Biology subjects

Gotlib, I. H.

Publications and source records attributed to Gotlib, I. H..

16 recordsLinked to original sources

bioRxiv2026

A Single, Reproducible Dimension Organizes Associations Between the Early Environment and the Developing Brain

Childhood adversity has enduring consequences for health and development across the lifespan, yet the organizing structure linking diverse environmental experiences to the developing brain remains poorly understood. We jointly modeled 104 measures of children's environments, spanning interpersonal, household, and neighborhood contexts, with 7 neuroimaging modalities of brain structure and function in 5,976 youth. Despite this breadth, only a single adversity-brain dimension emerged and replicated in held-out data. This dimension was defined primarily by socioeconomic and neighborhood deprivation and selectively expressed in lower cortical surface area and distributed functional connectivity concentrated in sensorimotor systems. Total cortical surface area reproduced 96% of the regional association, indicating a global rather than regionally specific structural signature. This dimension predicted poorer cognition and cardiometabolic health six years later. These findings reveal a strikingly selective, low-dimensional relation between children's environments and neurodevelopment, with socioeconomic deprivation emerging as the principal environmental axis associated with the developing brain.

neuroscience↗

bioRxiv2026

Prenatal exposure to PFAS and multimodal structural brain development across childhood

BackgroundPer- and polyfluoroalkyl substances (PFAS) are ubiquitous environmental pollutants that are posited to be neurotoxic to the developing brain; however, the impact of prenatal exposure to PFAS -- particularly to newer, short-chain PFAS -- on brain development across childhood is unclear. MethodsConcentrations of 9 PFAS were quantified in cord blood plasma of 459 infants who later participated in structural and diffusion magnetic resonance imaging (MRI) at ages 4.5, 6, 7.5, and 10.5 years, providing estimates of regional cortical thickness and surface area, subcortical volumes, and fractional anisotropy (FA) of key white matter tracts. Longitudinal mixed effect models estimated associations of PFAS with age 4.5 brain metrics and their developmental trajectories across childhood. ResultsHigher concentrations of long-chain PFAS (PFNA, PFHxS, PFDA) in cord blood were associated with lower surface area of the right paracentral lobule at age 4.5. PFHpA was associated with faster surface area growth in the left rostral anterior cingulate and slower growth in the right caudal middle frontal gyrus from 4.5 to 10.5 years. The short-chain compound PFBS was linked with greater FA in 17 of 27 white matter tracts at 4.5 years; those associations attenuated with age. Finally, PFOA was associated with lower FA in 6 tracts at 4.5 years. ConclusionsPrenatal exposure to PFAS was associated with altered development of frontal and paracentral regions and of white matter microstructure. These findings highlight the need for further research examining the long-term effects of prenatal exposure to PFAS on childrens neurodevelopment.

neuroscience↗

bioRxiv2026

Early Adversity Selectively Reshapes the Somato-Cognitive Action Network in the Developing Brain

Early adversity is among the most potent environmental influences on the developing brain; yet whether it remodels the areal layout of cortical networks, and along which dimensions, is unknown. In a longitudinal sample of 4,525 youth aged 9-18, we derived individualized cortical parcellations and related the topography of 15 networks to 10 data-driven dimensions of adversity. Greater exposure to threat, but not deprivation or unpredictability, predicted dose-dependent expansion of the somato-cognitive action network (SCAN), an effect nearly seven times larger than for any other network. The SCAN expanded toward the sensorimotor pole, encroached on neighbors, and shifted functional connectivity toward sensorimotor and away from control systems. Expansion mediated the association between threat and poorer cognition, replicating across waves, within children, and out-of-sample. Rather than diffusing across many systems, the imprint of adversity on the developing cortex converges on the territory of a single integrative network that couples brain to body.

neuroscience↗

bioRxiv2025

Deep Learning of Brain-Behavior Dimensions Identifies Transdiagnostic Biotypes in Youth with ADHD and Anxiety Disorders

