.org/0000-0001-7352-1745","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Barbara","middleName":"","lastName":"Sahakian","suffix":""},{"id":377260064,"identity":"35c1015c-0e57-458e-a947-723cb1c59339","order_by":52,"name":"Trevor Robbins","email":"","orcid":"https://orcid.org/0000-0003-0642-5977","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Trevor","middleName":"","lastName":"Robbins","suffix":""},{"id":377260065,"identity":"839ab646-af4e-40c0-ab79-96a66969ada1","order_by":53,"name":"Sylvane Desrivières","email":"","orcid":"https://orcid.org/0000-0002-9120-7060","institution":"Institute of Psychiatry, Psychology \u0026 Neuroscience, King's College London, United Kingdom","correspondingAuthor":false,"prefix":"","firstName":"Sylvane","middleName":"","lastName":"Desrivières","suffix":""},{"id":377260066,"identity":"d6b6255d-0fd0-4608-9a25-c0edec29f257","order_by":54,"name":"Gunter Schumann","email":"","orcid":"https://orcid.org/0000-0002-7740-6469","institution":"Charite Universitaetsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Gunter","middleName":"","lastName":"Schumann","suffix":""},{"id":377260067,"identity":"fec433e0-94da-4208-b241-4ba925d529a9","order_by":55,"name":"Jianfeng Feng","email":"","orcid":"https://orcid.org/0000-0001-5987-2258","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Jianfeng","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2024-11-05 16:55:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5397195/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5397195/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s44220-025-00577-2","type":"published","date":"2026-02-13T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73518858,"identity":"ae8a8664-5478-47de-8543-ec5370942b5c","added_by":"auto","created_at":"2025-01-10 18:04:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1169407,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of the stratified neural factors. a. \u003c/strong\u003eThe predictive performance of behavioral symptoms related to psychiatric symptoms with the task-based connectivity model. Task-based connectivity was estimated from the EFT (angry and neutral conditions), the MID task (reward anticipation, positive reward feedback and negative reward feedback conditions) and the SST (go-wrong, stop-success and stop-failure conditions). \u003cstrong\u003eb\u003c/strong\u003e. The correlation matrix of the behavioral symptoms. The externalizing and internalizing symptoms showed high intra-correlations within their respective psychiatric domain, but low correlations between each other. Externalizing symptoms consisted of ASD, ADHD, CD and ODD. Internalizing symptoms comprised GAD, Dep., ED and SP. \u003cstrong\u003ec\u003c/strong\u003e. The correlation matrix of the brain-predicted symptoms. \u003cstrong\u003ed\u003c/strong\u003e. With a two-step reliable analysis, we identified two stratified neural factors for externalizing and internalizing symptoms, respectively. We first identified task conditions with reliable stratified cross-disorder edges, which are defined as predictive edges that only predict externalizing but not internalizing symptoms and vice versa. We found that only conditions from the SST and MID task had significantly more stratified cross-disorder edges than a random observation. Then, we further identified which type of cross-disorder edges reliably predict externalizing or internalizing symptoms, which was termed the stratified factors. We discovered that the externalizing neural factor consisted of positive-positive cross-disorder edges (positively predicted at least two externalizing symptoms), while the internalizing neural factor comprised negative-negative cross-disorder edges (negatively predicted at least two internalizing symptoms). \u003cstrong\u003ee\u003c/strong\u003e. We checked the longitudinal predictive effects and developmental trajectories of the stratified factors across ages 14, 19 and 23. EFT, emotional face task; MID, monetary incentive delay task; SST, stop signal task; ADHD, attention-deficit/hyperactivity disorder; ASD, autism spectrum disorder; CD, conduct disorder; ODD, oppositional defiant disorder; GAD, general anxiety disorder; Dep., depression; ED, eating disorder; SP, specific phobia; 14-brain, brain at age 14; 19-brain, brain at age 19; 23-brain, brain at age 23; 14-exter., externalizing symptoms at age 14; 19-exter., externalizing symptoms at age 19; 23-exter., externalizing symptoms at age 23; 14-inter., internalizing symptoms at age 14; 19-inter., internalizing symptoms at age 19; 23-inter., internalizing symptoms at age 23;\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5397195/v1/d512ff9ee33976f54f3f1f14.png"},{"id":73518852,"identity":"096ba475-5043-4f58-a4a7-0a60c9077b41","added_by":"auto","created_at":"2025-01-10 18:04:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1574058,"visible":true,"origin":"","legend":"\u003cp\u003eMultilevel neuroanatomical characterization of the two stratified neural factors. a and b. The top 10% nodes and hub node connections (with high regional connections) in the externalizing and internalizing factor. The color bar indicates the normalized node degree (that is, the number of connections with other nodes). c. and d. The functional connections of the externalizing and internalizing factors shared similar large-scale network configurations that both were mainly localized between the motor, frontoparietal and salience networks. The color bar indicates the strength of normalized inter- or intra-network connections, where the number of connections between or within networks was divided by the largest connection number observed.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5397195/v1/cb57b88d9cbf6d1183dbdafe.png"},{"id":73518853,"identity":"cc2ca7e9-cfaa-4f43-bb96-34ebea8607a9","added_by":"auto","created_at":"2025-01-10 18:04:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1376704,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional specificity and generalization of the stratified neural factors.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e. The externalizing factor was specifically associated with most cognitive functions (14 of 25). The internalizing factor was specifically correlated with personality traits (5/5), especially neuroticism and anxiety. Two executive function measurements (between errors in SWM and proportion bet in CGT) and two personality traits (extroversion of NEO and reserve of TCI) showed distinct associations with externalizing and internalizing symptoms. The polygenic risk score (PRS) of ADHD and MDD showed a specific association with externalizing and internalizing factors, respectively. \u003cstrong\u003eb.\u003c/strong\u003e Generalization of the \u003cem\u003eNP\u003c/em\u003e factor across multiple developmental periods from preadolescence to adulthood in both population and clinical case-control datasets (ABCD, N = 1799; ADHD-200, N = 520; ABIDE II, N = 564; IMAGEN N = 998; STRATIFY and ESTRA, N = 433; and XiNan, N = 449). The significance level (that is, the grey color) was given as a false discovery rate (fdr) of 0.05. The \u003cem\u003eP\u003c/em\u003e values were reported as the original value and could survive the multiple testing correction with Benjamin–Hochberg procedure. AGN, Affective Go-No Go; BMI, body mass index; DD, Delay Discounting Task, which measured ‘waiting’ impulsivity\u003csup\u003e47\u003c/sup\u003e; MidOcci, middle occipital cortex; MidPFC, middle prefrontal cortex; NEO, NEO Personality Inventory; RVP: A, Target Sensitivity from Rapid Visual Information Processing task; PRM, Pattern Recognition Memory task; SURPS, Substance Use Risk Personality Scale; SWM, Spatial Working Memory task; TCI, Temperament and Character Inventory–Revised. AN, anorexia nervosa; BN, bulimia nervosa; AUD, alcohol use disorder ; MDD, major depressive disorder;\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5397195/v1/8d312834d5f63f8edab30918.png"},{"id":73520086,"identity":"119ad75d-7b7d-442d-b160-0b61708a9961","added_by":"auto","created_at":"2025-01-10 18:12:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1393679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterizing specificity of the cross-disorder neural factors at the levels of brain regions and extended networks\u003c/strong\u003e. \u003cstrong\u003ea.