Doctoral Dissertations
Date of Award
12-2001
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Major
Psychology
Major Professor
Joel F. Lubar
Committee Members
Deborah Welsh, Michael Nash, Larry James
Abstract
Depressive disorders are among the most common and familiar of all psychiatric disorders, ironically, individuals suffering from depressive disorders are likely to never be diagnosed or treated. With the depressive disorders now being considered the source of an emerging public health crisis, a variety of public and private sector agencies have sought to address the issues of under-diagnosis and under-treatment. Despite their best efforts, about half of individuals with depressive disorders are not accurately diagnosed. The absence of a "gold standard" biological marker that can be used adjunctively with psychometric diagnostic instruments may be a factor that hinders primary practice physicians from formulating accurate diagnoses. Though differential regional cerebral bloodflow (rCBF) is known to discriminate between depressives and non-depressives in laboratory settings, the technology required to use this measure is hazardous and very expensive. Using less expensive EEG technology, one neurophysiological correlate of depression has already been identified - Alpha wave activation asymmetry in the frontal lobes. Other features of the human EEG, particularly coherence and phase, were hypothesized as potential biological markers of depression. A 19-channel QEEG recording using the International 10/20 system for electrode placement was used to obtain data from depressed individuals. An analysis of the qeEG records revealed coherence anomalies at the electrode pair F7-F8 in the theta and beta bandpasses. Phase anomalies were found at the F1-F2 site in the alpha and beta bandpasses. Single-band analysis of amplitude topography revealed excessive amplitude in the frontal region at the Ihz, 2hz, and 3hz frequencies.
Recommended Citation
Askew, John Hancock, "The diagnosis of depression using psychometric instruments and quantitative measures of electroencephalographic activity. " PhD diss., University of Tennessee, 2001.
https://trace.tennessee.edu/utk_graddiss/8463