Publication Date

Spring 2020

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Leonard Wesley

Second Advisor

Robert Chun

Third Advisor

Yijie Sui

Keywords

Resting state, functional connectivity, graph theory analysis, clinical depression

Abstract

Clinical depression is a state of mind where the person suffers from persevering and overpowering sorrow. Existing examinations have exhibited that the course of action of arrangement in the brain of patients with clinical depression has a weird framework topology structure. In the earlier decade, resting-state images of the brain have been under the radar a. Specifically, the topological relationship of the brain aligned with graph hypothesis has discovered a strong connection in patients experiencing clinical depression. However, the systems to break down brain networks still have a couple of issues to be unwound. This paper attempts to give a machine-learning answer for the graph-based brain network investigations of resting state graphs analysis. This model attempts to determine a cost function for a given pair of nodes in the brain to check whether they are connected or not. It can be utilized by medicinal experts to treat patients experiencing clinical depression which helps in decision making of whether a node can be hit directly or not to cure the patient.

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