Classification of whole brain fMRI activation patterns

Functional magnetic resonance imaging (fMRI) is an imaging technology which is primarily used to perform brain activation studies by measuring neural activity in the brain. It is an interesting question whether patterns of activity in the brain as measured by fMRI can be used to predict the cognitive state of a subject. Researchers successfully employed a discriminative approach by training classifiers on fMRI data to predict the mental state of a subject from distributed activation patterns in the brain. In this thesis, we investigate the utility of feature selection methods in improving the prediction accuracy of classifiers trained on functional neuroimaging data. We explore the use of classification methods in the context of an event related functional neuroimaging experiment where participants viewed images of scenes and predicted whether they would remember each scene in a post-scan recognition-memory test. We view the application of our tool to this memory encoding task as a step toward the development of tools that will enhance human learning. We train support vector machines on functional data to predict participants' performance in the recognition test and compare the classifier's performance with participants' subjective predictions. We show that the classifier achieves better than random predictions and the average accuracy is close to that of the subject's own prediction. Our classification method consists of feature extraction, feature selection and classification parts.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Balcı, Serdar Kemal

Advisor dc:contributor.advisor

• Polina Golland.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/45854

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/45854