Biomedical Sciences ETDs

Publication Date

12-1-2009

Abstract

Objective: Progressive brain changes late in the disease course of schizophrenia may be detected with functional connectivity. This study compared functional connectivity between patients with schizophrenia late in the disease course with matched healthy controls. Method: Subjects included 18 patients with schizophrenia with minimum 15 years disease duration and 28 matched healthy controls from the MIND Clinical Imaging Consortium database. The functional magnetic resonance imaging paradigm was the auditory oddball task. We used independent components analysis to identify temporally cohesive but spatially distributed neural networks. We selected the executive control and default mode networks for additional analysis. The temporal course of each spatial component was then regressed with a model of the hemodynamic time course based on the experimental paradigm to measure functional connectivity. The beta weights from this regression were used for additional group level analysis. Results: The anterior default mode network had a main effect by group (patients with schizophrenia and healthy controls) and an interaction with group and aging. As the patient group aged, they had less negative modulation of the anterior default mode network. The patient group also had significantly less positive modulation of the executive control network. Conclusions: These results show evidence of changes in functional connectivity in the anterior default mode network late in the disease course of schizophrenia. The decreased functional connectivity may be attributable to the progressive disease course of schizophrenia.

Keywords

Schizophrenia, fMRI, Indepent Component Analysis, Duration of Illness

Document Type

Thesis

Language

English

Degree Name

Clinical Research

Level of Degree

Masters

Department Name

Biomedical Sciences Graduate Program

First Advisor

Bustillo, Juan

First Committee Member (Chair)

Calhoun, Vince

Second Committee Member

Shuttleworth, Bill

Share

COinS