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Detection of neural activity in functional MRI using canonical correlation analysis
Author(s) -
Friman Ola,
Cedefamn Jonny,
Lundberg Peter,
Borga Magnus,
Knutsson Hans
Publication year - 2001
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/1522-2594(200102)45:2<323::aid-mrm1041>3.0.co;2-#
Subject(s) - canonical correlation , correlation , functional magnetic resonance imaging , nuclear magnetic resonance , pattern recognition (psychology) , computer science , artificial intelligence , mathematics , neuroscience , physics , psychology , geometry
A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t ‐tests, F ‐tests, and ordinary correlation analysis are used. Such methods are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations. In addition, the proposed CCA method also makes it possible to detect activated brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of the hemodynamic response. Excellent performance on real fMRI data is demonstrated. Magn Reson Med 45:323–330, 2001. © 2001 Wiley‐Liss, Inc.