As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention. Multivariate techniques 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. We therefore propose a series of studies comparing multivariate approaches amongst each other and with traditional univariate approaches in dyadic reports and comprehensive review papers. For these studies we will use computer simulations as well as real-world neuroscience data sets. We will also extend and develop our own covariance approach further to enable adequate treatment of parametric within-subjects experimental designs and group-differences in one analysis step. Finally, we will provide a software analysis package that will integrate the most common features of multivariate approaches in a user-friendly manner. ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB006204-01A2
Application #
7197143
Study Section
Special Emphasis Panel (ZRG1-MDCN-K (52))
Program Officer
Cohen, Zohara
Project Start
2007-04-01
Project End
2010-01-31
Budget Start
2007-04-01
Budget End
2008-01-31
Support Year
1
Fiscal Year
2007
Total Cost
$290,154
Indirect Cost
Name
Columbia University (N.Y.)
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Habeck, Christian; Eich, Teal; Razlighi, Ray et al. (2018) Reference ability neural networks and behavioral performance across the adult life span. Neuroimage 172:51-63
Hamberger, Marla J; Habeck, Christian G; Pantazatos, Spiro P et al. (2014) Shared space, separate processes: Neural activation patterns for auditory description and visual object naming in healthy adults. Hum Brain Mapp 35:2507-20
Habeck, Christian; Steffener, Jason; Rakitin, Brian et al. (2012) Can the default-mode network be described with one spatial-covariance network? Brain Res 1468:38-51
Habeck, Christian; Rakitin, Brian; Steffener, Jason et al. (2012) Contrasting visual working memory for verbal and non-verbal material with multivariate analysis of fMRI. Brain Res 1467:27-41
Shamy, Jul Lea; Habeck, Christian; Hof, Patrick R et al. (2011) Volumetric correlates of spatiotemporal working and recognition memory impairment in aged rhesus monkeys. Cereb Cortex 21:1559-73
Habeck, Christian; Stern, Yaakov; Alzheimer’s Disease Neuroimaging Initiative (2010) Multivariate data analysis for neuroimaging data: overview and application to Alzheimer's disease. Cell Biochem Biophys 58:53-67
Habeck, Christian Georg (2010) Basics of multivariate analysis in neuroimaging data. J Vis Exp :
Carbon, Maren; Argyelan, Miklos; Habeck, Christian et al. (2010) Increased sensorimotor network activity in DYT1 dystonia: a functional imaging study. Brain 133:690-700
Siedlecki, Karen L; Habeck, Christian G; Brickman, Adam M et al. (2009) Examining the multifactorial nature of cognitive aging with covariance analysis of positron emission tomography data. J Int Neuropsychol Soc 15:973-81
Habeck, Christian; Foster, Norman L; Perneczky, Robert et al. (2008) Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease. Neuroimage 40:1503-15

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