NDLI: Analyzing the information distribution in the fMRI measurements by estimating the degree of locality

Content Provider	IEEE Xplore Digital Library
Author	Onal, I. Ozay, M. Firat, O. Oztekin, I. Yarman Vural, F.T.
Copyright Year	2013
Description	Author affiliation: Dept. of Psychol., Koc Univ., İstanbul, Turkey (Oztekin, I.) \|\| Dept. of Comput. Eng., Middle East Tech. Univ., Ankara, Turkey (Onal, I.; Ozay, M.; Firat, O.; Yarman Vural, F.T.)
Abstract	In this study, we propose a new method for analyzing and representing the distribution of discriminative information for data acquired via functional Magnetic Resonance Imaging (fMRI). For this purpose, we form a spatially local mesh with varying size, around each voxel, called the seed voxel. The relationship among each seed voxel and its neighbors is estimated using a linear regression model by minimizing the square error. Then, we estimate the optimal mesh size that represents the connections among each seed voxel and its surroundings by minimizing Akaike's Final Prediction Error (FPE) with respect to the mesh size. The degree of locality is represented by the optimum mesh size. Our results indicate that the local mesh size with the highest discriminative power varies across individual participants. The proposed method was tested on an fMRI study consisting of item recognition (IR) and judgment of recency (JOR) tasks. For each participant, the estimated arc weights of each local mesh with different mesh size are used to classify the type of memory judgment (i.e.IR or JOR). Classification accuracy for each participant was derived using k-Nearest Neighbor (k-NN) method. The results indicate that the proposed local mesh model with optimal mesh size can successfully represent discriminative information for neuroimaging data.
Starting Page	6772
Ending Page	6775
File Size	354925
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6611111
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Support vector machine classification Computational modeling Neuroscience Linear regression Vectors Brain modeling
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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