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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Related Experiment Video

Updated: Jan 17, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

MDFNet: a multi-dimensional feature fusion model based on structural magnetic resonance imaging representations for

Chenxiao Zhang¹, Pengzhi Nan¹, Limei Song²

¹School of Computer and Control Engineering, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai City, 264005, Shandong Province, China.

Magma (New York, N.Y.)

|September 18, 2025

Summary

This study introduces a novel Multi-Dimensional Feature Fusion Network (MDFNet) for accurate brain age estimation using structural MRI. The MDFNet significantly improves brain age gap analysis in Alzheimer's Disease patients.

Keywords:

Brain age estimation Deep learning Multi-dimensional feature fusion Structural MRI

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Last Updated: Jan 17, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

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:

Neuroimaging
Artificial Intelligence
Biomedical Engineering

Background:

Brain age estimation is crucial for understanding aging and neurodegenerative diseases.
Current methods often rely on limited data or complex imaging techniques.

Purpose of the Study:

To develop a unified Multi-Dimensional Feature Fusion Network (MDFNet) for enhanced brain age estimation.
To integrate diverse structural MRI features including whole brain, gray matter volume, and brain connectivity networks.

Main Methods:

MDFNet integrates whole-brain, tissue-level, node-based graph convolution, edge-based graph path convolution, and demographic data channels.
Validated on 1872 healthy subjects across four datasets and applied to an Alzheimer's Disease cohort.
Included interpretability analysis and normative modeling for robust validation.

Main Results:

MDFNet achieved superior performance with a Mean Absolute Error of 4.396 years and a Pearson Correlation Coefficient of 0.912.
Significantly greater brain age gap observed in Alzheimer's Disease patients compared to healthy controls.
Interpretability analysis confirmed the model's reliability at group and individual levels.

Conclusions:

The MDFNet effectively enhances brain age estimation using structural MRI.
Multi-dimensional feature integration is key to improving accuracy and reliability in brain age prediction.