Deviation in development of dorsal association tracts during preadolescence links to concurrent and future cognitive performance and transdiagnostic psychopathology

Domain: chemistry
Published: 2026-02-19
Editorial outcome: minor_revision

Abstract
Many psychiatric disorders begin during adolescence, coinciding with the rapid development of brain white matter (WM). However, it remains unclear whether deviations from normal WM development during this period contribute to psychopathology. In this study, we developed normative models of brain age based on specific WM tracts using three large-scale developmental datasets (~10,000 subjects). We found that tract-specific deviations in WM development of association and limbic/subcortical systems were linked to concurrent and future cognition and psychopathology. The spatial pattern of the association system aligned closely with high-order brain networks and mitochondrial maps. Importantly, delayed brain-age especially in dorsal association tracts predicted psychiatric disorders across diagnoses and disorder onset over a 2-year follow-up. By identifying tract-specific WM development during preadolescence as a predictor of cognitive capacity and psychiatric risks, this study provides a framework for tracking individualized brain development and understanding the neurobiological underpinnings of cognition and transdiagnostic psychopathology.

Introduction
Psychiatric disorders are among the most common causes of morbidity and mortality1,2, yet much is still unknown regarding their underlying developmental pathophysiologies3. One emerging hypothesis is that a large amount of variance in the risk for psychopathology comes from common “transdiagnostic” instead of disorder-specific factors. This is supported by findings showing that different psychiatric disorders share risk genes and show overlapping alterations in brain structure, connectivity and function4–6. Further, approximately 75% of all psychiatric disorders start before the age of 21, with 35% starting before age of 147, suggesting that deviations in neurodevelopment may contribute to psychopathology in a transdiagnostic manner.

Personalized characterization of white matter (WM) tract development may provide significant insight into links between disrupted neurodevelopment and psychopathology as there is significant temporal overlap between WM changes during preadolescence and the onset of many psychiatric disorders8–10. It is plausible that WM alterations may contribute to mental health issues given that WM tract integrity in association and limbic tracts is linked with cognitive and socioemotional processes in pediatric populations11. Developmental variance in the structural integrity of these critical pathways may have widespread influences on cognitive and emotional functioning, which may in turn influence risk for psychopathology12,13. Further, the energetic demands of WM is higher during the preadolescent and early adolescent window, driven by maturation-related increases in physiological processes such as myelin synthesis and axon–oligodendrocyte9. Mitochondria, which are central to energy metabolism, have been shown in histological studies to exhibit substantial alterations in individuals with psychiatric disorders14,15, implicating mitochondrial dysfunction as a potential contributor to impaired WM integrity. Given that WM enables efficient inter-regional communication and supports the transmission of neurotransmitter signals, its developmental disruption could have cascading effects on brain function. However, links between WM development, mitochondrial functioning, and psychopathology are poorly understood.

One way to assess normative growth and define pathological deviations in development is through the use of normative modeling which has been successfully applied in clinical contexts including growth charts in pediatrics16 and standardized achievement and intelligence (IQ) tests in psychology17. Recently, this framework has been extended to measures of brain structure and function to quantify person-specific deviations in developmental trajectories relative to typically developing controls18–21. In the context of assessing individual brain development/aging level, machine learning prediction modeling using magnetic resonance imaging (MRI) data has allowed for the estimation of the biological age of the brain of healthy individuals22. Deviations between this neuroimaging-based ‘brain age’ and chronological age, termed the brain age gap (BAG), may reflect disruptions in normal developmental/aging trajectories and provide mechanistic information into cognitive development and the pathophysiology of psychiatric disorders22–24. To date, this method has predominantly focused on studies using morphological measures of gray matter (GM), with aggregate results from these studies suggesting that psychopathology is associated with accelerated brain aging in adults and youth22–24. However, few BAG studies have used diffusion MRI (dMRI) measures of WM, and these studies are mostly in adults. Results from these WM-BAG studies also suggest that adult patients with single psychiatric diagnoses (e.g., schizophrenia and bipolar disorder) may have more advanced WM brain aging relative to non-psychiatric controls25,26. While these prior studies highlight the relevance of the BAG in psychopathology, the role of WM maturation, particularly the tract-specific development, in adolescents is still unclear. Our study seeks to fill this knowledge gap.

In the present study, we sought to characterize WM BAG patterns derived from tract-specific microstructure in preadolescents with varying levels of psychopathology and examine associations between tract-based BAGs and a broad set of behavioral assessments spanning cognitive and psychopathology domains in youth. First, we built and cross-validated WM tract-based brain age models using diffusion MRI (dMRI) data from the Lifespan Human Connectome Project Development (HCP-D) dataset (N=611). We then performed out-of-sample validation, using models derived from HCP-D data to predict brain ages in two independent datasets including Adolescent Brain Cognitive Development (ABCD) study (N = 8,688) and Healthy Brain Network (HBN) pediatric mental health study (N=978). Next, we used baseline data from the ABCD study and applied a multivariate sparse canonical correlation analysis (sCCA) to identify latent brain-behavior associations between WM-derived BAGs and cognition and psychopathology. Then, we assessed whether the tract-wise patterns of developmental levels linking cognition and psychopathology domains can be explained by tract-wise mitochondrial profiles from post-mortem data27. Lastly, to ascertain the clinical utility of WM BAGs in follow-up cognitive performance and risk for psychiatric illness, we examined tract-based BAG associations with i) the cognitive performance measured 2 or 3 years after baseline, and ii) the cumulative number of psychiatric diagnoses assessed concurrently and 2 years after baseline, as well as longitudinal transitions between healthy and psychiatrically diagnosed states over the 2-year study period.

Results
Whole-brain and individual WM tract profiles predicted chronological age
First, we extracted 54 WM tracts from dMRI in three datasets (HCP-D, HBN, and ABCD) and categorized the tracts into 7 systems (Fig. 1A). The GM regions connected by each tract are indicated in Supplementary Table 1. Tract-based features were constructed using diffusion fractional anisotropy (FA) profiles along tracts (Fig. 1B). Because dMRI metrics are highly site-dependent (Supplementary Fig. 1), which can hinder cross-site model training and application, we applied data harmonization (see Supplementary Fig. 1 for harmonization effects) prior to all downstream analyses. We then built brain age models using these tract-based features of individual tracts (25 combined bilateral association/projection tracts and 4 callosal tracts) or whole brain (concatenated all tracts) from typically developing adolescents in HCP-D dataset (N=611; 330 females, age range=5.58-21.92 years) (Fig. 1C). The models were trained by a Gaussian Process Regression (GPR) algorithm28, a nonparametric Bayesian machine learning approach capable of capturing the nonlinear developmental trajectories of WM microstructure. In addition, GPR has been widely adopted in brain-age research29,30, and previous comparative studies have demonstrated that GPR performs competitively or better than alternative algorithm30,31. Performance of the brain age model from whole-brain tracts is shown in Supplementary Fig. 2 and Supplementary Table 2.

The 5-fold cross-validation performance of the GPR model showed a Pearson R for the relationship between chronological age and predicted age of 0.855 and a root mean squared error (RMSE) of 2.264 years. To test the prediction performance of the brain age model, we selected 978 participants from the independent HBN dataset (366 females, age range=5.58–21.90 years) within the same age range as the HCP-D study. As shown in Supplementary Table 3 and Supplementary Fig. 2, whole-brain brain-age model significantly estimated age in HBN dataset with an R of 0.622 and a RMSE of 3.337 years. For the prediction of brain ages in another independent dataset, the ABCD study (N Baseline = 8,688, 4,205 females, N 2-y-follow-up = 5,883, 2,741 females; N 4-y-follow-up = 2,351, 1,118 females, age-range= 8.91-15.70 years), of which the age-range was much narrower and the prediction performance was with an R of 0.543 and an RMSE of 2.041 years (Supplementary Fig. 2).

To evaluate the effects of age-range and intra-subject variability on the predictive performance of the brain-age models, we performed two additional analyses. First, we repeatedly resampled the same number of HBN participants (N=100) across 100 iterations while systematically varying the age-range. Second, we resampled the same number of ABCD participants (N=500 per timepoint) with 100 repetitions, and compared samples composed of non-overlapping subjects versus samples including overlapping subjects across the three time points. As shown in Supplementary Fig. 3, predictive performance (R values) improved as the age-range becomes wider. In the ABCD resampling experiment, the distributions of R values were comparable between the results from overlapped (0.592 ± 0.010; Supplementary Fig. 4) and non-overlapped (0.580 ± 0.016; Supplementary Fig. 4) samples. The overlapped samples exhibited only 1.3% greater variance explained, indicating that intra-subject overlap had limited influence on overall model performance.

For brain age models built from FA profiles of individual tracts, all 29 tract-based brain age models significantly predicted the age in the 5-fold cross-validation dataset (HCP-D) after false discovery rate (FDR) correction for multiple comparisons. The cross-validation performance in HCP-D dataset was with Rs ranging from 0.411 to 0.781 (RMSEs ranging from 2.633 to 3.687). See Supplementary Table 2 for the cross-validation performance of HCP-D dataset. For the prediction of brain age in testing datasets (HBN and ABCD), all tracts also exhibited significant prediction for age (Supplementary Table 3-4).

For each participant, the WM tract predicted age is referred to as brain age, and the difference between the brain age and chronological age is defined as tract-based BAG. The tract-based BAG quantifies the extent to which an individual’s tract-specific biological age relative to other same-aged peers based on their tract-specific FA profiles. A negative BAG indicates that an individual’s predicted brain age is less than their chronological age (i.e., individual deviation under model-derived developmental trajectory), while a positive BAG indicates that an individual’s predicted brain age is higher than their chronological age (i.e., individual deviation over model-derived developmental trajectory). To correct the potential brain age bias caused by regression dilution, we further trained brain age models with bias correction, a linear transformation of chronological age32, on the training sets and applied them to the testing datasets. The predictive performance of the corrected tract-based brain ages is reported in Supplementary Table 2-4 and Supplementary Fig. 2. We used age-bias corrected BAGs for all primary statistical analyses, whereas uncorrected BAGs were used for validating brain age model performance. While there is ongoing debate regarding the optimal method for bias correction in brain age modeling33,34, our key findings remained robust regardless of whether age-bias correction was applied (see sensitivity analyses), as the BAGs were only adjusted by a linear model of chronological age derived from an independent dataset, and chronological age was additionally included as a covariate in all subsequent analyses.

Puberty and its accompanying hormonal changes play a critical role in shaping brain development. To validate that BAGs capture biologically meaningful developmental processes, we examined their associations with both pubertal stage assessments and salivary hormone levels (DHEA, testosterone, and estradiol). Across the first two time points, higher whole-brain tract BAGs (with age-bias correction) were significantly associated with more advanced pubertal stage (t(10,120) = 4.685, pFDR = 5.68 × 10−6, Supplementary Fig. 5), after controlling for age, sex, and visit. In addition, whole-brain tract BAGs showed significant positive associations with higher levels of DHEA (t(10,630) = 3.558, pFDR = 5.00 × 10−4), testosterone (t(9,872) = 4.705, pFDR = 5.68 × 10−6), and estradiol (t(4,583) = 2.238, pFDR = 0.025). These hormone analyses accounted for covariates including age, sex, visit, caffeine intake and physical activity in the past 12 hours, collection time since midnight, saliva collection duration, and time from collection to freezer storage.

Tract-based BAGs associated with cognition and psychopathology
To comprehensively characterize tract-based BAGs and their associations with general cognition and psychopathology, we examined associations of tract-based BAGs with a wide range of cognitive functions and psychopathological behaviors using multivariate sCCA, with age and sex as covariates. In the sCCA, we included 30 tract-based BAGs (29 tract-specific and a whole-brain tract BAGs with age-bias correction) and 51 “behavioral assessments” (20 neurocognitive and 31 psychopathology-related measures, see Methods) from the baseline ABCD dataset, which were utilized in our previous functional study35. The analysis showed that the first two canonical modes were statistically significant compared to null distributions generated by permutation tests (sCCA mode 1: R=0.19, pFDR < 0.001; sCCA mode 2: R=0.13, pFDR < 0.001). The scree plot of the covariance explained by all canonical modes is shown in Supplementary Fig. 6.

The loading patterns of the tract-based BAGs on the two significant sCCA modes were highly heterogeneous (Fig. 2A). BAGs obtained from most tracts in the dorsal and ventral association systems exhibited significant positive loadings for Mode 1. In contrast, BAGs from tracts mainly from the sensorimotor, limbic, cortico-striatum, cortico-thalamus and callosal systems had significant positive loadings for Mode 2. Therefore, in the following discussion, the 1st sCCA mode is referred to as Association BAG mode and the 2nd mode is referred to as Subcortical/limbic BAG mode. Cognitive measures had generally heavy and positive loadings on the two sCCA modes (Fig. 2B).

To evaluate the specific behavioral measures associated with each sCCA mode, we compared the distributions of loadings generated from 1000 bootstrap tests (see Methods). The measures with an effect size more than 0.5 were considered specific to the corresponding mode. As shown in Fig. 2B and Supplementary Table 5, the Association BAG mode significantly loaded on cognitive measures including total cognition composite and fluid cognition composite scores. Several psychopathological assessments, including Child Behavioral Checklist (CBCL) Attention Problems, Attention Deficit/Hyperactivity Disorder, and Total Problems exhibited significant and specific negative loadings on the Association BAG mode. For the Subcortical/limbic BAG mode, significant and specific cognitive scores included Little Man Task efficiency, Pattern Comparison processing speed and Stop Signal Reaction (SST) time, while Parent General Behavior Inventory (PGBI) Total Score of Mania showed significant negative loading.

