2 research outputs found
Machine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry
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.Comment: 20 pages, 2 figure
fMRI: preprocessing, classification and pattern recognition
As machine learning continues to gain momentum in the neuroscience community,
we witness the emergence of novel applications such as diagnostics,
characterization, and treatment outcome prediction for psychiatric and
neurological disorders, for instance, epilepsy and depression. Systematic
research into these mental disorders increasingly involves drawing clinical
conclusions on the basis of data-driven approaches; to this end, structural and
functional neuroimaging serve as key source modalities. Identification of
informative neuroimaging markers requires establishing a comprehensive
preparation pipeline for data which may be severely corrupted by artifactual
signal fluctuations. In this work, we review a large body of literature to
provide ample evidence for the advantages of pattern recognition approaches in
clinical applications, overview advanced graph-based pattern recognition
approaches, and propose a noise-aware neuroimaging data processing pipeline. To
demonstrate the effectiveness of our approach, we provide results from a pilot
study, which show a significant improvement in classification accuracy,
indicating a promising research direction.Comment: 20 pages, 1 figur