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Fusion analysis of functional MRI data for classification of individuals based on patterns of activation
University of British Columbia, Canada; University of British Columbia, Canada.
University of British Columbia, Canada.
Queens University, Canada.
Queens University, Canada.
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2015 (English)In: BRAIN IMAGING AND BEHAVIOR, ISSN 1931-7557, Vol. 9, no 2, 149-161 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Verlag (Germany) , 2015. Vol. 9, no 2, 149-161 p.
Keyword [en]
fMRI; Fusion analysis; Functional image analysis; jICA; Automatic classification
National Category
Basic Medicine
URN: urn:nbn:se:liu:diva-119235DOI: 10.1007/s11682-014-9292-1ISI: 000354966600002PubMedID: 24519260OAI: diva2:821275

Funding Agencies|Natural Sciences and Engineering Research Council (NSERC); Canadian Institutes of Health Research (CIHR)

Available from: 2015-06-15 Created: 2015-06-12 Last updated: 2015-06-15

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Johnsrude, Ingrid
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Disability ResearchFaculty of Arts and SciencesThe Swedish Institute for Disability Research
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