Fusion analysis of functional MRI data for classification of individuals based on patterns of activation.
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 com...
| Published in: | Brain Imaging & Behavior Vol. 9; no. 2; pp. 149 - 162 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Jun2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109738695&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109738695 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Jun2015 vid: 9 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109738695 NLM24519260 2013021003 10.1007/s11682-014-9292-1 NLM24519260 109738695 ppf: 149 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Fusion analysis of functional MRI data for classification of individuals based on patterns of activation. aug: au: Ramezani, Mahdi Abolmaesumi, Purang Marble, Kris Trang, Heather Johnsrude, Ingrid sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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