Rhythm of the Night: Brain Activity and Performance on a Sustained Attention Task is Modulated by Circadian Typology and Time of Day

Document Type


Publication Date



The human circadian system plays an important role in biological and psychological processes in both health and disease. Circadian typology (CT) is categorized into three general chronotypes: morning, evening, and neither. Research suggests that an individual’s diurnal preference may be associated with differences in cognitive abilities, personality traits, and incidence of psychiatric disorders. However, there is a dearth of research examining the relationship between chronotype, time of day, functional brain activity, and cognitive task performance in an age-controlled sample. Here we utilized a sustained attention to response task (SART) and electroencephalography (EEG) in a desynchrony protocol. College-aged morning type (MT) and evening type (ET) participants completed a SART task on two separate occasions during which brain activity from a 64-channel EEG system was recorded-at 8am and again at 6pm on a separate day, in counterbalanced order. This allowed for one test session to be completed when the participant was within their preferred testing time, presumably when underlying neural networks corresponding to alerting and sustained arousal and vigilance would be most efficient, as well as out-of-phase with diurnal preference. When examining both reaction times and response accuracy, a performance improvement was observed when participants were tested in-phase, when compared to performance out-of-phase. Further, an overall performance advantage was observed for MT participants when compared to age-matched ET participants. A graph theoretical approach was utilized to examine brain activity during the SART task. Distinct relationships between nodes within the task network were observed when a participant was tested in-phase when compared to out-of-phase with diurnal preference. This suggests that differences in task performance may be instantiated through transient changes in brain network function. These preliminary results may offer further insight into how task performance can change throughout the day, and the neural networks associated with those performance changes.

First Advisor

Lee Baugh

Second Advisor

Kelene Fercho

Research Area

Basic Biomedical Science

This document is currently not available here.