BayesFactorFMRI

BayesFactorFMRI performs Bayesian analyses of functional magnetic resonance imaging (fMRI) data to enable group-level inference and synthesis across studies in neuroimaging.

Key Features:

• Bayesian second-level analysis: Performs group-level (second-level) Bayesian statistical analysis of fMRI data.
• Bayesian meta-analysis: Supports Bayesian meta-analysis for synthesizing results across multiple neuroimaging studies.
• Multiprocessing capability: Distributes computational tasks across multiple processors to accelerate computationally intensive analyses.
• Multiple comparison correction: Implements mechanisms for correcting multiple comparisons to control false positives in voxelwise inference.
• Implemented in R and Python: Provides analysis functionality implemented using R and Python.
• Enhanced sensitivity: Employs Bayesian approaches that can yield greater sensitivity than classical techniques for detecting effects in fMRI data.

Scientific Applications:

• Group-level inference: Applied to infer effects at the group level in task-based or resting-state fMRI studies.
• Meta-analytic synthesis: Used to combine results across independent neuroimaging studies via Bayesian meta-analysis.
• Investigation of brain function and connectivity: Supports studies examining brain function, connectivity, and intervention effects on neural activity using Bayesian inference.

Methodology:

Applies Bayesian statistical techniques at the second-level (group) and meta-analysis stages and parallelizes computations using a multiprocessing framework; implemented in R and Python.