Attention-deficit/hyperactivity disorder and anxiety disorders are highly prevalent in youth and are characterized by substantial heterogeneity and frequent co-occurrence. This transdiagnostic complexity challenges conventional diagnostic frameworks that rely on symptom-based categories, which often obscure underlying dimensional and neurobiological mechanisms and offer limited neurobiological specificity. To address these issues, we developed a deep learning-based brain-behavior modeling framework that integrates clinically salient functional connectivity with cognitive and behavioral measures to identify interpretable dimensions and biologically grounded subtypes (biotypes). We applied our model to the Adolescent Brain Cognitive Development (ABCD) dataset comprising 3,508 children aged 9-11 years and revealed two reproducible brain-behavior dimensions that captured variation in cognitive control and emotion-attention regulation. These dimensions further yielded three distinct biotypes, each exhibiting unique symptom profiles and distinct brain development. We tested the robustness and generalizability of the dimensions and corresponding biotypes in an independent cohort of 224 age-matched participants from the Healthy Brain Network (HBN) and documented their early expression before symptom onset during adolescence. These findings highlight the utility of brain-behavior dimensions for elucidating heterogeneous psychiatric presentations and advance a biologically grounded framework for early classification and potential clinical translation in youth mental health.

neuroscience↗

bioRxiv2025

Refining RDoC Using Individual-Level Task fMRI Factor Models Reveals Reproducible Brain-wide Motifs

The Research Domain Criteria (RDoC) framework was introduced to guide psychiatric research using biologically grounded, dimensional constructs of mental function. However, its hierarchical domain structure remains largely unvalidated against individual-level brain and behavioral data. Building on prior group-level work, we applied a multi-stage validation framework to Human Connectome Project (HCP) task-fMRI data to test whether individual-level, data-driven models more accurately capture the organization of brain activity and behavior than RDoC-based models. Using confirmatory factor analysis in two independent cohorts, we found that data-driven bifactor models consistently outperformed RDoC-based models across multiple fit indices. The general factor derived from these models revealed a reproducible, low-dimensional axis spanning visual-attentional to default mode-auditory systems, aligning with canonical macroscale cortical gradients. Community detection further identified reproducible spatial motifs whose centroids corresponded to interpretable functional systems and whose alignment predicted individual performance on working memory and relational reasoning tasks. To assess whether these findings extended beyond neural data, we analyzed behavioral measures in HCP and in an independent transdiagnostic dataset (LA5c). In both datasets, data-driven behavioral models outperformed RDoC-based models, although the relative support for bifactor versus specific factor structure differed by dataset. Extending the neural analyses to LA5c, which included healthy controls and individuals with ADHD, bipolar disorder, and schizophrenia, showed that data-driven bifactor models generalized across diagnostic groups and that alignment with data-driven community centroids related to symptom severity, whereas RDoC-based representations showed weaker or no associations. Finally, topological analysis of task-evoked brain activity revealed that data-driven representations better captured the global organization of brain states than RDoC domains. Together, these findings demonstrate that individual-level, empirically derived models provide a more accurate, generalizable, and behaviorally relevant account of brain organization than the current RDoC framework. By integrating neural, behavioral, and clinical validation, this work advances precision neuroscience and supports the empirical refinement of dimensional psychiatric frameworks.

neuroscience↗

bioRxiv2025

Dissecting heterogeneity in cortical thickness abnormalities in major depressive disorder: a large-scale ENIGMA MDD normative modelling study

