Biology subjects

Naples, A.

Publications and source records attributed to Naples, A..

3 recordsLinked to original sources

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↗

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↗