Abstract
As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The following article attempts to provide a basic introduction with sample applications to simulated and real-world data sets.
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The literature on PCA is vast. A good didactic exposition with a historical overview and references can be found at http://en.wikipedia.org/wiki/Principal_component_analysis.
The larger the number of voxels in the data array, the more the empirical bootstrap distribution of individual voxel weights looks standard-normal. When the number of brain regions in the array is small, i.e., similar to, or a low-integer multiple of, the number of observations, the bootstrap distribution can deviate substantially from a standard-normal distribution.—Repeated personal observation by the authors.
The website is: http://www.loni.ucla.edu/ADNI/.
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Acknowledgments
Imaging data was provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (NIH U01AG024904). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., and Wyeth, as well as non-profit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health(www.fnih.org <http://www.fnih.org/> <http://www.fnih.org <http://www.fnih.org/>). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation. C. Habeck acknowledges grant support from NIH/NIBIB 5R01EB006204-03 (Multivariate approaches to neuroimaging analysis) and NIH/NIA 5R01AG026114-02 (Early AD Detection with ASL MRI & Covariance Analysis).
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Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or production of this report. A listing of ADNI authors is available at http://www.loni.ucla.edu/ADNI/Collaboration/ADNI_Manuscript_Citations.pdf.
Matlab code for spatial covariance analysis is downloadable at http://groups.google.com/group/gcva.
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Habeck, C., Stern, Y. & the Alzheimer’s Disease Neuroimaging Initiative. Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer’s Disease. Cell Biochem Biophys 58, 53–67 (2010). https://doi.org/10.1007/s12013-010-9093-0
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DOI: https://doi.org/10.1007/s12013-010-9093-0