Circadian rhythms are 24-hour fluctuations determining periodicity in a wide range of physiological processes, including neural activity and hormone secretion, which controls sleeping and feeding habits. Despite significant diurnal variation in human brain function, neuroscientists have rarely considered the effects of time-of-day on their studies. Moreover, there are interpersonal discrepancies in sleep-wake patterns, diurnal preferences, and daytime alertness (known as chronotypes), which can cause different diurnal profiles in human cognition and brain performance. The study of circadian typology differences has increased in recent years, however, examining the effects of both time-of-day and people's chronotype requires further elucidation. In the present study, we performed graph-theory based network analysis on resting-state functional MRI (rs-fMRI) to explore the topological differences in whole-brain functional networks between the morning and evening sessions, as well as between extreme morning-type and evening-type participants. To that end, 62 individuals (31 extreme morning- versus 31 evening-type) underwent two fMRI sessions: about 1 hour after the wake-up time (morning) and about 10 hours after the wake-up time (evening), scheduled in accord with their declared habitual sleep-wake pattern on a regular working day. The findings of this study revealed the effect of time-of-day on the functional connectivity patterns, and there was no significant difference in chronotype categories. Compared to the morning session, we found relatively higher network segregation (i.e., higher small-worldness and modularity) and higher synchronization in the evening session. Interestingly, local graph measures were altered predominantly across the left hemisphere in areas involved in language processing, sensorimotor control, as well as subcortical portions of the limbic system.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Industrial Engineering and Management Systems
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Vasheghani Farahani, Farzad, "Identifying Diurnal Variability of Brain Connectivity Patterns using Graph Theory" (2020). Electronic Theses and Dissertations, 2020-. 309.