\u003c/strong\u003e Specificity score comparison of the three cross-disorder networks using regional-level node degree as an example. We first normalized the node degree to facilitate subsequent cross-factor comparisons. Then, we used a weighted method for calculating the regional specificity score with the formula: specificity score of Factor 1 = Factor 1/Factor 2 + Factor 1/Factor 3. By weighting the contribution of the brain region in the other two cross-disorder factors (Factor 2 and Factor 3), the estimated specificity score provides a more robust measure of the unique contribution of each brain region within this cross-disorder factor (Factor 1). Building upon previous findings \u003csup\u003e13\u003c/sup\u003e, we hypothesize that as the brain scale increases from region to network, the specificity between cross-disorder factors will decrease. \u003cstrong\u003eb and c\u003c/strong\u003e. For each brain region and network, specificity scores were estimated for all three cross-disorder factors. The maximum specificity score for each ROI is considered indicative of its specificity to that cross-disorder factor. Then, we estimated a specificity distance, which is computed by subtracting the minimum specificity score from the maximum specificity score. This distance is interpreted as the uniqueness of this cross-disorder factor compared to others.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5397195/v1/815cc10053c6f884ceb81329.png"},{"id":73520087,"identity":"b3e67712-331e-4cda-9d39-16b21db4d686","added_by":"auto","created_at":"2025-01-10 18:12:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":721945,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of hierarchical cross-disorder neural networks.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5397195/v1/8e8c9444eb647fb2bbbcae3b.png"},{"id":102654445,"identity":"373c9688-5c1c-4b67-9464-4ced04fafda7","added_by":"auto","created_at":"2026-02-14 08:08:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8303420,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5397195/v1/cb50e6e0-3771-4c03-a72b-4e69d0ad88f0.pdf"},{"id":73518855,"identity":"cb174f08-c674-453a-b09a-b8c7a0a4b567","added_by":"auto","created_at":"2025-01-10 18:04:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":577181,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryresultsExterInter.docx","url":"https://assets-eu.researchsquare.com/files/rs-5397195/v1/31d39876d2a7a309103a53e1.docx"},{"id":73520404,"identity":"01880d4f-7210-4e0b-ae33-b90d41640f5d","added_by":"auto","created_at":"2025-01-10 18:20:41","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1682270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"nreditorialpolicychecklistEIP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5397195/v1/ce22962bed4622c446eac627.pdf"},{"id":73520115,"identity":"204b098d-585b-4f58-a3d0-2b0fb3afdf0c","added_by":"auto","created_at":"2025-01-10 18:12:41","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1668891,"visible":true,"origin":"","legend":"","description":"","filename":"nrreportingsummaryEIP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5397195/v1/09f84f0a123792a1402fee8f.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nT.B. served in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH and Shire. He received conference support or speaker’s fee from Lilly, Medice, Novartis and Shire. He has been involved in clinical trials conducted by Shire and Viforpharma. He received royalties from Hogrefe, Kohlhammer, CIP Medien and Oxford University Press. The present work is unrelated to the above grants and relationships. G.J.B. received honoraria from General Electric Healthcare for teaching scanner programming courses. All other authors declare no competing interests. GJB received honoraria for teaching from GE Healthcare","formattedTitle":"Hierarchical Neurocognitive Model of Externalizing and Internalizing Comorbidity","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsychiatric comorbidity is prevalent and often leads to more severe prognoses\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, posing a major challenge to the current mental health diagnostic system \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In response, the Hierarchical Taxonomy of Psychopathology (HiTOP) was proposed to categorize the complex psychiatric comorbidities into a general factor alongside multiple stratified transdiagnostic spectra, for instance, the externalizing (aggressive and hyperactive-impulsive) vs. internalizing (anxious and depressive) spectrum \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Recently, our research team identified a prefrontal-related general Neuropsychopathological (NP) factor underlying both externalizing and internalizing symptoms from preadolescence to early adulthood \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, the neurobiological mechanisms of externalizing and internalizing disorders and the interaction of general-stratified factors during development remain elusive.\u003c/p\u003e \u003cp\u003eAnother dimensional framework, the Research Domain Criteria (RDoC) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, was developed to advance the investigation of neurobiological foundations of dimensional psychopathology. However, the research progress in uncovering the stratified neural bases of externalizing and internalizing disorders has remained slow\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This sluggishness is partly due to the historical emphasis on localizing brain abnormalities at the regional level in psychiatric disorders \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. It is crucial to recognize that different brain regions do not function or develop independently; instead, they work in distributed and anatomically interconnected systems\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The above evidence hence suggests that distinct regionalized brain markers of psychiatric disorders might be located within a common psychopathological brain network. This hypothesis has recently gained support from normative network mapping and connectivity-based transdiagnostic studies\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, emphasizing the importance of network-based approaches in unifying region-level heterogeneous neural underpinnings of psychiatric disorders \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, previous transdiagnostic neuroimaging studies have predominantly employed a cross-sectional approach\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, thereby overlooking the developmental perspective on how the general and stratified neural substrates manifest and evolve longitudinally\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, especially during critical developmental periods like adolescence. Utilizing a longitudinal large-scale imaging dataset, we can further elucidate the nuanced interplay between the enduring and phasic neural mechanisms of psychiatric comorbidity \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This approach will significantly advance our understanding of the onset and progression of psychiatric comorbidity.\u003c/p\u003e \u003cp\u003eThe present study addresses three major questions regarding the specific transdiagnostic neural bases of externalizing and internalizing symptoms: (1) Can we identify stratified cross-disorder neural factors for externalizing and internalizing symptoms, respectively? (2) Do the two stratified neural factors exhibit distinct characterisations regarding neurobiological risk factors and clinical conditions? (3) How can we synthesize the general and stratified neural factors into a hierarchical neurocognitive model of comorbid psychopathology?\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStratified neural factors of externalizing and internalizing symptoms\u003c/h2\u003e \u003cp\u003eOur previous study found significant predictive effects of task-based connectomes on eight psychiatric symptoms in 14-year-old participants from the IMAGEN study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea \u003cb\u003eand Supplementary Tables\u0026nbsp;1 and 2\u003c/b\u003e, N\u0026thinsp;=\u0026thinsp;1,750) \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. For each psychiatric symptom, we generated a brain-predicted measure. Interestingly, we observed that there were significantly higher similarities between brain-predicted symptoms than the observed psychiatric symptoms (externalizing symptoms: brain-predicted \u003cem\u003er\u003c/em\u003e\u003csub\u003emean\u003c/sub\u003e = 0.91, observed \u003cem\u003er\u003c/em\u003e\u003csub\u003emean\u003c/sub\u003e = 0.37, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eperm\u003c/sub\u003e \u0026lt; 0.001 for the difference; internalizing symptoms: brain-predicted \u003cem\u003er\u003c/em\u003e\u003csub\u003emean\u003c/sub\u003e = 0.52, observed \u003cem\u003er\u003c/em\u003e\u003csub\u003emean\u003c/sub\u003e = 0.28, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eperm\u003c/sub\u003e \u0026lt; 0.001 for the difference) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb \u003cb\u003eand c\u003c/b\u003e). The results thus suggested substantial shared neural bases within externalizing and internalizing symptoms.