LAUSR

Introduction The beginning of the EEG was inspired by Berger attempting to study higher brain function. The matter was too complex for a starting technique and could have ended there. Instead, it became a breakthrough by enlightening centuries of inquire on the nature of seizures. Thereafter, the EEG was devoted almost entirely to sharpen the patchy relation between paroxysmal discharges and epilepsy. Nowadays, the technique has evolved to a point where several aspects of brain function can be addressed. EEG sources can be localized in 3D space. Functional networks can be outlined by correlating source activities, in close resemblance with other imaging techniques. Furthermore, directionality can be obtained from phase relationships, a millisecond time-space measure that remains exclusive of electromagnetic signals. On the other side, human behavior is too complex to be modeled only by task-related and default-mode networks. Interactions among these and other yet unknown networks ultimately emerge as a dynamic phenomenon. This work focuses on the effective connectivity in selected networks from the EEG of healthy volunteers looking for correlations with personality traits from the Minnesota Multiphasic Personality Inventory (MMPI). Methods Twenty individuals with no psychiatric or neurological disorder completed the MMPI-2 before recording their EEG with I 10–20, standard settings. Two minutes artifact free, awake, eyes-closed EEG signal was analyzed with Neuroguide software to generate Low Resolution Electromagnetic Topography (LORETA) scores for each of the Broadman Areas (BA). Pearson correlations of spectral power and phase differences for each BA pairs in the selected networks, based on their potential relevance to personality traits and higher brain function: Panic (PN), Dorsal Attention (DAN), Ventral Attention (VAN), Default-Mode (DMN), Affective (AN), Executive (EN), Memory (MN) and Language (LN). Connectivity Z-scores were calculated with Neuroguide normative database. Network connectivity Z-scores (zNC) were then correlated with the MMPI scores using multiple correlation analysis. Results None of the subjects showed out of range MMPI scores. The EEGs were normal at visual inspection. All zNC were within normal limits in all subjects. However, different deviation trends were seen across networks creating a particular “spider-web” pattern for each subject. Individual deviation patterns were significantly correlated with MMPI-2 profiles. Most noticeably, deviations in the PN and DMN were correlated with several MMPI-2 scales (p  Conclusion EEG network analysis is a promising tool for studying whole brain function, as well as for dissecting higher order processes. Overall and each network effective connectivity were normal in our subjects, yet deviation trends correlated with MMIP profiles, suggesting a potential use in personality theory as well as in psychopathology studies.