Data_Sheet_1_Classification of human chronotype based on fMRI network-based statistics.PDF

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.

睡眠时型（Chronotype）指个体内部昼夜生理节律与外部24小时明暗周期之间的关联，其与心理健康及认知功能的相关性正受到日益广泛的关注。晚睡型个体罹患抑郁症的风险显著升高，且在常规朝九晚五工作时段内认知表现会出现下降。然而，生理节律与支撑认知及心理健康的脑网络之间的相互作用机制仍未得到充分阐明。为解决这一科学问题，本研究采集了16名早睡型个体与22名晚睡型个体的静息态功能磁共振成像（rs-fMRI）数据，共开展三次扫描任务。我们构建了基于网络统计法（Network Based-Statistic）的分类框架，旨在探究脑功能网络中是否嵌入了可区分睡眠时型的特征信息，以及此类信息在一日内的动态变化规律。本研究发现，全天各时段均存在极端睡眠时型个体间存在显著差异的子网络，且此类差异可实现较高的分类准确率；我们明确了在晚间扫描时段实现97.3%分类准确率的严格阈值标准，并探究了相同条件下为何会降低其他扫描时段的分类性能。本研究揭示了极端睡眠时型个体脑功能网络的差异特征，为后续研究指明了方向——未来或可更精准地刻画内部生理节律、外部环境扰动、脑网络与精神疾病之间的关联机制。