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Generalized Prediction Model for Detection of Psychiatric Disorders Open Access


Other title
Machine Learning
Type of item
Degree grantor
University of Alberta
Author or creator
Sen, Bhaskar
Supervisor and department
Greiner, Russ (Computing Science) Brown, Matthew (Computing Science)
Examining committee member and department
Boulanger, Pierre (Computing Science)
Jagersand, Martin (Computing Science)
Department of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
Computer aided diagnosis of mental disorders like Attention Deficit Hyperactivity Disorder (ADHD) and Autism is a primary step towards automated detection and prognosis of these psychiatric diseases. This dissertation applies analyses based on learning models that use structural texture and functional connectivity to diagnose ADHD and also Autism, from (structural) 3-dimensional magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. One model learns texture-based filters that are used to extract features from MRI scans. Using these learned features, the model achieves 62.57% (baseline 54.97%) accuracy on the ADHD-200 hold-out dataset for differentiating between healthy control vs ADHD patients and also achieves 61.73% (baseline 51.57%) accuracy on the ABIDE (Autism) hold-out test for differentiating between healthy control vs Autism patients. Our next model examines temporal sequence of fMRI activation levels at various brain locations in order to make a diagnosis from fMRI scans. This incorporates spatial nonstationary independent component analysis of the fMRI scans in order to extract the uncorrelated components and decomposes fMRI scans into common spatial components and corresponding time courses. Using individual time courses of 45 independent components as features, our algorithm learns a classifier that yields an accuracy of 64.91% on the ADHD-200 hold-out dataset, and 62.33% accuracy on the ABIDE hold-out test. This result is higher (2.31% for ADHD and 2.33% for Autism) than previously published accuracies on these datasets using fMRI scans. Finally a combination of multimodal features yields 67.25% diagnosis accuracy on ADHD-200 and 64.31% accuracy on ABIDE. This result is significantly higher (4.65% for ADHD with one sided p = 0.01 and 4.31% for Autism with one sided p = 1.6172e-06) than previously published hold-out accuracies on these datasets using only imaging data. Our results indicate that combining multimodal features yields good classification accuracy for diagnosis of ADHD and Autism, which is an important step towards computer aided diagnosis of these psychiatric diseases.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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