\u003c/p\u003e \u003cp\u003eWe next aimed to identify specific neural factors, termed as \u0026lsquo;stratified neural factors\u0026rsquo;, comprising cross-disorder edges that predicted two or more symptoms from a single psychiatric domain (externalizing or internalizing), while not predicting any symptoms from the other domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). We found that stratified cross-disorder edges were consistently and reliably identified from task conditions relating to inhibitory control and reward sensitivity (i.e. stop success, stop failure, positive reward feedback and reward anticipation; all \u003cem\u003eP\u003c/em\u003e\u003csub\u003eperm\u003c/sub\u003e \u0026lt; 0.01, \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe further ascertained the neural mechanisms of the stratified cross-disorder edges in terms of their predictive effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Intriguingly, we observed a double dissociation effect: externalizing symptoms were reliably associated with positive-positive cross-disorder edges (\u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003eedge\u003c/em\u003e\u003c/sub\u003e = 1,268, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eperm\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.001, showing positive correlations with externalizing symptoms), whereas internalizing symptoms were reliably associated with negative-negative cross-disorder edges (\u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003eedge\u003c/em\u003e\u003c/sub\u003e = 469, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eperm\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.001, showing negative correlations with internalizing symptoms) (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). Therefore, in the following analyses, the summed functional connectivity (FC) strength of positive-positive cross-disorder edges will be referred to as the externalizing neural factor, and the summed strength of negative-negative cross-disorder edges as the internalizing neural factor.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLongitudinal analysis of stratified neural factors\u003c/h3\u003e\n\u003cp\u003eAs previous research has highlighted the stability of internalizing and externalizing behaviors over time \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, we next examined the longitudinal predictive performance of the two stratified neural factors from adolescence to early adulthood over a 10-year span (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). The externalizing factor demonstrated consistent performance for longitudinal prediction across ages 14, 19 and 23. To elaborate, the externalizing neural factor estimated at age 14 could significantly predict externalizing symptoms at ages 19 (N\u0026thinsp;=\u0026thinsp;1,045, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.095, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.09, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.001, \u003cb\u003eSupplementary Table\u0026nbsp;5A\u003c/b\u003e) and 23 (N\u0026thinsp;=\u0026thinsp;1,043, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.117, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.79, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 7.98E-05, \u003cb\u003eSupplementary Table\u0026nbsp;5A\u003c/b\u003e). Also, the externalizing neural factors estimated at ages 19 and 23 could predict their corresponding externalizing symptoms at the same age or later age (brain and symptoms both at age 19, N\u0026thinsp;=\u0026thinsp;1,095, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.096, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.20, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e =7.05E-04; brain at age 19 and symptoms at age 23, N\u0026thinsp;=\u0026thinsp;1,029, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.77, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e =0.038; brain and symptoms both at age 23, N\u0026thinsp;=\u0026thinsp;937, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.078, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.388, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e =0.009, \u003cb\u003eSupplementary Table\u0026nbsp;5C and 5E\u003c/b\u003e). In contrast, only the internalizing neural factor estimated at age 14 could significantly predict future internalizing symptoms measured at ages 19 and 23 (age 19, N\u0026thinsp;=\u0026thinsp;1,045, \u003cem\u003er\u003c/em\u003e = -0.080, \u003cem\u003et\u003c/em\u003e = -2.59, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.005; age 23, N\u0026thinsp;=\u0026thinsp;1,043, \u003cem\u003er\u003c/em\u003e = -0.088, \u003cem\u003et\u003c/em\u003e = -2.84, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.002, \u003cb\u003eSupplementary Table\u0026nbsp;5B\u003c/b\u003e), but not for the internalizing neural factors estimates at ages 19 and 23 (\u003cb\u003eSupplementary Table\u0026nbsp;5D and 5F\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe next examined the longitudinal changes of the two stratified neural factors across ages 14, 19 and 23 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). While both the externalizing and internalizing neural factors maintained highly significant positive FC strengths throughout ages 14, 19 and 23 (All \u003cem\u003et\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;100, Cohen \u0026lsquo;\u003cem\u003ed\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;3.1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), steady decreases in FC strength from age 14 to age 23 were also observed for both neural factors (externalizing slop \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003emean\u003c/em\u003e\u003c/sub\u003e = -9.44, \u003cem\u003et\u003c/em\u003e = -17.64, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = -0.68, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 1.95E-57; internalizing slop \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003emean\u003c/em\u003e\u003c/sub\u003e = -2.33, t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;18.89, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = -0.73, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 4.83E-64). During this critical developmental period, the normative decrease in connectivity strength could be explained by the neural pruning for a more efficient brain information process\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Lastly, we investigated the associations between psychiatric symptoms at age 14 and the rate of decline in the stratified neural factors from age 14 to 23. We observed that individuals with higher baseline externalizing symptoms may have undergone an under-pruning process of the externalizing neural factor from adolescence to early adulthood (N\u0026thinsp;=\u0026thinsp;575, \u003cem\u003er\u003c/em\u003e = -0.20, \u003cem\u003et\u003c/em\u003e = -4.92, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 1.12E-06); conversely, individuals with higher baseline internalizing symptoms experienced an over-pruning process of the internalizing neural factor from adolescence to early adulthood (N\u0026thinsp;=\u0026thinsp;575, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.96, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.003). These results indicated that while both the externalizing and internalizing behavioral domains showed strong within-domain intra-correlations for both observed and neural predicted symptoms, each behavior domain may be represented by a distinct cross-disorder neural substrate.