Furthermore, we performed a post-hoc statistical analysis to explore the univariate relationships between the 30 tract-based BAGs (with age-bias correction) and 51 behavioral measures. The heatmap of the t values is in Supplementary Figs. 7-8, showing significant associations of nearly all the cognitive measures with tract-based BAGs. The Manhattan plot in Fig. 2C visualizes the associations between all tract-based BAGs and behavioral measures. The top 10 tract-based BAGs showing the strongest correlations with either cognitive or psychopathology domains are highlighted. Across both domains, dorsal association tracts consistently ranked higher than tracts from other systems. Notably, the behavioral measures showing the strongest associations with dorsal association tracts included total intelligence, fluid intelligence, CBCL Total Problems, Attention Problems, and ADHD scores.

Functional decoding based on spatial association between brain regions connected by WM tracts and task-related brain activation
WM tracts connect distinct brain functional units that correspond to relevant task-related brain activations. To further characterize functions of GM regions connected by the WM tracts with loadings in the two identified sCCA modes, we utilized brain activation maps in the Neurosynth meta-analysis based on 100 topics36. For the functional activation map of each topic, we calculated the brain activation level within the GM regions connected by each association/projection WM tract. The topics (Supplementary Table 6) related to high-order cognitive ability, emotion and psychiatric disorders were selected to perform spatial correlation with the loading patterns of the two modes. A spatial permutation test (1000 times) was applied to test the significance of spatial correlation. As shown in Fig. 3A and Fig. 3B, similar spatial correlations were observed between the loading pattern of the Association BAG mode and distributions of cognitive-control (e.g., “Cognitive-control & task performance”) and language-related (e.g., “Reading & Words”) brain activation connected by WM tracts. Furthermore, the loading patten of the Subcortical/limbic BAG mode was correlated with reward or emotion related topics such as “Depression,” “Fear Conditioning & PTSD,” and “Reward” (Fig. 3A-B and Supplementary Table 7).

To qualitatively compare differences in functional networks connected by the tracts significantly loaded on the two sCCA modes, we projected their connected brain regions onto the cortical surface and overlapped with 7-network parcellation by Yeo et al.37. We observed that high-order networks (e.g., VAN, FPN and DMN) had the largest proportions of the brain regions connected by the tracts in the Association BAG mode. Moreover, the tracts in the Subcortical/limbic BAG mode connected mostly to the brain regions in sensorimotor and limbic networks (Fig. 3C).

Mitochondrial correlates of two cognition-psychopathology profiles
To explore potential biological mechanisms underlying the two cognition-psychopathology profiles, we incorporated the only available set of six high-resolution ex-vivo mitochondrial maps derived from a postmortem adult brain27, including 3 enzymatic activity maps (CI: NADH–ubiquinone oxidoreductase; CII: succinate dehydrogenase; CIV: cytochrome oxidase) reflecting energy-transformation, 1 mitochondrial content map (MitoD), and 2 derived maps reflecting tissue respiratory capacity (TRC; TRC = CI + CII + CIV) and mitochondrial respiratory capacity (MRC; MRC = TRC/MitoD)27 (see “Methods”). MRC refers to the ability of mitochondria to generate energy through oxidative phosphorylation, reflecting the energetic demands and metabolic support of specific tracts27. Tract-wise spatial mitochondrial distributions were quantified by overlaying the mitochondrial maps onto the HCP-1065 tract atlas38, from which mean mitochondrial values were extracted for each tract (Fig. 4A). Statistical significance of each mitochondrial profile was assessed using a spatial spin-permutation null model (1000 iterations). As shown in Fig. 4B, WM tracts with higher loadings in the Association BAG mode were significantly associated with increased enzymatic activity (CI: rho = 0.50, CII: rho = 0.48, CIV: rho = 0.51, pFDR < 0.05), elevated mitochondrial content (rho = 0.49, pFDR < 0.05) and higher MRC (rho = 0.52, pFDR < 0.05).

Baseline tract-based BAGs are prospectively linked to cognitive performance two to three years later
Having established the associations between deviation in development of WM tracts and both cognitive function and psychopathology, we next investigated whether developmental deviations in WM tracts could reflect meaningful variance in cognitive performance at follow-ups. To avoid circularity, we selected general cognitive measures39 that were not included in the prior sCCA analyses. Specifically, we included math ability (assessed via Stanford Mental Arithmetic Response Time Evaluation [SMARTE]), school grade and the overall performance of emotional Stroop task (measured by overall accuracy [STRP_ACC]), from a 2- or 3-year-follow-up (Fig. 5A). For the relationships with these three follow-up cognitive measures, the Association BAG mode demonstrated greater effect sizes (School Grade: F(1,7868) = 59.3, ΔR2adjusted = 0.729, pFDR = 1.48 × 10−14; SMARTE: F(1,5623) = 102.9, ΔR2adjusted = 1.698, pFDR = 5.76 × 10−24; STRP_ACC: F(1,5967) = 33.6, ΔR2adjusted = 0.538, pFDR = 6.84 × 10−9) compared to those of Subcortical/limbic BAG mode (School Grade: F(1,7868) = 24.6, ΔR2adjusted = 0.297, pFDR = 7.12 × 10−7; SMARTE: F(1,5623) = 69.6, ΔR2adjusted = 1.15, pFDR = 9.19 × 10−17; STRP_ACC: F(1,5967) = 14.3, ΔR2adjusted = 0.219, pFDR = 1.60 × 10−4). Post-hoc tract-wise analyses further revealed significant associations between tract-based BAGs association and the three cognitive measures, as shown in Fig. 5B and Supplementary Table 8-10. Among the three cognitive performances, more positive tract-based BAGs were associated with better cognitive performance assessed 2 to 3 years later.

To evaluate whether tract-specific BAGs capture more and distinct variance compared with tract-wise FA values, we conducted the supplementary statistical analyses using baseline tract-wise FA values to associate with follow-up cognitive performance. As shown in Supplementary Fig. 13, baseline FAs of the Cingulum Parahippocampal explained the greatest variance in 2-year follow-up school grades (F(1,7868) = 49.15, ΔR2adjusted = 0.602, pFDR = 2.57 × 10−12) and 3-year follow-up SMARTE performance (F(1,5623) = 88.31, ΔR2adjusted = 1.46, pFDR = 7.94 × 10−21), whereas baseline FAs of the FAT accounted for the highest variance in 3-year follow-up STRP_Acc (F(1,5967) = 21.47, ΔR2adjusted = 0.338, pFDR = 3.66 × 10−6) among the 30 tracts. Across all three follow-up cognitive measures, a greater number of tracts showed higher variance explained by tract-specific BAGs than by FA values (Supplementary Fig. 13A). In addition, we examined the similarity of effect patterns between tract-specific BAGs and FA values using Spearman correlation. As illustrated in Supplementary Fig. 13B, the FA-based effect patterns did not significantly resemble those derived from the tract-specific BAGs, suggesting that tract-specific BAGs provide complementary and differential variance linking to follow-up cognitive performance.

More negative tract-based BAGs at baseline concurrently associated with greater number of psychiatric (KSADS-5) diagnoses
We assessed whether tract-based BAGs were associated with psychiatric diagnoses using general linear models (GLMs). 8594 participants with valid dMRI and psychiatric assessments from the ABCD dataset at baseline were used for the main analysis. For each participant, categorical psychiatric diagnoses were measured using the parent-reported Kiddie Schedule for Affective Disorders and Schizophrenia for DSM-5 (KSADS-5). sCCA variate scores derived from the Association BAG mode at baseline showed a significant association with the cumulative numbers of KSADS-5 diagnoses assessed concurrently (F(2,8591) = 15.06, pFDR = 9.44 × 10−6, Fig. 6A-left), such that individuals with less tract-based age relative to chronological age had higher number of psychiatric disorders. sCCA variate scores derived from the Subcortical/limbic BAG mode showed a smaller effect for the association (F(2,8591) = 6.22, pFDR = 0.008, Fig. 6A-left).

In addition, we also conducted the post-hoc association analyses on the relationships of BAGs (with age-bias correction) obtained from individual tracts and whole-brain tracts, respectively, with the clinical diagnoses. BAG of the whole-brain tracts at baseline was significantly associated with the cumulative number of KSADS-5 diagnoses (0, 1, or >=2) assessed at the same time, after FDR correction (KSADS-5 effect: F(2,8591) = 7.05, pFDR = 0.004, Fig. 6B-upper). Moreover, BAGs of all tracts within dorsal association system at baseline showed significant associations with the cumulative number of diagnoses assessed concurrently (e.g., FAT: F(2,8591) = 9.78, pFDR = 0.001 and PAT: F(2,8591) = 13.89, pFDR = 1.52 × 10−5, Fig. 6B-upper). See Supplementary Table 11 for results of other tracts and post-hoc analyses. By contrast, none of the tract-wise FA values at baseline were significantly associated with the cumulative number of concurrent diagnoses when analyzed using the same statistical models (Supplementary Table 12).

Deviation in WM development trajectory assessed via BAGs at baseline prospectively predicted cumulative number of psychiatric (KSADS-5) diagnoses at 2-year follow-up
Next, the GLM analysis revealed that the scores derived from both sCCA variates at baseline were significantly associated with the cumulative number of diagnoses at 2-year follow-up (Association BAG mode: F(2,7904) = 11.90, pFDR = 2.21 × 10−4; Subcortical/limbic BAG mode: F(2,7904) = 7.20, pFDR = 0.004, Fig. 6A-middle). Further, post-hoc subgroup-wise analyses contrasting the difference between each pair of the subgroups showed that the scores of both sCCA variates were significantly lower in the subgroups with two or more diagnoses compared to the subgroup with no diagnosis.

Post-hoc tract-wise comparisons demonstrated that age-bias-corrected BAG of the whole-brain tracts at baseline were significantly associated with the cumulative number of diagnoses at 2-year follow-up (F(2,7904) = 7.03, pFDR = 0.005, Fig. 6B-middle), with the significantly more negative BAG in the subgroup with 2 or more diagnoses compared to the subgroup with no diagnosis. Age-bias-corrected BAGs of 6 association tracts and 5 limbic/cortico-subcortical tracts at baseline showed significant associations with the cumulative number of diagnoses at 2-year follow-up (e.g., FAT: F(2,7904) = 8.64, pFDR = 0.002; CS_P: F(2,7904) = 7.91, pFDR = 0.003; Fig. 6B-middle and Supplementary Table 11). In addition, supplementary comparative analyses using tract-wise FA values revealed that lower harmonized FA in the Corticospinal Tract (CST: F(2,7904) = 12.98, pFDR = 7.05 × 10−5) and Cingulum Parahippocampal Parietal (C_PHP: F(2,7904) = 5.14, pFDR = 0.044) were significantly associated with a greater number of diagnoses. Results for other tracts are provided in Supplementary Table 12. The tract-wise group effects based on FA exhibited a different directional pattern of variance in relation to the number of concurrent diagnoses compared with those observed for tract-wise BAGs (FA vs. BAG: rho = 0.366, pFDR = 0.1380, Supplementary Fig. 14).

Deviation in WM development trajectory assessed via BAGs at baseline prospectively associated with psychiatric diagnosis status transitions from baseline to 2-year follow-up
Brain variate scores of both sCCA modes at baseline significantly associated with the status transitions of diagnoses from baseline to 2-year follow-up (Association BAG mode: F(3,7826) = 10.52, pFDR = 2.11 × 10−5; Subcortical/limbic BAG mode: F(3,7826) = 4.75, pFDR = 0.008, Fig. 6A-right). Post-hoc subgroup-wise comparisons showed that the brain variate scores of both sCCA modes were significantly lower in Disorder-persistent (Association BAG mode: z = −4.410; p < 0.001; Subcortical/limbic BAG mode: z = −3.685; p = 0.001) compared to Healthy-persistent subgroup, while the brain variate scores of the Association BAG mode were significantly lower in Disorder-new-onset and Disorder-remitted subgroups compared to Healthy-persistent subgroup.

Furthermore, supplementary post-hoc tract-wise comparisons revealed that the age-bias-corrected BAG of the whole-brain tracts at baseline showed a significant association with diagnostic status transitions (F(3,7826) = 6.06, pFDR = 0.002, Fig. 6B-bottom). For BAGs (with age-bias correction) of individual tracts, all the tracts in the dorsal association system (e.g., AF: F(3,7826) = 7.04, pFDR = 0.001; FAT: F(3,7826) = 7.27, pFDR = 0.001) (Fig. 6B-bottom and Supplementary Fig. 6) were significantly associated with diagnostic status transitions. See Supplementary Table 11 for other tracts. Comparative analyses showed that the tract-wise FAs in tracts such as the CST and the C_PHP were significantly associated with diagnostic status transitions. However, no significant relationship was observed between FA- and BAG-based group effects related to the diagnostic status transitions (rho = 0.205, pFDR = 0.278, Supplementary Fig. 14).

Consistency of the BAG variates and their associations with cognitive performance and psychiatric diagnoses
To further evaluate the consistency of the two sCCA BAG variates, we repeated the sCCA analyses using behavioral measures from either the cognition or psychopathology domain. In both domain-specific analyses, the sCCA variates of Association and Subcortical/limbic BAG modes still showed significance after permutation testing. The loading patterns of the re-derived sCCA variates showed significant consistency with the Association and Subcortical/limbic BAG modes using full behavioral set, respectively, on both behavioral and BAG sides. See Supplementary Figs. 10–12 for more details.

