Fusion analysis of functional MRI data for classification of individuals based on patterns of activation.

Authors

Mahdi Ramezani, Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada & Robotics and Control Laboratory, Department of Electrical Engineering, The University of British Columbia, 2332 Main Mall, Vancouver, BC, Canada V6T 1Z4Follow
Purang Abolmaesumi, Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada
Kris Marble, Department of Psychology, Queen’s University, Kingston, ON, Canada
Heather Trang, Department of Psychology, Queen’s University, Kingston, ON, Canada
Ingrid Johnsrude, Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada & Department of Psychology, Queen’s University, Kingston, ON, Canada & Linnaeus Centre for Hearing and Deafness, Department of Behavioural Sciences and Learning, Linköping University, Linköping, SwedenFollow

Document Type

Article

Publication Date

6-1-2015

Journal

Brain Imaging and Behavior

Volume

9

Issue

2

First Page

149

Last Page

161

URL with Digital Object Identifier

10.1007/s11682-014-9292-1

Abstract

Classification of individuals based on patterns of brain activity observed in functional MRI contrasts may be helpful for diagnosis of neurological disorders. Prior work for classification based on these patterns have primarily focused on using a single contrast, which does not take advantage of complementary information that may be available in multiple contrasts. Where multiple contrasts are used, the objective has been only to identify the joint, distinct brain activity patterns that differ between groups of subjects; not to use the information to classify individuals. Here, we use joint Independent Component Analysis (jICA) within a Support Vector Machine (SVM) classification method, and take advantage of the relative contribution of activation patterns generated from multiple fMRI contrasts to improve classification accuracy. Young (age: 19-26) and older (age: 57-73) adults (16 each) were scanned while listening to noise alone and to speech degraded with noise, half of which contained meaningful context that could be used to enhance intelligibility. Functional contrasts based on these conditions (and a silent baseline condition) were used within jICA to generate spatially independent joint activation sources and their corresponding modulation profiles. Modulation profiles were used within a non-linear SVM framework to classify individuals as young or older. Results demonstrate that a combination of activation maps across the multiple contrasts yielded an area under ROC curve of 0.86, superior to classification resulting from individual contrasts. Moreover, class separability, measured by a divergence criterion, was substantially higher when using the combination of activation maps.