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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: Apr 11, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

NeuroFM: Toward Precision Neuroimaging with Foundation Models for Individualized Brain Health Estimation.

Austin Dibble¹, Connor Dalby¹, Michele Sevegnani²

¹School of Psychology & Neuroscience, University of Glasgow, Glasgow, UK.

Medrxiv : the Preprint Server for Health Sciences

|April 10, 2026

Summary

This summary is machine-generated.

NeuroFM, a novel foundation model, analyzes brain structure from MRI scans to create individualized health profiles. This disease-naïve approach enables early detection of neurological conditions like dementia.

Keywords:

brain health foundation model neuroimaging precision medicine precision neuroimaging

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Last Updated: Apr 11, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Area of Science:

Neuroimaging
Artificial Intelligence
Computational Neuroscience

Background:

Current neuroimaging lacks scalable, quantitative brain health summaries.
Existing models entangle task-specific objectives with cohort/disease signals.
Biologically grounded anatomical pattern identification is needed.

Purpose of the Study:

Introduce NeuroFM, a foundation model for precision neuroimaging.
Develop a disease-naïve model for brain health assessment.
Enable quantitative, individualized brain health profiling.

Main Methods:

Trained NeuroFM exclusively on 100,000 healthy synthetic brain volumes.
Predicted morphometric and demographic targets without disease labels.
Evaluated generalization across 136,361 multi-cohort volumes.

Main Results:

NeuroFM organizes brain structure into population-level patterns encoding health differences.
Representations transferred across neuroscience domains without adaptation.
Enabled linear readouts for clinical, cognitive, and developmental factors.
Estimated future dementia risk years before diagnosis.

Conclusions:

Established a disease-naïve foundation model for precision neuroimaging.
NeuroFM supports quantitative brain health assessments across diverse settings.
Demonstrated potential for early risk identification and personalized brain health monitoring.