Next, we examined the associations between psychopathology-derived sCCA BAG variates and follow-up cognitive performance. Consistent with the full-behavior-derived results, psychopathology-derived Association BAG variate (School Grade: F(1,7868) = 58.86, ΔR2adjusted = 0.72, p = 1.90 × 10−14; SMARTE: F(1,5623) = 93.50, ΔR2adjusted = 1.55, p = 6.00 × 10−22; STRP_ACC: F(1,5967) = 32.13, ΔR2adjusted = 0.51, p = 1.51 × 10−8;) exhibited stronger associations with follow-up cognitive performance than the Subcortical/limbic BAG variate (School Grade: F(1,7868) = 19.62, ΔR2adjusted = 0.23, p = 9.57 × 10−6; SMARTE: F(1,5623) = 65.47, ΔR2adjusted = 1.08, p = 7.16 × 10−16; STRP_ACC: F(1,5967) = 11.63, ΔR2adjusted = 0.18, p = 0.001).

Furthermore, we also investigated the relationships between cognition-derived sCCA BAG variates and the cumulative number of KSADS-5 diagnoses at baseline, at 2-y-follow-up, and the transition of the transdiagnostic status between baseline and 2-y-follow-up. GLM analyses demonstrated the statistical patterns were consistent with the behavior-derived results: more negative Association and Subcortical/limbic BAG variate scores were associated with more comorbid psychiatric disorders both at baseline (Association mode: F(2,8591) = 14.96, p = 3.24 × 10−7; Subcortical/limbic mode: F(2,8591) = 6.54, p = 0.0015) and at 2-y-follow-up (Association mode: F(2,7904) = 11.94, p = 6.67 × 10−6; Subcortical/limbic mode: F(2,7904) = 7.74, p = 0.0005), and the longitudinal transition of KSADS-5 diagnosis status (Association mode: F(3,7826) = 10.44, p = 7.49 × 10−7; Subcortical/limbic mode: F(3,7826) = 5.07, p = 0.0017).

Sensitivity analysis for the effects of puberty, socioeconomic status (SES) and age-bias correction
We conducted several sensitivity analyses to evaluate the robustness of our findings and determine if they remained significant after controlling for potential confounding factors. First, to test whether age-bias correction influenced the magnitude and significance of associations observed between WM BAGs and psychiatric diagnoses, we repeated the analyses using two more alternative strategies: (i) uncorrected BAGs with chronological age included as a covariate, and (ii) age-bias-corrected BAGs without age as a covariate. The results (Supplementary Table 13-14) remained consistent, indicating that the observed associations were not dependent on the bias correction procedure.

Second, we sought to account for potential effects of pubertal development on our outcomes, by re-running our main analyses including Pubertal Development Scale (PDS) scores as an additional covariate. This analysis yielded unchanged results, suggesting that puberty did not confound the observed associations (Supplementary Table 15).

Lastly, to account for effects related to SES on our outcomes, we reran our main analyses including two measures of SES (family income and parental education) as covariates. The results of this analysis (Supplementary Table 16) were unchanged from our main results, indicating that the associations observed between tract-based BAG and psychiatric diagnoses could not be better accounted for by SES-related factors.

Methods
Participants
This study complies with all relevant ethical regulations. All datasets included in this study are publicly available and anonymized human brain sources, which have been approved by their institutional boards or ethics committees71–73. No new human subjects were recruited for this study. The written informed consent was obtained for all participants in the original studies. For participants under the age of 18, written consent was provided by their legal guardians and assent was obtained from the participants. As the study focus on tract-based developmental correlates without a prior hypothesis regarding sex differences, sex was included as a covariate in all primary analyses, and no sex- or gender-specific analyses were conducted.

Human Connectome Project in Development (HCP-D) Study. The HCP-D dataset, designed to characterize healthy brain development in children and adolescents, was utilized as training and cross-validation for building a white-matter tract-based brain age model. This developmental dataset featured a predominantly cross-sectional design and comprised 652 individuals (aged 5.58 to 21.92 years; 351 females) with typical development71, of whom are without the following exclusion criteria: 1) Serious neurological condition; (2) History of serious head injury; (3) Long term use of immunosuppressants or steroids; (4) Premature birth; (5) Claustrophobia; (6) Hospitalization >2 days for certain physical or psychiatric conditions or substance use; (7) Treatment >12 months for psychiatric conditions or (9) Pregnancy or other MRI contraindications. All MRI data were acquired on 3T Siemens Prisma Scanners at four imaging sites. See more details about scanning parameters in Supplementary Table 17-18. The study was approved by a central Institutional Review Board at Washington University in St. Louis.

Healthy Brain Network pediatric mental health (HBN) Study. To validate the prediction performance in an independent dataset with the similar age range, we included 1687 subjects (600 females) from 5.58 to 21.90 years old from HBN study72. The MRI data was collected with 3T Siemens Prisma scanners at City College of New York and the CitiGroup Cornell Brain Imaging Center, and with a 3T Siemens Tim Trio scanner at Rutgers University Brain Imaging Center. See Supplementary Table 17-18 for more scanning details. The study was approved by the Chesapeake Institutional Review Board.

Adolescent Brain Cognitive Development (ABCD) Study. We used the brain age model built from the HCP-D data to predict brain age for participants in the ABCD study, which is a prospective, ongoing, multi-site assessment of development in U.S. children from age 9–10 years though adulthood73. The MRI and behavioral data (v5.1) at two timepoints, baseline, 2-year- and 4-year-follow-ups, were collected from 11,875 children. The T1-weighted (T1w) anatomical MRI and dMRI were acquired with magnetization-prepared rapid acquisition gradient echo (MPRAGE) and simultaneous multi-slice/multiband echo-planar imaging (EPI) sequences, respectively, using a unified protocol designed for different types of 3T MRI scanner platforms including Siemens Prisma and Prisma Fit, General Electronic Discovery 750, and Philips Medical System Ingenia and Achieva dStream. See Supplementary Table 17-18 for more scanning parameter details.

MRI data preprocessing and quality control (QC)
HCP-D. This dataset underwent the HCP’s minimal preprocessing workflow74, including the steps of b0 intensity normalization, and corrections of EPI distortion, eddy-current, head-motion and gradient nonlinearity. The preprocessed files and QC metrics of 636 subjects were downloaded from the Fiber Data Hub (link: https://brain.labsolver.org/hcp_d.html). The data identified as “outlier” in the QC table were excluded (N=15), which were with lower neighboring diffusion weighted imaging (DWI) correlation values (0.46~0.70).

HBN Study. The dMRI data was preprocessed by QSIPrep according to75. Preprocessed 3T dMRI data of 1,769 subjects were downloaded from AWS S3 (link: s3://fcp-indi/data/Projects/HBN/BIDS_curated/derivatives/qsiprep/). Because the dMRI data was collected according to 15 different acquisition parameters, we only included the subjects with the most common dMRI acquisition scheme of ‘SOTE_64-dir_most_common’. Quality control (QC) procedure was from previous studies75,76. The subjects whose dMRI quality score (‘xgb_qc_score’, or ‘dl_qc_score’ if ‘xgb_qc_score’ was not available) less than 0.5 were excluded. A total of 1,102 subjects passed the quality control procedure.

ABCD Study. Standard dMRI preprocessing was performed by the ABCD Data Analysis, Informatics and Resource Center77, with the following steps of eddy-current correction, head-motion correction, adjusting diffusion gradients for head motion, robust tensor fitting, correcting B0 distortion and gradient distortion using opposite phase encoding pairs of b0 images, registering b0 images to T1w images using mutual information and cubic interpolation to resample at a 1.7mm isotropic resolution. The QC procedure included both manual and automated checks, which were described elsewhere77. Specifically, we excluded the images that met one of the following criteria: (1) with artifacts preventing radiology reads or with incidental findings (mrif_score=0, mrif_score=3 or mrif_score=4); (2) failed raw QC or total repetitions for all OK scans was less than 103 for dMRI data (iqc_dmri_ok_ser = 0 or iqc_dmri_ok_nreps < 103); (3) with dMRI B0 unwarp unavailable, with registration of dMRI to T1w larger than 17mm, or dMRI maximum dorsal and ventral cutoff scores larger than 47 and 54 respectively (apqc_dmri_bounwarp_flag=1, apqc_dmri_reg-t1_rigid > 17, apqc_dmri_fov_cutoff_dorsal > 47 or apqc_dmri_fov_cutoff_ventral > 54); (4) failed manual post-processing QC for dMRI data (dmri_dti_postqc_qc = 0) and (5) failed raw QC for T1w data (iqc_t1_ok_ser=0). See Supplementary Fig. 15 for more details.

Assessments of cognition and psychopathology
The behavioral dataset, as used in our previous study35, comprised multisource data from the ABCD study assessing cognitive functioning and psychopathology across multiple domains. The dataset included 20 neurocognitive and 31 psychopathology-related assessments. The cognitive assessments covered different aspects of cognition including fluid cognition, crystalized cognition, overall cognition, processing speed, working memory, inhibitory control, episodic memory, vocabulary, reading, visuospatial ability, attention and cognitive flexibility, derived from NIH Toolbox cognition battery, Matrix Reasoning Test from the Wechsler Intelligence Scale for Children-V (WISC-V), Rey Auditory Verbal Learning Test (RAVLT), Little Man Task, Recognition memory, Emotional n-back fMRI task, Stop-signal fMRI task and Monetary Incentive Delay task. See Supplementary Table 19 for more details of the above neurocognitive measurements. The psychopathology-related assessments included the scales of the Child Behavioral Checklist (CBCL)78, the personality measures from the Modified Urgency, Perseverance, Premeditation and Sensation-seeking (UPPS-P) for Children from PhenX79,80, the mania scale from the 73-item Parent General Behavior Inventory (PGBI), psychosis risk symptom subscales from Youth-report Prodromal Questionnaire Brief Version (PQ-B)81 and the subscales assessing motivational systems from a modified Behavioral Inhibition & Behavioral Activation Scales. See Supplementary Table 20 for details of psychopathology-related subscales.

Assessments of clinical diagnosis and status transitions
We used parent-reported Kiddie Schedule for Affective Disorders and Schizophrenia for DSM-5 (KSADS-5)80 to measure the current categorical psychiatric diagnoses at baseline and 2-year follow-up. Psychiatric disorders (Depressive Disorder [MDD], Psychotic Disorder [PSD], Bipolar Disorder [BPD], Attention-deficit/hyperactivity Disorder [ADHD], Substance Use Disorder [SUD], Alcohol Use Disorder [AUD], Obsessive-compulsive Disorder [OCD], Social Anxiety Disorder [SAD], Generalized Anxiety Disorder [GAD], Separation Anxiety Disorder [SED], Eating Disorder [ED], Conduct Disorder [CD], Posttraumatic Stress Disorder [PTSD], Oppositional Defiant Disorder [ODD], Specific Phobia [SP], Panic Disorder [PAD], Agoraphobia [AGO], Disruptive mood dysregulation disorder [DMDD], Enuresis and Encopresis Disorder [EED], and Tic Disorder [TD]) were used for the analyses. For each specific disorder, the score would be labeled as “0” for “absence of diagnosis” and as “1” for “definitive diagnosis”.

Diffusion MRI preprocessing
The dMRI data were reconstructed using generalized q-sampling imaging82 with a diffusion sampling length ratio of 1.25. A deterministic fiber tracking algorithm83 was used with augmented tracking strategies84 to improve reproducibility. We utilized default parameters for fiber tracking38. Topology-informed pruning85 was applied to tractography with 2 iteration(s) to remove false connections. The above preprocessing was performed using DSI studio (https://dsi-studio.labsolver.org38).

Tract profile quantification and data harmonization
A total of 54 tracts were automatically extracted using DSI studio. These tracts included 6 pairs of bilateral dorsal association tracts (left and right Arcuate Fasciculus [AF], Frontal Aslant Tract [FAT], Parietal Aslant Tract [PAT], Superior Longitudinal Fasciculus 1 [SLF1], Superior Longitudinal Fasciculus 2 [SLF2], Superior Longitudinal Fasciculus 3 [SLF3] tracts), 4 pairs of bilateral ventral association tracts (left and right Inferior Fronto Occipital Fasciculus [IFOF], Inferior Longitudinal Fasciculus [ILF], Uncinate Fasciculus [UF] and Middle Longitudinal Fasciculus [MdLF] tracts), 6 pairs of bilateral limbic tracts (left and right Cingulum Frontal Parahippocampal [C_FPH], Cingulum Frontal Parietal [C_FP], Cingulum Parahippocampal [C_PH], Cingulum Parahippocampal Parietal [C_PHP], Cingulum Parolfactory [C_PO] and Fornix [F] tracts), 3 pairs of bilateral sensory-motor tracts (left and right Corticospinal Tract [CST], Optic Radiation [OR] and Vertical Occipital Fasciculus [VOF] tracts), 3 pairs of bilateral thalamic radiation tracts (left and right Thalamic Radiation Anterior [TR_A], Thalamic Radiation Posterior [TR_P] and Thalamic Radiation Superior [TR_S] tracts), 3 pairs bilateral corticostriatal tracts (left and right Corticostriatal Tract Anterior [CS_A], Corticostriatal Tract Posterior [CS_P], and Corticostriatal Tract Superior [CS_S]) and 4 corpus callosum tracts (Corpus Callosum Body [CC_Body], Corpus Callosum Forceps Major [CC_Major], Corpus Callosum Forceps Minor [CC_Minor] and Corpus Callosum Tapetum [CC_Tap]). The 54 tracts are illustrated in Fig. 1A. The GM regions mainly connected by the tracts are indicated in Supplementary Table 1. For each identified tract, 100 equal-length segments (or nodes thereafter) were sampled along the tract from one end to the other (Fig. 1B). We then computed the FA value for each node in all the tracts using a kernel density estimator with the default bandwidth setting. The 100-node FA tract-profile was generated for each tract of each participant. 611 participants in HCP-D dataset (aged 5.58 to 21.92 years; 281 females and 330 males), 978 participants in HBN dataset (aged from 5.58 to 21.90 years, 366 females), and 8688 at baseline, 5883 at 2-year- and 2531 at 4-year-follow-up in ABCD dataset (age-range=8.91-15.70 years) had successful automatic tract extraction for all 54 WM tracts.

