The Statistical Analysis of Multi-Voxel Patterns in Functional Imaging
Kai Schreiber; Bart Krekelberg · 2013 · PLoS ONE
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
The goal of multi-voxel pattern analysis (MVPA) in BOLD imaging is to determine whether patterns of activation across multiple voxels change with experimental conditions. MVPA is a powerful technique, its use is rapidly growing, but it poses serious statistical challenges. For instance, it is well-known that the slow nature of the BOLD response can lead to greatly exaggerated performance estimates. Methods are available to avoid this overestimation, and we present those here in tutorial fashion. We go on to show that, even with these methods, standard tests of significance such as Students' T
Abstract by Kai Schreiber; Bart Krekelberg, PLoS ONE (2013) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1371/journal.pone.0069328