Ian H. Gotlib

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

The paper identifies a single adversity-brain dimension linking childhood environments to neurodevelopment, primarily driven by socioeconomic deprivation.

Prenatal exposure to PFAS and multimodal structural brain development across childhood

This study examines the impact of prenatal PFAS exposure on childhood brain development using MRI data, finding associations with cortical surface area and white matter microstructure changes.

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

This study shows early adversity increases the size of the somato-cognitive action network in the developing brain, particularly in response to threat, and this change is linked to poorer cognition.

Efficient Deep Learning Models for Predicting Individualized Task Activation from Resting-State Functional Connectivity

This study evaluates deep learning models for predicting task activation from resting-state fMRI, finding that architectural changes improve efficiency without sacrificing accuracy.

Childhood Maltreatment and Deviations from Normative Brain Structure: Results from 3,711 Individuals from the ENIGMA MDD and ENIGMA PTSD

The paper examines how childhood maltreatment affects brain structure in 3,711 individuals with MDD and PTSD, finding age- and sex-specific brain morphometric differences.

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

The paper uses deep learning to identify brain-behavior dimensions and biotypes in youth with ADHD and anxiety disorders, revealing distinct symptom profiles and brain development patterns.

Refining RDoC Using Individual-Level Task fMRI Factor Models RevealsReproducible and Clinically Relevant Brain-Wide Motifs

This paper validates data-driven factor models over RDoC-based models using fMRI and behavioral data, showing better reproducibility and clinical relevance across neural and behavioral domains.

A Scalable Toolkit for Modeling 3D Surface-based Brain Geometry

The paper introduces a scalable toolkit for 3D surface-based brain analysis, demonstrating its utility in mapping subcortical shape variations across 21 international samples.

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

This study validates the RDoC framework using a latent variable approach, showing a bifactor model better fits fMRI data than the current framework and highlights underrepresentation of the arousal and regulatory systems domain.

A Large-Scale ENIGMA Multisite Replication Study of Brain Age in Depression

The paper replicates a brain age gap in depression using a large-scale multisite study, finding a similar effect size to a prior study.

Cortical Thickness Trajectories across the Lifespan: Data from 17,075 healthy individuals aged 3-90 years

The paper analyzes cortical thickness trajectories in 17,075 individuals to show peak thickness in childhood, steep decline in early adulthood, and slower decline later, with notable exceptions and significant inter-individual variability.

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

The paper examines brain network coherence differences between adolescents with depression and suicidal or non-suicidal self-injurious thoughts, identifying specific neural patterns associated with these behaviors.

Greater male than female variability in regional brain structure across the lifespan

The paper analyzes sex differences in brain structure variability across the lifespan using a large dataset, finding greater male variability in subcortical volumes, cortical surface area, and thickness in most regions.

Brain Aging in Major Depressive Disorder: Results from the ENIGMA Major Depressive Disorder working group

This paper examines brain aging in major depressive disorder (MDD) using a large dataset, finding that MDD patients show increased brain age differences compared to controls.

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

This study examines how family income-to-needs ratio relates to brain volume changes in adolescents, finding sex differences in these associations.

Subcortical Volume Trajectories across the Lifespan: Data from 18,605 healthy individuals aged 3-90 years

This study analyzes subcortical volume changes across the lifespan using MRI data from 18,605 individuals, finding age-related declines in some structures and increases in others.

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

This study identifies subcortical shape differences in MDD patients using ENIGMA data, highlighting region-specific changes in surface area and thickness.

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

The paper examines how threat-related early life stress affects the relationship between cortisol, DHEA, and testosterone during puberty using longitudinal data from adolescents.

Sex differences in the effects of gonadal hormones on white matter microstructure development in adolescence

The paper examines how sex differences in gonadal hormone changes affect white matter microstructure development in adolescents using longitudinal MRI and hormone data.

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

This study uses simultaneous fPET-fMRI to show reduced dopamine release and neural activity in the striatum in MDD during reward processing, with decoupling linked to rumination.

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

The paper shows that early life stress severity moderates the effect of PM2.5 air pollution on adolescent brain volume changes.

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

The paper develops deep learning models to assess the quality of subcortical brain shape models from MRI, reducing human rater time by 46-70%.

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

This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.