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EEG-based clusters differentiate psychological distress, sleep quality and cognitive function in adolescents1.22 MB
Preprint VersionCC BY V4.0, Open Access
Preprint
EEG-based clusters differentiate psychological distress, sleep quality and cognitive function in adolescents
bioRxiv, Vol.15 October 2021
Cold Spring Harbor Laboratory Press
2021
Abstract
Introduction: To better understand the relationships between brain activity, cognitive function and mental health risk in adolescence there is value in identifying data-driven subgroups based on measurements of brain activity and function, and then comparing cognition and mental health symptoms between such subgroups.
Methods: Here we implement a multi-stage analysis pipeline to identify data-driven clusters of 12-year-olds (M = 12.64, SD = 0.32) based on frequency characteristics calculated from resting state, eyes-closed electroencephalography (EEG) recordings. EEG data was collected from 59 individuals as part of their baseline assessment in the Longitudinal Adolescent Brain Study (LABS) being undertaken in Queensland, Australia. Applying multiple unsupervised clustering algorithms to these EEG features, we identified well-separated subgroups of individuals. To study patterns of difference in cognitive function and mental health symptoms between core clusters, we applied Bayesian regression models to probabilistically identify differences in these measures between clusters.
Results: We identified 5 core clusters which were associated with distinct subtypes of resting state EEG frequency content. EEG features that were influential in differentiating clusters included Individual Alpha Frequency, relative power in 4 Hz bands up to 16 Hz, and 95% Spectral Edge Frequency. Bayesian models demonstrated substantial differences in psychological distress, sleep quality and cognitive function between these clusters. By examining associations between neurophysiology and health measures across clusters, we have identified preliminary risk and protective profiles linked to EEG characteristics.
Conclusion: In this work we have developed a flexible and scaleable pipeline to identify subgroups of individuals in early adolescence on the basis of resting state EEG activity. These findings provide new clues about neurophysiological subgroups of adolescents in the general population, and associated patterns of health and cognition that are not observed at the whole group level. This approach offers potential utility in clinical risk prediction for mental and cognitive health outcomes throughout adolescent development. Competing Interest Statement The authors have declared no competing interest.
Details
- Title
- EEG-based clusters differentiate psychological distress, sleep quality and cognitive function in adolescents
- Authors
- Owen Forbes (Corresponding Author) - Queensland University of TechnologyPaul E Schwenn (Author) - University of the Sunshine Coast, Queensland, Thompson InstitutePaul Pao-Yen Wu (Author) - Queensland University of TechnologyEdgar Santos-Fernandez (Author) - Queensland University of TechnologyHong-Bo Xie (Author) - Queensland University of TechnologyJim Lagopoulos (Author) - University of the Sunshine Coast, Queensland, Thompson InstituteLarisa T Mcloughlin (Author) - University of the Sunshine Coast, Queensland, Thompson InstituteDashiell D Sacks (Author) - University of the Sunshine Coast, Queensland, Thompson InstituteKerrie Mengersen (Author) - Queensland University of TechnologyDaniel F Hermens (Author) - University of the Sunshine Coast, Queensland, Thompson Institute
- Publication details
- bioRxiv, Vol.15 October 2021
- Publisher
- Cold Spring Harbor Laboratory Press
- Date published
- 2021
- DOI
- 10.1101/2021.10.14.464347
- ISSN
- 2692-8205
- Organisation Unit
- University of the Sunshine Coast, Queensland; Thompson Institute
- Language
- English
- Record Identifier
- 99662198302621
- Output Type
- Preprint
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