"Characterizing Structural Neural Networks in Major Depressive Disorder using Diffusion Tensor Imaging"

Advisor:
Xiaoping Hu, Ph.D. (Georgia Institute of Technology/Emory University)

Committee:
Helen S. Mayberg, M.D. (Emory University)
Paul E. Holtzheimer, M.D. (Dartmouth College / Emory University)
Xiaoming Huo, Ph.D. (Georgia Institute of Technology)
Shella Keilholz, Ph.D. (Georgia Institute of Technology / Emory University)
Robert Gross, M.D. Ph.D. (Georgia Institute of Technology / Emory University)

Diffusion tensor imaging (DTI) is a noninvasive MRI technique used to assess white matter (WM) integrity, fiber orientation, and structural connectivity (SC) using water diffusion properties. DTI techniques are rapidly evolving and are now having impact on the study of neuropsychiatric disorders including Major Depression. Major depressive disorder (MDD) is highly prevalent and a leading cause of worldwide disability and is increasingly viewed as a disorder of neural circuitry. Although many studies have evaluated the structural connectivity of putative circuits involved in this disorder using DTI, the results are highly variable likely to many technical and analytical limitations.

During the course of this research, we performed a comprehensive set of analyses of structural WM integrity in a large sample of depressed patients to clarify and extend past observations in MDD and to evaluate the utility of these methods to potentially optimize surgical targeting using deep brain stimulation in treatment resistant depressed patients. Whole brain, graph theory and hypothesis driven approaches were to used to assess and characterize the structural organization of putative depression circuits towards these mechanistic and clinical goals.