About

Shafali S. Jeste is a Professor and Department Chair of Pediatrics at UCLA's David Geffen School of Medicine. Her research focuses on neurodevelopmental disorders, particularly autism spectrum disorder (ASD), and she is involved in clinical trials and research activities aimed at understanding and predicting ASD in high-risk infants. Her work includes investigating electrophysiological biomarkers of sleep and cognition in syndromes such as Dup15q and Tuberous Sclerosis Complex, as well as exploring neural predictors of language function after intervention in children with autism. Dr. Jeste's contributions extend to studying developmental trajectories, neural responses to language, and social attention in neurogenetic syndromes, with a goal of informing clinical trial endpoints and advancing neuropsychiatric care for neurodevelopmental disorders.

Research topics

• Medicine
• Psychology
• Psychiatry
• Neuroscience
• Developmental psychology
• Computer Science
• Machine Learning
• Audiology
• Family medicine
• Genetics
• Medical physics
• Pathology
• Internal medicine
• Business
• Biology

Selected publications

• 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 access

  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

• Visual Cortical Response Variability in Infants at High Familial Likelihood for Autism

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-09 · 1 citations

  articleOpen access

  Abstract Visual processing undergoes rapid refinement in the first year of life, supporting the emergence of higher-order cognitive, language, and motor functions. Visual evoked potentials (VEPs) provide a non-invasive measure of visual system maturation that may shed light on heterogeneous developmental trajectories among infants at high familial likelihood for autism. Infants with an older sibling with autism spectrum disorder (N = 177 at 6 months; N = 132 at 12 months) participated in the Infant Brain Imaging Study–Early Prediction (IBIS-EP) study. Pattern-reversal VEPs were recorded at 6 and 12 months, and developmental skills were assessed at 24 months using the Bayley Scales of Infant and Toddler Development (Bayley-III). VEP components were characterized by P1 amplitude, latency, and trial-to-trial variability in latency. Associations with 24-month cognitive, language, and motor scores were examined using general linear models controlling for age, site, sex, and trial count. Robust VEPs were observed at both timepoints, showing age-appropriate morphology and expected developmental changes, including decreases in P1 latency and amplitude from 6 to 12 months. Greater trial-to-trial variability in P1 latency at both timepoints was significantly associated with higher cognitive and language scores at 24 months. Variability in visual cortical response timing was the strongest neural correlate of developmental skills in infancy. These findings suggest that temporal variability in early neural responses may reflect adaptive sensory circuit flexibility rather than inefficiency, potentially facilitating experience-dependent tuning of visual pathways. VEPs offer a mechanistic window into how developing sensory systems scaffold individual differences in early developmental trajectories. Research Highlights Trial-to-trial variability in visual cortical response timing predicts cognitive and language outcomes at 24 months in infants at familial likelihood for autism. Mean P1 latency did not predict outcomes, suggesting variability is a more sensitive early neural marker than average response timing. Greater neural response variability in infancy may reflect adaptive sensory circuit flexibility rather than noise or inefficient processing. VEP-based biomarkers provide a scalable mechanistic window into how early sensory processing scaffolds cognitive and language development.

  PublisherOA PDFDOI

• Wasserstein Boxplots for the Analysis of EEG Power Spectral Densities With Applications to Autism

  Statistics in Medicine · 2026-04-01

  article

  Functional boxplots are an informative exploratory tool for visualizing functional data and providing a concise summary of a dataset's central tendency, spread, and potential outliers. Motivated by within- and across-sample visualization of electroencephalogram (EEG) power spectral densities in autism studies, we propose Wasserstein boxplots for summarizing a sample of densities. Densities take on nonnegative values and integrate to 1. In order to take into account the constraints of the signal and to avoid metric distortions associated with transformations, the proposed Wasserstein boxplots utilize the 2-Wasserstein metric for quantifying distances between densities. In addition, the proposed boxplots extend the traditional approach of summarizing central tendency within a single dataset to comparative settings where central tendency of data within a target sample is quantified with respect to a reference group. Cross-sample Wasserstein boxplots are motivated by quantification of deviations in power spectra of autistic children (target group) from neurotypical development (reference group). Finally, covariate-adjusted boxplots are proposed for quantifying deviations in the target group from the Fréchet mean in the reference group, conditional on covariates. A unique feature of EEG power spectra is the peak alpha frequency (PAF), which shifts to higher frequencies as children age. Hence, covariate-adjusted boxplots are used to quantify deviations in the autistic sample from neurotypical spectra, conditional on age. The proposed exploratory tools, especially the comparative analyses are applicable more broadly, beyond the motivating autism context, to studies involving both a target and a reference group.

