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

Resting State Network Dynamics Across Wakefulness and Sleep

The function of sleep is a longstanding mystery of the brain. By contrast, the function of resting state networks (RSNs) is one of its most recent mysteries. The relationship between RSNs and neuronal activity has been unclear since RSNs were discovered during the advent of functional magnetic resonance imaging (fMRI). Somewhat paradoxically, investigating these enigmatic phenomena in parallel can help to illuminate the function of both. The three studies described as part of this thesis all involve an evaluation of RSN dynamics across wakefulness and sleep. They are all based on the same dataset, derived from an experimental paradigm in which healthy, non sleep-deprived participants (N=36, 21 female) slept in an MRI scanner, as their brain activity was recorded using simultaneous electroencephalography (EEG)-fMRI. An independent component analysis (ICA) was performed in the first study. Spatial boundaries of components in each sleep stage were compared with those of wakefulness, in the first attempt to catalogue RSNs across all healthy alternate functional modes of the brain. Against expectations, all non-wake-RSN components were positively identified as noise. This indicated that sleep is supported by much the same RSN architecture as wakefulness, despite the unique operations performed during sleep. In the second study, between-RSN functional connectivity (FC) dynamics were evaluated across wakefulness and sleep, in order to determine whether they reflect known cortical neurophysiological dynamics. This was confirmed, highlighting the connection between RSNs and neuronal activity. Moreover, the dynamic pattern suggested that one of the functions of sleep may be to homeostatically counterbalance wakefulness RSN FC. A further pattern, indicating increased FC of “higher-order” RSNs (e.g., default mode network), suggested that slow wave sleep might manifest an altered, rather than a reduced state of awareness, in contrast to historical depictions. Finally, the third study correlated frequency-banded oscillatory activity, as measured by EEG, with RSN activity, as measured with fMRI. This was done in order to track changes in representations of frequency-banded neuronal activity in each RSN across stages. It was discovered that the pattern of frequency band representation dynamics reflects the aforementioned cortical neurophysiological dynamics, further strengthening the connection between RSNs and neuronal activity