\u003c/p\u003e\n\u003ch3\u003eNeuroanatomical characterization of stratified neural factors\u003c/h3\u003e\n\u003cp\u003eWe characterized the above two stratified neural factors at multiple neuroanatomical levels. In terms of the regional network degree, the externalizing neural factor was mainly located in brain regions such as the middle cingulate cortex (MCC), precentral gyrus (PreCG), precuneus (PreCun), supramarginal gyrus (SMG), and putamen (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u003cb\u003eand Supplementary table 6a\u003c/b\u003e), which were commonly implicated in the habitual control process \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In contrast, the internalizing neural factor was primarily enriched in regions such as the precuneus (PreCun), ventromedial prefrontal cortex (vmPFC), medial orbitofrontal cortex (mOFC), and caudate (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb \u003cb\u003eand Supplementary table 6b\u003c/b\u003e), all known to play crucial roles in goal-directed processing \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNext, while the two stratified neural factors did not share overlapping edges by definition, they did share similar network configurations at a higher neuroanatomical level. For instance, the externalizing and internalizing neural factors exhibited similar network-level configurations, primarily in the motor, frontoparietal, and salience networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, d \u003cb\u003eand Supplementary table 7\u003c/b\u003e). Notably, the salience network plays a pivotal role in attending to motivational stimuli and recruiting appropriate functional brain-behavior networks to modulate behavior \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Therefore, the hyperconnectivity of the externalizing neural factor (e.g. in high-risk individuals) might be associated with excessive perception of external stimuli and a lack of inhibitory control over automatic responses \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Conversely, the hypoconnectivity of the internalizing neural factor might be related to limited salience processing, resulting in difficulties in engaging goal-directed behaviors in individuals with internalizing disorders \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eFunctional and genetic bases of stratified neural factors\u003c/h3\u003e\n\u003cp\u003eWe then investigated the associations between task performance measures with the two stratified neural factors. Both the externalizing and internalizing neural factors showed significant negative correlations with accuracies in the monetary incentive delay (MID) task (externalizing: N\u0026thinsp;=\u0026thinsp;1,620, \u003cem\u003er\u003c/em\u003e = -0.14, \u003cem\u003et\u003c/em\u003e = -5.73, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 1.20E-08; internalizing: N\u0026thinsp;=\u0026thinsp;1,620, \u003cem\u003er\u003c/em\u003e = -0.08, \u003cem\u003et\u003c/em\u003e = -3.22, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.001) and the go-trials in the stop-signal task (SST) (externalizing: N\u0026thinsp;=\u0026thinsp;1,567, \u003cem\u003er\u003c/em\u003e = -0.26, \u003cem\u003et\u003c/em\u003e = -10.62, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 1.81E-25; internalizing: N\u0026thinsp;=\u0026thinsp;1,567, \u003cem\u003er\u003c/em\u003e = -0.20, \u003cem\u003et\u003c/em\u003e = -8.01, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 2.30E-15), but no significant associations with the reaction time in the MID task (externalizing: N\u0026thinsp;=\u0026thinsp;1,620, \u003cem\u003er\u003c/em\u003e = -0.05, \u003cem\u003et\u003c/em\u003e = -1.97, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.05; internalizing: N\u0026thinsp;=\u0026thinsp;1,620, \u003cem\u003er\u003c/em\u003e = -0.02, \u003cem\u003et\u003c/em\u003e = -0.84, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.40) nor stop signal delay in the SST (externalizing: N\u0026thinsp;=\u0026thinsp;1,567, \u003cem\u003er\u003c/em\u003e = -0.04, \u003cem\u003et\u003c/em\u003e = -1.51, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.13; internalizing: N\u0026thinsp;=\u0026thinsp;1,567, \u003cem\u003er\u003c/em\u003e = -0.04, \u003cem\u003et\u003c/em\u003e = -1.77, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.08). These results were similar to the general neural factor (i.e. the \u003cem\u003eNP\u003c/em\u003e-factor) findings in our previous study \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNext, we examined the functional specificity of the two stratified neural factors across a wide range of cognitive-behavioral phenotypes. The externalizing neural factor exhibited specific associations with impulsive and substance use behaviors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cb\u003eSupplementary Table\u0026nbsp;8a\u003c/b\u003e), where impulsivity is a characteristic feature of externalizing disorders and a known risk factor for future substance abuse \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In contrast, the internalizing neural factor was primarily correlated with maladaptive traits, such as neuroticism and negative thinking (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cb\u003eSupplementary Table\u0026nbsp;8b\u003c/b\u003e), where neuroticism plays a pivotal role in longitudinally predicting various internalizing disorders, like anxiety and depression \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt last, we investigated whether the two stratified neural factors had different genetic substrates by examining their associations with the polygenic risk scores (PRS) of attention-deficit/hyperactivity disorder (ADHD) and major depressive disorder (MDD) as the representations of externalizing and internalizing disorders, respectively. We observed a significant association of the externalizing neural factor with an increased PRS of ADHD (N\u0026thinsp;=\u0026thinsp;1,594) (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.082, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.27, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 5.31E-05), but not with the PRS of MDD (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.33. Conversely, a lower internalizing neural factor was only correlated with the PRS of MDD (N\u0026thinsp;=\u0026thinsp;1,594) (\u003cem\u003er\u003c/em\u003e = -0.04, \u003cem\u003et\u003c/em\u003e = -1.74, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.041), but not with that of ADHD (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.74). The above results hence suggested that the externalizing and internalizing neural factors have distinct behavioral and genetic implications for their corresponding psychiatric comorbidity.\u003c/p\u003e\n\u003ch3\u003eGeneralization of stratified neural factors\u003c/h3\u003e\n\u003cp\u003eWe evaluated the generalization performance of the two stratified neural factors in multiple population and clinical datasets.\u003c/p\u003e \u003cp\u003eFirst, with the MID task and SST in the Adolescent Brain Cognitive Development cohort (ABCD) dataset\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, we found that the externalizing neural factor was significantly correlated with a wide range of externalizing symptoms at age 10 (N\u0026thinsp;=\u0026thinsp;1,799), including externalizing (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.059, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.51, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.006), rule break (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.070, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.97, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.002), conduct (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.063, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.69, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.006), aggressive (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.057, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.41, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.008), oppositional defiant (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.054, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.30, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.011), and attention symptoms (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.050, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.10, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.018). In addition, the externalizing neural factor estimated at age 10 could also predict the summary score of all externalizing symptoms at age 11 (N\u0026thinsp;=\u0026thinsp;1,042, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.052, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.67, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.047), as well as the subscores (ADHD: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.078, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.49, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.007; opposite: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.062, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.03, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.021; rule break: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.062, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.00, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.022). In contrast, the internalizing neural factor showed no significant association with the internalizing-related symptom at ages 10 or 11.