Data-Driven Clustering of Functional Signals Reveals Gradients in Processing Both within the Anterior Hippocampus and across Its Long Axis.

A particularly elusive puzzle concerning the hippocampus is how the structural differences along its long anteroposterior axis might beget meaningful functional differences, particularly in terms of the granularity of information processing. One measure posits to quantify this granularity by calculating the average statistical independence of the BOLD signal across neighboring voxels, or intervoxel similarity (IVS), and has shown the anterior hippocampus to process coarser-grained information than the posterior hippocampus. This measure, however, has yielded opposing results in studies of developmental and healthy aging samples, which also varied in fMRI acquisition parameters and hippocampal parcellation methods.

Variability in the analysis of a single neuroimaging dataset by many teams.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data.