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Related Experiment Video

Updated: Sep 13, 2025

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SAGEFusionNet: An Auxiliary Supervised Graph Neural Network for Brain Age Prediction as a Neurodegenerative

Suraj Kumar1, Suman Hazarika2, Cota Navin Gupta1

  • 1Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati 781039, India.

Brain Sciences
|July 29, 2025
PubMed
Summary

This study introduces SAGEFusionNet, a novel Graph Neural Network (GNN) for predicting brain age and identifying Parkinson's disease (PD) biomarkers. The model accurately estimates biological age, offering insights into neurodegeneration.

Keywords:
anatomical graphbrain agegraph neural networkgrey matter volumesMRIwhite matter volume

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Graph Neural Networks

Background:

  • Graph Neural Networks (GNNs) show promise for analyzing brain structural patterns in neurodegenerative diseases like Parkinson's disease (PD).
  • Brain age prediction is an emerging technique to identify aging patterns as potential biomarkers for disorders.
  • Existing GNNs face challenges with depth, leading to oversmoothing and vanishing gradients.

Purpose of the Study:

  • To propose SAGEFusionNet, a GNN architecture for enhanced brain age prediction.
  • To assess PD-related brain aging patterns using T1-weighted structural MRI (sMRI).
  • To overcome limitations of deep GNNs by incorporating ROI-aware pooling and multi-layer feature fusion.

Main Methods:

  • Developed SAGEFusionNet with ROI-aware pooling and multi-layer feature fusion for multi-scale structural information.
  • Utilized T1-weighted sMRI scans from ADNI (580 healthy individuals) and PPMI (215 PD patients).
  • Constructed anatomical graphs using grey matter (GM) and white matter (WM) volumes, with GM volume as node features.

Main Results:

  • SAGEFusionNet achieved a mean absolute error (MAE) of 4.24±0.38 years and a Pearson's Correlation Coefficient (PCC) of 0.72±0.03 on healthy individuals.
  • On PD patients, the model showed a mean MAE of 13.36 years, with 213 out of 215 individuals exhibiting higher predicted brain ages.
  • The model demonstrated effectiveness in identifying accelerated aging patterns in PD.

Conclusions:

  • Brain age prediction using SAGEFusionNet offers valuable insights into neurodegenerative disease patterns.
  • The proposed method effectively captures multi-scale structural information and enhances gradient flow for improved GNN performance.
  • SAGEFusionNet shows potential as a tool for biomarker discovery in Parkinson's disease and other neurodegenerative conditions.