Classification of human chronotype based on fMRI network-based statistics

Sophie L. Mason*, Leandro Junges, Wessel Woldman, Elise R. Facer-Childs, Brunno M. De Campos, Andrew P. Bagshaw, John R. Terry

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

45 Downloads (Pure)

Abstract

Chronotype—the relationship between the internal circadian physiology of an individual and the external 24-h light-dark cycle—is increasingly implicated in mental health and cognition. Individuals presenting with a late chronotype have an increased likelihood of developing depression, and can display reduced cognitive performance during the societal 9–5 day. However, the interplay between physiological rhythms and the brain networks that underpin cognition and mental health is not well-understood. To address this issue, we use rs-fMRI collected from 16 people with an early chronotype and 22 people with a late chronotype over three scanning sessions. We develop a classification framework utilizing the Network Based-Statistic methodology, to understand if differentiable information about chronotype is embedded in functional brain networks and how this changes throughout the day. We find evidence of subnetworks throughout the day that differ between extreme chronotypes such that high accuracy can occur, describe rigorous threshold criteria for achieving 97.3% accuracy in the Evening and investigate how the same conditions hinder accuracy for other scanning sessions. Revealing differences in functional brain networks based on extreme chronotype suggests future avenues of research that may ultimately better characterize the relationship between internal physiology, external perturbations, brain networks, and disease.
Original languageEnglish
Article number1147219
Number of pages22
JournalFrontiers in Neuroscience
Volume17
DOIs
Publication statusPublished - 5 Jun 2023

Bibliographical note

Funding:
SM acknowledges the financial support of the University of Birmingham through an Alumni Scholarship. LJ acknowledges support from a Waterloo Foundation research grant (Ref No. 1970-4687). WW acknowledges the financial support of Epilepsy Research UK through an Emerging Leader Fellowship (F2002). BC acknowledges the financial support of São Paulo Research Foundation (2013/07559-3). EF-C acknowledges financial support for this work from the Biotechnology and Biological Sciences Research Council (BBSRC, BB/J014532/1). EF-C has received funding from the Department of Industry, Innovation and Science, Australia, (#ICG000899 and #ICG001546) and was supported by a Science Industry Endowment Fund Ross Metcalf STEM+ Business Fellowship administered by the Commonwealth Scientific and Industrial Research Organization. EF-C and AB acknowledge the Engineering and Physical Sciences Research Council (EPSRC, EP/J002909/1) and an Institutional Strategic Support Fund Accelerator Fellowship from the Wellcome Trust (Wellcome, 204846/Z/16/Z). JT acknowledges the financial support of UKRI (EPSRC) via Fellowship EP/T027703/1. The publication of this article was supported by the University of Birmingham's Open Access Fund.

Keywords

  • chronotype (morningness-eveningness)
  • functional connectivity
  • fMRI
  • classifier
  • network-based statistical (NBS) analysis
  • functional networks
  • graph theory

Fingerprint

Dive into the research topics of 'Classification of human chronotype based on fMRI network-based statistics'. Together they form a unique fingerprint.

Cite this