A New Representation of fMRI Signal by a Set of Local Meshes for Brain Decoding

Onal I., Ozay M., Mizrak E., GİLLAM İ., YARMAN VURAL F. T.

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, vol.3, no.4, pp.683-694, 2017 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 3 Issue: 4
  • Publication Date: 2017
  • Doi Number: 10.1109/tsipn.2017.2679491
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
  • Page Numbers: pp.683-694
  • Keywords: Brain decoding, classification, functional magnetic resonance imaging (fMRI), voxel connectivity, HUMAN VISUAL-CORTEX, COGNITIVE STATES, MACHINE, GRAPHS
  • Middle East Technical University Affiliated: Yes


How neurons influence each other's firing depends on the strength of synaptic connections among them. Motivated by the highly interconnected structure of the brain, in this study, we propose a computational model to estimate the relationships among voxels and employ them as features for cognitive state classification. We represent the sequence of functional Magnetic Resonance Imaging (fMRI) measurements recorded during a cognitive stimulus by a set of local meshes. Then, we represent the corresponding cognitive state by the edge weights of these meshes each of which is estimated assuming a regularized linear relationship among voxel time series in a predefined locality. The estimated mesh edge weights provide a better representation of information in the brain for cognitive state or task classification. We examine the representative power of ourmesh edge weights on visual recognition and emotional memory retrieval experiments by training a support vector machine classifier. Also, we use mesh edge weights as feature vectors of inter-subject classification onHuman Connectome Project task fMRI dataset, and test their performance. We observe that mesh edge weights perform better than the popular fMRI features, such as, raw voxel intensity values, pairwise correlations, features extracted using PCA and ICA, for classifying the cognitive states.