In order to eliminate the influence of the differences across acquisition sites, we harmonized the FA tract-profile data using a ComBat algorithm in Matlab (https://github.com/Jfortin1/ComBatHarmonization/tree/master/Matlab)86. In our study, KSADS-5 diagnosis, age and sex were designed as the biological variables to reserve.

Brain age prediction models and brain age gap
Brain age prediction models. An overview of the brain age prediction pipeline is outlined in the Fig. 1C. We used the HCP-D dataset for training and cross-validation of brain age prediction models because it included high-quality dMRI from a moderate cohort of healthy participants (N=611) covering a wide age range (5.58–21.90 years). We then applied these models to predict brain age in the independent ABCD and HBN datasets. The 25 pairs of projection/association tracts (each pair of bilateral tracts were concatenated) and 4 corpus callosum tracts were used to build “tract-specific” models respectively, while all 54 tracts were concatenated to build a “whole-brain” model. Using a 5-fold cross-validation on 80% subjects from HCP-D dataset, a Gaussian Process Regression (GPR) model28,30 (https://github.com/garedaba/brainAges) in Python was trained to predict individual age based on the whole-brain tract-profile or 29 tract-specific profiles respectively. The remaining 20% HCP-D subjects were used to validate the brain age model performance in a 5-fold cross-validation procedure.

The trained brain age prediction models from the HCP-D cohort were then used to predict brain ages of the subjects at baseline and 2-year-follow-up in the ABCD dataset and the subjects in the HBN dataset. Model accuracy was evaluated in the cross-validation dataset and the independent testing dataset using the R (Pearson correlation between chronological age and predicted age) and the RMSE (root mean squared error). To assess whether tract-specific brain-age prediction exceeded chance, we generated an empirical null distribution by randomly permuting chronological ages across participants and re-establishing the prediction model on each permuted dataset. This procedure was repeated 1000 times, yielding a null distribution of R values under the assumption that there is no predictive utility of WM features on chronological age prediction. The p value is determined as the proportion of permutation-based R values derived greater than or equal to the observed R. Values of p<0.05 were considered as the predictive performance significantly exceeded chance. Multiple comparisons across tracts (n=30) were implemented using the Benjamini-Hochberg false-discovery-rate (FDR) procedure.

Brain age gap calculation and brain-age bias correction. For each subject, brain age gap (BAG) was calculated as the difference between predicted brain age and chronological age. Age-bias correction was conducted by the method proposed by Beheshti et al.32. In the training dataset, we performed linear regression between chronological age and BAG, as follows:

BAG = αAgeChronological + β (1)

The derived slope (α) and intercept (β) were applied to the chronological ages of the individuals in the cross-validation and the independent testing datasets. We then subtracted the calculated values from the predicted BAGs to correct for age bias, as follows:

BAGCorreced = BAGUncorreced − (αAgeChronological + β) (2)

To avoid potential prediction accuracy overestimates34, we only reported the prediction performance by uncorrected brain ages. Tract-based BAGs without age-bias correction were used for testing the robustness of our results described in the primary statistical analysis section.

Biological interpretation of BAGs through developmental markers
We further included pubertal stage scales and salivary hormone levels as developmental bench markers. Pubertal maturation of the ABCD subjects was measured by the Pubertal Development Scale (PDS)87. We utilized the parent-reported PDS as it is likely more reliable than the self-reported version for the age range of our study participants and widely used in other ABCD studies88. Because the ABCD 4-year follow-up data were not available in the baseline and 2-year follow-up data. Of 8688 participants at baseline and 5833 participants at 2-year-follow-up, pubertal development data assessed by the Pubertal Development Scale (PDS) were available for 8263 baseline participants and 4532 follow-up participants. The PDS provides puberty category measures ranging from prepubertal to post-pubertal. For male participants, the puberty category score was calculated by summing items related to voice deepening, body hair growth, and facial hair growth. These scores were then categorized as follows89: prepubertal (=3), early pubertal (4 or 5), mid pubertal (≥ 6 and ≤ 8), late puberty (≥ 9 and ≤ 11); post pubertal (=12). For female participants, the puberty category score was derived by summing items on body hair growth and breast development, with categorization as follows: prepubertal (=2), early pubertal (=3 and no menarche), mid pubertal (>3 and no menarche), late puberty (≤7 and menarche); post pubertal (=8 and menarche). See Supplementary Fig. 5 for distributions of sex-specific pubertal stage distributions across two timepoints. Linear Mixed Effects Models (LMMs) were conducted to explore the relationship between pubertal stage and whole-brain BAGs with age-bias correction:

BAG = β0 + β1Hormone + β2Age + β3Sex + β4Timepoint + (1|Subject) (3)

Salivary hormone concentrations of DHEA, testosterone, and estradiol were assayed by Salimetrics (https://salimetrics.com), with samples collected between 7:00 am and 7:00 pm. Hormone data were obtained from the ABCD variables of hormone_scr_dhea_mean (DHEA), hormone_scr_ert_mean (testosterone), and hormone_scr_hse_mean (estradiol) in table phy_y_sal_horm. Data cleaning and quality control followed the previous published studies90,91: i) participants with mismatched biological sex (reported at saliva collection versus biological sex) were excluded; ii) hormone levels outside biologically plausible reference ranges (DHEA: 5~1000 pg/ml; Testosterone: 5~500 pg/ml; Estradiol: 0~1500 pg/ml) were removed; iii) factors of age, sex, visit timepoint, whether have caffeine intake and physical activity in the past 12 hours, saliva collection times since midnight (minutes), saliva collection duration (minutes), and time from collection to freezer storage (minutes) were included as the confounding effects in all analyses. After QC, hormone assessments were available for 10,302 participants for DHEA (N Baseline = 8266, N 2-y-follow-up = 2036), 10,684 participants for testosterone (N Baseline = 8323, N 2-y-follow-up = 2361), and 6413 participants for estradiol (N Baseline = 3944, N 2-y-follow-up = 2469). Statistical analyses were performed using LMMs:

BAG = β0 + β1Hormone + β2Age + β3Sex + β4Timepoint + β5CaffineIntake + β6PhysicalActivity + β7CollectionTime + β8CollectionDuration + β9FreezeTime + (1|Subject) (4)

Statistical analysis for clinical applications
Associations between tract BAGs and behavioral assessments. To explore the multivariate associations between the tract-based BAGs with age-bias correction and the multidomain behavioral assessments, we performed sparse canonical correlation analysis (sCCA) between Z-scored brain and behavioral measurements of the ABCD baseline datasets (https://github.com/cedricx/sCCA/tree/master/sCCA/code/final)92. Prior to sCCA analysis, we regressed age and sex out of both the brain and behavioral measures. sCCA identifies multiple latent variables that show maximal correlations between the brain and behavior datasets, with regularization to handle multicollinearity issues and achieves sparsity.

argmax u:v uTXTYv, subject to ||u||2 2 = 1, ||v||2 2 = 1, ||u||1 = C1 and ||v||2 = C2 (5)

Here, X and Y are the multi-dimensional tract-based BAG and behavioral datasets, respectively. In addition, u and v are the coefficients used to project the datasets to latent variables. The regularization parameters include the sparsity of u and v, which were determined by the following grid-search and nested cross-validation procedure within the training dataset. Both parameters were incremented by 0.1 from 0 to 1, resulting 100 combinations of different C1 and C2 values. Subsequently, we randomly resampled two-thirds of the subjects for ten times. Through two-thirds of the sample, the coefficients of the sCCA model with C1 and C2 were estimated. The combination of the regularization parameters with the largest canonical correlation coefficient was used for the final model. Furthermore, we performed 1000 times of permutation tests to assess the statistical significance of canonical correlation modes, with shuffling the behavioral set order during each iteration. To determine the significance of each behavioral measure within each mode, we used a bootstrap resampling procedure, repeated 1000 times. Behavioral and brain measures for each sCCA mode were deemed significant if their 95% confidence intervals did not include zero.

Additionally, to assess the specificity of the sCCA modes, we performed paired t-tests to compare the behavioral loading distributions derived from the bootstrap tests. A behavioral measure was considered specific to an sCCA mode if its effect size (Cohen’s d value) was more than 0.5.

In addition, a post-hoc analysis investigating the relationships between tract BAGs and each cognitive or clinical score was conducted by generalized linear model (GLM) with the covariates of sex and age as follows:

BAG = β0 + β1Behavior + β2Age + β3Sex (6)

All tested associations (30×51 tests) were corrected for multiple comparisons by using the FDR method. Corrected p and t values are shown in Supplementary Figs. 7–8.

Associations between tract-BAG measures and clinical diagnoses at baseline. To assess whether inter-individual differences in the tract-BAG measures capture clinically relevant differences in psychiatric diagnoses, we characterized associations of these BAGs with the cumulative number of KSADS-5 diagnoses in the ABCD dataset. We categorized the participants into three subgroups based on their cumulative number of their KSADS-5 diagnoses at baseline. Participants in the first subgroup were with no KSADS-5 diagnosis (N=6466), in the second subgroup with a single KSADS-5 diagnosis (N=1337), and in the third subgroup with at least two KSADS-5 diagnosis (N=791). Tract-BAG measures (including sCCA variate scores and individual tract BAGs) versus cumulative number of KSADS-5 diagnoses were assessed using general linear models (GLMs) controlled for age and sex, as follows:

BAGBaseline = β0 + β1KSADSBaseline + β2Age + β3Sex (7)

where categorical variable KSADSBaseline is the cumulative number of KSADS-5 diagnoses (0, 1, or ≥2) at baseline. We then performed Tukey tests for post-hoc comparisons. The GLM analysis was conducted using ‘glm’ in R.

We also conducted a validation analysis using GLMs to evaluate the associations between tract-BAG measures and transdiagnostic status in participants with available 2-year follow-up data. The participants, who had both brain and clinical measures available at the 2-year follow-up, were categorized into three groups: those without a KSADS-5 diagnosis (N=4131), those with one KSADS-5 diagnosis (N=741), and those with at least two KSADS-5 diagnoses (N=336).

Associations between tract-BAG measures and clinical diagnoses at 2-year follow-up. To explore the predictive potential of tract-BAG measures for clinical diagnoses of psychiatric disorders at 2-year follow-up, we used GLMs to assess associations between the tract-BAG measures at baseline and cumulative number of KSADS-5 diagnoses at 2-year-follow-up, as follows:

BAGBaseline = β0 + β1KSADSfollowup + β2Age + β3Sex (8)

where variable KSADSfollowup is the cumulative number of KSADS-5 diagnoses at 2-year-follow-up. The participants were categorized into three subgroups according: those without KSADS-5 diagnosis (N=6288), those with a single KSADS-5 diagnosis (N=1116) and those with at least two KSADS-5 diagnoses (N=503).

Associations between tract-BAG measures at baseline and status transitions in KSADS-5 diagnoses from baseline to 2-year follow-up. We investigated whether tract-BAG measures associated with diagnostic status transitions. Based on whether the participants had psychiatric diagnoses at baseline and/or 2-year follow-up, they were categorized into 4 subgroups: Healthy-persistent (0 psychiatric diagnosis at both baseline and 2-year follow-up, NHH = 5,236), Disorder-remitted (≥1 psychiatric diagnosis at baseline and 0 psychiatric diagnosis at 2-year follow-up, NPH = 995), Disorder-new-onset (0 psychiatric diagnosis at baseline and ≥1 psychiatric diagnosis at 2-year follow-up, NHP = 751) and Disorder-persistent (≥1 psychiatric diagnosis at both baseline and 2-year follow-up, NPP = 848). GLMs were conducted to assess the association between tract BAGs versus the diagnostic transition status of psychiatric diagnosis, as follows:

BAGBaseline = β0 + β1KSADS Transition + β2Age + β3Sex (9)

Associations between tract-BAG measures at baseline and cognitive performance at 2-year or 3-year follow-up. To evaluate longitudinal cognitive outcomes, we examined the associations between baseline tract-based BAG measures and follow-up cognitive performance not included in the prior sCCA analysis. Three general cognitive assessments from follow-up visits were used: Emotional Stroop Task (3-Year Follow-Up)93: This task assessed processing speed/attention, inhibitory control, and emotional information processing. We used the overall accuracy score as the measure. Self-Reported Academic Grade (SAG; 2-Year Follow-Up)39: School performance was evaluated using self-reported grades, ranging from 1 (A+) to 12 (failing), with lower scores indicating better academic performance. Stanford Mental Arithmetic Response Time Evaluation (SMARTE)39: This measure of math ability included dot enumeration, single-digit, and mixed-digit arithmetic problems. The total number of correct responses was used as the performance measure.

