Anatomically Informed Bayesian Model Selection for fMRI Group Data Analysis
- Cite this paper as:
- Keller M., Lavielle M., Perrot M., Roche A. (2009) Anatomically Informed Bayesian Model Selection for fMRI Group Data Analysis. In: Yang GZ., Hawkes D., Rueckert D., Noble A., Taylor C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5762. Springer, Berlin, Heidelberg
A new approach for fMRI group data analysis is introduced to overcome the limitations of standard voxel-based testing methods, such as Statistical Parametric Mapping (SPM). Using a Bayesian model selection framework, the functional network associated with a certain cognitive task is selected according to the posterior probabilities of mean region activations, given a pre-defined anatomical parcellation of the brain. This approach enables us to control a Bayesian risk that balances false positives and false negatives, unlike the SPM-like approach, which only controls false positives. On data from a mental calculation experiment, it detected the functional network known to be involved in number processing, whereas the SPM-like approach either swelled or missed the different activation regions.
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