About

James McPartland is an Associate Professor of Child Psychiatry and Psychology at the Yale Child Study Center. He graduated magna cum laude in Psychology from Harvard University and received a doctoral degree in Child Clinical Psychology from the University of Washington in 2005. He completed pre- and post-doctoral clinical fellowships focusing on autism and related disorders at Yale. Dr. McPartland is a licensed child psychologist and serves as the Director of the Yale Developmental Disabilities Clinic. He is also the Director of Undergraduate Studies at the Child Study Center and offers an undergraduate seminar focused on autism spectrum disorder. His program of research investigates the brain bases of neurodevelopmental disabilities to develop biologically-based tools for detection and treatment.

Research topics

• Psychology
• Medicine
• Medical physics
• Biology
• Internal medicine
• Psychiatry
• Genetics

Selected publications

• Are Differences in Social Drive in Childhood and Adolescence Diagnosis Specific? A Multimeasurement Subtyping Approach.

  UNC Libraries · 2026-04-10

  articleOpen accessSenior author

  Altered social drive is a common feature across psychiatric disorders and is embedded in multiple diagnostic criteria, underscoring the need for a transdiagnostic approach. However, the extent to which social drive alterations vary across diagnoses and clinical presentations remains poorly characterized. This study examined whether distinct social drive profiles-defined by differences in social reticence, seeking, and maintaining challenges-relate to variations in clinical features and show specificity to particular neurodevelopmental and neuropsychiatric conditions.Data were drawn from the Healthy Brain Network (N = 2,380; ages 5-21 years, mean [SD] age = 10.27 [3.39] years; 68% male) and included youth with attention-deficit/hyperactivity disorder, anxiety disorders, autism spectrum disorder, oppositional defiant/conduct disorder, depressive disorders, obsessive-compulsive disorder, and tic disorders. Latent profile analysis identified distinct social drive profiles based on constellations of social reticence, seeking, and maintaining challenges. Profiles were compared across demographic, social functioning, and clinical measures, and the distribution of diagnostic categories within each profile was assessed.Five profiles emerged: engaged (n = 1,530), inhibited (n = 477), aloof (n = 189), avoidant (n = 143), and constrained (n = 50). Profile differences were evident in demographic factors, social functioning, and clinical features. No single diagnosis mapped exclusively onto any profile; rather, participants with distinct neurodevelopmental or neuropsychiatric diagnoses were distributed across all 5 profiles.Psychiatric diagnoses alone may not fully capture alterations in social drive, which appear to transcend diagnostic boundaries. These findings support a transdiagnostic framework and challenge disorder-specific models of social drive differences.

  PublisherDOI

• Optimizing functional connectivity scanning conditions for predicting autistic traits

  Nature Mental Health · 2026-04-21

  articleOpen access

  Autism is a heterogeneous condition, and functional magnetic resonance imaging-based studies have advanced understanding of neurobiological correlates of autistic features. Little work has focused on the optimal brain states to reveal brain–phenotype relationships. Here, using connectome-based predictive modeling, we interrogated four datasets to determine scanning conditions that boost prediction of clinically relevant phenotypes and assess generalizability. In dataset one, a sample of youth with autism and neurotypical participants (n = 63), we found that a sustained attention task resulted in high prediction performance of autistic traits compared with a free-viewing social attention task and a resting-state condition. In dataset two (n = 25), we observed the predictive network model of autistic traits generated from the sustained attention task generalized to predict measures of attention in neurotypical adults. In datasets three and four, we determined the same predictive network model further generalized to predict measures of social responsiveness in the Autism Brain Imaging Data Exchange (n = 229) and the Healthy Brain Network (n = 643). Our data suggest an in-scanner sustained attention challenge can help delineate robust markers of autistic traits. This research investigates optimal brain states for predicting autistic traits using connectome-based predictive modeling across four datasets. Findings indicate that a sustained attention task enhances prediction accuracy, generalizing effectively to neurotypical adults and various measures of social responsiveness.