ImportanceMajor depressive disorder (MDD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology, which may obscure identification of structural brain abnormalities in MDD. To explore this, we used normative modeling to index regional patterns of variability in cortical thickness (CT) across individual patients. ObjectiveTo use normative modeling in a large dataset from the ENIGMA MDD consortium to obtain individualised CT deviations from the norm (relative to age, sex and site) and examine the relationship between these deviations and clinical characteristics. Design, setting, and participantsA normative model adjusting for age, sex and site effects was trained on 35 CT measures from FreeSurfer parcellation of 3,181 healthy controls (HC) from 34 sites (40 scanners). Individualised z-score deviations from this norm for each CT measure were calculated for a test set of 2,119 HC and 3,645 individuals with MDD. For each individual, each CT z-score was classified as being within the normal range (95% of individuals) or within the extreme range (2.5% of individuals with the thinnest or thickest cortices). Main outcome measuresZ-score deviations of CT measures of MDD individuals as estimated from a normative model based on HC. ResultsZ-score distributions of CT measures were largely overlapping between MDD and HC (minimum 92%, range 92-98%), with overall thinner cortices in MDD. 34.5% of MDD individuals, and 30% of HC individuals, showed an extreme deviation in at least one region, and these deviations were widely distributed across the brain. There was high heterogeneity in the spatial location of CT deviations across individuals with MDD: a maximum of 12% of individuals with MDD showed an extreme deviation in the same location. Extreme negative CT deviations were associated with having an earlier onset of depression and more severe depressive symptoms in the MDD group, and with higher BMI across MDD and HC groups. Extreme positive deviations were associated with being remitted, of not taking antidepressants and less severe symptoms. Conclusions and relevanceOur study illustrates a large heterogeneity in the spatial location of CT abnormalities across patients with MDD and confirms a substantial overlap of CT measures with HC. We also demonstrate that individualised extreme deviations can identify protective factors and individuals with a more severe clinical picture. Key points QuestionCan z-scores derived from normative modelling shed light on the heterogeneous group-level findings of cortical thickness abnormalities in major depression and what characterises individuals at the extreme ends of cortical thickness abnormalities? FindingWe confirmed a large overlap in z-score distributions between depressed individuals and healthy controls and a heterogeneous spatial distribution of extreme z-deviations across brain regions across individual patients. Lower z-scores for cortical thickness were related to more severe clinical characteristics. MeaningOur findings confirm the heterogeneity in individual variation in the location and extent of CT abnormalities across patients with MDD and stress the importance of individualised predictions when examining cortical thickness abnormalities.

neuroscience↗

bioRxiv2024

Predicting Task Activation Maps from Resting-State Functional Connectivity using Deep Learning

Deep learning models have demonstrated the potential to predict task-evoked brain activation from resting-state fMRI, offering a pathway toward individualized brain mapping without requiring task-based data. In this study, we systematically evaluate architectural strategies for improving the efficiency and scalability of such models. Using data from the Human Connectome Project, we replicate the BrainSurfCNN framework and introduce two extensions: BrainSERF, which incorporates channel-wise attention through squeeze-and-excitation modules, and BrainSurfGCN, a graph-based model that leverages cortical mesh topology for efficient message passing. Across multiple evaluation metrics, including spatial correlation, Dice score, Dice AUC, and subject identification accuracy, all models achieve comparable predictive performance. Despite similar accuracy, the proposed models offer distinct advantages. BrainSERF provides modest improvements in capturing individual-specific features, while BrainSurfGCN achieves substantial reductions in model size and training time, highlighting a favorable trade-off between performance and computational efficiency. Beyond architectural comparisons, we investigate factors driving variability in prediction accuracy. We find that behavioral task performance, resting-state data quality, and inter-subject variability in task activation jointly constrain prediction fidelity. In particular, contrasts with lower signal reliability and higher variability exhibit reduced predictability across all models. Together, these findings demonstrate that incorporating topological and functional structural priors can improve the efficiency of deep learning models without sacrificing accuracy, while also emphasizing that prediction performance is fundamentally limited by the reliability of the underlying neural signals.

neuroscience↗

bioRxiv2024

A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria (RDoC) Framework

Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns, we employed a latent variable approach using bifactor analysis. We examined 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with 6,192 participants. A curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set, and the remaining held-out maps formed the internal validation set. External validation was conducted using 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Here, we show that a bifactor model incorporating a task-general domain and splitting the cognitive systems domain better fits the examined corpus of tfMRI data than the current RDoC framework. We also identify the domain of arousal and regulatory systems as underrepresented. Our data-driven validation supports revising the RDoC framework to reflect underlying brain circuitry more accurately.

neuroscience↗

bioRxiv2020

Default Mode and Salience Network Alterations in Suicidal and Non-Suicidal Self-Injurious Thoughts and Behaviors in Adolescents with Depression