  PublisherDOI

• Sex-Related Measurement Bias in Autism Spectrum Disorder Symptoms in the Baby Siblings Research Consortium

  JAMA Network Open · 2025-08-08 · 6 citations

  articleOpen access

  Importance: Disparities exist in age of diagnosis and prevalence of autism spectrum disorder (ASD) for female compared with male children. Correcting for sources of bias is critical for improving equitable ASD identification. Objective: To determine whether sex differences exist in measurement of ASD symptoms using the Autism Diagnostic Observation Schedule (ADOS) among young children at high familial likelihood (HFL) and low familial likelihood (LFL) of ASD. Design, Setting, and Participants: This cohort study collected longitudinal, prospective data from the Baby Siblings Research Consortium between January 1, 2003, and December 31, 2021. Participants included 3106 children who had an older sibling with ASD (HFL group) and 1444 without (LFL group). Data from as many as 3 visits when participants were aged 20 to 40 months were included. Analysis occurred between March 1, 2023, and May 29, 2025. Exposures: Child sex and age and ASD diagnosis. Main Outcomes and Measures: Measurement invariance by sex and age was examined across item-level ADOS data. Diagnostic group and sex differences were then examined using mixed-effect models on corrected scores. Results: Repeated visits (n = 7557) from 4550 participants (2548 [56.0%] male) were included, of whom 1444 (31.7%) were in the LFL and 3016 (68.3%) in the HFL groups. Confirmatory factor analysis indicated social communication and restricted and repetitive behaviors models fit the data well in the HFL group but poorly in the LFL group. In the HFL group, females were rated as less impaired in eye contact (differential item functioning estimate [SE] = 0.088 [0.033]; P = .01), and their response to joint attention (differential item functioning estimate [SE] = 0.290 [0.105]; P = .01) and quality of social overtures (differential item functioning estimate [SE] = 0.053 [0.019]; P = .005) was associated with less underlying social communication difficulties compared with males. Adjusting for differential item functioning by age and sex resulted in moderate levels of measurement differences. Females showed milder autistic traits than males, although this gap was smaller in the participants diagnosed with ASD. Conclusions and Relevance: Sex differences exist in the general population in many social communication traits, yet ASD diagnostic thresholds do not account for these sex differences. Future instrument development, as well as clinician training, should acknowledge milder presentation (fewer difficulties with eye contact or quality of social impairments) in many females. This may help identify developmental differences earlier and improve outcomes for autistic females (estimate [SE] = -0.160 [0.061]; P = .009).

  PublisherOA PDFDOI

• Isolated Neurologic Manifestations of Adolescent-Onset Wilson Disease (P2-6.013)

  Neurology · 2025-04-07

  article

  We report a case of Wilson Disease in an adolescent presenting with isolated neurological symptoms in the absence of symptomatic hepatic, psychiatric or ophthalmologic involvement.

  PublisherDOI

• Central Posterior Envelopes for Bayesian Longitudinal Functional Principal Component Analysis

  Statistics in Biosciences · 2025-07-05

  articleOpen access
  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

• Covariate Adjusted Functional Mixed Membership Models

  Statistics and data science in imaging. · 2025-10-30

  articleOpen access

  Mixed membership models are a flexible class of models used for unsupervised learning that allow each observation to partially belong to multiple clusters or features. In this article, we extend the framework of functional mixed membership models to allow for covariate-dependent modeling structures. The framework uses a multivariate Karhunen-Loève decomposition, which allows for a scalable and flexible model. Within this framework, we establish a set of sufficient conditions to ensure the identifiability of the mean, covariance, and allocation structure up to a permutation of the labels. This article is primarily motivated by studies on functional brain imaging through electroencephalography (EEG) of children with autism spectrum disorder (ASD). Using the proposed framework, we provide novel insight into the heterogeneity of developmental changes in alpha oscillations and show that individuals with ASD have smaller developmental changes compared to their typically developing counterparts.

