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Representation of Cognitive Processes Using the Minimum Spanning Tree of Local Meshes
Date
2013-07-07
Author
Firat, Orhan
Ozay, Mete
Onal, Itir
GİLLAM, İLKE
Yarman Vural, Fatoş Tunay
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
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A new graphical model called Cognitive Process Graph (CPG) is proposed, for classifying cognitive processes based on neural activation patterns which are acquired via functional Magnetic Resonance Imaging (fMRI) in brain. In the CPG, first local meshes are formed around each voxel. Second, the relationships between a voxel and its neighbors in a local mesh, which are estimated by using a linear regression model, are used to form the edges among the voxels (graph nodes) in the CPG. Then, a minimum spanning tree (MST) of the CPG which spans all the voxels in the region of interest is computed. The arc weights of the MST are used to represent the underlying cognitive processes. The proposed method reduces the curse of dimensionality problem that is caused by very large dimension of the feature space of the fMRI measurements, compared to number of instances. Finally, the arc weights computed over the path of the MST called MST-Features (MST-F) are used to train a statistical learning machine.
Subject Keywords
Functional connectivity
,
Brain activity
,
Networks
,
States
,
FMRI
,
Principles
,
Patterns
,
Graphs
,
Memory
URI
https://hdl.handle.net/11511/53170
Collections
Department of Computer Engineering, Conference / Seminar