NDLI: Nonlinear Dimension Reduction and Activation Detection for fMRI Dataset

Content Provider	IEEE Xplore Digital Library
Author	Xilin Shen Meyer, F.G.
Copyright Year	2006
Description	Author affiliation: University of Colorado at Boulder, USA (Xilin Shen)
Abstract	Functional magnetic resonance imaging (fMRI) has been established as a powerful method for brain mapping. Different physical phenomena contribute to the dynamical changes in the fMRI signal, the task-related hemodynamic responses, non-task-related physiological rhythms, machine and motion artifacts, etc. In this paper, we propose a new approach for fMRI data analysis. Each fMRI time series is viewed as a point in RT . We are interested in learning the organization of the points in high dimensions and extracting useful information for data classification. A nonlinear manifold learning technique is applied to obtain a low dimensional embedding of a dataset. The embedding differentiates time series with different temporal patterns. By assuming that the subset of activated time series forms a low dimensional structure, we partition the dataset and separate subsets of points with low dimensionality. The correspondence between low dimensional subsets and time series that contain task-related responses is verified and the activation maps are generated accordingly. The proposed approach is data-driven. It does not require a model for the hemodynamic response. We have conducted several experiments with synthetic and in-vivo datasets that demonstrate the performance of our approach.
Starting Page	90
Ending Page	90
File Size	631179
Page Count	1
File Format	PDF
ISBN	0769526462
DOI	10.1109/CVPRW.2006.144
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Geometry Data analysis Scanning probe microscopy Magnetic resonance imaging Electric variables measurement Independent component analysis Brain mapping Rhythm Hemodynamics Principal component analysis
Content Type	Text
Resource Type	Article

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