Data visualization for inference in tomographic brain imaging
Cyril Pernet; Christopher R. Madan · 2019 · European Journal of Neuroscience
WASTE classifies this as Null Dataset · AI classification, approximate
A negative-result dataset — reusable evidence that an expected effect wasn't there.
Abstract
Tomographic imaging (i.e. magnetic resonance imaging [MRI], positron emission tomography [PET], X-ray computed tomography [CT]) offers a unique window in understanding structure-function relationships in the living brain. Nowadays, it is standard practice to acquire entire brain volumes and perform statistical analyses at each voxel, an approach known as statistical parametric mapping (Friston, Frith, Liddle, & Frackowiak, 1991). Because it is impossible to report the statistical results in every voxel, summary tables and figures are of importance. The choice made by authors to create such fig
Abstract by Cyril Pernet; Christopher R. Madan, European Journal of Neuroscience (2019) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1111/ejn.14430