Authors:
Rudolph L. Mappus IV
;
David Minnen
and
Charles Lee Isbell Jr.
Affiliation:
College of Computing, Georgia Tech, United States
Keyword(s):
Dimensionality reduction, ICA, fMRI.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Functional magnetic resonance imaging (fMRI) captures brain activity by measuring the hemodynamic response. It is often used to associate specific brain activity with specific behavior or tasks. The analysis of fMRI scans seeks to recover this association by differentiating between task and non-task related activation and by spatially isolating brain activity. In this paper, we frame the association problem as a convolution of activation patterns. We project fMRI scans into a low dimensional space using manifold learning techniques. In this subspace, we transform the time course of each projected fMRI volume into the frequency domain. We use independent component analysis to discover task related activations. The combination of these methods discovers sources that show stronger correlation with the activation reference function than previous methods.