Low entropy maps as patterns of the pathological alteration specificity of brain regions: A meta-analysis dataset
Donato Liloia,
Franco Cauda,
Andrea Nani,
Jordi Manuello,
Sergio Duca,
Peter T. Fox,
Tommaso Costa
Affiliations
Donato Liloia
GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
Franco Cauda
GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy; Corresponding author at: Department of Psychology, University of Turin, Turin, Italy.
Andrea Nani
GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
Jordi Manuello
GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
Sergio Duca
GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
Peter T. Fox
Research Imaging Institute, University of Texas Health Science Center at San Antonio, USA; South Texas Veterans Health Care System, San Antonio, TX, USA
Tommaso Costa
GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Department of Psychology, University of Turin, Turin, Italy; FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
The data presented in this article are related to the research article entitled “The alteration landscape of the cerebral cortex” (Cauda et al., 2018). Here, we applied a metric called alteration negentropy (A-negentropy) on a large human neuroimaging dataset, in order to denote the “low structural alteration variety” of the altered brain areas. Furthermore, we reported the overview of the selection strategy, as well as the description and distribution of the selected studies from the voxel-based morphometry database of BrainMap (Vanasse et al., 2018). For all of the analyzed brain areas, we reported the number of pathologies affecting them (both local maxima and mean value), as well as the peak and average values of A-negentropy. Regions altered by a small number of brain disorders exhibit high values of A-negentropy.
