Lack of univariate, clinically-relevant biomarkers of autism in resting state EEG: a study of 776 participants

Dede, Adam J. O.; Xiao, Wen‐Yi; Vaci, Nemanja; Cohen, Michael X; Milne, Elizabeth

doi:10.1101/2023.05.21.23290300

Cited by 3 publications

(1 citation statement)

References 60 publications

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“…Contrary to work implicating involving beta waveform shape in Parkinson’s disease (Cole, van der Meij, et al, 2017), we found no significant differences in alpha-band rhythm features for either of two prominent neurodevelopmental disorders. This is consistent with recent work from Dede et al, 2023 that failed to find a single biomarker for ASD after extracting a high number of measures from resting-state EEG (in sensor space, including band-power and frequency measures) in a dataset of 776 individuals. There is large inter-individual variation in alpha-band rhythm features, but high internal consistency (Lopez et al, 2023), which hints at the fact that usage of longitudinal data (Gabard-Durnam et al, 2019) may be a promising approach to capture diagnosis-related information sufficiently.…”

Section: Discussionsupporting

confidence: 91%

Resting-state is not enough: alpha and mu rhythms change shape across development, but lack diagnostic sensitivity

Bender,

Voytek,

Schaworonkow

2023

Preprint

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In the human brain, the alpha rhythm in occipital cortex and the mu rhythm in sensorimotor cortex are among the most prominent rhythms, with both rhythms functionally implicated in gating modality-specific information. Separation of these rhythms is non-trivial due to the spatial mixing of these oscillations in sensor space. Using a computationally efficient processing pipeline, we show that alpha and mu rhythms can be separated from electroencephalography recordings performed on nearly 3000 children aged 5-18. Using the extracted time series for each rhythm, we characterized the waveform shape on a cycle-by-cycle basis and examined whether and how the waveform shape differs across development and in attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Alpha and mu rhythms both exhibit nonsinusoidal waveform shape that changes significantly across development. We found no differences in the resting-state features of these alpha-band rhythms for either ADHD or ASD in comparison to typically developing participants. While waveform shape is ignored by traditional Fourier spectral analyses, these nonsinusoidal properties may be informative for building more constrained generative models for different types of alpha-band rhythms. Our results highlight the difficulties in identifying targeted biomarkers for ADHD and ASD from resting-state alpha-band rhythms.

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Section: Discussionsupporting

confidence: 91%

Resting-state is not enough: alpha and mu rhythms change shape across development, but lack diagnostic sensitivity

Bender,

Voytek,

Schaworonkow

2023

Preprint

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Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction

Bailey

Fulcher

Caldwell

et al. 2023

Preprint

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Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, this research has mostly examined power in different frequency bands. Here we compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences between meditators and non-meditators. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7,381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC showed above-chance classification accuracy (67%, pFDR = 0.007), for which 405 features significantly distinguished meditators (all pFDR < 0.05). Top-performing features indicated that meditators exhibited more consistent statistical properties across shorter subsegments of their EEG time-series (higher stationarity) and displayed an altered distributional shape of values about the mean. By contrast, classifiers trained with traditional band-power measures did not distinguish the groups (pFDR > 0.05). Our novel analysis approach suggests the key signatures of meditators' brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.

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A systematic review of aperiodic neural activity in clinical investigations

Donoghue

2024

Preprint

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In the study of neuro-electrophysiological recordings, aperiodic neural activity - activity with no characteristic frequency - has increasingly become a common feature of study. This interest has rapidly extended to clinical work, with many reports investigating aperiodic activity from patients from a broad range of clinical disorders. This work typically seeks to evaluate aperiodic activity as a putative biomarker relating to diagnosis or treatment response, and/or as a potential marker of underlying physiological activity. There is thus far no clear consensus on if and how aperiodic neural activity relates to clinical disorders, nor on the best practices for how to study it in clinical research. To address this, this systematic literature review, following PRISMA guidelines, examines reports of aperiodic activity in electrophysiological recordings with human patients with psychiatric and/or neurological disorders, finding 143 reports across 35 distinct disorders. Reports within and across disorders are summarized to evaluate current findings and examine what can be learned as pertains to the analysis, interpretations, and overall utility of aperiodic neural activity in clinical investigations. Aperiodic activity is commonly reported to relate to clinical diagnoses, with 31 of 35 disorders reporting a significant effect in diagnostic and/or treatment related studies. However, there is variation in the consistency of results across disorders, with the heterogeneity of patient groups, disease etiologies, and treatment status arising as common themes across different disorders. Overall, the current variability of results, potentially confounding covariates, and limitations in current understanding of aperiodic activity suggests further work is needed before aperiodic activity can be established as a potential biomarker and/or marker of underlying pathological physiology. Finally, a series of recommendations are proposed, based on the findings, limitations, and key discussion topics of the current literature to assist with guiding productive future work on the clinical utility of studying aperiodic neural activity.

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Lack of univariate, clinically-relevant biomarkers of autism in resting state EEG: a study of 776 participants

Cited by 3 publications

References 60 publications

Resting-state is not enough: alpha and mu rhythms change shape across development, but lack diagnostic sensitivity

Resting-state is not enough: alpha and mu rhythms change shape across development, but lack diagnostic sensitivity

Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction

A systematic review of aperiodic neural activity in clinical investigations

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