The self is the core of our mental life. Previous investigations have demonstrated a strong neural overlap between self‐related activity and resting state activity. This suggests that information about self‐relatedness is encoded in our brain's spontaneous activity. The exact neuronal mechanisms of such “rest‐self containment,” however, remain unclear. The present EEG study investigated temporal measures of resting state EEG to relate them to self‐consciousness. This was obtained with the self‐consciousness scale (SCS) which measures Private, Public, and Social dimensions of self. We demonstrate positive correlations between Private self‐consciousness and three temporal measures of resting state activity: scale‐free activity as indexed by the power‐law exponent (PLE), the auto‐correlation window (ACW), and modulation index (MI). Specifically, higher PLE, longer ACW, and stronger MI were related to higher degrees of Private self‐consciousness. Finally, conducting eLORETA for spatial tomography, we found significant correlation of Private self‐consciousness with activity in cortical midline structures such as the perigenual anterior cingulate cortex and posterior cingulate cortex. These results were reinforced with a data‐driven analysis; a machine learning algorithm accurately predicted an individual as having a “high” or “low” Private self‐consciousness score based on these measures of the brain's spatiotemporal structure. In conclusion, our results demonstrate that Private self‐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 self‐related information is temporally contained in the brain's resting state. “Rest‐self containment” can thus be featured by a temporal signature.

We present our work investigating the feasibility of combining intraoperative ultrasound for brain shift correction and augmented reality (AR) visualization for intraoperative interpretation of patient-specific models in image-guided neurosurgery (IGNS) of brain tumors. We combine two imaging technologies for image-guided brain tumor neurosurgery. Throughout surgical interventions, AR was used to assess different surgical strategies using three-dimensional (3-D) patient-specific models of the patient's cortex, vasculature, and lesion. Ultrasound imaging was acquired intraoperatively, and preoperative images and models were registered to the intraoperative data. The quality and reliability of the AR views were evaluated with both qualitative and quantitative metrics. A pilot study of eight patients demonstrates the feasible combination of these two technologies and their complementary features. In each case, the AR visualizations enabled the surgeon to accurately visualize the anatomy and pathology of interest for an extended period of the intervention. Inaccuracies associated with misregistration, brain shift, and AR were improved in all cases. These results demonstrate the potential of combining ultrasound-based registration with AR to become a useful tool for neurosurgeons to improve intraoperative patient-specific planning by improving the understanding of complex 3-D medical imaging data and prolonging the reliable use of IGNS.

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