To assess the relationship between baseline tract-based BAGs and each follow-up cognitive measure, we employed generalized additive models (GAMs), which are widely used in large-scale datasets94. Age and sex were included as covariates in all models. The unique contribution of each tract-based BAG measure was quantified by computing the change in model variance (adjusted ΔR², expressed as a percentage) when the BAG predictor was added to a null model containing covariates only. Both effect sizes and two-sided p-values with FDR correction were reported. To assess whether tract-specific BAGs account for more variance in follow-up cognitive performance than raw FA values, we conducted the same statistical analyses using tract-wise FA values, averaged from the harmonized tract-wise FA values.

Sensitivity analysis on effects of pubertal maturation. Pubertal maturation of the ABCD subjects was measured by the Pubertal Development Scale (PDS)87. We utilized the parent-reported PDS as it is likely more reliable than the self-reported version for the age range of our study participants and widely used in other ABCD studies88. Of 8,681 baseline participants, 8,263 had pubertal development data assessed by the Pubertal Development Scale (PDS), which included puberty category measures ranging from prepubertal to post-pubertal. For male participants, the puberty category score was calculated by summing items related to voice deepening, body hair growth, and facial hair growth. These scores were then categorized as follows89: prepubertal (=3), early pubertal (4 or 5), mid pubertal (≥ 6 and ≤ 8), late puberty (≥ 9 and ≤ 11); post pubertal (=12). For female participants, the puberty category score was derived by summing items on body hair growth and breast development, with categorization as follows: prepubertal (=2), early pubertal (=3 and no menarche), mid pubertal (>3 and no menarche), late puberty (≤7 and menarche); post pubertal (=8 and menarche). To assess the impact of pubertal maturation on our results, we included the puberty category as an additional covariate in the GLM analysis described above.

Functional decoding based on spatial association between brain regions connected by WM tracts and task-related brain activation
To validate the functional significance of the tract-based BAGs of the sCCA modes, we assessed spatial correlation between brain regions connected by these tracts and specific task-related brain activation patterns. For task-related brain activation maps, we used data from the latest Neurosynth database (version 7)36, a meta-analytic tool that aggregates findings from 14,371 published fMRI studies. We selected 31 topics (See Supplementary Table 6) related to cognitive-control, language, emotion, or psychiatric conditions from a set of 100 topics generated using Latent Dirichlet Allocation (LDA). These topic maps were generated through reverse inference, denoting the probability of a topic being relevant given a particular brain activation. For the white matter tracts, we utilized the Population-Probability Atlas38, which provides voxel probability maps for 25 association and projection WM tracts included in our sCCA study. To refine this atlas, we excluded voxels with a probability lower than 0.5 and those with less than 50% probability according to the GM probability map (MNI-maxprob-thr50-2mm.nii.gz in FSL). We calculated the average probability of topic activation levels within the generated tract regions of interest (ROIs). To evaluate their spatial correlation, we conducted Spearman correlation analyses between the loadings of two sCCA modes and the z-scores from the brain activation meta-analytic maps for each topic. Statistical significance was evaluated using a spin-based spatial permutation test (1000 iterations; https://github.com/murraylab/brainsmash/tree/master)95, which preserves the spatial autocorrelation structure of GM maps. During each iteration, the voxel-wise task activation map was spatially rotated and the brain activation levels of the GM regions connected by each tract were recomputed. The empirical p-value was defined as the proportion of permuted Spearman correlation coefficients that exceeded the observed correlation and corrected by the FDR method.

