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使用图形神经网络对有效大脑连接的成像进行痴呆症分类

Jun Cao¹, Lichao Yang², Ptolemaios Georgios Sarrigiannis³

¹School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire, MK43 0AL, UK; School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK.

Computers in biology and medicine

|November 20, 2023

概括

这项研究引入了一种新的图形神经网络 (GNN),用于使用有效的大脑连接 (EBC) 诊断阿尔茨海默病 (AD) 和帕金森病 (PD). 这种新方法在区分患者和健康对照中实现了高准确度.

科学领域:

神经科学和计算精神病学
医学成像和机器学习

背景情况:

阿尔茨海默病 (AD) 和帕金森病 (PD) 是主要的神经退行性疾病.
有效的大脑连接 (EBC) 显示了区分AD,PD和健康对照 (HC) 的潜力.
目前使用EBC诊断疾病的方法有限,特别是复杂的大脑网络.

研究的目的:

开发和验证一种新的图形神经网络 (GNN) 方法,用于使用EBC诊断神经退行性疾病.
探索将EBC估计与功率频谱密度 (PSD) 特征进行分类的有效性.
在疾病诊断中同时应用单变量和多变量特征的框架.

主要方法:

开发一个定向结构学习GNN (DSL-GNN),适用于EBC中的定向信息处理.
将DSL-GNN应用于来自神经成像数据的EBC估计和PSD特征.
在四个区分任务中验证DSL-GNN框架:AD对HC,PD对HC,AD对PD和AD对PD对HC.

主要成果:

拟议的DSL-GNN在所有歧视任务中,与现有方法相比,实现了更高的性能.
记录的最高准确率为94.0% (AD与HC),94.2% (PD与HC),97.4% (AD与PD) 和93.0% (AD与PD与HC).
该框架成功地整合了单变量和多变量特征,以加强分类.

结论:

开发的DSL-GNN为分析具有因果定向信息的复杂大脑网络提供了强大的分析框架.
这种方法显示了改善AD和PD等常见神经退行性疾病的诊断准确性的巨大潜力.
该研究强调了将GNN与EBC结合起来,促进神经退行性疾病研究的有效性.

关键词:

阿尔茨海默氏症的疾病是阿尔茨海默氏症.定向结构学习图表神经网络神经网络这是一个EEGEEGEEGEEGEEGEEGEEG.有效的大脑连接性有效的大脑连接性帕金森病的疾病.

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