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Paper   IPM / Cognitive / 12794
School of Cognitive Sciences
  Title:   Decoding brain states using backward edge elimination and graph kernels in fMRI connectivity networks
1.  Fatemeh Mokhtari
2.  Gholam Ali Hossein-Zadeh-Dehkordi
  Status:   Published
  Journal: Journal of Neuroscience Methods
  Vol.:  212
  Year:  2013
  Pages:   259-268
  Supported by:  IPM
In the current study, we present a new approach for decoding brain states based on the connectivity graphs extracted from functional magnetic resonance imaging (fMRI) data. fMRI connectivity graphs are constructed in different brain states and fed into an iterative support vector classifier that is enriched by shortest-path kernel. The classifier prunes the graphs of insignificant edges via a backward edge elimination procedure. The iteration in which maximum classification performance occurs is considered as optimum iteration. The edges and nodes that survive in the optimum iteration form discriminant networks between states. We apply “one-versus-one” approach to extend the proposed method into a multi-class classifier. This classifier is used to distinguish between five cognitive brain states from a blocked design fMRI data: (1) fixation, (2) detection of a single stimulus, (3) perceptual matching, (4) attentional cueing, and (5) delayed match-to-sample. The proposed method results in multi-class classification accuracy of 86.32

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