\u003c/p\u003e \u003cp\u003eNext, we compared diagnosed patients (marked as severe or high risk for at least one externalizing or internalizing disorder) with healthy controls for the two stratified neural factors in the IMAGEN and ABCD datasets. For the externalizing neural factor, we found that externalizing participants had significantly higher factor scores than control groups in both IMAGEN (externalizing participants N\u0026thinsp;=\u0026thinsp;93, controls N\u0026thinsp;=\u0026thinsp;859, t\u0026thinsp;=\u0026thinsp;7.10, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.78, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 1.24e-12) and ABCD datasets (externalizing participants N\u0026thinsp;=\u0026thinsp;206, controls N\u0026thinsp;=\u0026thinsp;1,596, t\u0026thinsp;=\u0026thinsp;2.67, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.003). However, the comparison of the internalizing neural factor between diagnosed patients and healthy controls was only significant in IMAGEN (internalizing participants: N\u0026thinsp;=\u0026thinsp;46, controls N\u0026thinsp;=\u0026thinsp;859, t = -3.42, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.52, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 3.29E-04), but not in ABCD dataset (internalizing participants N\u0026thinsp;=\u0026thinsp;32, controls N\u0026thinsp;=\u0026thinsp;1,596, t\u0026thinsp;=\u0026thinsp;1.85, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.97).\u003c/p\u003e \u003cp\u003eFurther, we investigated whether the two stratified neural factors had clinical relevance in the case-control STRATIFY and ESTRA cohort (age\u0026thinsp;=\u0026thinsp;23) with the SST\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. We found that the externalizing and internalizing neural factors were differentially associated with psychiatric disorders. To elaborate, the externalizing neural factor of alcohol use disorder (AUD) patients (N\u0026thinsp;=\u0026thinsp;127) was significantly higher than in the healthy controls (N\u0026thinsp;=\u0026thinsp;64) (t\u0026thinsp;=\u0026thinsp;1.82, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.035), but no significant group differences were found for this neural factor between healthy controls (N\u0026thinsp;=\u0026thinsp;64) and patients with internalizing disorders (Anorexia Nervosa: N\u0026thinsp;=\u0026thinsp;55, t = -0.54, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.059; Bulimia Nervosa: N\u0026thinsp;=\u0026thinsp;44, t\u0026thinsp;=\u0026thinsp;1.47, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.15; Major Depression: N\u0026thinsp;=\u0026thinsp;143, t\u0026thinsp;=\u0026thinsp;1.44, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.15). On the other hand, the internalizing neural factor was significantly lower in patients with internalizing disorders than in healthy controls (N\u0026thinsp;=\u0026thinsp;64) (internalizing patients: N\u0026thinsp;=\u0026thinsp;242, t = -2.31, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.011; Anorexia Nervosa: N\u0026thinsp;=\u0026thinsp;55, t = -3.04, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.002; Bulimia Nervosa: N\u0026thinsp;=\u0026thinsp;44, t = -0.83, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.16, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.20; Major Depression: N\u0026thinsp;=\u0026thinsp;143, t = -2.04, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.021), but no significant group difference was found for the alcohol use disorder (N\u0026thinsp;=\u0026thinsp;127, t = -0.92, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003etwo\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.36).\u003c/p\u003e \u003cp\u003eFinally, we found that the externalizing factor generated using resting-state fMRI (with the same set of FC as defined in the IMAGEN cohort) was significantly higher in the ASD patients (N\u0026thinsp;=\u0026thinsp;233) than in healthy controls (N\u0026thinsp;=\u0026thinsp;331) (ABIDEII, Mean age\u0026thinsp;=\u0026thinsp;10.5, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.90, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.01). A significant difference were also obersved in the group comparison between ADHD patients (N\u0026thinsp;=\u0026thinsp;292) and healthy controls (N\u0026thinsp;=\u0026thinsp;228) (ADHD-200, Mean age\u0026thinsp;=\u0026thinsp;11, t\u0026thinsp;=\u0026thinsp;2.06, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.18, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 0.020). Furthermore, the internalizing factor generated using resting-state fMRI (with the same set of FC as defined in the IMAGEN cohort) of depressive patients (N\u0026thinsp;=\u0026thinsp;277) was significantly lower than the control group (N\u0026thinsp;=\u0026thinsp;172) (XiNan dataset, Mean age\u0026thinsp;=\u0026thinsp;36.1 \u003cem\u003et\u003c/em\u003e = -3.11, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = -0.30, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eone\u0026minus;tailed\u003c/em\u003e\u003c/sub\u003e = 9.67e-04). These results further supported the distinct contribution of externalizing and internalizing neural factors to externalizing and internalizing comorbidity, respectively.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNeural specificities of the three cross-disorder networks\u003c/h2\u003e \u003cp\u003eOur previous study identified a general neural factor (\u003cem\u003eNP\u003c/em\u003e factor) across the externalizing and internalizing symptoms. Here, we would like to closely examine the specific configurations of the three cross-disorder neural factors (one general and two stratified neural factors) based on the Specificity Score, i.e., the contribution of a factor after controlling the other two factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, with further details available in the Method).\u003c/p\u003e \u003cp\u003eWe first estimated the Specificity Score of each brain region for the three cross-disorder neural factors respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, total 268 regions). We found that 79 regions exhibited predominant associations (i.e. with the highest Specificity Score) with the general \u003cem\u003eNP\u003c/em\u003e factor, with the most prominent regions including the ventral precuneus, middle occipital cortex, and inferior frontal cortex (\u003cb\u003eSupplementary Table S9 A\u003c/b\u003e). Additionally, 97 regions were associated predominantly with the externalizing neural factor, most notably the primary sensorimotor cortex areas such as the precentral and middle cingulate cortex (\u003cb\u003eSupplementary Table S9B\u003c/b\u003e). Finally, 92 regions showed predominant associations with the internalizing neural factor, with notable regions including the medial prefrontal cortex and orbitofrontal cortex (\u003cb\u003eSupplementary Table S9C\u003c/b\u003e). The Specificity Scores of the three cross-disorder neural factors were significantly different (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2,265)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;15.80, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.31E-07), with the general \u003cem\u003eNP\u003c/em\u003e factor demonstrating significantly higher scores compared to either the externalizing or internalizing counterparts (\u003cem\u003eNP\u003c/em\u003e vs externalizing: \u003cem\u003et\u003c/em\u003e\u003csub\u003e(174)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.70, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;5.17E-06; \u003cem\u003eNP\u003c/em\u003e vs internalizing: \u003cem\u003et\u003c/em\u003e\u003csub\u003e(169)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.85, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1.63E-04).