Suicidal ideation (SI) and non-suicidal self-injury (NSSI) are two distinct yet often co-occurring risk factors for suicide in adolescents. Elucidating the neurobiological patterns that specifically characterize SI and NSSI in adolescents is needed to inform the use of these markers in intervention studies and to develop brain-based treatment targets. Here, we clinically assessed 70 adolescents--49 adolescents with depression and 21 healthy controls--to determine SI and NSSI history. Twenty-eight of the depressed adolescents had a history of SI and 29 had a history of NSSI (20 overlapping). All participants underwent a resting-state fMRI scan. We compared groups in network coherence of subdivisions of the central executive network (CEN), default mode network (DMN), and salience network (SN). We also examined group differences in between-network connectivity and explored brain-behavior correlations. Depressed adolescents with SI and with NSSI had lower coherence in the ventral DMN compared to those without SI or NSSI, respectively, and healthy controls (all ps<0.043). Depressed adolescents with NSSI had lower coherence in the anterior DMN and in insula-SN (all ps<0.030), and higher CEN-DMN connectivity compared to those without NSSI and healthy controls (all ps<0.030). Lower network coherence in all DMN subnetworks and insula-SN were associated with higher SI and NSSI (all ps<0.001). Thus, SI and NSSI are related to brain networks associated with difficulties in self-referential processing and future planning, while NSSI specifically is related to brain networks associated with disruptions in interoceptive awareness. Intrinsic network patterns may be reliable biomarkers of SI and NSSI in adolescents.

neuroscience↗

bioRxiv2020

Cross-sectional and longitudinal associations of family income-to-needs ratio with cortical and subcortical brain volume in adolescent boys and girls

Deviations in neurodevelopment may underlie the association between lower childhood socioeconomic status and difficulties in cognitive and socioemotional domains. Most previous investigations of the association between childhood socioeconomic status and brain morphology have used cross-sectional designs with samples that span wide age ranges, occluding effects specific to adolescence. Sex differences in the association between childhood socioeconomic status and neurodevelopment may emerge or intensify during adolescence. We used tensor-based morphometry, a whole brain approach, to examine sex differences in the cross-sectional association between normative variation in family income-to-needs ratio (INR) and cortical and subcortical gray and white matter volume during early adolescence (ages 9-13 years, N=147), as well as in the longitudinal association between in INR and change in volume from early to later adolescence (ages 11-16 years, N=109). Biological sex interacted with INR to explain variation in volume in several areas cross-sectionally and longitudinally. Effects were primarily in cortical gray matter areas, including regions of the association cortex and sensorimotor processing areas. Effect sizes tended to be larger in boys than in girls. Biological sex may be an important variable to consider in analyses of the effects of family income on structural neurodevelopment during adolescence. HighlightsO_LISex-specific associations of SES with neurodevelopment may emerge in adolescence. C_LIO_LIWe used a whole-brain approach to examine gray and white matter volume. C_LIO_LISex interacted with SES to explain variation in volume across adolescence. C_LIO_LISex is an important variable to consider in analyses of SES and brain volume. C_LI

developmental biology↗

bioRxiv2019

Decoupling of Dopamine Release and Neural Activity in Major Depressive Disorder during Reward Processing Assessed by Simultaneous fPET-fMRI

The interaction of the midbrain dopaminergic system and the striatum is implicated in reward processing; it is still unknown, however, how this interaction is altered in Major Depressive Disorder (MDD). In the current study, we related the dopamine release/binding inferred by [11C] Raclopride functional Positron Emission Tomography (fPET) to neural activity monitored by blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) in adults diagnosed with MDD and healthy controls (CTL). Participants completed a monetary incentive delay (MID) task during simultaneous [11C] Raclopride fPET and fMRI. Instead of the usual kinetic modeling method for analyzing dynamic PET time activity curves (TACs), we used a simpler general linear model (GLM) approach, which includes introducing a fPET dopamine activation response function to model changes in the TAC associated with the MID task. In addition, using simulations, we show that the GLM approach has several advantages over kinetic modeling. This is achieved without invoking erroneous steady-state assumptions or selecting a suitable reference region. Our results include the observation of both decreased fMRI activation and dopamine release/binding in the striatum in the MDD cohort, implying a reduced reward processing capacity in MDD. Furthermore, in the MDD group, individuals with lower fMRI activations in the right middle putamen and ventral medial prefrontal cortex (vmPFC) had higher reflection rumination scores, and individuals with lower dopamine release/binding in the left putamen and the right nucleus accumbens (NAcc) also had higher reflection rumination scores. Significant cross-modal inter-subject and intra-subject correlations of dopamine release/binding and fMRI activation were observed in the CTL group, but not in the MDD group. The intra-subject correlation of the two modalities was negatively associated with reflection rumination scores in the CTL group, indicating that decoupling of the dopaminergic system and striatum may be important in the pathophysiology of MDD.

