TY - GEN
T1 - An information theoretic approach to classify cognitive states using fMRI
AU - Onal, Itir
AU - Ozay, Mete
AU - Firat, Orhan
AU - Öztekin, Ilke
AU - Vural, Fatos T.Yarman
PY - 2013/12/1
Y1 - 2013/12/1
N2 - 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. The samples are generated during an fMRI experiment employing item recognition (IR) and judgment of recency (JOR) tasks. For each sample, estimated arc weights of the local mesh with optimal size are used to classify whether it belongs to IR or JOR tasks. Results indicate that the suggested connectivity model with optimal mesh size for each sample represent the information distribution in the brain better than the state-of-the art methods.
AB - 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. The samples are generated during an fMRI experiment employing item recognition (IR) and judgment of recency (JOR) tasks. For each sample, estimated arc weights of the local mesh with optimal size are used to classify whether it belongs to IR or JOR tasks. Results indicate that the suggested connectivity model with optimal mesh size for each sample represent the information distribution in the brain better than the state-of-the art methods.
UR - http://www.scopus.com/inward/record.url?scp=84894136089&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894136089&partnerID=8YFLogxK
U2 - 10.1109/BIBE.2013.6701565
DO - 10.1109/BIBE.2013.6701565
M3 - Conference contribution
AN - SCOPUS:84894136089
SN - 9781479931637
T3 - 13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013
BT - 13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013
T2 - 13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013
Y2 - 10 November 2013 through 13 November 2013
ER -