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09:06
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
Published on: June 9, 2018
Deep learning models identify brain changes during the progression of Alzheimer's disease
Jinhui Sun1, Jing-Dong J Han2, Weiyang Chen3
1School of Cyber Science and Engineering, Qufu Normal University, Qufu, China.
NPJ Systems Biology and Applications
|February 19, 2026
Summary
This study introduces a novel AI network to analyze brain scans over time, improving Alzheimer's disease (AD) diagnosis. The findings reveal dynamic changes in brain regions during AD progression, aiding early detection.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Neurodegenerative Diseases
Background:
- Alzheimer's disease (AD) diagnosis often relies on single time-point data, neglecting its progressive nature.
- Longitudinal analysis of structural magnetic resonance imaging (sMRI) is crucial for understanding AD progression.
- Integrating multi-tissue features and temporal dynamics is needed for advanced AD diagnostic models.
Purpose of the Study:
- To develop and validate a novel deep learning network for Alzheimer's disease diagnosis using longitudinal sMRI data.
- To investigate the dynamic changes in brain regions associated with Alzheimer's disease and normal aging.
- To enhance the pathological understanding and early diagnostic capabilities for Alzheimer's disease.
Main Methods:
- Proposed a Multi-Branch Fusion Channel Attention Network (MBFCA-Net) for analyzing multi-time series sMRI data.
- Leveraged temporal correlations across longitudinal scans for Alzheimer's disease detection.
- Conducted retrospective interpretability analysis to quantify regional brain contributions across disease stages.
Main Results:
- The MBFCA-Net effectively utilized longitudinal scan data for Alzheimer's disease diagnosis.
- Identified dynamic changes in the importance of brain regions like the amygdala, parahippocampal gyrus, and temporal lobe during AD progression.
- Observed a developmental trend in AD-related voxel clusters, shifting from the hippocampus to the temporal lobe.
Conclusions:
- The study highlights the significance of longitudinal data and advanced AI models for Alzheimer's disease diagnosis.
- Revealed dynamic, stage-specific alterations in brain regions critical for understanding AD.
- Provides novel insights into longitudinal AD patterns, supporting early diagnosis and disease understanding.
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