Autism or Autism Spectrum Disorder (ASD) is a development disability which generally begins during childhood and may last throughout the lifetime of an individual. It is generally associated with difficulty in communication and social interaction along with repetitive behavior. One out of every 59 children in the United States is diagnosed with ASD [11] and almost 1% of the world population has ASD [12]. ASD can be difficult to diagnose as there is no definite medical test to diagnose this disorder. The aim of this thesis is to extract features from resting state functional Magnetic Resonance Imaging (rsfMRI) data as well as some personal information provided about each subject to train variations of a Graph Convolutional Neural Network to detect if a subject is Autistic or Neurotypical. The time series information as well as the connectivity information of specific parts of the brain are the features used for analysis. The thesis converts fMRI data into a graphical representation where the vertex represents a part of the brain and the edge represents the connectivity between two parts of the brain. New adjacency matrix filters were added to the Graph CNN model and the model was altered to add a time dimension.

Library of Congress Subject Headings

Autism--Diagnosis; Magnetic resonance imaging--Data processing; Neural networks (Computer science); Image analysis

Publication Date


Document Type


Student Type


Degree Name

Computer Engineering (MS)

Department, Program, or Center

Computer Engineering (KGCOE)


Raymond Ptucha

Advisor/Committee Member

Shanchieh Jay Yang

Advisor/Committee Member

Clark Hochgraf


RIT – Main Campus

Plan Codes