 The framework comprises: tools to automatically convert ADNI data into the BIDS standard , pipelines for image preprocessing and feature extraction , baseline classifiers and a rigorous CV procedure. We demonstrate the use of the framework through assessing the influence of diffusion tensor imaging (DTI) metrics (fractional anisotropy - FA, mean diffusivity - MD), feature types, imaging modalities (diffusion MRI or T1w MRI) , data imbalance and FS bias. First , voxel-wise features generally gave better performances than regional features.
<meaning-changed>  The framework comprises: tools to automatically convert ADNI data into the BIDS standard , pipelines for image preprocessing and feature extraction , baseline classifiers and a rigorous CV procedure. We demonstrate the use of the framework through assessing the influence of diffusion tensor imaging (DTI) metrics (fractional anisotropy - FA, mean diffusivity - MD), feature types, imaging modalities (diffusion MRI or T1w MRI) , data imbalance and FS bias. First , voxel-wise features generally gave better performances than regional features.
 The framework comprises: tools to automatically convert ADNI data into the BIDS standard , pipelines for image preprocessing and feature extraction . We then apply the framework to compare different components. First, FS has a positive impact on classification results: highest balanced accuracy (BA) improved from 0.76 to 0.82 for task CN vs AD. Secondly , voxel-wise features generally gave better performances than regional features.