SAGEFusionNet:作为神经退行性生物标志物,用于大脑年龄预测的辅助监督图形神经网络
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
概括
这项研究介绍了SAGEFusionNet,这是一个新的图形神经网络 (GNN),用于预测大脑年龄和识别帕金森病 (PD) 生物标志物. 该模型准确地估计了生物年龄,为神经退行提供了洞察力.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 图形神经网络 (GNN) 显示出分析像帕金森病 (PD) 这样的神经退行性疾病中的大脑结构模式的前景.
- 脑年龄预测是一种新兴的技术,用于识别衰老模式作为疾病的潜在生物标志物.
- 现有的GNN面临着深度的挑战,导致过度平滑和消失的梯度.
研究的目的:
- 提出SAGEFusionNet,一个用于增强大脑年龄预测的GNN架构.
- 通过使用T1加权结构MRI (sMRI) 来评估PD相关的大脑衰老模式.
- 通过结合ROI意识的聚合和多层特征融合,克服深度GNN的局限性.
主要方法:
- 开发了SAGEFusionNet,具有ROI意识的聚合和多层特征融合,用于多尺度的结构信息.
- 利用ADNI (580名健康人) 和PPMI (215名PD患者) 的T1加权sMRI扫描.
- 使用灰质 (GM) 和白质 (WM) 体积构建解剖图,将GM体积作为节点特征.
主要成果:
- 在健康个体中,SAGEFusionNet实现了4.24±0.38年的平均绝对误差 (MAE) 和0.72±0.03的皮尔森相关系数 (PCC).
- 在PD患者中,该模型显示平均MAE为13.36年,在215个人中,有213人表现出更高的预测大脑年龄.
- 该模型在识别PD加速衰老模式方面表现出有效性.
结论:
- 使用SAGEFusionNet进行大脑年龄预测,为神经退行性疾病模式提供了宝贵的见解.
- 拟议的方法有效地捕获多个尺度的结构信息,并增强梯度流,以提高GNN性能.
- SAGEFusionNet显示出作为帕金森病和其他神经退行性疾病中的生物标志物发现工具的潜力.
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