Fighting or embracing multiplicity in neuroimaging? neighborhood leverage versus global calibration

Publication date: Available online 5 November 2019Source: NeuroImageAuthor(s): Gang Chen, Paul A. Taylor, Robert W. Cox, Luiz PessoaAbstractNeuroimaging faces the daunting challenge of multiple testing – an instance of multiplicity – that is associated with two other issues to some extent: low inference efficiency and poor reproducibility. Typically, the same statistical model is applied to each spatial unit independently in the approach of massively univariate modeling. In dealing with multiplicity, the general strategy employed in the field is the same regardless of the specifics: trust the local “unbiased” effect estimates while adjusting the extent of statistical evidence at the global level. However, in this approach, modeling efficiency is compromised because each spatial unit (e.g., voxel, region, matrix element) is treated as an isolated and independent entity during massively univariate modeling. In addition, the required step of multiple testing “correction” by taking into consideration spatial relatedness, or neighborhood leverage, can only partly recoup statistical efficiency, resulting in potentially excessive penalization as well as arbitrariness due to thresholding procedures. Moreover, the assigned statistical evidence at the global level heavily relies on the data space (whole brain or a small volume). The present paper reviews how Stein's paradox (1956) motivates a Bayesian multilevel (BML) approach that, rather than fighting multiplicity, embrac...
Source: NeuroImage - Category: Neuroscience Source Type: research