TY - GEN
T1 - Functional mesh learning for pattern analysis of cognitive processes
AU - Firat, Orhan
AU - Ozay, Mete
AU - Önal, Itir
AU - Oztekin, Ilke
AU - Vural, Fatoş T.Yarman
PY - 2013/12/9
Y1 - 2013/12/9
N2 - We propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning machine, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using functional neighborhood concept. In order to define functional neighborhood, the similarities between the time series recorded for voxels are measured and functional connectivity matrices are constructed. Then, the local mesh for each voxel is formed by including the functionally closest neighboring voxels in the mesh. The relationship between the voxels within a mesh is estimated by using a linear regression model. These relationship vectors, called Functional Connectivity aware Local Relational Features (FC-LRF) are then used to train a statistical learning machine. The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-Nearest Neighbor and Support Vector Machine, are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the Functional Mesh Learning model, which range in 62-68% is superior to the classical multi-voxel pattern analysis (MVPA) methods, which range in 40-48%, for ten semantic categories.
AB - We propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning machine, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using functional neighborhood concept. In order to define functional neighborhood, the similarities between the time series recorded for voxels are measured and functional connectivity matrices are constructed. Then, the local mesh for each voxel is formed by including the functionally closest neighboring voxels in the mesh. The relationship between the voxels within a mesh is estimated by using a linear regression model. These relationship vectors, called Functional Connectivity aware Local Relational Features (FC-LRF) are then used to train a statistical learning machine. The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-Nearest Neighbor and Support Vector Machine, are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the Functional Mesh Learning model, which range in 62-68% is superior to the classical multi-voxel pattern analysis (MVPA) methods, which range in 40-48%, for ten semantic categories.
UR - http://www.scopus.com/inward/record.url?scp=84889005439&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84889005439&partnerID=8YFLogxK
U2 - 10.1109/ICCI-CC.2013.6622239
DO - 10.1109/ICCI-CC.2013.6622239
M3 - Conference contribution
AN - SCOPUS:84889005439
SN - 9781479907816
T3 - Proceedings of the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013
SP - 161
EP - 167
BT - Proceedings of the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013
T2 - 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013
Y2 - 16 July 2013 through 18 July 2013
ER -