Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H<sub>2</sub><sup>15</sup>O-, and FDG-PET

Moeller, James R.; Habeck, Christian G.

doi:https://doi.org/10.1155/IJBI/2006/79862

International Journal of Biomedical Imaging

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Recent Advances in Mathematical Methods for the Analysis of Biomedical Images

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Volume 2006 | Article ID 079862 | https://doi.org/10.1155/IJBI/2006/79862

Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H₂¹⁵O-, and FDG-PET

James R. Moeller^1,2,3and Christian G. Habeck^2,4

Received09 Feb 2006

Revised17 Aug 2006

Accepted18 Aug 2006

Published06 Dec 2006

Abstract

In brain mapping studies of sensory, cognitive, and motor operations, specific waveforms of dynamic neural activity are predicted based on theoretical models of human information processing. For example in event-related functional MRI (fMRI), the general linear model (GLM) is employed in mass-univariate analyses to identify the regions whose dynamic activity closely matches the expected waveforms. By comparison multivariate analyses based on PCA or ICA provide greater flexibility in detecting spatiotemporal properties of experimental data that may strongly support alternative neuroscientific explanations. We investigated conjoint multivariate and mass-univariate analyses that combine the capabilities to (1) verify activation of neural machinery we already understand and (2) discover reliable signatures of new neural machinery. We examined combinations of GLM and PCA that recover latent neural signals (waveforms and footprints) with greater accuracy than either method alone. Comparative results are illustrated with analyses of real fMRI data, adding to Monte Carlo simulation support.

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Copyright © 2006 James R. Moeller and Christian G. Habeck. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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