Journal
HUMAN BRAIN MAPPING
Volume 42, Issue 11, Pages 3343-3351Publisher
WILEY
DOI: 10.1002/hbm.25452
Keywords
activation likelihood estimation; Bayes' factor; coordinate‐ based meta‐ analysis; fMRI; reverse inference; voxel‐ based morphometry
Funding
- Fondazione Carlo Molo
Ask authors/readers for more resources
This study introduces a new tool called BACON for reverse inference in functional and structural neuroimaging data, aiming to determine the extent of association between patterns of cerebral activation or alteration and specific mental functions or brain pathologies. BACON utilizes Bayes' factor and activation likelihood estimation derived-maps to obtain posterior probability distributions on evidence of specificity concerning a particular mental function or brain pathology.
Over the past decades, powerful MRI-based methods have been developed, which yield both voxel-based maps of the brain activity and anatomical variation related to different conditions. With regard to functional or structural MRI data, forward inferences try to determine which areas are involved given a mental function or a brain disorder. A major drawback of forward inference is its lack of specificity, as it suggests the involvement of brain areas that are not specific for the process/condition under investigation. Therefore, a different approach is needed to determine to what extent a given pattern of cerebral activation or alteration is specifically associated with a mental function or brain pathology. In this study, we present a new tool called BACON (Bayes fACtor mOdeliNg) for performing reverse inference both with functional and structural neuroimaging data. BACON implements the Bayes' factor and uses the activation likelihood estimation derived-maps to obtain posterior probability distributions on the evidence of specificity with regard to a particular mental function or brain pathology.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available