TY - GEN
T1 - Functional networks of anatomic brain regions
AU - Velioglu, Burak
AU - Aksan, Emre
AU - Onal, Itir
AU - Firat, Orhan
AU - Ozay, Mete
AU - Vural, Fatos T.Yarman
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/10
Y1 - 2014/10/10
N2 - In this study, we propose a new approach to construct a two-level functional brain network. The nodes of the first-level network are the voxels of the functional Magnetic Resonance Images (fMRI) recorded during an object recognition task. The nodes of the network at the second-level are the anatomic regions of the brain. The arcs of the first level are estimated by a linear regression equation for the meshes formed around each voxel. Neighbors of each voxel are determined by using a functional similarity metric. The node degree distributions of the voxel-level functional brain network are then used to estimate the node attributes and arc weights between the nodes of anatomic regions at the second level. The region-level functional brain network is then used to analyze the relationship among the anatomic regions of the brain during a cognitive process. Our results indicate that, although the neighborhood is defined functionally, voxels tend to make connections within the anatomic regions. Therefore, it can be deduced that nearby voxels work coherently during the cognitive task compared to the voxels apart from each other.
AB - In this study, we propose a new approach to construct a two-level functional brain network. The nodes of the first-level network are the voxels of the functional Magnetic Resonance Images (fMRI) recorded during an object recognition task. The nodes of the network at the second-level are the anatomic regions of the brain. The arcs of the first level are estimated by a linear regression equation for the meshes formed around each voxel. Neighbors of each voxel are determined by using a functional similarity metric. The node degree distributions of the voxel-level functional brain network are then used to estimate the node attributes and arc weights between the nodes of anatomic regions at the second level. The region-level functional brain network is then used to analyze the relationship among the anatomic regions of the brain during a cognitive process. Our results indicate that, although the neighborhood is defined functionally, voxels tend to make connections within the anatomic regions. Therefore, it can be deduced that nearby voxels work coherently during the cognitive task compared to the voxels apart from each other.
KW - Functional Brain Network
KW - MVPA
KW - fMRI
UR - http://www.scopus.com/inward/record.url?scp=84911111624&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84911111624&partnerID=8YFLogxK
U2 - 10.1109/ICCI-CC.2014.6921441
DO - 10.1109/ICCI-CC.2014.6921441
M3 - Conference contribution
AN - SCOPUS:84911111624
T3 - Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014
SP - 53
EP - 60
BT - Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014
A2 - Patel, Shushma
A2 - Wang, Yingxu
A2 - Kinsner, Witold
A2 - Patel, Dilip
A2 - Fariello, Gabriele
A2 - Zadeh, Lotfi A.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014
Y2 - 18 August 2014 through 20 August 2014
ER -