\u003c/p\u003e \u003cp\u003eAs previous findings have suggested that heterogeneous regions of the same psychiatric disorder might be linked in a common brain network \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, we next investigated whether the three cross-disorder factors may also share common network configurations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, 55 networks in total). We found that 20 networks were predominantly associated with the general neural factor, primarily consisting of connections with the superior medial frontal (SMF) network (\u003cb\u003eSupplementary Table S10A\u003c/b\u003e). Additionally, 18 networks were predominantly associated with the externalizing neural factor, mainly encompassing connections with the motor areas networks (Mot) (\u003cb\u003eSupplementary Table S10B\u003c/b\u003e). Moreover, 17 networks were predominantly associated with the internalizing neural factor, primarily connected to the default mode network (DMN) (\u003cb\u003eSupplementary Table S10C\u003c/b\u003e). However, at the network level, we observed indifferentiable Specificity Scores among the three cross-disorder networks (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2,54)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.83). In summary, our findings indicated that the general and stratified factors exhibited increased neural specificity along the neuroanatomical coarse-fine gradient from the network level to the region level.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, based on a large longitudinal cohort from adolescence to early young adulthood, we identified two stratified neural factors, respectively, underlying the externalizing and internalizing symptoms, each characterized by unique neurobiological configurations, genetic underpinnings, and clinical relevance. These two stratified neural factors, along with the previously identified general neuropsychopathological factor (\u003cem\u003eNP\u003c/em\u003e factor), collectively constitute a hierarchical neurocognitive model that characterizes neural mechanisms underlying psychiatric comorbidity, with implications for early prevention and therapeutics in psychiatry (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe externalizing neural factor is characterized by hyperconnectivity of primary sensory and motor regions, which has specific associations with higher impulsivity and inhibitory deficits compared to other behavioral phenotypes. This neural factor may serve as a neural mechanism underlying poor impulse control, a core dimension of externalizing psychopathology \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Additionally, the externalizing neural factor was longitudinally predictive of externalizing symptoms across developmental stages, from preadolescence to adulthood, elucidating neural mechanisms behind the enduring impact of impulsivity on the externalizing spectrum \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In contrast, the internalizing factor is characterized by hypoconnectivity of the ventromedial prefrontal cortex and medial orbitofrontal cortex, and both are crucial components of the goal-directed circuitry \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Notably, the internalizing neural factor was specifically linked to neuroticism/negative affect traits, which predisposed individuals to experience negative emotional states and life events and was proposed as a common vulnerability factor for internalizing psychopathology \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Hypoconnectivity of the internalizing neural factor might lead to challenges in responding adaptively to negative events, which perpetuate negative emotional states and result in the development of mood disorders \u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, we previously identified a general cross-disorder neural factor (\u003cem\u003eNP\u003c/em\u003e factor), characterized by hyperconnectivity of executive control networks, which inefficiently regulate/support other neural networks\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Our studies help to clarify how the three cross-disorder networks interact in the manifestation of comorbid neuropsychopathology (summarized as a hierarchical neurocognitive spectra model in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e): externalizing comorbid symptoms may result from the combination of hyperconnectivity in the impulsivity circuit and the executive control network's failure to inhibit impulsive behaviors, whereas internalizing comorbid symptoms may stem from the combination of the hypoconnectivity of the goal-directed circuit and the executive control network\u0026rsquo;s failure to initiate adaptive behavior.\u003c/p\u003e \u003cp\u003eNevertheless, several limitations necessitate further investigation in future research endeavors. First, this study mainly focused on delineating the cross-disorder neural foundations associated with internalizing and externalizing symptoms from preadolescence to adulthood, overlooking the dimension of psychotic experiences, which typically manifest in late developmental stages \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Future research could investigate whether early internalizing and externalizing neural factors (the shared and stratified ones) are risk factors for subsequent thought disorders, which, such as bipolar disorder, also demonstrate impaired behavior that falls into externalizing and internalizing domains. Second, we failed to identify a stable cross-disorder neural basis in the emotional face task, in which the standard emotional face was used to induce basic emotion. Future investigations could capitalize on more ecologically naturalistic paradigms, such as movie-watching, to illuminate the shared emotion-related neural substrates across psychiatric disorders\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Last, this study focused on identifying comorbidity networks at age 14 (wherein functional connections are linked to at least two psychiatric disorders at the same time). Nonetheless, previous longitudinal clinical studies had reported prevalent temporal comorbidity between psychiatric disorders \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, implying the presence of a transition neural network. For instance, this network was initially associated with externalizing symptoms but not internalizing symptoms, later transitioning to associate with internalizing symptoms while not externalizing symptoms. Integrating persistent and transition comorbidity networks in future research will offer a more comprehensive understanding of the evolution and interaction mechanisms underlying psychiatric comorbidity.\u003c/p\u003e \u003cp\u003eIn conclusion, we identified two cross-disorder neural factors for internalizing and externalizing symptoms that persist longitudinally from adolescence to early adulthood. The two stratified neural factors demonstrated neuroanatomical specificity and are further delineated by cognitive, behavioral and genetic risk factors. Combining with the previously identified general \u003cem\u003eNP\u003c/em\u003e factor, we present a hierarchical neurocognitive spectra model for psychiatric comorbidity. These findings might help provide a unified neurobehavioral-based psychiatric nosology that could improve diagnostic precision and treatment efficacy \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003cp\u003e \u003cb\u003eStudy overview.\u003c/b\u003e In the present study, we aimed to identify a stratified cross-disorder neural factor for externalizing and internalizing symptoms with multivariate associations between psychiatric symptoms and task-based functional connectivity, which were estimated at age 14. These multivariate associations have also been used in the identification of general neural factor in the previous study \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Briefly, we employed a mutually exclusive approach to identify specific externalizing and internalizing edges. For example, externalizing edges, composed of functional connectivity, were predictive only of externalizing symptoms and not internalizing symptoms, and vice versa for internalizing edges. Only those stratified edges surviving permutation-based reliability analysis were identified as the externalizing or internalizing neural factor. Then, we checked the longitudinal persistence of the externalizing and internalizing neural factors across age 19 and age 23. Last, we conducted multilevel specificity characterisations of the externalizing and internalizing neural factors, such as anatomical, cognitive-behavioral, genetic and generalisation analyses. Detailed information about the psychiatric questionnaire (Development and Well-Being Assessment (DAWBA) and the Strengths and Difficulties Questionnaire (SDQ)), task design (Monetary incentive delay (MID) task, Stop-signal task (SST) and Emotional face task (EFT)), and brain-behaviour modelling were provided in the Supplementary Method.