About

Mirella Dapretto is a professor-in-residence in the Department of Psychiatry and Biobehavioral Sciences at the University of California Los Angeles (UCLA). She also serves as the Department Vice Chair in Psychiatry and Biobehavioral Sciences. Her research focuses on understanding the neural and genetic mechanisms underlying autism spectrum disorder and related neurodevelopmental conditions. Her work involves investigating brain activity, neural responses, and connectivity patterns in infants, children, and youth at high likelihood for developing autism, as well as exploring the relationships between brain function, gut metabolites, and autism symptomatology. Dapretto's contributions include studying atypical neural responses to language and social stimuli, the role of the cerebellum in behavioral development, and the genetic factors associated with autism. Her research also extends to examining sensory over-responsivity, early neural responses, and the biological mechanisms shared between autism and other conditions such as anxiety. She has contributed to advancing the understanding of autism through neuroimaging, genetic studies, and the investigation of brain oscillations and connectivity, with a focus on identifying biomarkers and mechanisms that can inform diagnosis and intervention strategies.

Research topics

• Psychology
• Medicine
• Developmental psychology
• Neuroscience
• Computer Science
• Audiology
• Cognitive psychology
• Artificial Intelligence
• Machine Learning
• Internal medicine
• Virology
• Pathology

Selected publications

• Atypical early neural responses to native and non-native language in infants at high likelihood for developing autism

  Molecular Autism · 2025-02-03 · 5 citations

  articleOpen accessSenior author

  BACKGROUND: Language difficulties are common in autism spectrum disorder (ASD), a neurodevelopmental condition characterized by impairments in social communication as well as restricted and repetitive behaviors. Amongst infant siblings of children with an ASD diagnosis - who are at higher likelihood for developing ASD - a high proportion also show difficulties and delays in language acquisition. METHODS: In this study, we used functional magnetic resonance imaging (fMRI) to examine differences in language processing in 9-month-old infants at high (HL) and typical (TL) familial likelihood for ASD. Infants were presented with native (English) and novel (Japanese) speech while sleeping naturally in the scanner. Whole-brain and a priori region-of-interest analyses were conducted to evaluate neural differences in language processing based on likelihood group and language condition. RESULTS: HL infants showed attenuated responses to speech in general, particularly in left temporal language areas, as well as a lack of neural discrimination between the native and novel languages compared to the TL group. Importantly, we also demonstrate that HL infants show distinctly atypical patterns of lateralization for speech processing, particularly during native speech processing, suggesting a failure to left-lateralize. LIMITATIONS: The sample size, particularly for the TL group, is relatively modest because of the challenges inherent to collecting auditory stimulus-evoked data from sleeping participants, as well as retention and follow-up difficulties posed by the COVID-19 pandemic. The groups were not matched on some demographic variables, but the present findings held even after accounting for these differences. CONCLUSIONS: To our knowledge, this is the first fMRI study to directly measure autism-associated atypicalities in native language uptake during infancy. These findings provide a better understanding of the neurodevelopmental underpinnings of language delay in ASD, which is a prerequisite step for developing earlier and more effective interventions for autistic children and HL siblings who experience language impairments.

  PublisherOA PDFDOI

• Relationships between brain activity, tryptophan-related gut metabolites, and autism symptomatology

  Nature Communications · 2025-04-14 · 28 citations

  articleOpen access

  While it has been suggested that alterations in the composition of gut microbial metabolites may play a causative role in the pathophysiology of autism spectrum disorder (ASD), it is not known how gut microbial metabolites are associated with ASD-specific brain alterations. In this cross-sectional, case-control observational study, (i) fecal metabolomics, (ii) task-based functional magnetic resonance imaging (fMRI), and (iii) behavioral assessments were obtained from 43 ASD and 41 neurotypical (NT) children, aged 8-17. The fMRI tasks used socio-emotional and sensory paradigms that commonly reveal strong evoked brain differences in ASD participants. Our results show that fecal levels of specific tryptophan-related metabolites, including kynurenate, were significantly lower in ASD compared to NT, and were associated with: 1) alterations in insular and cingulate cortical activity previously implicated in ASD; and 2) ASD severity and symptoms (e.g., ADOS scores, disgust propensity, and sensory sensitivities). Moreover, activity in the mid-insula and mid-cingulate significantly mediated relationships between the microbial tryptophan metabolites (indolelactate and tryptophan betaine) and ASD severity and disgust sensitivity. Thus, we identify associations between gut microbial tryptophan metabolites, ASD symptoms, and brain activity in humans, particularly in brain regions associated with interoceptive processing.

  PublisherOA PDFDOI

• 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

• Beyond motor learning: Insights from infant magnetic resonance imaging on the critical role of the cerebellum in behavioral development

