Christian Habeck|Yaakov Stern
Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer’s Disease
Columbia University|Columbia University
clinical cognitive neuroscience mature need sophisticated neuroimaging analysis becomes apparent multivariate analysis techniques recently received increasing attention many attractive features cannot easily realized commonly used univariate voxelwise techniques multivariate approaches evaluate correlationcovariance activation across brain regions rather proceeding voxelbyvoxel basis thus results easily interpreted signature neural networks univariate approaches hand cannot directly address functional connectivity brain covariance approach also result greater statistical power compared univariate techniques forced employ stringent often overly conservative corrections voxelwise multiple comparisons multivariate techniques also lend much better prospective application results analysis one dataset entirely new datasets multivariate techniques thus well placed provide information mean differences correlations behavior similarly univariate approaches potentially greater statistical power better reproducibility checks contrast advantages high barrier entry use multivariate approaches preventing widespread application community neuroscientist becoming familiar multivariate analysis techniques initial survey field might present bewildering variety approaches although algorithmically similar presented different emphases typically people mathematics backgrounds believe multivariate analysis techniques sufficient potential warrant better dissemination researchers able employ informed accessible manner following article attempts provide basic introduction sample applications simulated realworld data sets