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Proceedings Paper

Discriminating between brain rest and attention states using fMRI connectivity graphs and subtree SVM
Author(s): Fatemeh Mokhtari; Shahab K. Bakhtiari; Gholam Ali Hossein-Zadeh; Hamid Soltanian-Zadeh
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Paper Abstract

Decoding techniques have opened new windows to explore the brain function and information encoding in brain activity. In the current study, we design a recursive support vector machine which is enriched by a subtree graph kernel. We apply the classifier to discriminate between attentional cueing task and resting state from a block design fMRI dataset. The classifier is trained using weighted fMRI graphs constructed from activated regions during the two mentioned states. The proposed method leads to classification accuracy of 1. It is also able to elicit discriminative regions and connectivities between the two states using a backward edge elimination algorithm. This algorithm shows the importance of regions including cerebellum, insula, left middle superior frontal gyrus, post cingulate cortex, and connectivities between them to enhance the correct classification rate.

Paper Details

Date Published: 24 February 2012
PDF: 7 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83144C (24 February 2012); doi: 10.1117/12.911203
Show Author Affiliations
Fatemeh Mokhtari, Univ. of Tehran (Iran, Islamic Republic of)
Shahab K. Bakhtiari, Univ. of Tehran (Iran, Islamic Republic of)
Gholam Ali Hossein-Zadeh, Univ. of Tehran (Iran, Islamic Republic of)
Hamid Soltanian-Zadeh, Univ. of Tehran (Iran, Islamic Republic of)
Henry Ford Health System (United States)

Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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