  PublisherOA PDFDOI

• Conceptualizing Autism as a Behavioral Network: Transdiagnostic Associations with Co-occurring Psychiatric Conditions

  Research on Child and Adolescent Psychopathology · 2026-03-14 · 1 citations

  articleOpen accessSenior author

  Autism is characterized by marked heterogeneity in behavioral presentation and high rates of co-occurring psychiatric symptoms, which hinder diagnostic precision, personalized intervention, and long-term quality of life. Approach and withdrawal behaviors—subserved by core motivational systems underlying action and emotion—may serve as transdiagnostic processes linking autism with common co-occurring conditions in childhood. Guided by this framework, we examined how autism-related approach–withdrawal behaviors interrelate and connect to internalizing and externalizing symptoms. Using data from 280 autistic children aged 6 to 11 years enrolled in the Autism Biomarkers Consortium for Clinical Trials, we constructed a Gaussian graphical model of approach–withdrawal behaviors. Core behaviors were identified using expected influence centrality. Autism, when conceptualized as a system of interconnected approach–withdrawal behaviors, was positively associated with common co-occurring psychiatric conditions, with strongest associations observed for anxiety and attention-deficit/hyperactivity disorder. Affect regulation–related nodes were most relevant to internalizing symptoms, whereas arousal regulation and sensory nodes were uniquely related to externalizing symptoms. These findings integrate transdiagnostic theories of approach-withdrawal motivation with network analysis and highlight clinically relevant targets for diagnostic refinement and intervention.

  PublisherOA PDFDOI

• Harnessing Trial-to-Trial Variability of EEG Spectral Characteristics to Understand Autism

  Journal of Autism and Developmental Disorders · 2025-11-22

  articleOpen access

  PURPOSES: There is a great need for mechanistically informed biomarkers to understand autism spectrum disorder (ASD) and guide treatment. Electroencephalography (EEG) is a non-invasive method for identifying objective biomarkers, but traditional trial-averaged metrics may mask neural variability, a meaningful feature of ASD reflecting sensory, attentional, and cognitive differences. METHODS: This study investigates whether across-trial EEG variability enhances ASD classification compared to conventional mean EEG features. We hypothesize that capturing dynamic within-subject neural variability improves classification accuracy and offers deeper insights into ASD-related neural disruptions. We analyzed EEG power spectral features in individuals with and without ASD, extracting across-trial variability in five frequency bands alongside traditional mean EEG power metrics. Using machine learning, we compared classification performance and identified the most predictive neural markers. RESULTS: Results show that across-trial EEG variability outperformed mean EEG metrics, achieving 70.7% classification accuracy. Variability in delta and gamma bands is critical for distinguishing ASD, with robust cross-validation results and significant correlations with behavioral scores, supporting the clinical relevance and generalizability of neural variability as an ASD biomarker. CONCLUSIONS: By incorporating neural variability into machine learning models, this study introduces a novel framework for improving biomarker-driven assessments. These findings highlight the potential for personalized tools that inform targeted interventions while offering insights into ASD neurophysiology. Future research should integrate longitudinal EEG analyses and multimodal neuroimaging to advance precision diagnostics in autism.

  PublisherOA PDFDOI

• Imaging Metabotropic Glutamate Receptor 5 and Excitatory Neural Activity in Autism

  American Journal of Psychiatry · 2025-12-10 · 3 citations

  article

  OBJECTIVE: Autism spectrum disorder is a prevalent and heterogeneous condition with features ranging from social and communication differences to sensory sensitivities. Differences in excitatory neurotransmission have been identified in autism, but the molecular underpinnings are poorly understood. To investigate the mechanism underlying these observed differences, the authors assessed glutamatergic receptor density in autistic adults using positron emission tomography (PET) and related it to a functional EEG measure of excitatory activity. METHODS: were calculated using Spearman's rho. RESULTS: Across all brain regions, mGlu5 availability was significantly lower (by ~15%) in autistic relative to neurotypical control participants. Group differences were generally greatest in the cerebral cortex. Within the autistic group, mGlu5 availability in all regions was significantly correlated with the slope of the EEG (e.g., cerebral cortex, r=0.67), such that shallower slope was associated with lower mGlu5 availability. CONCLUSIONS: This brain-wide investigation of mGlu5 availability with PET revealed pervasive lower mGlu5 availability across multiple brain areas in autism. Additionally, multimethod analyses revealed associations with a noninvasive electrophysiological index of excitatory neurotransmission. These results indicate that lower brain-wide mGlu5 availability may represent a molecular mechanism underlying altered excitatory neurotransmission that has the potential to stratify the heterogeneous autism phenotype.

