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. 2010 Feb 1;49(3):2509-19.
doi: 10.1016/j.neuroimage.2009.08.036. Epub 2009 Aug 24.

Classification of spatially unaligned fMRI scans

Ariana Anderson  1 , Ivo D Dinov, Jonathan E Sherin, Javier Quintana, A L Yuille, Mark S Cohen
Affiliations

Classification of spatially unaligned fMRI scans

Ariana Anderson et al. Neuroimage. .

Abstract

The analysis of fMRI data is challenging because they consist generally of a relatively modest signal contained in a high-dimensional space: a single scan can contain millions of voxel recordings over space and time. We present a method for classification and discrimination among fMRI that is based on modeling the scans as distance matrices, where each matrix measures the divergence of spatial network signals that fluctuate over time. We used single-subject independent components analysis (ICA), decomposing an fMRI scan into a set of statistically independent spatial networks, to extract spatial networks and time courses from each subject that have unique relationship with the other components within that subject. Mathematical properties of these relationships reveal information about the infrastructure of the brain by measuring the interaction between and strength of the components. Our technique is unique, in that it does not require spatial alignment of the scans across subjects. Instead, the classifications are made solely on the temporal activity taken by the subject's unique ICs. Multiple scans are not required and multivariate classification is implementable, and the algorithm is effectively blind to the subject-uniform underlying task paradigm. Classification accuracy of up to 90% was realized on a resting-scanned schizophrenia/normal dataset and a tasked multivariate Alzheimer's/old/young dataset. We propose that the ICs represent a plausible set of imaging basis functions consistent with network-driven theories of neural activity in which the observed signal is an aggregate of independent spatial networks having possibly dependent temporal activity.

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Figures

Figure 1
Figure 1. Phase Space for Primary Components
Figure 2
Figure 2. Subject Matrices showing unequal number of component
Figure 3
Figure 3. Embedding of Matrices
Figure 4
Figure 4. Geodesic Distance Calculation
Figure 5
Figure 5. Number of Components Extracted by Selection Method
Figure 6
Figure 6. SZ Accuracy by Extracted Eigenvalues
Figure 7
Figure 7. AD Accuracy by Extracted Eigenvalues
Figure 8
Figure 8. AD Parameter Choice
Figure 9
Figure 9. SZ Parameter Choice
See this image and copyright information in PMC

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