caa a22 4500 445298529 CHVBK 20180317142549.0 cr unu---uuuuu 170323e20101101xx s 000 0 eng 10.1007/s12013-010-9093-0 doi (NATIONALLICENCE)springer-10.1007/s12013-010-9093-0 Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer's Disease [Elektronische Daten] [Christian Habeck, Yaakov Stern] 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. Springer Science+Business Media, LLC, 2010 Alzheimer's disease nationallicence Multivariate analysis nationallicence Principal components analysis nationallicence Brain reading nationallicence Classification nationallicence Cross validation nationallicence Nonparametric inference nationallicence Split-sample simulations nationallicence Habeck Christian Cognitive Neuroscience Division, The Taub Institute for Research on Aging and Alzheimer's Disease, Columbia University, 10032, New York, NY, USA aut Stern Yaakov Cognitive Neuroscience Division, The Taub Institute for Research on Aging and Alzheimer's Disease, Columbia University, 10032, New York, NY, USA aut Cell Biochemistry and Biophysics Humana Press Inc 58/2(2010-11-01), 53-67 1085-9195 58:2<53 2010 58 12013 https://doi.org/10.1007/s12013-010-9093-0 text/html Onlinezugriff via DOI 1 review-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s12013-010-9093-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Habeck Christian Cognitive Neuroscience Division, The Taub Institute for Research on Aging and Alzheimer's Disease, Columbia University, 10032, New York, NY, USA aut NATIONALLICENCE 700 1- Stern Yaakov Cognitive Neuroscience Division, The Taub Institute for Research on Aging and Alzheimer's Disease, Columbia University, 10032, New York, NY, USA aut NATIONALLICENCE 773 0- Cell Biochemistry and Biophysics Humana Press Inc 58/2(2010-11-01), 53-67 1085-9195 58:2<53 2010 58 12013 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer