Biology subjects

Jeste, S.

Publications and source records attributed to Jeste, S..

8 recordsLinked to original sources

bioRxiv2026

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

Visual processing undergoes rapid development 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-4). VEP components (P1 and N1) were characterized by their amplitude and latency, as well as trial-to-trial variability in these measures. 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 time points, with 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 time points was associated with higher cognitive and language scores at 24 months. In contrast, conventional measures of mean P1 latency and amplitude were not associated with developmental outcomes. These findings suggest that temporal variability in early visual responses may index adaptive sensory-circuit flexibility during a period of rapid experience-dependent development. VEP response-timing variability may therefore provide an early mechanistic marker of sensory-circuit organization relevant to later developmental trajectories. Research HighlightsO_LIGreater trial-to-trial variability in visual cortical response timing was associated with higher cognitive and language scores in infants at high familial likelihood for autism. C_LIO_LIConventional average VEP measures were unrelated to outcomes, suggesting temporal variability may more sensitively capture relevant neural-circuit differences in this population. C_LIO_LIGreater variability in early visual responses may reflect adaptive sensory-circuit flexibility during a critical period of neurodevelopment. C_LIO_LIVEP response-timing measures provide an early mechanistic window into sensory-circuit organization and its relation to later developmental trajectories. C_LI

neuroscience↗

bioRxiv2025

Altered aperiodic EEG spectral power during speech perception task is associated with verbal communication in youths with Autism Spectrum Disorder

Most children with Autism Spectrum Disorder (ASD) have co-occurring language impairment, but its neural mechanisms are not well known. Excitation (E) / inhibition (I) imbalance is considered as a key neurobiological mechanism of ASD, and several electroencephalography (EEG)-based E/I balance metrics have been proposed in the previous studies. The goal of the present research was to focus on these metrics abstracted from the speech perception task to investigate their relation to language/communication in autistic youths. We used a high-density 128-channel EEG to register neural responses during speech perception task in the sex- and age-matched groups of youths with ASD (N = 162) and typically developing (TD) controls (N = 144), aged 7-18 years old. The results revealed alterations in the E/I measures in the ASD group, pointing to a higher level of excitation or neural noise in the cortex as well as broadband reduction of spectral power during speech perception. A greater neural noise reflected in the reduction of aperiodic exponent and offset was associated with lower verbal communication skills in youths with ASD. The findings suggested that the higher noisiness in the cortical systems may be a relevant marker to monitor in relation to language/communication in ASD.

neuroscience↗

bioRxiv2025

Structural Determinants of Signal Speed: A Multimodal Investigation of Face Processing in Autism Spectrum Disorder

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. TeaserEstimated axonal latency is a non-invasive, spatially detailed measure of neuronal speed to inform brain function and disorder.

neuroscience↗

bioRxiv2021

Altered Salience Network Connectivity in 6-Week-Old Infants at Risk for Autism

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 (SN) - an early-emerging neural network involved in orienting attention to the most salient aspects of ones internal and external environment - may predict the development of ASD markers such as reduced social attention and atypical sensory processing. Six-week-old infants at high-risk for ASD exhibited stronger SN connectivity with sensorimotor regions; low-risk infants demonstrated stronger SN 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 SN connectivity predicted subsequent ASD symptomatology, providing a plausible mechanistic account for the unfolding of atypical developmental trajectories associated with ASD risk.

neuroscience↗

bioRxiv2021

Electrophysiological signatures of brain aging in autism spectrum disorder.

Emerging evidence suggests that aging processes may be altered in adults with autism spectrum disorder (ASD). However, it remains unclear if oscillatory slowing, a key neurophysiological change in the aging brain, manifests atypically in this population. This study sought to examine patterns of age-related oscillatory slowing in adults with ASD, captured by reductions in the brains peak alpha frequency. Resting-state EEG data from adults (18-70 years) with ASD (N=93) and age-matched neurotypical (NT) controls (N=87) were pooled from three independent datasets. A robust curve-fitting procedure quantified the peak frequency of alpha oscillations (7-13Hz) across all brain regions. Associations between peak alpha frequency and age were assessed and compared between groups. Consistent with characteristic patterns of oscillatory slowing, peak alpha frequency was negatively associated with age across the entire sample (p<.0001). A significant group by age interaction revealed that this relationship was more pronounced in adults with ASD (p<.01), suggesting that that age-related oscillatory slowing may be accelerated in this population. Scalable EEG measures such as peak alpha frequency could provide insights into neural aging that are crucially needed to inform care plans and preventive interventions that can promote successful aging in ASD.

neuroscience↗

bioRxiv2020

Altered Thalamocortical Connectivity in Six-Week Old Infants at High Familial Risk for Autism Spectrum Disorder