  Developmental Cognitive Neuroscience · 2025-01-27 · 2 citations

  reviewOpen accessSenior authorCorresponding

  Although the cerebellum is now recognized for its crucial role in non-motor functions such as language, perceptual processes, social communication, and executive function in adults, it is often overlooked in studies of non-motor behavioral development in infancy. Recent magnetic resonance imaging (MRI) research increasingly shows the cerebellum is key to understanding the emergence of complex human behaviors and neurodevelopmental conditions. This review summarizes studies from diverse MRI modalities that link early cerebellar development from birth to age two with emerging non-motor behaviors and psychiatric symptomatology. Our focus centered on both term and preterm infants, excluding studies of perinatal injury and cerebellar pathology. We conclude that the cerebellum is implicated in many non-motor behaviors and implicit learning mechanisms in infancy. The field’s current limitations include inconsistencies in study design, a paucity of gold-standard infant neuroimaging tools, and treatment of the cerebellum as a uniform structure. Moving forward, the cerebellum should be considered a structure of greater interest to the developmental neuroimaging community. Studies should test developmental hypotheses about the behavioral roles of specific cerebro-cerebellar circuits, and theoretical frameworks such as Olson’s “model switch” hypothesis of cerebellar learning. Large-scale, longitudinal, well-powered neuroimaging studies of typical and preterm development will be key. • The cerebellum is a developmentally critical region of the brain in infancy • Individual differences in early cerebellar development may heavily influence the development of complex non-motor behaviors • Future MRI research in infants should include the cerebellum as a key region, ensuring that adequate tools are utilized • More studies are needed that explicitly test developmental hypotheses relating to early cerebellar growth and function • Longitudinal, well-powered studies of typical and preterm development will be key to expanding this area of work

  PublisherDOI

• Number of Alpha Peaks in the Electroencephalogram Is Associated With Clinical Phenotype and Copy Number Variants in Youths With Autism

  Biological Psychiatry Cognitive Neuroscience and Neuroimaging · 2025-10-15 · 2 citations

  articleOpen access
  PublisherOA PDFDOI

• Language network functional connectivity in infancy predicts developmental language trajectories

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-19 · 1 citations

  preprintOpen accessSenior authorCorresponding

  Abstract Although developmental language delays affect approximately 10% of children in the general population, the neurodevelopmental mechanisms that support normative language acquisition, and atypicalities that may predict later language delay, across the first year of life are poorly understood. Here, resting-state fMRI data from the Baby Connectome Project was used to evaluate age-related changes in language network functional connectivity and alterations associated with suboptimal language development. Additionally, a data-driven machine learning algorithm was used to partition our sample into three groups who showed Delayed, Typical, and Advanced trajectories of language development. These groups reliably differed on several assessments of language ability during infancy and toddlerhood. Using a priori brain regions involved in adult language processing, a seed-based functional connectivity analysis showed broad age-related increases in functional synchrony and specialization throughout the infant language network. Additionally, the Delayed group showed several atypical patterns of functional connectivity with language regions. Importantly, the magnitude of connectivity differences consistently predicted later language scores at two-year outcome across several different language assessments. These findings add to our understanding of normative neurodevelopmental patterns underlying language acquisition, and identify several potential biomarkers associated with language delay that could serve as future targets to inform diagnoses and clinical interventions. Highlights The language network undergoes significant maturational changes during infancy With age, functionally similar nodes integrate, while dissimilar nodes segregate Language-delayed infants show atypical connectivity in the language network These early atypicalities predict later language development at two-year outcome We identify several neural signatures associated with early language delay

  PublisherDOI

• 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

• Variability of bedtime in infancy and its relationship to parent-reported and auto-videosomnography sleep metrics

  Sleep Medicine · 2025-04-23 · 1 citations

  article
  PublisherDOI

• Salience network connectivity is altered in 6-week-old infants at heightened likelihood for developing autism

  Communications Biology · 2024-04-22 · 19 citations

  articleOpen accessSenior author

  Converging evidence implicates disrupted brain connectivity in autism spectrum disorder (ASD); however, the mechanisms linking altered connectivity early in development to the emergence of ASD symptomatology remain poorly understood. Here we examined whether atypicalities in the Salience Network - an early-emerging neural network involved in orienting attention to the most salient aspects of one's internal and external environment - may predict the development of ASD symptoms such as reduced social attention and atypical sensory processing. Six-week-old infants at high likelihood of developing ASD based on family history exhibited stronger Salience Network connectivity with sensorimotor regions; infants at typical likelihood of developing ASD demonstrated stronger Salience Network connectivity with prefrontal regions involved in social attention. Infants with higher connectivity with sensorimotor regions had lower connectivity with prefrontal regions, suggesting a direct tradeoff between attention to basic sensory versus socially-relevant information. Early alterations in Salience Network connectivity predicted subsequent ASD symptomatology, providing a plausible mechanistic account for the unfolding of atypical developmental trajectories associated with vulnerability to ASD.

  PublisherOA PDFDOI

• Atypical Neural Responses to Native and Non-Native Language in Infants at High Likelihood for Developing Autism

  Research Square · 2024-12-03

  preprintOpen accessSenior author
  PublisherOA PDFDOI

Recent grants

• 8/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT CHLA

  NIH · $28.7M · 2015–2027

• NIH Grant U01MH109589

  NIH · $17.1M · 2022

• Genetics and Biomarkers Core

  NIH · $60.0M · 2007–2024

• NIH Grant R03DC005159

  NIH · $229k · 2005

• Parsing ASD Heterogeneity: Neuroendophenotypes of Social Attention and Sensory Responsivity

  NIH · $3.8M · 2018–2025

Frequent coauthors

• Susan Y. Bookheimer

  Neurobehavioral Systems

  298 shared

• Leanna M. Hernandez

  University of California, Los Angeles

  95 shared

• Shulamite A. Green

  87 shared

• Sara Jane Webb

  Seattle Children's Hospital

  76 shared

• Shafali Jeste

  61 shared

• Raphael Bernier

  University of Washington

  59 shared

• Daniel H. Geschwind

  Center for Autism and Related Disorders

  55 shared

• Marco Iacoboni

  University of California, Los Angeles

  49 shared

Education

• Ph.D., Psychology

  UCLA

  1994

Awards & honors

• Suzanne Eaton, Ph.D. Memorial Prize
• Taylor M. Brown Memorial Award