  PublisherDOI

• Clinical determinants of psychiatric care in genetic neurodevelopmental disorders: a cross-sectional analysis

  Journal of Neurodevelopmental Disorders · 2025-10-07

  articleOpen access

  This study aims to identify clinical and developmental factors associated with psychotropic medication exposure and subspecialty psychiatric service utilization among patients with genetic neurodevelopmental disorders (GNDDs). We conducted a retrospective analysis of 316 patients from the Care and Research in Neurogenetics (CARING) Clinic at the University of California, Los Angeles (UCLA). We assessed the association between neurodevelopmental and psychiatric diagnoses, behavioral histories, family history, and service utilization with two outcomes: (1) the number of psychotropic medication classes trialed before clinic intake and (2) whether the patient was evaluated by a CARING psychiatrist. Poisson and logistic regression models were used to evaluate associations while adjusting for demographic and clinical covariates. Individuals with more severe behavioral disturbances had higher psychiatric service needs, while intellectual disability was associated with greater psychotropic medication exposure but not increased psychiatric consultation, possibly due to prior community-based care. The presence of a pathogenic/likely pathogenic genetic variant was not associated with either outcome, suggesting that genetic diagnosis alone does not predict psychiatric needs. Instead, behavioral comorbidities, not genetic status, were the primary drivers of psychotropic use and psychiatric referrals. A history of developmental delay was negatively associated with psychiatric consultation, and mediation analyses indicated that early intervention services partly explained this relationship. Additionally, patients receiving behavioral therapies had higher psychotropic exposure, reflecting greater clinical complexity and frequent use of multimodal treatment strategies. Our findings suggest that psychiatric needs in GNDDs are more closely tied to behavioral comorbidities than to genetic diagnosis status, reinforcing the importance of symptom-driven psychiatric evaluation. The observed relationship between early developmental interventions and psychiatric service utilization warrants further longitudinal investigation. These results highlight opportunities to optimize psychiatric care pathways through early screening, integrated behavioral and pharmacologic interventions, and targeted resource allocation for individuals with neurodevelopmental disorders.

  PublisherOA PDFDOI

• Characterizing developmental and behavioral profiles in developmental synaptopathies to inform clinical trial endpoints

  2025-04-14

  preprintOpen access

  Background: The Developmental Synaptopathies Consortium is a multi-site natural history network studying rare, neurogenetic syndromes associated with synaptic dysfunction and developmental delays. One aim of the Consortium is clinical trial readiness, including identifying clinical concepts and validating their measurement. Methods: We evaluated the scope and limitations of conventional cognitive and behavioral measurement strategies in 2–21-year-olds with Phelan-McDermid syndrome (PMS, N=98), Tuberous Sclerosis Complex (TSC, N=98) and PTEN Hamartoma Tumor syndrome (PHTS, N=69). Results: On average, intellectual disability (ID) severity was severe-to-profound in PMS, mild-to-moderate for TSC, and borderline (or absent) in PHTS. Severity of ID invalidated the use of many assessments, including standardized autism diagnostic measures. Conclusions: These results will inform trial planning for these and other similarly medically complex neurodevelopmental conditions.

  PublisherDOI

Recent grants

• Administrative Core

  NIH · $138.5M · 2015–2026

• Genetics and Biomarkers Core

  NIH · $60.0M · 2007–2024

• Mechanisms of Change with Early Intervention in Tuberous Sclerosis Complex

  NIH · $2.6M · 2017–2023

• Genetics and Biomarkers Core

  NIH · $3.9M · 2007–2022

• NIH Grant K23MH094517

  NIH · $906k · 2016

Frequent coauthors

• Charles A. Nelson

  Harvard University

  126 shared

• Damla Şentürk

  University of California, Los Angeles

  91 shared

• Charlotte DiStefano

  82 shared

• Sara Jane Webb

  Seattle Children's Hospital

  78 shared

• Catherine A. Sugar

  Neurobehavioral Systems

  74 shared

• Mirella Dapretto

  61 shared

• Carly Hyde

  UCLA Health

  59 shared

• Susan Faja

  Harvard University

  58 shared

Education

• MD

  Harvard Medical School

  2002

• BA

  Yale University

  1997