Machine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry
This work addresses diagnostic classification challenges in neurology and psychiatry by providing a standardized pipeline for biomarker discovery, but it appears incremental as it combines existing methods without introducing a fundamentally new approach.
The authors tackled the problem of diagnostic pattern recognition from neuroimaging data by proposing a common data analysis pipeline using various ML algorithms and processing toolboxes, and they applied it to discover new biomarkers for epilepsy and depression based on clinical and MRI/fMRI data.
We consider a problem of diagnostic pattern recognition/classification from neuroimaging data. We propose a common data analysis pipeline for neuroimaging-based diagnostic classification problems using various ML algorithms and processing toolboxes for brain imaging. We illustrate the pipeline application by discovering new biomarkers for diagnostics of epilepsy and depression based on clinical and MRI/fMRI data for patients and healthy volunteers.