Mitochondrial mapping analyses
We based our analysis on whole-brain maps of mitochondrial properties developed by Mosharov et al.27. Six mitochondrial maps (CI, CII, CIV, MitoD, TRC and MRC) were included in our study. For each tract, we averaged the mitochondrial values within the voxels whose probability higher than 50% in Population-Probability Atlas38 in MNI152 volumetric space. The tract-wise mitochondrial profiles were shown in Fig. 4A. Spatial correlation between sCCA loading pattern and mitochondrial profiles were performed by Spearman correlation. Statistical significance was evaluated using a spin-based spatial permutation test (10,000 iterations; https://github.com/frantisekvasa/rotate_parcellation)96. We then estimated the number of null correlation coefficients that were significantly higher than expected by chance as p-value and corrected by the FDR method.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "General Assessment\nThis manuscript presents a rigorous and ambitious analysis of white matter brain-age modelling in youth, using three large-scale neuroimaging cohorts (ABCD, HCP-D, and HBN). The authors build tract-specific diffusion-based brain age prediction models and link resulting brain age gaps (BAGs) to both cognitive performance and transdiagnostic psychopathology, with supporting analyses. The study is well-written and addresses important questions in developmental neuroscience and psychiatry using state-of-the-art computational approaches. However, several conceptual and methodological issues, particularly regarding the interpretation of brain age gap (BAG) in youth, the implications of age-bias correction, and the handling of scanner/site effects, require clarification. These issues affect how the findings can be interpreted and generalised, and warrant revision before the manuscript is suitable for publication.\nMajor Comments\n1. Interpretation and Conceptual Clarity of Brain Age Gap (BAG)\nThe manuscript treats BAG as a proxy for \"maturation\" or \"delay,\" yet this assumes a linear and biologically grounded trajectory of white matter development. In reality, white matter volume and FA follow curvilinear trajectories across development, typically increasing between ages 9–14 before plateauing or decreasing later. Interpreting positive BAGs as \"more mature\" and negative ones as \"delayed\" is conceptually problematic, particularly when such deviations may reflect normal individual variation or artefacts of modeling rather than meaningful maturational differences. The current framing risks conflating statistical deviation with biological interpretation.\n2. Handling and Interpretation of Age-Bias Correction\nWhile the authors aim for transparency by including both corrected and uncorrected BAGs, the use of both in parallel without clear analytic separation introduces interpretive ambiguity. The combination of age-bias correction and inclusion of age as a covariate may result in compounded adjustments that are difficult to interpret, and the implications of this approach are not clearly addressed.\n3. Over-interpretation of Predictive Findings\nThe manuscript repeatedly uses predictive language (e.g., BAGs “predicted” later outcomes) based on associations in observational data. While some associations are longitudinal, the analyses do not constitute true prediction or causal inference. This wording overstates the findings and could mislead readers about the strength of evidence.\n4. Cross-Dataset Generalisability and Domain Shift\nThe authors train models in one dataset and apply them to others without detailed evaluation of generalisability. Domain shifts between cohorts (e.g., due to scanner differences or population composition) may distort predictions. Without clearly presenting out-of-sample performance or calibration by cohort, it is difficult to assess the stability and transportability of the models.\n5. Harmonisation\nDiffusion metrics like FA are sensitive to scanner and acquisition parameters, yet the manuscript does not describe any harmonisation approach (e.g. longCombat). This raises concerns about site-related confounding and biases in BAG estimates across cohorts.\n6. Interpretation of BAG Directionality\nWhile the authors include sensitivity analyses controlling for pubertal status and SES, the biological interpretation of BAG directionality remains speculative. The assumption that smaller BAGs reflect developmental “delay” or risk is not validated through direct associations with developmental benchmarks such as age, pubertal stage, or sex-specific trajectories. It remains unclear how BAG varies across these axes or aligns with known nonlinear patterns of white matter maturation.\n7. Functional and Mitochondrial Decoding Over-interpretation\nThe use of a single adult brain for mitochondrial mapping limits generalisability, particularly in a paediatric sample. Despite acknowledging this limitation in the discussion, the main results treat the interpretation too strongly given the mismatch in age and sample source.\n8. Terminology Around “Maturation”\nThe manuscript frequently equates positive BAG with “advanced maturation” and negative BAG with “delay.” This language presumes a normative linear developmental trajectory and may not reflect true neurobiological differences. It also risks misinterpretation by clinicians or translational researchers.\n9. Literature overview and “filling gaps”\nThere is a lack of engagement with earlier dMRI brain age literature. The study would benefit from highlighting these, especially studies in in youth that may have investigated cognition, psychopathology, similar.\nMinor Comments\n1. RMSE and Pearson’s R are not reported, though they are standard metrics for model evaluation. Their absence makes it harder to contextualise the predictive performance.\n2. The sentence \"...are poorly understand\" should be “poorly understood”.\n3. Some references have gaps between the word before it (e.g. reference 1) while others do not.\n4. The label “Association Development mode” used to describe sCCA components implies a specificity that may not be warranted by the data.\n5. The manuscript briefly mentions differences between GM- and WM-derived BAGs but does not integrate this contrast meaningfully into the main interpretation.\n6. Figures do not consistently indicate whether the BAG values shown are corrected or uncorrected.\n7. The choice of Gaussian Process Regression (GPR) is not justified against alternative modeling approaches.", "reviewer_2": "The manuscript by Wang et al assesses whether adolescents’ white matter brain-age gap (BAG) relates to cognition or psychopathology. Here, BAG is the difference in true ages and ages predicted by a machine learning model trained on white-matter tracts’ fractional anisotropy. The model is trained in HCPD and applied to primarily ABCD (with further validation in HBN). The authors also characterize the covariance structure of tract-wise BAG and a variety of cognitive and behavioral traits as assessed in the ABCD sample using sparce canonical correlation analysis (sCCA), describing two primary modes of covariance - Association and Subcortical/limbic development modes. Finally, they test whether the spatial patterning of these modes correlated with post-mortem patterns of metabolic markers and if individual BAGs across these modes, across all tracts, and/or within specific brain tracts are associated with cognitive metrics or diagnostic status. Overall, the paper is expansive and intended to address the very relevant issue of how non-typical white matter development in various tracts may be associated with cognitive function and psychopathology. Strengths include the use of 3 independent datasets and the inclusion of sensitivity analyses for pubertal stage and the effects of age-correction. However there are several major areas of concern relating to clarity, possible circularity, and other methodological issues. These concerns significantly limit the potential impact of the paper especially relative to the many other BAG papers published in the last several years with overlapping samples and research questions. We hope the following specific comments are helpful.\n1. It is not adequately justified why BAG model is fitted in HCPD (smaller sample, bigger age range) and then tested in ABCD. This is especially important given that the out of sample r2 of the age predictions in ABCD is quite low (0.185) raising significant concerns about the interpretability of the downstream BAG analyses.\n2. There is concern that several of the analyses presented are redundant or circular. For instance, the two sCCA modes were defined based on BAG’s covariance with cognitive and behavioral measures. This makes their subsequent tests of sCCA’s spatial covariance with neurosynth terms (Figure 3) and sCCA variants’ associations with KSADS and cognitive variables (which are almost certainly highly covariant with the cognitive and behavioral variables used to define the modes) problematic.\n3. Similarly, repeating associations between cognitive/clinical markers and BAG at the level of sCCA mode, whole-brain, tract, and network are presented without clarity as to the interdependence of these tests. Greater justification, clearer interpretation, or clarity as to which analyses are considered supplementary would likely be helpful.\n4. It is not clear why the BAG tends to be the outcome variable in GLMs when the authors are interested in how much BAG contributes to current/future psychiatric/cognitive symptoms. Why aren’t the models set up so that behavior/cognition are the outcome variables and BAGs are the predictor variables (while controlling for demographic covariates)?", "reviewer_3": "Overview: This study developed a model of brain age using diffusion-weighted imaging data from the HCP development dataset and validated it in the ABCD and Healthy Brain Network (HBN) cohorts with modest effect sizes for their validation. To determine clinical relevance, they identify associations between derived brain age estimates and cognition and clinical measures in ABCD at baseline and at 2-year follow-up. Finally, they use postmortem data to link finding to mitochondrial WM profiles. This is a lot of work and the overall result is a relatively cohesive characterization of how individual differences in brain-age derived estimates of WM may link to clinical and cognitive features during preadolescence. However, there are some methodological and ‘big picture’ concerns, as described below:\nBig picture: In recent years a number of studies have sought to build predictive models of chronological age using structural MRI data as input. Larger gaps between brain age and actual age, i.e., the ‘brain age gap’ (BAG) are often interpreted as indicative of poorer brain health or to reflect developmental ‘disruptions’. However, many different factors may influence brain age predictions – for example, a single dose of ibuprofen can reduce brain age estimates generated from grey matter morphometry data by 1 year https://pmc.ncbi.nlm.nih.gov/articles/PMC6510235/ - making interpretation of findings difficult. Nonetheless, here the authors expand on the brain age literature by estimating this using diffusion tensor imaging data, specifically fractional anisotropy (FA), in a large sample of youth from the ABCD study. While the concept of studying brain age within the context of white matter maturation in a developmental cohort is thus somewhat novel, it is highly likely that brain age estimates derived from white matter data are also influenced by a large number of factors, including, but not limited to, those that are well-recognized confounds in diffusion-weighted imaging and in the use of simplistic tensor derived measures such as FA https://pubmed.ncbi.nlm.nih.gov/22846632/. Thus, one primary concern with this study is the concept of brain age itself, including as indexed using FA. For this reason, use of terms such as ‘delayed maturation’ (as is done throughout the discussion) to summarize findings should be used with caution.\nAdditional comments:\nAs far as I can tell, brain age predictions in ABCD were applied to both baseline and year 2 follow-up data in a single model (only a single r2 value is provided). However, given the sample size of each time point, there must have been overlap between the baseline and year two samples. Brain age estimates from the same interval calculated two years apart are presumably not independent. How was this addressed?\nThe model’s performance in ABCD is modest, particularly when done without age-bias correction. This is glossed over and presented in a supplemental table, with the in-text summary simply stating that all tracts were significant, however procedures for significance testing (e.g., permutation testing, multiple comparisons correction?) are not included in the methods (presumably this should be included around lines 813-814).\nTo test clinical relevance, they identify associations between derived brain age estimates and cognition and clinical measures using sparse CCA in ABCD at baseline. They identify two components/modes – an Association mode and a Subcortical/Limbic mode. Both map onto various cognitive and clinical measures at baselines, as indicated by the CCA. The authors then test whether these modes predict academic performance, emotional Stroop performance and math ability at 2 and 3 year follow-up. However, it is unclear if this is driven my associations between the baseline behavioral variables, by the brain age estimates, or both. Finally, the authors use SVR to predict academic performance, emotional Stroop performance and math ability at 2 and 3 year follow-up specifically using the tract-based BAGs alone (i.e., not the CCA modes) and find some modest predictive power. Overall, this collection of analyses is somewhat unsatisfying and it is not clear if, for example, simple individual participant FA values for each tract, rather than BAGs, would do equally well (or better) in predicting these measures. The same concern applies to the prediction of KSADS diagnoses (i.e., are BAGs better predictors than ‘raw’ FA values)?\nMinor: please define all acronyms in figure legends (not just in Table S1)", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you to the authors for their effort in addressing my comments. I have no further concerns for the manuscript and wish the authors the best of luck going forward.", "reviewer_2": "This is a substantive revision with detailed responses to each of our points. The responses to points 1,3,4, and 5 are very good and we no longer have concerns about these points. While the paper is thus significantly improved, the response to point 2 does not fully address our concerns about the methodological approach, in that it remains unclear whether the experiments justify some of the inferences made by the authors. This concern limits the impact of the paper and is not something that we believe can be addressed on further review. We also agree with Reviewer 3 that the manuscript is only somewhat novel, and it is hard to differentiate its added value from other brain age gap papers.", "reviewer_3": "The authors have been responsive to the reviewer comments and the submission now has greater clarity.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to Reviewers:\n______________________________________________________________________________\nContent\nResponse to all Reviewers Page 1\nResponse to Reviewer #1 Page 3\nResponse to Reviewer #2 Page 25\nResponse to Reviewer #3 Page 35\n______________________________________________________________________________\nResponse to all Reviewers:\nWe thank all the reviewers for their constructive and thoughtful feedback, which we have\nintegrated into this revised manuscript. We believe that the revisions made based on your feedback\nhave greatly strengthened the manuscript and improved its clarity and readability. Below we\nsummarize the major revisions:\n• In response to reviewer feedback, we have reframed how we describe brain age gap (BAG) to\nemphasize that tract-based BAG reflects statistical deviation from age-normative white-matter\ntrajectories, rather than a direct biological measure of accelerated or delayed maturation. We\nnow provide additional validation analyses demonstrating that higher whole-brain BAG is\nassociated with more advanced pubertal stages and higher pubertal hormone levels, supporting\nits neurodevelopmental relevance.\n• To address concerns regarding overlapping samples and age-range effects on prediction, we\nincorporated newly and partially released ABCD 4-year follow-up dMRI data (Annual Curated\nRelease 5.1). Using non-overlapping samples and matched sample sizes, we observed\nconsistent and improved predictive performance, particularly in testing datasets with wider age\nranges.\n• In addition to the full-behavior-derived model, we conducted supplementary analyses\nrerunning our brain-behavior sparse canonical correlation analyses (sCCA) using only\ncognitive or only psychopathological measures. Results from these supplemental analyses\nwere similar to our original findings. Across all three cases (cognition-, psychopathology-, and\nfull-behavior-derived), we observed highly consistent tract-/behavioral- loading patterns and\ndownstream associations with cognitive/psychopathological measures, highlighting the\nrobustness of the identified BAG-behavior relationships.\n• We added supplementary analyses comparing the explained variance of tract-based BAG with\ntract-level fractional anisotropy (FA) values for cognitive/clinical measures. Although both\ncontained meaningful variance, BAG explained additional variance in cognitive and clinical\nmeasures beyond FA in multiple tracts, suggesting that BAG captures complementary\ndevelopmental information not fully represented by raw FA.\nBelow we provide detailed responses to each reviewer comment. We emphasize all reviewer\ncomments with gray-colored text and provide individual responses below in black font. For\n1\nconvenience, we incorporated resulting changes to the manuscript directly in the responses in\nquotation marks. Major changes are also highlighted with light green color in our revised\nmanuscript.