fMRI: preprocessing, classification and pattern recognition

TL;DR

This paper reviews neuroimaging data preprocessing and pattern recognition techniques, emphasizing noise-aware pipelines and demonstrating improved classification accuracy in clinical applications of fMRI data.

Contribution

It introduces a noise-aware preprocessing pipeline for fMRI data and demonstrates its effectiveness through a pilot study with improved classification results.

Findings

Significant improvement in classification accuracy using the proposed pipeline

Advantages of pattern recognition in clinical neuroimaging applications

Effective graph-based pattern recognition approaches

Abstract

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.