  PublisherDOI

• Advancing scientific understanding of the drive to socially engage: from broad constructs to transdiagnostic ‘building blocks’

  Molecular Psychiatry · 2025-08-26

  review
  PublisherDOI

• Sex-specific Axonal Conduction Velocity Development Drives Differential Changes in Frontal, Parietal, and Insular Cortices in Autism Spectrum Disorder

  medRxiv · 2025-10-30 · 3 citations

  preprintOpen access

  Abstract Neither the neurological underpinnings of autism spectrum disorder (ASD) nor their contribution to sex differences are well understood. In previous cross-sectional studies of axonal conduction velocity, the speed of action potential transmission, was observed to be decreased in autistic individuals, and this deficiency was associated with cognitive and behavioral differences. This longitudinal study aims to better understand how changes in neuronal microstructure contribute to the developmental trajectory of individuals with ASD and specifically to sex-differences in behavior during the adolescent period. Eighty-two participants (34 ASD, 41 female) completed multi-year longitudinal behavioral and neuroimaging testing. Pubertal development significantly mediated and accelerated age-related increases in conduction velocity, with girls with autism exhibiting greater increases in cortex over time and boys exhibiting greater increases in white matter (WM). Girls with autism exhibited more rapid increases in frontal and parietal cortices while boys showed relatively higher increases in insular cortex compared to girls. Across all boys, conduction velocity increased in WM at a higher rate than girls, but increased more slowly in autistic relative to non-autistic boys. Parent-reported anxious and depressive symptomatology also increased over time in girls with autism, whereas behavioral metrics associated with ASD declined, especially in boys. Notably, conduction velocity showed significant associations with parent-reported anxious and depressive symptomatology in many of the same brain regions that showed sex-specific developmental changes. These results indicate that neurodevelopmental changes in conduction velocity may underlie sex-linked biological mechanisms and contribute to differences in behavioral expression in autistic and non-autistic development.

  PublisherOA PDFDOI

• Reduced social prioritization: An underlying mechanism driving slower latency to look at faces in autism.

  Journal of Psychopathology and Clinical Science · 2025-10-16 · 1 citations

  articleOpen access

  = 118) children. Participants completed an eye tracking (ET) assay in which they viewed arrays of social and nonsocial stimuli. To determine why autistic children were slower to look at faces, we computed two ET metrics that reflected either deprioritization of social information or prolonged prioritization of nonsocial information. Deprioritization of social information was operationalized by face look number (FLN), which was defined as the numerical look sequence position of the first face look. Prolonged prioritization of nonsocial information was operationalized by time per object prior, which was defined as the average looking time per nonsocial object prior to the first face look. We found that autistic children were slower to look at faces compared to NT children and had higher FLN but not time per object prior compared to NT children. FLN was associated with measures of the autism phenotype. In summary, this work suggests deprioritization of social information better explains slower latency to look at faces in autistic children than prolonged prioritization of nonsocial information, with the reduced prioritization of faces contributing to difficulties in dynamic social interaction commonly observed in autism. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

  PublisherDOI

• Structural determinants of signal speed: Estimated axonal latency and its multimodal validation during face processing in autism

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-19 · 3 citations

  preprintOpen access

  Abstract It has not previously been possible to investigate the fundamental relationship between axonal structure – which dictates action potential transmission – and human neuronal function in vivo . Here, we introduce a novel metric of axonal signal speed, estimated axonal latency (EAL), derived from the relationship between axonal diameter, myelination, and length measured via MRI. We validate EAL along two pathways of the face processing network by relating it to N170 latency, an electrophysiological marker of face processing speed measured via EEG. Our results show that EAL along these pathways predicts N170 latency specifically during face processing. Moreover, we demonstrate that individuals with and without autism rely upon different pathways, potentially providing a structural account for autism-related face processing differences. By establishing this relationship between EEG-based electrical function and MRI-based axonal microstructure, we provide a non-invasive, spatially detailed estimate of neuronal processing speed that can inform our understanding of brain function, development, and disorder. Teaser Estimated axonal latency is a non-invasive, spatially detailed measure of neuronal speed to inform brain function and disorder.

  PublisherOA PDFDOI

• Multilevel Multivariate Functional Principal Component Analysis of Evoked and Induced Event-Related Spectral Perturbations

  Statistics in Biosciences · 2025-12-29

  articleOpen access
  PublisherOA PDFDOI

Recent grants

• Administrative Core

  NIH · $138.5M · 2015–2026

• NIH Grant R03MH079908

  NIH · $83k · 2008

• Neural Mechanisms for Social Interactions and Eye Contact in ASD

  NIH · $3.2M · 2016–2022

• NIH Grant R01MH107426

  NIH · $651k · 2018

• NIH Grant R01MH100173

  NIH · $1.3M · 2017

Frequent coauthors

• Fred R. Volkmar

  Yale University

  587 shared

• Benjamin Aaronson

  Simons Foundation

  554 shared

• Moira Lewis

  Children's Healthcare of Atlanta

  500 shared

• Christopher J. McDougle

  Massachusetts General Hospital

  494 shared

• Hope Morris

  396 shared

• Carolyn A. Doyle

  Indiana University School of Medicine

  378 shared

• Francesca Happé

  King's College London

  348 shared

• Brian Reichow

  UConn Health

  334 shared