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结构相似性网络揭示了痴呆症中大脑的脆弱性

Marcella Montagnese¹, Amir Ebneabbasi², Natalia García-San-Martín³

¹Department of Psychology, University of Cambridge, Downing Pl, Cambridge, CB2 3EB, UK.

medRxiv : the preprint server for health sciences

|June 30, 2025

概括

这项研究引入了一个新的框架,使用脑成像 (MIND网络) 和人工智能建模来精确确定阿尔茨海默病 (AD) 个体大脑变化,改善诊断和预后.

科学领域:

神经成像是一种神经成像.
计算神经科学是一种神经科学.
生物统计学生物统计学

背景情况:

阿尔茨海默病 (AD) 在大脑退化方面呈现出显著的个体变异性,阻碍了精确的诊断和预后.
目前对阿尔茨海默病的诊断和预后工具缺乏准确性,原因是神经退行变化的个体间异质性.

研究的目的:

开发和验证一个新的框架,整合了形态测量逆分歧 (MIND) 网络和等级贝叶斯模型.
在阿尔茨海默病中识别个体级神经解剖学偏差.

主要方法:

使用MIND网络,使用多变量MRI特征量化大脑区域相似性.
在英国生物库数据 (N=35,133) 上训练了一种规范模型,并应用于国家阿尔茨海默氏症协调中心队列 (N=3,567).
在临床阶段,APOE基因型,死亡风险和神经病理负担中分析了大脑偏差.

主要成果:

降低的MIND值 (负偏差) 显著分层了阿尔茨海默病的阶段 (p<0.01) 并且在受影响的功能网络中得到了丰富.
在APOE ε4同胞体中观察到较大的负偏差,与死后神经病理严重程度相关 (p=0.032).
神经生物学解码将这些偏差与神经递质受体分布和皮质组织联系起来.

结论:

开发的人口神经成像建模框架使个性化大脑映射成为可能.
这种方法为改善阿尔茨海默病的诊断和预后提供了直接的实用性.
该框架有助于理解阿尔茨海默病的潜在生物机制.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.在MIND网络中,MIND网络是MIND网络.规范性建模规范性建模大脑网络大脑网络形态学上的相似性.神经退行症的神经退行症神经病理学神经病理学个性化药物是个性化的药物.结构性核磁共振成像 (MRI)

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