DisConICA

DisConICA assesses reproducibility and discriminability of functional brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) to identify candidate biomarkers for brain disorders.

Key Features:

• Discover-Confirm Methodology: Employs a discover-confirm approach beginning with gRAICAR (generalized Ranking and Averaging Independent Component Analysis by Reproducibility) to identify reproducible independent components representing brain networks.
• Reproducibility Assessment: Identifies functional networks that are highly reproducible within separate clinical or control groups but not reproducible when groups are combined, as potential individual-level discriminators.
• Unsupervised Clustering Analysis: Applies unsupervised clustering to evaluate and validate the discriminative ability of discovered components between clinical and control groups.
• Integration with SPM, FSL and DICOM support: Interfaces with SPM (Statistical Parametric Mapping) and FSL (FMRIB Software Library) and processes raw DICOM images as part of neuroimaging analysis workflows.

Scientific Applications:

• Biomarker discovery from rs-fMRI: Facilitates identification of functional brain networks that may serve as robust indicators for distinguishing brain disorders from rs-fMRI data.
• Clinical differentiation of PTSD and PCS: Demonstrated on rs-fMRI data from US Army soldiers with PTSD, comorbid PCS + PTSD, and matched healthy combat controls to evaluate real-world discriminability.

Methodology:

Hypothesizes that networks reproducible within separate groups but not when merged are effective discriminators; uses gRAICAR in the discover phase for reproducibility assessment and unsupervised clustering in the confirm phase to validate discriminative power.