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

Abstract

37 Biomarker development is currently a high priority in neurodevelopmental disorder research. For 38 many types of biomarkers (particularly biomarkers of diagnosis), reliability over short time periods is 39 critically important. In the field of autism spectrum disorder (ASD), resting electroencephalography 40 (EEG) power spectral densities (PSD) are well-studied for their potential as biomarkers. Classically, 41 such data have been decomposed into pre-specified frequency bands (e.g., delta, theta, alpha, beta, 42 and gamma). Recent technical advances, such as the Fitting Oscillations and One-Over-F (FOOOF) 43 algorithm, allow for targeted characterization of the features that naturally emerge within an EEG 44 PSD, permitting a more detailed characterization of the frequency band-agnostic shape of each 45individual's EEG PSD. Here, using two resting EEGs collected a median of 6 days apart from 22 46 children with ASD and 25 typically developing (TD) controls during the Feasibility Visit of the 47 Autism Biomarkers Consortium for Clinical Trials, we estimate within visit test-retest reliability 48 based on characterization of the PSD shape in two ways: (1) Using the FOOOF algorithm we 49 estimate six parameters (offset, slope, number of peaks, and amplitude, center frequency and 50 bandwidth of the largest alpha peak) that characterize the shape of the EEG PSD; and (2) using 51 nonparametric functional data analyses, we decompose the shape of the EEG PSD into a reduced set 52 of basis functions that characterize individual power spectrum shapes. We show that individuals 53 exhibit idiosyncratic PSD signatures that are stable over recording sessions using both 54 characterizations. Our data show that EEG activity from a brief two-minute recording provides an 55 efficient window into understanding brain activity at the single-subject level with desirable 56 psychometric characteristics that persist across different analytical decomposition methods. This is a 57 necessary step towards analytical validation of biomarkers based on the EEG PSD, and provides 58 insights into parameters of the PSD that offer short-term reliability (and thus promise as potential 59 biomarkers of trait or diagnosis) versus those that are more variable over the short term (and thus 60 may index state or other rapidly dynamic measures of brain function). Future research should 61 address longer-term stability of the PSD, for purposes such as monitoring development or response to 62 treatment. 63 64 65 Development of translational biomarkers is a crucial step towards clinical trial readiness for 66 neurodevelopmental disorders such as Autism Spectrum Disorder (ASD). 1 The recent failure of 67 several promising clinical trials 2,3 underscores the importance of biomarker development, and the 68 need for a range of biomarkers serving a range of purposes. For example, a diagnostic biomarker can 69 confirm presence or absence of a disorder, or identify individuals with a biologically-defined subtype 70 thereof, 4 in order to guide patient s...

Electroencephalogram aperiodic power spectral slope can be reliably measured and predicts ADHD risk in early development

“…Although we focus on ADHD risk, excitatory/inhibitory imbalance and differences in the aperiodic exponent of the power spectrum have been observed in other neurodevelopmental disorders as well. Racz et al (2021) identified a trend toward shallower exponents in a small sample of adults with schizophrenia as compared to typically developing adults, and there is interest in whether disruptions appear in autism spectrum disorder (Levin et al, 2020). Crucially, as others have highlighted (Donoghue, Haller, et al, 2020;Mamiya et al, 2021;Voytek & Knight, 2015), the excitatory/inhibitory balance is critical for optimal synaptic plasticity and neuronal developmental, generally, and for maximizing signal to noise ratio in neural circuitry, specifically.…”