neuroscience↗

bioRxiv2019

Severity of early life stress moderates the effect of fine particle air pollution on adolescent brain development

Air pollution is currently the greatest environmental threat to public health, but we know little about its effects on adolescent brain development. In this context, exposure to air pollution co-occurs, and could interact, with social factors that also affect brain development, such as early life stress (ELS). Here, we show that severity of ELS moderates the association between fine particle air pollution (particulate matter 2.5; PM2.5) and structural brain development. We interviewed adolescents about ELS, used census-tract data to characterize PM2.5 concentrations, and conducted longitudinal tensor-based morphometry to assess regional changes in brain volume over a two-year period. Across various cortical, thalamic, and white matter tract regions, there was a remarkably consistent effect of PM2.5 on volumetric change for adolescents who had experienced less, rather than more, severe ELS. Furthermore, exposure to higher levels of PM2.5 and experiencing moderate to severe ELS were associated with comparable volumetric changes in the brain in adolescence.

neuroscience↗

bioRxiv2019

Coupling of waking cortisol with DHEA and testosterone across the pubertal transition: Associations with threat-related early life stress

Atypical regulation of the hypothalamic-pituitary-adrenal (HPA) axis is a putative mechanism underlying the association between exposure to early life stress (ELS) and the subsequent development of mental and physical health difficulties. Recent research indicates that puberty is a period of HPA-axis plasticity during which the effects of exposure to ELS on cortisol regulation may change. In particular, increases in the sex hormones that drive pubertal maturation, including dehydroepiandrosterone (DHEA) and testosterone, may be implicated in pubertal recalibration of cortisol regulation. In the current study, we examined the associations among levels of objectively-rated threat-related ELS and salivary waking cortisol, DHEA, and testosterone in a sample of 178 adolescents (55% female) who were in early puberty at baseline (Tanner stages 1-3; mean Tanner stage[SD]=1.93[0.64]; mean age[SD]=11.42[1.04]) and were followed up approximately two years later (mean Tanner stage[SD]=3.46[0.86]; mean age[SD]=13.38[1.06]). Using multi-level modeling, we disaggregated the effects of between-individual levels and within-individual increases in pubertal stage and sex hormones on change in cortisol. Controlling for between-individual differences in average pubertal stage, the association between levels of cortisol and DHEA was more strongly positive among adolescents who evidenced greater within-individual increases in pubertal stage across time. Both higher average levels and greater within-individual increases in DHEA and testosterone were associated with increases in cortisol across time, indicating positive coupling of developmental changes in these hormones; however, coupling was attenuated in adolescents who were exposed to more severe threat-related ELS prior to puberty. These findings advance our understanding of the development of the HPA-axis and its association with childhood environmental risk during puberty.

developmental biology↗

bioRxiv2019

Subcortical Shape Alterations in Major Depressive Disorder: Findings from the ENIGMA Major Depressive Disorder Working Group

Alterations in regional subcortical brain volumes have been widely investigated as part of the efforts of an international consortium, ENIGMA, to determine reliable structural brain signatures for Major Depressive Disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work to precisely map localized MDD-related differences in subcortical regions using shape analysis. In this meta-analysis of subcortical shape from the ENIGMA-MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta-analysis. Relative to CTL, patients with MDD had lower surface area in the subiculum of the hippocampus, the basolateral amygdala, and the nucleus accumbens shell. Relative to CTL, patients with adolescent-onset MDD ([≤] 21 years) had lower thickness and surface area of the subiculum of the hippocampus and the basolateral amygdala. Relative to first-episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala. Our results suggest that previously reported MDD-associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.

neuroscience↗

bioRxiv2018

Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks.

neuroscience↗

bioRxiv2017

Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.

neuroscience↗