Utilization of Spatial Coherence in Functional Neuroimage-Based Classification

Functional magnetic resonance imaging provides a non-invasive mechanism for monitoring brain activity of subjects during performance of a task. While this approach has been used extensively for human brain mapping activities, automated classification of subjects based on neural activation patterns i...

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Bibliographic Details
Main Authors: Mitra, P., Gopalakrishnan, V., McNamee, R.L.
Format: Conference Proceeding
Language:English
Subjects:
Biomedical engineering
Biomedical informatics
Brain
Filtering
Humans
Magnetic resonance imaging
Monitoring
Pharmaceuticals
Principal component analysis
Spatial coherence
Online Access:Request full text
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Description
Summary:Functional magnetic resonance imaging provides a non-invasive mechanism for monitoring brain activity of subjects during performance of a task. While this approach has been used extensively for human brain mapping activities, automated classification of subjects based on neural activation patterns is also of interest. However, due to the high dimensionality of the image data, classification accuracy is highly dependent upon the adequacy of the features used in the models. In this work 1 , we present a new feature refinement strategy that uses spatial coherence information to eliminate irrelevant features from consideration. For a neurobehavioral disinhibition dataset, we show that this new approach for feature selection using spatially coherent voxels (SCV) outperforms conventional methods.
ISSN:2151-7614
2151-7622
DOI:10.1109/ICBBE.2009.5163742