Orhan Firat, Itir Onal, Emre Aksan, Burak Velioglu, Ilke Oztekin, Fatos T. Yarman Vural

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Data mining and Machine Learning, Magnetic ResonanceImaging, Medical Image Processing, Brain State Decoding,Functional Connectivity, MVPA.


Functional Magnetic Resonance Imaging (fMRI) data con- sists of time series for each voxel recorded during a cog- nitive task. In order to extract useful information from this noisy and redundant data, techniques are proposed to select the voxels that are relevant to the underlying cog- nitive task. We propose a simple and efficient algorithm for decoding the brain states by modelling the correlation patterns between the voxel time series. For each stimulus during the experiment, a separate functional connectivity matrix is computed in voxel level. The elements in con- nectivity matrices are then filtered out by making use of a minimum spanning tree formed using a global connectivity matrix for the entire experiment in order to reduce dimen- sionality. For a recognition memory experiment with nine subjects, functional connectivity matrices are computed for encoding and retrieval phases. The class labels of the re- trieval samples are predicted within a k-nearest neighbour space constructed by the traversed entries in the functional connectivity matrices for encoding samples. The proposed method is also adapted to large scale functional connectiv- ity tasks by making use of graphics boards. Classification performance in ten categories is comparable and even bet- ter compared to both classical and enhanced methods of multi-voxel pattern analysis techniques.

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