Doctoral Dissertations

Date of Award


Degree Type


Degree Name

Doctor of Philosophy



Major Professor

Debora R. Baldwin

Committee Members

Gregory L. Stuart, Jacob J. Levy, John H. Dougherty


INTRODUCTION: Recent research exploring cortical functional connectivity defines a default network (DNt) of brain function and activation of a core midline network (CMS) in the processing of self. The electroencephalographic (EEG) activity in these components of the human DNt and CMS is not well understood. METHODS: This study was conducted with 63 participants. Individuals were recorded during eyes-closed (ECB) and eyes-opened (EOB) baselines and active task (AT) conditions (e.g., self-referential, self-image, self-concept, recent symptomology, other face and object processing). We estimated EEG source localization with standardized low resolution electromagnetic tomography (sLORETA). Subjective experience was obtained for baselines and photographic conditions. RESULTS: The ECB resting condition shows higher activity in all frequencies as compared to all other conditions. Likewise, the active tasks show differential effects for increased activity as compared to EOB for each region of interest (ROI) in each frequency domain. CONCLUSION: The data are in agreement with other neuroimaging techniques (fMRI/PET) investigating the DNt of brain function and further shows that the 3-dimensional localization accuracy of LORETA EEG is sufficient for the study of the DNt. In examining both within and between functional core regions there was a higher degree of activity in lower frequency bands during eyes closed; however, this pattern does not extend to all ROIs for all frequency domains. The differences may represent functional connectivity relating to endogenous/exogenous attention states as opposed to the simple concept of “resting” or “non-activity”. Further study of the functional relationships between EEG frequencies within and between regions in the default network and during self-specific processing may prove important to understanding the complex nature of neocortical functional integration.

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