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BrainInsights: a comprehensive framework for pre-processing, analysis, and interpretation of neuroimaging data using
Mageshwar Selvakumar1, Andrea Mendez Torrijos1, Laura Cristina Konerth1
1Institute of Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Frontiers in Neuroinformatics
|May 1, 2026
Summary
BrainInsights simplifies complex neuroimaging analysis for researchers. This automated GUI framework integrates statistics and machine learning, enabling faster discovery of clinical insights from brain data.
Area of Science:
- Neuroscience
- Medical Imaging
- Computational Biology
Background:
- Neuroimaging data complexity presents analytical challenges.
- Existing frameworks lack scalability, statistical integration, and require programming skills.
- This hinders researchers from focusing on core neuroscience questions.
Purpose of the Study:
- To present BrainInsights, an automated GUI-based pipeline ecosystem.
- To facilitate flexible analysis of multi-modal or multi-parametric neuroimaging data.
- To bridge hypotheses-driven statistics with data-driven machine learning.
Main Methods:
- BrainInsights comprises MARIA, ML Pipeline, and ML DaViz tools.
- The system is deployed as a singularity container for reproducibility and scalability.
- Validated using multi-parametric MRI data from Anorexia Nervosa and Rheumatoid Arthritis studies.
Main Results:
- Distinguished Anorexia Nervosa patients from controls with 65% balanced accuracy.
- Predicted Rheumatoid Arthritis treatment response with up to 95.4% balanced accuracy.
- Demonstrated high subgroup separation and treatment success prediction.
Conclusions:
- BrainInsights effectively integrates statistical and machine learning analyses.
- Enables uncovering biologically plausible bio-signatures using interpretability tools like SHAP.
- Accelerates the translation of neuroimaging data into clinical insights.
