Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer’s disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aβ biomarkers and identify the Aβ-related dominant brain regions involved with cognitive impairment. We employed Aβ biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aβ biomarkers on the test set. To identify Aβ-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aβ in MCI compared to controls and a stronger correlation between Aβ and cognition, particularly in Braak stages III–IV and V–VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aβ biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aβ biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aβ biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.

中文翻译：

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使用 Braak 分期淀粉样蛋白生物标志物和机器学习预测认知功能障碍和区域中心

轻度认知障碍（MCI）是正常衰老和早期阿尔茨海默病（AD）之间的过渡阶段。Braak 区域细胞外淀粉样蛋白 (Aβ) 的存在表明与 MCI/AD 认知功能障碍有关。研究区域 Aβ 生物标志物与认知功能之间的多变量预测关系有助于早期发现和预防 AD。我们引入了机器学习方法来根据区域 Aβ 生物标志物估计认知功能障碍，并识别与认知障碍相关的 Aβ 相关主导大脑区域。我们使用来自同一个人的 Aβ 生物标志物和认知测量来训练支持向量回归 (SVR) 和人工神经网络 (ANN) 模型，并仅根据测试集上的 Aβ 生物标志物来预测认知表现。为了识别参与认知预测的 Aβ 相关主导大脑区域，我们构建了局部可解释模型不可知解释 (LIME) 模型。我们发现与对照组相比，MCI 中的 Aβ 升高，并且 Aβ 与认知之间存在更强的相关性，特别是在 Braak III-IV 期和 V-VII 期（p < 0.05）生物标志物中。SVR 和 ANN，尤其是 ANN，在区域 Aβ 生物标志物和认知障碍之间显示出很强的预测关系 (p < 0.05)。LIME 与 ANN 的整合表明，海马旁回、颞下回和海马是预测认知能力下降最决定性的 Braak 区域。与之前的研究结果一致，这种新方法表明 Aβ 生物标志物与认知障碍之间的关系。所提出的分析框架可以根据 Braak 分期 Aβ 生物标志物估计认知障碍，并描绘共同参与 AD 病理生理学的主要大脑区域。