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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: May 2, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

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.

Keywords:

clinical translation interpretable models machine learning framework multi-modal neuroimaging reproducibility statistical analysis

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Related Experiment Videos

Last Updated: May 2, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

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.