liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
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.
Show others and affiliations
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
Identifiers
URN: urn:nbn:se:liu:diva-119235DOI: 10.1007/s11682-014-9292-1ISI: 000354966600002PubMedID: 24519260OAI: oai:DiVA.org:liu-119235DiVA: diva2:821275
Note

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

Open Access in DiVA

No full text

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Johnsrude, Ingrid
By organisation
Disability ResearchFaculty of Arts and SciencesThe Swedish Institute for Disability Research
Basic Medicine

Search outside of DiVA

GoogleGoogle ScholarTotal: 1 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 62 hits
ReferencesLink to record
Permanent link

Direct link