Functional mesh learning for pattern analysis of cognitive processes

Orhan Firat, Mete Ozay, Itir Önal, Ilke Oztekin, Fatoş T.Yarman Vural

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013
Pages161-167
Number of pages7
DOIs
Publication statusPublished - 2013 Dec 9
Externally publishedYes
Event12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013 - New York, NY, United States
Duration: 2013 Jul 162013 Jul 18

Publication series

NameProceedings of the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013

Other

Other12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013
CountryUnited States
CityNew York, NY
Period13/7/1613/7/18

ASJC Scopus subject areas

  • Information Systems
  • Software

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    Firat, O., Ozay, M., Önal, I., Oztekin, I., & Vural, F. T. Y. (2013). Functional mesh learning for pattern analysis of cognitive processes. In Proceedings of the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013 (pp. 161-167). [6622239] (Proceedings of the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013). https://doi.org/10.1109/ICCI-CC.2013.6622239