Journal article
Clustered components analysis for functional MRI
Division of Imaging Sciences, Department of Radiology, Indiana University, School of Medicine, Indianapolis, IN, USA. sechen@iupui.edu1
A common method of increasing hemodynamic response (SNR) in functional magnetic resonance imaging (fMRI) is to average signal timecourses across voxels. This technique is potentially problematic because the hemodynamic response may vary across the brain. Such averaging may destroy significant features in the temporal evolution of the fMRI response that stem from either differences in vascular coupling to neural tissue or actual differences in the neural response between two averaged voxels.
Two novel techniques are presented in this paper in order to aid in an improved SNR estimate of the hemodynamic response while preserving statistically significant voxel-wise differences. The first technique is signal subspace estimation for periodic stimulus paradigms that involves a simple thresholding method.
This increases SNR via dimensionality reduction. The second technique that we call clustered components analysis is a novel amplitude-independent clustering method based upon an explicit statistical data model. It includes an unsupervised method for estimating the number of clusters. Our methods are applied to simulated data for verification and comparison to other techniques.
A human experiment was also designed to stimulate different functional cortices. Our methods separated hemodynamic response signals into clusters that tended to be classified according to tissue characteristics.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2004 |
| Pages: | 85-98 |
| ISSN: | 1558254x and 02780062 |
| Types: | Journal article |
| DOI: | 10.1109/TMI.2003.819922 |
Algorithms Biomedical engineering Biomedical imaging Biomedical measurements Blood flow Brain Brain Mapping Cluster Analysis Clustering methods Computer Simulation Hemodynamics Humans Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Magnetic Resonance Imaging Magnetic analysis Magnetic resonance imaging Neurons Pattern Recognition, Automated Predictive models Principal Component Analysis Radiology Reproducibility of Results Sensitivity and Specificity amplitude-independent clustering method biological tissues biomedical MRI brain clustered components analysis dimensionality reduction explicit statistical data model functional MRI functional cortices functional magnetic resonance imaging haemodynamics hemodynamic response medical image processing neural response neural tissue neurophysiology pattern clustering periodic stimulus paradigms signal subspace estimation signal timecourses simple thresholding method statistical analysis vascular coupling voxels