Converging evidence from neuroimaging studies has revealed altered connectivity in cortical-subcortical networks in youth and adults with autism spectrum disorder (ASD). Comparatively little is known about the development of cortical-subcortical connectivity in infancy, before the emergence of overt ASD symptomatology. Here we examined early functional and structural connectivity of thalamocortical networks in infants at high familial risk for ASD (HR) and lowrisk controls (LR). Resting-state functional connectivity (rs-fcMRI) and diffusion tensor imaging (DTI) data were acquired 52 six-week-old infants. Functional connectivity was examined between six cortical seeds -prefrontal, motor, somatosensory, temporal, parietal, and occipital regions- and bilateral thalamus. We found significant thalamic-prefrontal underconnectivity, as well as thalamic-occipital and thalamic-motor overconnectivity in HR infants, relative to LR infants. Subsequent structural connectivity analyses also revealed atypical white matter integrity in thalamic-occipital tracts in HR infants, compared to LR infants. Notably, aberrant rs-fcMRI and DTI connectivity indices at 6 weeks predicted atypical social development between 6 and 36 months of age, as assessed with eye-tracking and diagnostic measures. These findings indicate that thalamocortical connectivity is disrupted at both the functional and structural level in HR infants as early as six weeks of age, providing a possible early marker of risk for ASD.

neuroscience↗

bioRxiv2019

Functional connectivity differences in early infancy precede autism symptoms: a multivariate pattern analysis

Functional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Mapping pre-symptomatic functional disruptions in ASD could identify infants based on neural risk, providing a crucial opportunity to mediate outcomes before behavioral symptoms emerge. Here we quantify functional connectivity using scalable EEG measures of oscillatory phase coherence (6-12Hz). Infants at high and low familial risk for ASD (N=65) underwent an EEG recording at 3 months of age and were assessed for ASD symptoms at 18 months using the Autism Diagnostic Observation Schedule-Toddler Module. Multivariate pattern analysis was used to examine early functional patterns that are associated with later ASD symptoms. Support vector regression (SVR) algorithms accurately predicted observed ASD symptoms at 18 months from EEG data at 3 months (r=0.76, p=0.02). Specifically, lower frontal connectivity and higher right temporo-parietal connectivity predicted higher ASD symptoms. The SVR model did not predict non-verbal cognitive abilities at 18 months (r=0.15, p=0.36), suggesting specificity of these brain alterations to ASD. These data suggest that frontal and temporo-parietal dysconnectivity play important roles in the early pathophysiology of ASD. Early functional differences in ASD can be captured using EEG during infancy and may inform much-needed advancements in the early detection of ASD.

neuroscience↗

bioRxiv2019

Within Visit Test-Retest Reliability of EEG Profiles in Children with Autism Spectrum Disorder and Typical Development

Biomarker development is currently a high priority in neurodevelopmental disorder research. For many types of biomarkers (particularly biomarkers of diagnosis), reliability over short time periods is critically important. In the field of autism spectrum disorder (ASD), resting electroencephalography (EEG) power spectral densities (PSD) are well-studied for their potential as biomarkers. Classically, such data have been decomposed into pre-specified frequency bands (e.g., delta, theta, alpha, beta, and gamma). Recent technical advances, such as the Fitting Oscillations and One-Over-F (FOOOF) algorithm, allow for targeted characterization of the features that naturally emerge within an EEG PSD, permitting a more detailed characterization of the frequency band-agnostic shape of each individuals EEG PSD. Here, using two resting EEGs collected a median of 6 days apart from 22 children with ASD and 25 typically developing (TD) controls during the Feasibility Visit of the Autism Biomarkers Consortium for Clinical Trials, we estimate within visit test-retest reliability based on characterization of the PSD shape in two ways: (1) Using the FOOOF algorithm we estimate six parameters (offset, slope, number of peaks, and amplitude, center frequency and bandwidth of the largest alpha peak) that characterize the shape of the EEG PSD; and (2) using nonparametric functional data analyses, we decompose the shape of the EEG PSD into a reduced set of basis functions that characterize individual power spectrum shapes. We show that individuals exhibit idiosyncratic PSD signatures that are stable over recording sessions using both characterizations. Our data show that EEG activity from a brief two-minute recording provides an efficient window into understanding brain activity at the single-subject level with desirable psychometric characteristics that persist across different analytical decomposition methods. This is a necessary step towards analytical validation of biomarkers based on the EEG PSD, and provides insights into parameters of the PSD that offer short-term reliability (and thus promise as potential biomarkers of trait or diagnosis) versus those that are more variable over the short term (and thus may index state or other rapidly dynamic measures of brain function). Future research should address longer-term stability of the PSD, for purposes such as monitoring development or response to treatment.

neuroscience↗