\nThis manuscript presents a rigorous and ambitious analysis of white matter brain-age modelling in\nyouth, using three large-scale neuroimaging cohorts (ABCD, HCP-D, and HBN). The authors\nbuild tract-specific diffusion-based brain age prediction models and link resulting brain age gaps\n(BAGs) to both cognitive performance and transdiagnostic psychopathology, with supporting\nanalyses. The study is well-written and addresses important questions in developmental\nneuroscience and psychiatry using state-of-the-art computational approaches. However, several\nconceptual and methodological issues, particularly regarding the interpretation of brain age gap\n(BAG) in youth, the implications of age-bias correction, and the handling of scanner/site effects,\nrequire clarification. These issues affect how the findings can be interpreted and generalised, and\nwarrant revision before the manuscript is suitable for publication.\nWe appreciate the reviewer for the positive evaluation of our work and for the thoughtful and\nconstructive feedback. The reviewer’s comments (see below) are very helpful that lead to\nclarification of our manuscript, particularly in conceptual and methodological issues. We also\nperformed additional analyses based on suggestions of the reviewer to strengthen the manuscript\nwith more comprehensive information about the study. We believe that our manuscript has been\nsignificantly improved with these modifications.\n• We have revised the interpretation of brain age gap (BAG) to clarify that BAG reflects the\ndegree of deviation of an individual’s tract-based microstructural profile from age-normative\ntrajectory, rather than a biological measure of accelerated or delayed maturation.\n• To validate the neurodevelopmental underpinnings of the BAG in youth, we now add the\nassociation analysis between whole-brain BAG and developmental benchmarks (pubertal\nstages and hormone levels). We observed that significantly positive correlations between\nwhole-brain BAGs and more advanced pubertal stages, as well as higher pubertal hormone\nlevels.\n• We now make more clarifications for the scanner/site effects of our utilized diffusion MRI\n(dMRI) features and data harmonization. We also make more clear clarifications of the results\nwith or without age-bias correction.\nReviewer #1, Comment 1:\n1. Interpretation and Conceptual Clarity of Brain Age Gap (BAG)\nThe manuscript treats BAG as a proxy for \"maturation\" or \"delay,\" yet this assumes a linear and\nbiologically grounded trajectory of white matter development. In reality, white matter volume and\nFA follow curvilinear trajectories across development, typically increasing between ages 9–14\nbefore plateauing or decreasing later. Interpreting positive BAGs as \"more mature\" and negative\nones as \"delayed\" is conceptually problematic, particularly when such deviations may reflect\nnormal individual variation or artefacts of modeling rather than meaningful maturational\ndifferences. The current framing risks conflating statistical deviation with biological interpretation.\nResponse:\nmaturation” might risk conflating statistical deviation with biological interpretation, particularly\ngiven the potential influence of modeling artefacts. To address this concern, we have revised the\nmanuscript to clarify that tract-based BAG reflects the degree of deviation of an individual’s tract-\nbased microstructural profile from age-normative trajectory, rather than a definitive measure of\naccelerated or delayed maturation. We have also added supplementary association analyses linking\nBAGs to developmental benchmarks (pubertal stages and hormone levels) to validate its\nunderpinning neurodevelopmental meanings (See our responses to Reviewer #1, Comment 6 for\nmore details).\nWe updated the manuscript accordingly:\nTitle:\n“Deviation in Development of Dorsal Association Tracts during Preadolescence Links to\nConcurrent and Future Cognitive Performance and Transdiagnostic Psychopathology”\nAbstract:\nPage 2, line 41-42:\n“However, it remains unclear whether deviations from normal WM development during\nthis period contribute to psychopathology.”\nPage 2, line 43-44:\n“We found that tract-specific deviations in WM development of association and\nlimbic/subcortical systems …”\nPage 2, line 46-47:\n“Importantly, delayed brain-age especially in dorsal association tracts predicted\npsychiatric disorders across diagnoses and disorder onset over a 2-year follow-up.”\nPage 2, line 48-49:\n“… this study provides a valuable framework for tracking individualized brain\ndevelopment and understanding the neurobiological underpinnings of cognition and\ntransdiagnostic psychopathology”\nResults:\nPage 8, line 176-182:\n“For each participant, the WM tract predicted age is referred to as brain age, and the\ndifference between the brain age and chronological age is defined as tract-based BAG.\nThe tract-based BAG quantifies the extent to which an individual’s tract-specific biological\nage relative to other same-aged peers based on their tract-specific FA profiles. A negative\nBAG indicates that an individual’s predicted brain age is less than their chronological age\n(i.e., individual deviation under model-derived developmental trajectory), while a positive\nage (i.e., individual deviation over model-derived developmental trajectory).”\nPage 16, line 332:\n“Baseline tract-based BAGs are prospectively linked to cognitive performance two to\nthree years later”\nPage 16, line 345-346:\n“Among the three cognitive performances, more positive tract-based BAGs were\nassociated with better cognitive performance assessed 2 to 3 years later.”\nPage 17, line 378-379:\n“More negative tract-based BAGs at baseline concurrently associated with greater\nnumber of psychiatric (KSADS-5) diagnoses”\nPage 20, line 424-425:\n“Deviation in WM developmental trajectory assessed via BAGs at baseline prospectively\npredicted cumulative number of psychiatric (KSADS-5) diagnoses at 2-year follow-up”\nPage 20, line 447-448:\n“Deviation in WM developmental trajectory assessed via BAGs at baseline prospectively\npredicted psychiatric diagnosis status transitions from baseline to 2-year follow-up”\nDiscussion:\nPage 24, line 539-547:\n“Conceptually, BAG represents the statistical deviation from the age-normative trajectory.\nHowever, the calculation of BAG could be impacted by both physiological factors (e.g.,\nneurodevelopmental deviation) and technical noise (e.g. MRI data quality). Therefore,\nvalidating that BAG captures neurodevelopmentally meaningful variance is essential. In\nour study, tract-based BAG showed significant positive associations with pubertal\ndevelopment, including both pubertal stage and hormone levels. This is in line with\nprevious findings showing that puberty is associated with increased FA in major tracts of\nadolescents45, and that higher FAs in WM regions such as the CC and cingulum are linked\nto elevated gonadal hormone levels in adolescent females46. Together, these results provide\ninitial evidence that tract-based BAGs track with known metrics of developmental\nbenchmarks and are consistent with prior BAG findings derived from GM morphology47.”\nAnd in the Limitations and Future Work section of the Discussion (Page 28-29, line 673-\n680):\n“Second, BAGs may be influenced by multiple factors beyond true neurodevelopmental\nvariation. Previous studies have shown that both technical aspects (e.g. dMRI acquisition\nand preprocessing schemes65) and non-technical factors (e.g. pharmacological\nmanipulation66) can affect BAG estimations. Although we validated the relationships\nbetween tract-based BAG and developmental benchmarks (pubertal stages and hormone\nlevels), tract-based BAGs should nonetheless be interpreted as relative indices of deviation\nfrom normative WM developmental trajectories. Further work leveraging large-scale\nlongitudinal datasets, integrating a broader range of lifestyle and environmental factors,\nand more biologically informed tract-based imaging modalities may help disentangle these\nsources of variability and improve interpretability.”\nReference:\n45. Piekarski DJ, Colich NL, Ho TC. The effects of puberty and sex on adolescent white\nmatter development: A systematic review. Dev Cogn Neurosci 60, 101214 (2023).\n46. Ho TC, Colich NL, Sisk LM, Oskirko K, Jo B, Gotlib IH. Sex differences in the effects\nof gonadal hormones on white matter microstructure development in adolescence. Dev\nCogn Neurosci 42, 100773 (2020).\n47. Whitmore LB, Weston SJ, Mills KL. BrainAGE as a measure of maturation during early\nadolescence. Imaging Neurosci 1, (2023).\n65. Jones DK, Knosche TR, Turner R. White matter integrity, fiber count, and other\nfallacies: the do's and don'ts of diffusion MRI. Neuroimage 73, 239-254 (2013).\n66. Le TT, et al. Effect of Ibuprofen on BrainAGE: A Randomized, Placebo-Controlled,\nDose-Response Exploratory Study. Biol Psychiatry Cogn Neurosci Neuroimaging 3, 836-\n843 (2018).\nReviewer #1, Comment 2:\n2. Handling and Interpretation of Age-Bias Correction\nWhile the authors aim for transparency by including both corrected and uncorrected BAGs, the\nuse of both in parallel without clear analytic separation introduces interpretive ambiguity. The\ncombination of age-bias correction and inclusion of age as a covariate may result in compounded\nadjustments that are difficult to interpret, and the implications of this approach are not clearly\naddressed.\nResponse:\nWe thank the Reviewer for pointing out this confusion and are happy to clarify. We have further\nclarified the results with and without age-bias correction in the revised manuscript accordingly. In\nthis study, we separated the analytic purposes for using corrected and uncorrected BAGs.\nSpecifically, the uncorrected BAGs were only used for validation of brain age models (e.g.,\nevaluation of prediction performance) to avoid overestimating prediction accuracy, whereas the\nage-bias corrected BAGs were used for all primary analyses regarding clinical or behavioral\nassociations, thereby minimizing residual correlations with chronological age.\nTo avoid compounded adjustments, we conducted additional sensitivity analyses using three\nadjustment strategies: i) uncorrected BAGs with chronological age included as a covariate\n(Supplementary Table 14); ii) corrected BAGs without age covariate (Supplementary Table 15);\nand iii) corrected BAGs with age covariate (primary results, Supplementary Table 12). As shown\n6\nin Supplementary Table 12 and Supplementary Table 14-15, the statistical results were highly\nconsistent across these three strategies, as BAGs in ABCD dataset were age-bias-corrected through\na linear model derived from the independent training dataset:\nWe have now further clarified the used of age-bias-corrected and uncorrected BAGs in the Results\nand Supplementary Material sections:\nResults:\nPage 8-9, line 185-190:\n“We used age-bias corrected BAGs for all primary statistical analyses, whereas\nuncorrected BAGs were used for validating brain age model performance. While there is\nongoing debate regarding the optimal method for bias correction in brain age\nmodeling33,34, our key findings remained robust regardless of whether age-bias correction\nwas applied (see sensitivity analyses), as the BAGs were only adjusted by a linear model\nof chronological age derived from an independent dataset, and chronological age was\nadditionally included as a covariate in all subsequent analyses.”\nPage 9, line 205-206:\n“In the sCCA, we included 30 tract-based BAGs (29 tract-specific and a whole-brain tract\nBAGs with age-bias correction) …”\nPage 11, line 249-250:\n“Furthermore, we performed a post-hoc statistical analysis to explore the univariate\nrelationships between the 30 tract-based BAGs (with age-bias correction) and 51\nbehavioral measures.”\nLegend of Figure 5. Page 17:\n“(B) Relationships between tract-based measures (with age-bias correction) and follow-\nup cognitive measures.”\nLegend of Figure 6. Page 19:\n“(B) Associations between baseline tract-based BAGs (with age-bias correction) and the\ncumulative number of KSADS-5 diagnoses at baseline, 2-y-follow-up and 2-y-follow-up\ntransdiagnostic status transitions.”\nPage 21, line 457-458:\n“Furthermore, supplementary post-hoc tract-wise comparisons revealed that the age-bias-\ncorrected BAG of the whole-brain tracts at baseline showed a significant association with\ndiagnostic status transitions”\nPage 22, line 496-499:\n7\n“… we repeated the analyses using two more alternative strategies: (i) uncorrected BAGs\nwith chronological age included as a covariate, and (ii) age-bias-corrected BAGs without\nage as a covariate. The results (Supplementary Table 14-15) remained consistent,\nindicating that the observed associations were not dependent on the bias correction\nprocedure.”\nSupplementary materials:\nSupplementary Table 14-15\nReference:\n33. Gaser C, Kalc P, Cole JH. A perspective on brain-age estimation and its clinical\npromise. Nat Comput Sci 4, 744-751 (2024).\n34. Butler ER, et al. Pitfalls in brain age analyses. Hum Brain Mapp 42, 4092-4101 (2021).\nReviewer #1, Comment 3:\n3. Over-interpretation of Predictive Findings\nThe manuscript repeatedly uses predictive language (e.g., BAGs “predicted” later outcomes) based\non associations in observational data. While some associations are longitudinal, the analyses do\nnot constitute true prediction or causal inference. This wording overstates the findings and could\nmislead readers about the strength of evidence.\nResponse:\nWe agree that the use of predictive terms such as “predict” may unintentionally imply causal\ninference or out-of-sample predicting, which could not be implied by our study. Our analyses are\nprospectively observational and, although they include longitudinal associations (baseline BAGs\nlinked to follow-up cognitive and clinical outcomes), they do not establish causality nor predictive\nmodeling.\nTo address this concern, we have revised the manuscript by using more appropriate terminologies.\nSpecifically:\nResults:\nPage 16, line 332:\n“Baseline tract-based BAGs are prospectively linked to cognitive performance two to\nthree years later”\nPage 16, line 334-335:\n“…, we next investigated whether developmental deviations in WM tracts could reflect\nmeaningful variance in cognitive performance at follow-ups.”\nPage 16, line 345-346:\n“Among the three cognitive performances, more positive tract-based BAGs were\nassociated with better cognitive performance assessed 2 to 3 years later.”\nDiscussion:\nPage 26, line 620-622:\n“Advanced BAG of specific tracts as a potential predictive biomarker of later cognitive\nperformance\nOur findings also highlight the potential of WM maturation in linking to cognitive\nperformance two to three year later…”\nReviewer #1, Comment 4:\n4. Cross-Dataset Generalisability and Domain Shift\nThe authors train models in one dataset and apply them to others without detailed evaluation of\ngeneralisability. Domain shifts between cohorts (e.g., due to scanner differences or population\ncomposition) may distort predictions. Without clearly presenting out-of-sample performance or\ncalibration by cohort, it is difficult to assess the stability and transportability of the models.\nResponse:\nWe quite agree with the reviewer on this comment. Although diffusion tensor imaging (DTI)\nderived metrics show high reproducibility, differences related to scanner, acquisition protocols and\npopulation composition could introduce domain shifts of FA values across sites. Therefore, it is\nimportant to remove the site-specific variations prior to combining different sites. We made the\nfollowing modifications in the revised manuscript: First, we added clarification of domain shifts\nbetween sites and cohorts. Second, we have added the Supplementary Figure 1 to demonstrate the\ndomain shifts of FA values and the site-wise distributions of FA values after removing the site\neffects by data harmonization approach.\nWe further clarified on these aspects in the Results and added the analyses to the Supplementary\nMaterial:\nResults:\nPage 5, line 116-119:\n“Because dMRI metrics are highly site-dependent (Supplementary Figure 1), which can\nhinder cross-site model training and application, we applied data harmonization (see\nSupplementary Figure 1 for harmonization effects) prior to all downstream analyses.”\nSupplementary materials:\nSupplementary Figure 1:\ncohorts and the effects of data harmonization. Ridge density plots depict the distributions of raw\n(left) and harmonized (right) whole-brain averaged FA values across multiple sites in three\ndevelopmental cohorts (HCP-D, HBN, and ABCD). Each row corresponds to a site, with density\nshading reflecting the relative frequency of FA values (darker = higher density). Prior to\nharmonization, noticeable site-related shifts and variability in FA distributions were evident. After\nharmonization, distributions became more consistent across sites. Abbreviations: HCP-D, Human\nConnectome Project in Development); ABCD, Adolescent Brain Cognitive Development; HBN,\nHealthy Brain Network pediatric mental health.\nReviewer #1, Comment 5:\n5. Harmonisation\nDiffusion metrics like FA are sensitive to scanner and acquisition parameters, yet the manuscript\ndoes not describe any harmonisation approach (e.g. longCombat). This raises concerns about site-\nrelated confounding and biases in BAG estimates across cohorts.\nResponse:\nWe apologize for the unclear statement of harmonization procedure. We conducted harmonization\nprocedure to correct site-wise differences for FA values. However, we only described this\nprocedure in Methods section and did not demonstrate the harmonization performance. In the\nrevised manuscript, we have added a description of harmonization procedure in the Results section\nand the site-wise distributions of whole-brain FA values before and after data harmonization. The\nrelevant changes are:\nPage 5, line 116-119:\n“Because dMRI metrics are highly site-dependent (see Supplementary Figure 1 for domain\nshifts of FAs across sites), which can hinder cross-site model training and application, we\napplied data harmonization (see Supplementary Figure 1 for harmonization effects) prior\nto all downstream analyses.”