The temporal signature of self: Temporal measures of restingâ state EEG predict selfâ consciousness

The self is the core of our mental life. Previous investigations have demonstrated a strong neural overlap between selfâ related activity and resting state activity. This suggests that information about selfâ relatedness is encoded in our brain’s spontaneous activity. The exact neuronal mechanisms of such â restâ self containment,â however, remain unclear. The present EEG study investigated temporal measures of resting state EEG to relate them to selfâ consciousness. This was obtained with the selfâ consciousness scale (SCS) which measures Private, Public, and Social dimensions of self. We demonstrate positive correlations between Private selfâ consciousness and three temporal measures of resting state activity: scaleâ free activity as indexed by the powerâ law exponent (PLE), the autoâ correlation window (ACW), and modulation index (MI). Specifically, higher PLE, longer ACW, and stronger MI were related to higher degrees of Private selfâ consciousness. Finally, conducting eLORETA for spatial tomography, we found significant correlation of Private selfâ consciousness with activity in cortical midline structures such as the perigenual anterior cingulate cortex and posterior cingulate cortex. These results were reinforced with a dataâ driven analysis; a machine learning algorithm accurately predicted an individual as having a â highâ or â lowâ Private selfâ consciousness score based on these measures of the brain’s spatiotemporal structure. In conclusion, our results demonstrate that Private selfâ consciousness is related to the temporal structure of resting state activity as featured by temporal nestedness (PLE), temporal continuity (ACW), and temporal integration (MI). Our results support the hypothesis that selfâ related information is temporally contained in the brain’s resting state. â Restâ self containmentâ can thus be featured by a temporal signature.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147871/1/hbm24412.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147871/2/hbm24412_am.pd