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExternalizing and internalizing factor.\u003c/b\u003e The stratified neural factor was constructed to represent specific brain signatures to externalizing or internalizing symptoms. Specifically, we first removed the general cross-disorder edges that were associated with both externalizing and internalizing symptoms simultaneously \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Next, we identified two types of stratified cross-disorder edges: (1) the externalizing edges that predict at least two externalizing symptoms and (2) the internalizing edges that predict at least two internalizing symptoms. Then, for each task condition, we investigated if the number of stratified edges identified was significantly higher than a random observation using a permutation test (see the supplementary methods for more details). Only the significant task conditions and their stratified edges were kept in the following analyses. Next, to improve interpretability of results, the stratified edges were split into four different groups according to the predictive effect directions: positive-positive (or negative-negative) edges that have the same predictive effect to externalizing/internalizing symptoms positively (or negatively); positive-negative (or negative-positive) edges that have different predictive directions to externalizing/internalizing symptoms. We found that only the positive-positive externalizing edges (i.e. positively associated with externalizing symptoms) and the negative-negative internalizing edges (i.e. negatively associated with internalizing symptoms) were significantly higher than a random observation. Therefore, the two groups of stratified edges were termed the externalizing factor (positive-positive externalizing edges) and internalizing factor (negative-negative internalizing edges), respectively, and used in the following analyses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSpecificity score of cross-disorder neural factors.\u003c/b\u003e In delineating the specific underpinnings of each cross-disorder neural factor, we devised a specificity score for the brain measurements, assessing their relative contribution. At the brain region level, we initially normalized the region degree by dividing the total sum of degrees for each cross-disorder factor respectively, then plus 1 to prevent potential singularities in subsequent calculations. This normalized region degree ranged from 1 to 2, indicating the relative importance of each brain region to the cross-disorder neural factor. Last, for each brain region, the normalized brain degree was divided by that of the other two cross-disorder neural factors, and the sum of these two ratios is then calculated as the specificity score. This score reflects the importance of this brain region to the cross-disorder factor and controls for its influence on the other two cross-disorder factors. The same computational steps were applied at the network level.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGeneralization datasets.\u003c/b\u003e To investigate whether the two cross-disorder factors identified with the adolescent population-based IMAGEN dataset could be generalized into other developmental periods and clinical conditions, we utilised multiple large-scale population-based datasets (the Adolescent Brain Cognitive Development cohort\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, \u003cb\u003eABCD\u003c/b\u003e) and clinical case-control datasets (\u003cb\u003eThe\u003c/b\u003e \u003cb\u003eSTRATIFY\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eESTRA\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, \u003cb\u003eADHD-200\u003c/b\u003e\u003csup\u003e48\u003c/sup\u003e, \u003cb\u003eABIDE II; XiNan\u003c/b\u003e). The details of these datasets are provided in the supplementary method.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability.\u0026nbsp;\u003c/strong\u003eIMAGEN data are available from a dedicated database: https://imagen2.cea.fr; Stratify data are also available from the IMAGEN database: https://imagen2.cea.fr; ABCD data are available from a dedicated database: https://abcdstudy.org/; HCP data are available from a dedicated database: https://www.humanconnectome.org/; ADHD-200 data are available from a dedicated database: http://fcon_1000.projects.nitrc.org/indi/adhd200. ABIDEII data are available from a dedicated database: http://fcon_1000.projects.nitrc.org/indi/abide/abide_II.html. XiNan and STRATIFY/ESTRA datasets are available from the principal investigator of the study and are subject to local ethics committee requirements. Shen 268 parcellation is available from https://www.nitrc.org/frs/?group_id=51.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability.\u0026nbsp;\u003c/strong\u003eAll data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Information. Custom code will be uploaded to GitHub before publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u0026nbsp;\u003c/strong\u003eWe thank R. Kotov for comments on a previous draft. This work received support from the following sources: the National Natural Science Foundation of China (T2122005, 82150710554,82302288 and 81801773), National Key R and D Program of China (2019YFA0709501, 2022CSJGG1000, 2021YFC2501402, 2018YFC1312900, 2019YFA0709502 and 2018YFC1312904),the China Postdoctoral Science Foundation (2023M740659 and BX20230075), the Shanghai Pujiang Project (18PJ1400900), the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286), the Horizon 2020 funded ERC Advanced Grant \u0026lsquo;STRATIFY\u0026rsquo; (Brain network based stratification of reinforcement-related disorders) (695313), Horizon Europe \u0026lsquo;environMENTAL\u0026rsquo;, grant no: 101057429, UK Research and Innovation (UKRI) Horizon Europe funding guarantee (10041392 and 10038599), Human Brain Project (HBP SGA 2, 785907, and HBP SGA 3, 945539), The German Center for Mental Health (DZPG), the Bundesministerium f\u0026uuml;r Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B; Forschungsnetz IMAC-Mind 01GL1745B), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940, TRR 265, NE 1383/14-1), the Medical Research Foundation (MRF-058-0014-F-ZHAN-C0866) and Medical Research Council (grants MR/R00465X/1, MR/S020306/1 and MRF-058-0004-RG-DESRI: \u0026lsquo;ESTRA: Neurobiological underpinning of eating disorders: integrative biopsychosocial longitudinal analyses in adolescents\u0026rsquo;; MR/S020306/1 and MRF-058-0009-RG-DESR-C0759: \u0026lsquo;Establishing causal relationships between biopsychosocial predictors and correlates of eating disorders and their mediation by neural pathways\u0026rsquo;).the National Institutes of Health (NIH) funded ENIGMA-grants 5U54EB020403-05, 1R56AG058854-01 and U54 EB020403 as well as NIH R01DA049238, the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797). NSFC grant 82150710554. Further support was provided by grants from: - the ANR (ANR-12-SAMA-0004, AAPG2019 - GeBra), the Eranet Neuron (AF12-NEUR0008-01 - WM2NA; and ANR-18-NEUR00002-01 - ADORe), the region lle de France (QIM-VEAVE project, convention n\u0026deg;23002745-230002747), the Fondation de France (00081242), the Fondation pour la Recherche M\u0026eacute;dicale (DPA20140629802), the Mission Interminist\u0026eacute;rielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-H\u0026ocirc;pitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l\u0026rsquo;Avenir (grant AP-RM-17-013 ), the F\u0026eacute;d\u0026eacute;ration pour la Recherche sur le Cerveau. ImagenPathways \u0026quot;Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways\u0026quot; is a collaborative project supported by the European Research Area Network on Illicit Drugs (ERANID), the Medical Research Council/Arts and Humanities Research Council/Economic and Social Research Council Adolescence, Mental Health and the Developing Mind initiative as part of the EDIFY programme (grant number MR/W002418/1). US receives salary support from the National Institute of Health Research (NIHR) Biomedical Research Centre (BRC) at the South London and Maudsley (SLaM) NHS Foundation Trust and King\u0026rsquo;s College London (KCL).