24 February 2012 Discriminating between brain rest and attention states using fMRI connectivity graphs and subtree SVM
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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.
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Fatemeh Mokhtari, Fatemeh Mokhtari, Shahab K. Bakhtiari, Shahab K. Bakhtiari, Gholam Ali Hossein-Zadeh, Gholam Ali Hossein-Zadeh, Hamid Soltanian-Zadeh, Hamid Soltanian-Zadeh, } "Discriminating between brain rest and attention states using fMRI connectivity graphs and subtree SVM", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83144C (24 February 2012); doi: 10.1117/12.911203; https://doi.org/10.1117/12.911203
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