Discriminating between brain rest and attention states using fMRI connectivity graphs and subtree SVM

Fatemeh Mokhtari; Shahab K. Bakhtiari; Gholam Ali Hossein-Zadeh; Hamid Soltanian-Zadeh

doi:10.1117/12.911203

24 February 2012 Discriminating between brain rest and attention states using fMRI connectivity graphs and subtree SVM

Fatemeh Mokhtari, Shahab K. Bakhtiari, Gholam Ali Hossein-Zadeh, Hamid Soltanian-Zadeh

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83144C (2012) https://doi.org/10.1117/12.911203
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

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.

Citation Download Citation

Fatemeh Mokhtari, Shahab K. Bakhtiari, Gholam Ali Hossein-Zadeh, and 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); https://doi.org/10.1117/12.911203

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