Functional Mesh Learning for Pattern Analysis of Cognitive Processes

Date

2013-07-18

Author

Firat, Orhan
Ozay, Mete
Onal, Itir
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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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.

Subject Keywords

Graph-theoretical analysis, States, Connectivity, Brain networks

URI

https://hdl.handle.net/11511/54099

Conference Name

12th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI CC)

Collections

Department of Computer Engineering, Conference / Seminar

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Representation of Cognitive Processes Using the Minimum Spanning Tree of Local Meshes Firat, Orhan; Ozay, Mete; Onal, Itir; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-07-07) 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 t...
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Citation Formats

O. Firat, M. Ozay, I. Onal, İ. GİLLAM, and F. T. Yarman Vural, “Functional Mesh Learning for Pattern Analysis of Cognitive Processes,” presented at the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI CC), Fordham Univ, New York, NY, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54099.