Modeling Voxel Connectivity for Brain Decoding


Onal I., Ozay M., YARMAN VURAL F. T.

2015 International Workshop on Pattern Recognition in NeuroImaging PRNI 2015, California, United States Of America, 10 - 12 June 2015, pp.5-8 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/prni.2015.26
  • City: California
  • Country: United States Of America
  • Page Numbers: pp.5-8

Abstract

The massively dynamic nature of human brain cannot be represented by considering only a collection of voxel intensity values obtained from fMRI measurements. It has been observed that the degree of connectivity among voxels provide important information for modeling cognitive activities. Moreover, spatially close voxels act together to generate similar BOLD responses to the same stimuli. In this study, we propose a local mesh model, called Local Mesh Model with Temporal Measurements (LMM-TM), to first estimate spatial relationship among a set of voxels using spatial and temporal data measured at each voxel, and then employ the relationship for the construction of a connectivity model for brain decoding. For this purpose, we first construct a local mesh around each voxel (called seed voxel) by connecting it to its spatially nearest neighbors. Then, we represent the BOLD response of each seed voxel in terms of linear combination of the BOLD responses of its p-nearest neighbors. The relationship between a seed voxel and its neighbors is estimated by solving a linear regression problem. The estimated mesh arc weights are used to model local connectivity among the voxels that reside in a spatial neighborhood. Using these weights as features, we train Support Vector Machines and k-Nearest Neighbor classifiers. We test our model on a visual object recognition experiment. In the experimental analysis, we observe that classifiers that employ our features perform better than classifiers that employ raw voxel intensity values, local mesh model weights and features extracted using distance metrics such as Euclidean distance, cosine similarity and Pearson correlation.