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A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT

Abstract: 

Practical limitations on the duration of individual fMRI scans have led neuroscientist to consider the aggregation of data from multiple subjects. Differences in anatomical structures and functional topographies of brains require aligning data across subjects. Existing functional alignment methods serve as a preprocessing step that allows subsequent statistical methods to learn from the aggregated multi-subject data. Despite their success, current alignment methods do not leverage the labeled data used in the subsequent methods. In this work we propose a semi-supervised scheme that simultaneously learns the alignment and performs the analysis. We derive a specific instance of the scheme using the Shared Response Model for alignment and Multinomial Logistic Regression for classification. In our experiments this method improves the average classification accuracy from 65.5% to 68.5%, and from 5.3% to 6.1% over the independently-trained methods. Furthermore, our method achieves similar prediction with almost half the samples used for alignment.

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Paper Details

Authors:
Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge
Submitted On:
2 March 2017 - 12:56pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
Javier Turek
Paper Code:
3367
Document Year:
2017
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Document Files

Semi-Supervised fMRI Functional Alignment

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[1] Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge, "A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1587. Accessed: Jun. 28, 2017.
@article{1587-17,
url = {http://sigport.org/1587},
author = {Javier S. Turek; Theodore L. Willke; Po-Hsuan Chen; Peter J. Ramadge },
publisher = {IEEE SigPort},
title = {A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT},
year = {2017} }
TY - EJOUR
T1 - A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT
AU - Javier S. Turek; Theodore L. Willke; Po-Hsuan Chen; Peter J. Ramadge
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1587
ER -
Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge. (2017). A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT. IEEE SigPort. http://sigport.org/1587
Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge, 2017. A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT. Available at: http://sigport.org/1587.
Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge. (2017). "A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT." Web.
1. Javier S. Turek, Theodore L. Willke, Po-Hsuan Chen, Peter J. Ramadge. A SEMI-SUPERVISED METHOD FOR MULTI-SUBJECT FMRI FUNCTIONAL ALIGNMENT [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1587