Classifying fMRI-derived resting-state connectivity patterns according to their daily rhythmicity

Abstract

The vast majority of biological functions express rhythmic fluctuations across the 24-hour day. We investigated the degree of daily modulation across fMRI (functional Magnetic Resonance Imaging) derived resting-state data in 15 subjects by evaluating the time courses of 20 connectivity patterns over 8h (4 sessions). For each subject, we determined the chronotype, which describes the relationship between the individual circadian rhythm and the local time. We could therefore analyze the daily time course of the connectivity patterns controlling for internal time. Furthermore, as the participants' scan times were staggered as a function of their chronotype, we prevented sleep deprivation and kept time awake constant across subjects. Individual functional connectivity within each connectivity pattern was defined at each session as connectivity strength measured by a mean z-value and, in addition, as the spatial extent expressed by the number of activated voxels. Highly rhythmic connectivity patterns included two sub-systems of the Default-Mode Network (DMN) and a network extending over sensori-motor regions. The network characterized as the most stable across the day is mainly associated with processing of executive control. We conclude that the degree of daily modulation largely varies across fMRI derived resting-state connectivity patterns, ranging from highly rhythmic to stable. This finding should be considered when interpreting results from fMRI studies.