In a recent published commentary “Chen, G., Taylor, P.A., Stoddard, J., Cox, R.W., Bandettini, P.A., Pessoa, L., 2022. Sources of Information Waste in Neuroimaging: Mishandling Structures, Thinking Dichotomously, and Over-Reducing Data. Aperture Neuro 2021, 46.”, we make the following four suggestions to alleviate information waste and to improve reproducibility:
(1) abandon strict dichotomization;
(2) report full results;
(3) quantify effects;
(4) model data hierarchy.
One particular suggestion for voxel-wise modeling approach is full result reporting: avoid hard threshodling and adopt a “highlight but not hide” methodology as shown in Fig. 1F in the paper:
We believe that the hierarchical perspective helps reveal the information loss associated with two aspects of the conventional modeling approach: the implicit assumption of uniform distribution and the artificial dichotomization required in handling multiplicity. In fact, these two aspects are two sides of the same coin: the conventional modeling methodology focuses only on local relatedness among neighboring spatial units, but ignores the global information shared across the whole brain. Consequently, the various approaches of adjustment for multiple testing adopted in the field may lead to excessive penalties and overconservative inferences. For these considerations, when voxelwise analysis is performed under the conventional massively univariate framework, we believe that the Bayesian multilevel framework lends an important perspective: a threshold or a set of spatial blobs purely based on statistical evidence is only suggestive but not rigid. To avoid further information waste, any statistical evidence should be viewed – regardless of the adopted framework – as intrinsically embedded with some underlying and implicit assumptions; it should be considered as a continuum both in result reporting as well as during the research reviewing process.
This result reporting approach is more clearly illustrated in a recent manuscript:
Taylor, P.A., Reynolds, R.C., Calhoun, V., Gonzalez-Castillo, J., Handwerker, D.A., Bandettini, P.A., Mejia, A.F., Chen, G., 2022. Highlight Results, Don’t Hide Them: Enhance interpretation, reduce biases and improve reproducibility.