Unsupervised fMRI Analysis

author: David R. Hardoon, London's Global University
published: Feb. 25, 2007,   recorded: December 2006,   views: 568

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Recently machine learning methodology has been used increasing to analyze the relationship between stimulus categories and fMRI responses [2, 14, 15, 11, 13, 8, 9, 1, 12, 7]. Here, we introduce a new unsupervised machine learning approach to fMRI analysis approach, in which the simple categorical description of stimulus type (e.g. type of task) is replaced by a more informative vector of stimulus features. We compared this new approach with a standard Support Vector Machine (SVM) analysis of fMRI data using a categorical description of stimulus type. The following study differs from conventional unsupervised approaches in that we make use of the stimulus characteristics. We use kernel Canonical Correlation Analysis (KCCA) to learn the correlation between the fMRI volume and the corresponding stimulus features presented at a particular time point. CCA can be seen as the problem of finding basis vectors for two sets of variables such that the correlation of the projections of the variables onto these basis vectors are mutually maximised. KCCA first projects the data into a higher dimensional feature space before performing CCA in the new feature space.

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