\nSupplementary materials:\nSupplementary Figure 1 (see our response to “Reviewer #1, Comment 5”)\nReviewer #1, Comment 6:\n6. Interpretation of BAG Directionality\nWhile the authors include sensitivity analyses controlling for pubertal status and SES, the\nbiological interpretation of BAG directionality remains speculative. The assumption that smaller\nBAGs reflect developmental “delay” or risk is not validated through direct associations with\ndevelopmental benchmarks such as age, pubertal stage, or sex-specific trajectories. It remains\nunclear how BAG varies across these axes or aligns with known nonlinear patterns of white matter\nmaturation.\nResponse:\nWe thank the reviewer for this excellent suggestion on validating the associations between BAGs\nand developmental benchmarks. Specifically, we added analyses showing the distribution of\nwhole-brain BAG across five pubertal categories. In addition, we also tested the associations\nbetween whole-brain BAG and biologically developmental benchmarks of puberty categories and\nhormone levels of DHEA, Testosterone (ERT) and Estradiol (HSE). As shown in the\nSupplementary Figure 5, more positive whole-brain BAG is associated with more advanced\npubertal stages, as well as higher DHEA, ERT and HSE levels.\nThe manuscript has been updated as follows:\nResults:\nPage 9, line 191-199:\n“Puberty and its accompanying hormonal changes play a critical role in shaping brain\ndevelopment. To validate that BAGs capture biologically meaningful developmental\nprocesses, we examined their associations with both pubertal stage assessments and\nsalivary hormone levels (DHEA, testosterone, and estradiol). Across the first two\ntimepoints, higher whole-brain tract BAGs (with age-bias correction) were significantly\nassociated with more advanced pubertal stage (t = 4.685, 𝑝 < 0.001,\n(10,120) !\"#\nSupplementary Figure 5), after controlling for age, sex, and visit. In addition, whole-brain\ntract BAGs showed significant positive associations with higher levels of DHEA (t =\n(10,630)\n3.558, 𝑝 < 0.001), testosterone (t = 4.705, 𝑝 < 0.001), and estradiol (t =\n!\"# (9,872) !\"# (4,583)\n2.238, 𝑝 = 0.025). These hormone analyses accounted for covariates including age,\n!\"#\n11\nsex, visit, caffeine intake and physical activity in the past 12 hours, collection time since\nmidnight, saliva collection duration, and time from collection to freezer storage.”\nDiscussion:\nPage 24, line 542-547:\n“In our study, tract-based BAG showed significant positive associations with pubertal\ndevelopment, including both pubertal stage and hormone levels. This is in line with\nprevious findings showing that puberty is associated with increased FA in major tracts of\nadolescents45, and that higher FAs in WM regions such as the CC and cingulum are linked\nto elevated gonadal hormone levels in adolescent females46. Together, these results provide\ninitial evidence that tract-based BAGs track with known metrics of developmental\nbenchmarks and are consistent with prior BAG findings derived from GM morphology47”\nMethods:\nPage 35-36, line 874-907:\nBiological interpretation of BAGs through developmental bench markers\nWe further included pubertal stage scales and salivary hormone levels as developmental\nbench markers. Pubertal maturation of the ABCD subjects was measured by the Pubertal\nDevelopment Scale (PDS)88. We utilized the parent-reported PDS as it is likely more\nreliable than the self-reported version for the age range of our study participants and\nwidely used in other ABCD studies89. Because the ABCD 4-year follow-up data are only\npartially released, analyses in this section focused on the baseline and 2-year follow-up\ndata. Of 8,688 participants at baseline and 5,833 participants at 2-year follow-up, pubertal\ndevelopment data assessed by the Pubertal Development Scale (PDS) were available for\n8,263 baseline participants and 4,532 follow-up participants. The PDS provides puberty\ncategory measures ranging from prepubertal to post-pubertal. For male participants, the\npuberty category score was calculated by summing items related to voice deepening, body\nhair growth, and facial hair growth. These scores were then categorized as follows90:\nprepubertal (= 3), early pubertal (4 or 5), mid pubertal (≥ 6 and ≤ 8), late puberty (≥ 9\nand ≤ 11); postpubertal (= 12). For female participants, the puberty category score was\nderived by summing items on body hair growth and breast development, with\ncategorization as follows: prepubertal (= 2), early pubertal (=3 and no menarche), mid\npubertal (> 3 and no menarche), late puberty (≤ 7 and menarche); postpubertal (= 8 and\nmenarche). See Supplementary Figure 5 for distributions of sex-specific pubertal stage\ndistributions across two timepoints. Linear Mixed Effects Models (LMMs) were conducted\nto explore the relationship between pubertal stage and whole-brain BAGs with age-bias\ncorrection:\n𝐵𝐴𝐺 = 𝛽 + 𝛽 𝐻𝑜𝑟𝑚𝑜𝑛𝑒+𝛽 𝐴𝑔𝑒+ 𝛽 𝑆𝑒𝑥 + 𝛽 𝑇𝑖𝑚𝑒𝑝𝑜𝑖𝑛𝑡 + (1|𝑆𝑢𝑏𝑗𝑒𝑐𝑡)\n$ % & ' (\nSalivary hormone concentrations of DHEA, testosterone, and estradiol were assayed by\nSalimetrics (https://salimetrics.com), with samples collected between 7:00 am and 7:00 pm.\nHormone data were obtained from the ABCD variables of hormone_scr_dhea_mean\n(DHEA), hormone_scr_ert_mean (testosterone), and hormone_scr_hse_mean (estradiol)\nin table phy_y_sal_horm. Data cleaning and quality control followed the previous\npublished studies91,92: i) participants with mismatched biological sex (reported at saliva\n12\ncollection versus biological sex) were excluded; ii) hormone levels outside biologically\nplausible reference ranges (DHEA: 5~1000 pg/ml; Testosterone: 5~500 pg/ml; Estradiol:\n0~1500 pg/ml) were removed; iii) factors of age, sex, visit timepoint, whether have caffeine\nintake and physical activity in the past 12 hours, saliva collection time since midnight\n(minutes), saliva collection duration (minutes), and time from collection to freezer storage\n(minutes) were included as the confounding effects in all analyses. After QC, hormonenassessments were available for 10,302 participants for DHEA (𝑁 = 8,266,\n)*+,-./,\n𝑁 = 2,036), 10,684 participants for testosterone (𝑁 = 8,323,\n&01023--34056 )*+,-./,\n𝑁 = 2,361), and 6,413 participants for estradiol (𝑁 = 3,944,\n&01023--34056 )*+,-./,\n𝑁 = 2,469). Statistical analyses were performed using LMMs:\n&01023--34056\n𝐵𝐴𝐺 = 𝛽 + 𝛽 𝐻𝑜𝑟𝑚𝑜𝑛𝑒+𝛽 𝐴𝑔𝑒+ 𝛽 𝑆𝑒𝑥 + 𝛽 𝑇𝑖𝑚𝑒𝑝𝑜𝑖𝑛𝑡+ 𝛽 𝐶𝑎𝑓𝑓𝑖𝑛𝑒𝐼𝑛𝑡𝑎𝑘𝑒\n$ % & ' ( 7\n+ 𝛽 𝑃ℎ𝑦𝑠𝑖𝑐𝑎𝑙𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦+ 𝛽 𝐶𝑜𝑙𝑙𝑒𝑐𝑡𝑖𝑜𝑛𝑇𝑖𝑚𝑒\n8 9\n+ 𝛽 𝐶𝑜𝑙𝑙𝑒𝑐𝑡𝑖𝑜𝑛𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛+ 𝛽 𝐹𝑟𝑒𝑒𝑧𝑒𝑇𝑖𝑚𝑒+(1|𝑆𝑢𝑏𝑗𝑒𝑐𝑡)\n: ;\nReference:\n45. Piekarski DJ, Colich NL, Ho TC. The effects of puberty and sex on adolescent white\nmatter development: A systematic review. Dev Cogn Neurosci 60, 101214 (2023).\n46. Ho TC, Colich NL, Sisk LM, Oskirko K, Jo B, Gotlib IH. Sex differences in the effects\nof gonadal hormones on white matter microstructure development in adolescence. Dev\nCogn Neurosci 42, 100773 (2020).\n47. Whitmore LB, Weston SJ, Mills KL. BrainAGE as a measure of maturation during early\nadolescence. Imaging Neurosci (Camb) 1, (2023).\n88. Petersen AC, Crockett L, Richards M, Boxer A. A self-report measure of pubertal status:\nReliability, validity, and initial norms. Journal of Youth and Adolescence 17, 117-133\n(1988).\n89. Rasmussen AR, et al. Validity of self-assessment of pubertal maturation. Pediatrics 135,\n86-93 (2015).\n90. Kraft D, Alnaes D, Kaufmann T. Domain adapted brain network fusion captures\nvariance related to pubertal brain development and mental health. Nat Commun 14, 6698\n(2023).\n91. Heller C, et al. Hormonal contraceptive intake during adolescence and cortical brain\nmeasures in the ABCD Study. npj Women's Health 3, 53 (2025).\n92. Herting MM, et al. Correspondence Between Perceived Pubertal Development and\nHormone Levels in 9-10 Year-Olds From the Adolescent Brain Cognitive Development\nStudy. Front Endocrinol (Lausanne) 11, 549928 (2020).\nSupplementary materials:\nSupplementary Figure 5:\nBAGs.\n(A) Distributions of pubertal categories (prepubertal, early pubertal, mid pubertal, late pubertal,\nand postpubertal) at baseline and 2-year follow-up visits in females and males. Subject counts\nwithin each category are shown separately by sex and timepoint.\n(B) Distributions of whole-brain brain age gaps (BAGs) across pubertal categories at baseline\n(top) and 2-year follow-up (bottom), stratified by sex. Ridge plots illustrate how BAGs vary across\npubertal stages, with earlier categories (prepubertal/early pubertal) generally showing younger\npredicted brain age relative to chronological age compared to later stages.\nReviewer #1, Comment 7:\n7. Functional and Mitochondrial Decoding Over-interpretation\npaediatric sample. Despite acknowledging this limitation in the discussion, the main results treat\nthe interpretation too strongly given the mismatch in age and sample source.\nResponse:\nWe thank the Reviewer for this thoughtful comment. The interpretation of the functional and\nmitochondrial decoding results should be made with caution, particularly given the mismatch in\nage between the functional activation and mitochondrial maps and the lack of inter-individual\nvariability in these maps. While our study introduces a novel and explainable framework linking\ntract-wise patterns to existing functional and postmortem maps, we acknowledge that these\nfindings require further validation using more matched maps.\nBased on the Reviewer’s suggestion, we have clarified the source of the maps and softened the\ntone in the Discussion to emphasize that these encoding results are exploratory and hypothesis-\ngenerating rather than conclusive. Specifically, we now describe the observed spatial\ncorrespondence between tract-based BAG patterns and mitochondrial maps as a preliminary\nindication of a potential and initial link between metabolically demanding association tracts and\ntheir heightened vulnerability to psychiatric diseases during development. These revisions help\nprevent overstatement and ensure that results are interpreted within the appropriate developmental\nand methodological constraints:\nThe manuscript has been updated accordingly:\nResults:\nPage 14, line 305-306:\n“To explore potential biological mechanisms underlying the two cognition-\npsychopathology profiles, we incorporated the only available set of six high-resolution ex-\nvivo mitochondrial maps derived from a postmortem adult brain27, …”\nDiscussion:\nPage 25, line 579-582:\n“Our results showing cognitive and psychopathology associations with WM BAGs specific\nto association and subcortical/limbic tracts reinforces previous brain-behavior findings\nand may offer preliminary insights into how WM development contributes to distinct brain\nfunctional activation patterns derived from meta-analyses.”\nPage 25, line 587-589:\n“Through mitochondrial decoding analyses, our tract-based analyses provide exploratory\nevidence that WM tracts most strongly implicated in cognition and psychopathology also\nshow higher mitochondrial enrichment (Fig. 4).”\nPage 26, line 599-601:\n15\n“Our findings align with post-mortem studies that have shown reduced WM mitochondrial\ndensity in patients with psychiatric disorders compared to non-psychiatric controls14,\nproviding the preliminary support for the “mitochondrial dysfunction” hypothesis in\npsychiatric disorders15.”\nReference:\n14. Senko D, et al. White matter lipidome alterations in the schizophrenia brain.\nSchizophrenia 10, 123 (2024).\n15. Manji H, et al. Impaired mitochondrial function in psychiatric disorders. Nat Rev\nNeurosci 13, 293-307 (2012).\n27. Mosharov EV, et al. A human brain map of mitochondrial respiratory capacity and\ndiversity. Nature 641, 749-758 (2025).\nReviewer #1, Comment 8:\n8. Terminology Around “Maturation”\nThe manuscript frequently equates positive BAG with “advanced maturation” and negative BAG\nwith “delay.” This language presumes a normative linear developmental trajectory and may not\nreflect true neurobiological differences. It also risks misinterpretation by clinicians or translational\nresearchers.\nResponse:\nWe would like to refer to the above comment (please see Reviewer #1, Comment 1). We have\nrevised the terminology throughout the Results to avoid implying biological “maturation.”\nSpecifically, we replaced terms such as “advanced maturation” and “delayed maturation” with\nmore objective descriptors, such as “more positive BAG” and “more negative BAG,” to emphasize\nthat BAG reflects deviation from age-normative predictions rather than a direct measure of\nbiological maturity. These changes aim to prevent overinterpretation and ensure clearer expression\nfor both clinical and translational audiences.\nReviewer #1, Comment 9:\n9. Literature overview and “filling gaps”\nThere is a lack of engagement with earlier dMRI brain age literature. The study would benefit from\nhighlighting these, especially studies in in youth that may have investigated cognition,\npsychopathology, similar.\nResponse:\nWe agree with the reviewer and now have substantially expanded our engagement with the existing\ndMRI brain age literature, particularly studies in youth populations and those investigating\ncognitive and psychopathological correlates. Our literature survey reveals that this study provides\nthe first evidence on how dMRI-derived brain age gaps (BAGs), particularly tract-based BAGs,\nrelate to both cognition and psychopathology in adolescents. While previous research has explored\nvarious aspects of dMRI brain age, no prior work has simultaneously examined tract-specific aging\npatterns and their associations with cognitive performance and psychiatric symptoms in youth.\nPrior dMRI BAG studies (Tønnesen et al., 2020; Zhu et al., 2023) in adult populations with\n16\nschizophrenia and bipolar disorder reported more positive whole-brain WM BAGs (accelerated\naging), which contrasts with our findings in adolescents, highlighting the importance of age-\nspecific investigations. Notably, our whole-brain BAG results align with the results by Roy et al.\n(2024), who found that more positive BAGs were associated with greater educational opportunity\n(an environmental measure serving as a proxy for cognitive enrichment) in the ABCD cohort, and\nUllman et al. (2016) demonstrated that higher BAGs related to better working memory and\nnumerical ability in 6-year-old children. Previous research also found more positive dMRI BAGs\nin schizophrenia patients aged >30 years, but not in patients <30 years old, and our study provides\nsupportive evidence for the \"maturational model\" in psychiatric patients (Wang et al., 2021).\nUnlike previous whole-brain approaches, our tract-based analysis identifies the crucial roles of\ndorsal association tracts during adolescence, revealing tract-specific vulnerabilities that may be\nneglected in global metrics. As summarized in the table below, existing dMRI brain age studies in\nyouth have been limited in scope, sample size, or have not examined the intersection of tract-\nspecific aging with both cognitive and psychopathological outcomes. Our study uniquely bridges\nmultiple gaps by focusing on the critical adolescent developmental period, employing tract-\nspecific rather than solely whole-brain metrics, simultaneously examining cognitive and\npsychopathological correlates, and providing evidence for developmental specificity in the\nrelationship between white matter maturation and clinical outcomes.\nThe dMRI brain age literature in youth or linking BAGs to cognition or psychopathology is\nsummarized in the following table:\nReviewer Table 1. Literature review of WM dMRI brain age studies through youth or linking WM\ndMRI BAGs to cognition or psychopathology\nLinks to cognition\ndMRI features Reference Title Subjects\nor psychopathology\n576 total and Beck, et al., Biol. Dimensions of 8,834 ABCD NA\nregional measures Psychiatry (2024) early life adversity subjects (age range:\n(e.g. FA, MD, AD) are differentially 8.9-13.8 years)\nassociated with from two\npatterns of delayed timepoints\nand accelerated\nbrain maturation\nDiffusion kurtosis Roy, et al., Dev. Differences in 6,410 baseline data NA\nFA profiles of 28 Cogn. Neurosci. educational and 4,770 2-y-\ntracts (2024) opportunity predict follow-up data of\nwhite matter ABCD dataset\ndevelopment\nFA and MD Richie-Halford, et Multidimensional 1,651 subjects with NA\nprofiles of 18 tracts al., PLoS Comput. analysis and ages 5-21 of HBN\nBiol. (2021) detection of dataset; 76 subjects\ninformative with ages 6-50 of\nfeatures in human Weston-Havens\nbrain white matter. dataset\nFA voxel-wise Ullman, H. et al., C Timing of White Data set 1: The predicted brain\nmaps ereb. Cortex (2016) Matter age positively\n82 subjects (6-20\nDevelopment years old) correlated with\nDetermines working memory\nData set 2:\nCognitive Abilities performance and\n17\nat School Entry but 31 subjects (age: numerical ability (P\nNot in Late 6.8 ± 0.3 years) < 0.01, P < 0.05) in\nAdolescence the 6-year-old\nchildren;\nThis association\nbetween brain age\nand working\nmemory was no\nlonger significant\nin children older\nthan 13 years of age\nFA, AD and RD Shokri-Kojori, E. et Estimates of brain 100 participants Higher WM age\nvalues of 41 al., Brain Res. age for gray matter (18-78 years old) scores were\nbilateral WM (2021) and white matter in associated with\nregions younger and older lower crystalized\nadults: Insights into intelligence\nhuman intelligence (measured by\nWASI vocabulary\ntest) in older\nsubjects (age > 40)\nFA, MD, RD and Tønnesen, S. et al., Brain Age Training set: More positive\nAD skeleton values Biol. Psychiatry: Prediction Reveals BAGs in patients\n928 healthy control\n(96 measures) Cogn. Neurosci. Aberrant Brain (HC) subjects (18- with SCZ and BD\nNeuroimaging White Matter in 94 years of age);\n(2020) Schizophrenia and\nTesting set:\nBipolar Disorder: A\n648 patients with\nMultisample\nschizophrenia\nDiffusion Tensor\nImaging Study (SCZ), 185 patients\nwith bipolar\ndisorder (BD), 990\nHC subjects\nFA voxel-wise Zhu, et al., Transl. Investigating brain Training set: 10 WM regions\nmaps for 48 white- Psychiatry (2023). aging trajectory 230 HC subjects showed more\nmatter regions deviations in positive BAGs in\n(age range: 20-84\ndifferent brain SCZs compared to\nyears)"}]

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