Biology subjects

McPartland, J.

Publications and source records attributed to McPartland, J..

3 recordsLinked to original sources

bioRxiv2026

Heterogeneity of Brain Dynamics in Genetic and Psychiatric Conditions

Whether the heterogeneity of psychiatric conditions converges on shared neurophysiological alterations or translates into distinct signatures remains unclear. We assembled high-density electroencephalogram (hd-EEG) resting-state recordings from 4,812 individuals aged 5 months to 66 years across 11 psychiatric conditions, a broad spectrum of rare genetic variants, and typically developing (TD) individuals. We established normative developmental trajectories of source-space EEG across spectral organization, connectivity, and signal complexity. Psychiatric conditions showed small deviations, revealing a shared transdiagnostic profile. In contrast, single rare variants showed substantially larger, distinct and sometimes mirror-opposite signatures that collapsed toward the psychiatric profile when pooled. Autism Spectrum Disorder showed some of the smallest group-level effects yet the largest individual deviations, indicating substantial but directionally inconsistent alterations. EEG deviations followed a cortical gradient, with larger effects in sensorimotor regions. We demonstrate that sample sizes in the hundreds are required for robust associations with psychiatric diagnoses. This interactive open resource provides normative scores to benchmark future results.

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↗

bioRxiv2024

Widespread Associations between Behavioral Metrics and Brain Microstructure in ASD

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. Our lab has previously found that g-ratio, the proportion of axon width to myelin diameter, and axonal conduction velocity, which is associated with the capacity of an axon to carry information, are both decreased in ASD individuals. By associating these differences with performance on cognitive and behavioral tests, this study aims to first associate a broad array of behavioral metrics with neuroimaging markers of ASD, and to explore the prevalence of ASD subtypes using a neuroimaging driven perspective. Analyzing 273 participants (148 with ASD) ages 8 to 17 through an NIH-sponsored Autism Centers of Excellence network (MH100028), we observe widespread associations between behavioral and cognitive evaluations of autism and between behavioral and microstructural metrics, alongside different directional correlations between different behavioral metrics. Stronger associations with individual subcategories from each test rather than summary scores suggest that different neuronal profiles may be masked by composite test scores. Machine learning cluster analyses applied to neuroimaging data reinforce the association between neuroimaging and behavioral metrics and suggest that age-related maturation of brain metrics may drive changes in ASD behavior. This suggests that if ASD can be definitively subtyped, these subtypes may show different behavioral trajectories across the developmental period. Clustering identified a pattern of restrictive and repetitive behavior in some participants and a second group that was defined by high sensory sensitivity and language performance.

neuroscience↗