An Information Theoretic Approach to Classify Cognitive States Using fMRI

Date

2013-11-13

Author

Onal, Itir
Ozay, Mete
Firat, Orhan
GİLLAM, İLKE
Yarman Vural, Fatoş Tunay

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this study, an information theoretic approach is proposed to model brain connectivity during a cognitive processing task, measured by functional Magnetic Resonance Imaging (fMRI). For this purpose, a local mesh of varying size is formed around each voxel. The arc weights of each mesh are estimated using a linear regression model by minimizing the squared error. Then, the optimal mesh size for each sample, that represents the information distribution in the brain, is estimated by minimizing various information criteria which employ the mean square error of linear regression model. The estimated mesh size shows the degree of locality or degree of connectivity of the voxels for the underlying cognitive process.

Subject Keywords

Brain modeling, Vectors, Standards, Mathematical model, Accuracy, Support vector machine classification

URI

https://hdl.handle.net/11511/54432

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

I. Onal, M. Ozay, O. Firat, İ. GİLLAM, and F. T. Yarman Vural, “An Information Theoretic Approach to Classify Cognitive States Using fMRI,” 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54432.