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis paper is based on independent research commissioned and funded in England by the National Institute for Health Research (NIHR) Policy Research Programme (project ref. PR-ST-0416-10001). The views expressed in this article are those of the authors and not necessarily those of the national funding agencies or ERANID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.J., G.S., T.W.R. and J.F. conceptualized the study. C.X. and T.J. designed the analytic approach. C.X. analyzed the data. C.X. and T.J. wrote the manuscript. S.X. preprocessed the neuroimaging data. Y.L. and S.X. helped with visualization. J.K. calculated the PRS. T.W.R., G.S., B.J.S. and J.F. revised the first draft. T.B., G.J.B., A.L.W.B., C.B., S.D., J.F., H.F., A.G., H.G., P.G., A.H., B.I., J.-L.M., M.-L.P.M., F.N., L.P., J.H.F., M.N.S., H.W., R.W. and G.S. were the principal investigators of IMAGEN. T.B., G.J.B., A.L.W.B., C.B., H.F., A.G., H.G., P.G., A.H., B.I., J.-L.M., M.-L.P.M., F.N., D.P.O., L.P., J.H.F., M.N.S., H.W., R.W. and G.S. acquired the data. All authors critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIMAGEN Consortium\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEric Artiges\u003csup\u003e16,17\u003c/sup\u003e, Marina Bobou\u003csup\u003e4,18\u003c/sup\u003e, M. John Broulidakis\u003csup\u003e19\u003c/sup\u003e, Tobias Banaschewski\u003csup\u003e20\u003c/sup\u003e, Andreas Becker\u003csup\u003e21\u003c/sup\u003e, Christian B\u0026uuml;chel\u003csup\u003e22\u003c/sup\u003e, Patricia Conrod\u003csup\u003e23\u003c/sup\u003e, Tahmine Fadai\u003csup\u003e24\u003c/sup\u003e, Herta Flor\u003csup\u003e25,26\u003c/sup\u003e, Antoine Grigis\u003csup\u003e27\u003c/sup\u003e, Yvonne Grimmer\u003csup\u003e20\u003c/sup\u003e, Hugh Garavan\u003csup\u003e28\u003c/sup\u003e, Penny Gowland\u003csup\u003e29\u003c/sup\u003e, Andreas Heinz\u003csup\u003e30\u003c/sup\u003e, Corinna Insensee\u003csup\u003e31\u003c/sup\u003e, Viola Kappel\u003csup\u003e32\u003c/sup\u003e, Herv\u0026eacute; Lema\u0026icirc;tre\u003csup\u003e33\u003c/sup\u003e, Jean-Luc Martinot\u003csup\u003e16\u003c/sup\u003e, Marie-Laure Paill\u0026egrave;re Martinot\u003csup\u003e16,34\u003c/sup\u003e, Betteke Maria van Noort\u003csup\u003e35\u003c/sup\u003e, Frauke Nees\u003csup\u003e20,25,36\u003c/sup\u003e, Dimitri Papadopoulos Orfanos\u003csup\u003e27\u003c/sup\u003e, Jani Penttil\u0026auml;\u003csup\u003e37\u003c/sup\u003e, Juliane H. Fr\u0026ouml;hner\u003csup\u003e39\u003c/sup\u003e, Ulrike Schmidt\u003csup\u003e40,41\u003c/sup\u003e, Julia Sinclair\u003csup\u003e42\u003c/sup\u003e, Michael N.Smolka\u003csup\u003e22\u003c/sup\u003e, Maren Struve\u003csup\u003e32\u003c/sup\u003e, Henrik Walter\u003csup\u003e30\u003c/sup\u003e, Robert Whelan\u003csup\u003e43\u003c/sup\u003e, Barbara J. Sahakian\u003csup\u003e1,45\u003c/sup\u003e, Trevor W. Robbins\u003csup\u003e1,45\u003c/sup\u003e, Sylvane Desrivi\u0026egrave;res\u003csup\u003e5\u003c/sup\u003e, Gunter Schumann\u003csup\u003e1,4,46,47,48\u003c/sup\u003e, Tianye Jia\u003csup\u003e1,2,5,10\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTRATIFY Consortium\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNilakshi Vaidya\u003csup\u003e4\u003c/sup\u003e, Zuo Zhang\u003csup\u003e5,6\u003c/sup\u003e, Lauren Robinson\u003csup\u003e5,7\u003c/sup\u003e, Jeanne Winterer\u003csup\u003e8,9\u003c/sup\u003e, Yuning Zhang\u003csup\u003e10\u003c/sup\u003e, Sinead King\u003csup\u003e5\u003c/sup\u003e, Gareth J. Barker\u003csup\u003e11\u003c/sup\u003e, Arun L. Bokde\u003csup\u003e12\u003c/sup\u003e, R\u0026uuml;diger Br\u0026uuml;hl\u003csup\u003e13\u003c/sup\u003e, Hedi Kebir\u003csup\u003e4\u003c/sup\u003e, Herv\u0026eacute; Lema\u0026icirc;tre\u003csup\u003e33\u003c/sup\u003e, Frauke Nees\u003csup\u003e20,25,36\u003c/sup\u003e, Dimitri Papadopoulos Orfanos\u003csup\u003e27\u003c/sup\u003e, Ulrike Schmidt\u003csup\u003e40,41\u003c/sup\u003e, Julia Sinclair\u003csup\u003e42\u003c/sup\u003e, Robert Whelan\u003csup\u003e43\u003c/sup\u003e, Henrik Walter\u003csup\u003e30\u003c/sup\u003e, Sylvane Desrivi\u0026egrave;res\u003csup\u003e5\u003c/sup\u003e, Gunter Schumann\u003csup\u003e1,4,46,47,48\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eESTRA Consortium\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZuo Zhang\u003csup\u003e5,6\u003c/sup\u003e, Marina Bobou\u003csup\u003e4,18\u003c/sup\u003e, Lauren Robinson\u003csup\u003e5,7\u003c/sup\u003e, Arun L. Bokde\u003csup\u003e12\u003c/sup\u003e, Herv\u0026eacute; Lema\u0026icirc;tre\u003csup\u003e33\u003c/sup\u003e, Dimitri Papadopoulos Orfanos\u003csup\u003e27\u003c/sup\u003e, Henrik Walter\u003csup\u003e30\u003c/sup\u003e, Ulrike Schmidt\u003csup\u003e40,41\u003c/sup\u003e, Trevor W. Robbins\u003csup\u003e1,45\u003c/sup\u003e, Sylvane Desrivi\u0026egrave;res\u003csup\u003e5\u003c/sup\u003e, Gunter Schumann\u003csup\u003e1,4,46,47,48\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZIB Consortium\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChao Xie\u003csup\u003e1,2\u003c/sup\u003e, Shitong Xiang\u003csup\u003e1,2\u003c/sup\u003e, Wei Cheng\u003csup\u003e1,2\u003c/sup\u003e, Gunter Schumann\u003csup\u003e1,4,46,47,48\u003c/sup\u003e, Jianfeng Feng\u003csup\u003e1,2,49,50,51\u003c/sup\u003e, Tianye Jia\u003csup\u003e1,2,5,10\u003c/sup\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFusar-Poli, P., \u003cem\u003eet al.\u003c/em\u003e Transdiagnostic psychiatry: a systematic review. World psychiatry: official journal of the World Psychiatric Association (WPA) 18, 192\u0026ndash;207 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRegier, D.A., Kuhl, E.A. \u0026amp; Kupfer, D.J. 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However, the exact neurobiological characterizations of these multilevel factors remain elusive. In this study, leveraging the brain-behavior predictive framework with a 10-year longitudinal imaging-genetic cohort (IMAGEN, ages 14, 19 and 23, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,750), we constructed two neural factors underlying externalizing and internalizing symptoms, which were reproducible across six clinical and population-based datasets (ABCD, STRATIFY/ ESTRA, ABIDE II, ADHD-200 and XiNan, from age 10 to age 36, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3,765). These two neural factors exhibit distinct neural configurations: hyperconnectivity in impulsivity-related circuits for the externalizing symptoms and hypoconnectivity in goal-directed circuits for the internalizing symptoms. Both factors also differ in their cognitive-behavior relevance, genetic substrates and developmental profiles. Together with previous studies, these findings propose a hierarchical neurocognitive spectral model of comorbid mental illnesses from preadolescence to adulthood: a general neuropsychopathological (NP) factor (manifested as inefficient executive control) and two stratified factors for externalizing (deficient inhibition control) and internalizing (impaired goal-directed function) symptoms, respectively. These holistic insights are crucial for the development of stratified therapeutic interventions for mental disorders.\u003c/p\u003e","manuscriptTitle":"Hierarchical Neurocognitive Model of Externalizing and Internalizing Comorbidity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 18:04:36","doi":"10.21203/rs.3.rs-5397195/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-mental-health","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natmentalhealth","sideBox":"Learn more about [Nature Mental Health](https://www.nature.com/natmentalhealth/)","snPcode":"44220","submissionUrl":"https://mts-natmentalhealth.nature.com/cgi-bin/main.plex","title":"Nature Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"25216f9b-6900-44ed-8078-9d95564f3b0d","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40163501,"name":"Health sciences/Diseases/Psychiatric disorders"},{"id":40163502,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control"}],"tags":[],"updatedAt":"2026-02-14T08:07:55+00:00","versionOfRecord":{"articleIdentity":"rs-5397195","link":"https://doi.org/10.1038/s44220-025-00577-2","journal":{"identity":"nature-mental-health","isVorOnly":false,"title":"Nature Mental Health"},"publishedOn":"2026-02-13 05:00:00","publishedOnDateReadable":"February 13th, 2026"},"versionCreatedAt":"2025-01-10 18:04:36","video":"","vorDoi":"10.1038/s44220-025-00577-2","vorDoiUrl":"https://doi.org/10.1038/s44220-025-00577-2","workflowStages":[]},"version":"v1","identity":"rs-5397195","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5397195","identity":"rs-5397195","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}