Classification of human chronotype based on fMRI network-based statistics

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.

Automated summary

The article "The Impact of Chronotype on Cognition and Mental Health" suggests that individuals with a late chronotype have a higher likelihood of developing depression and exhibit poorer cognitive performance during the day. By analyzing rs-fMRI data from both early and late chronotypes, the study reveals that differential information about chronotype is embedded in functional brain networks and this embedding changes throughout the day. These findings provide insights into the relationship between internal physiology, external perturbations, brain networks, and disease.