PDF(Pubmed)
Abstract：
UNASSIGNED: Alzheimer\'s disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers.
UNASSIGNED: Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers.
UNASSIGNED: On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors.
UNASSIGNED: Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.
UNASSIGNED: Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers.
UNASSIGNED: On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors.
UNASSIGNED: Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.
摘要：
■阿尔茨海默病（AD）是一种进行性神经退行性疾病。当前核心脑脊液（CSF）AD生物标志物，广泛用于诊断，需要进行腰椎穿刺,使它们作为筛选工具不切实际。考虑到睡眠障碍在AD中的作用，最近的研究表明，定量睡眠脑电图特征是AD病理的潜在非侵入性生物标志物。然而,综合多导睡眠图（PSG）信号的定量分析仍然相对不足。PSG是一种非侵入性测试，可对各种参数进行定性和定量分析，与其他生物标志物一起提供额外的见解。机器学习（ML）因其在复杂数据集中辨别复杂模式的能力而受到关注。在AD神经病理学检测中提供了希望。因此,本研究旨在评估多模式ML方法预测ADCSF核心生物标志物的有效性.
■轻度-中度AD患者前瞻性招募PSG，然后检测CSF和血液样本的生物标志物。PSG信号经过预处理以提取非线性，时域和频域统计量化特征。使用四个输入特征子集训练多个ML算法：临床变量(CLINVAR)，常规PSG参数(SLEEPVAR)，定量PSG信号特征(PSGVAR)和所有子集的组合(ALL)。采用交叉验证技术来评估模型性能并确保泛化性。开发回归模型以确定用于解释生物标志物中的方差的最有效的变量组合。
■关于49个科目，梯度提升回归因子在估计生物标志物水平方面取得了最好的结果，对每个生物标志物使用不同的损失函数:Aβ42的最小绝对偏差(LAD),p-tau的最小二乘(LS)和t-tau的Huber。ALL子集显示了所有三种生物标志物的最低训练误差，尽管具有不同的测试性能。具体来说,SLEEPVAR子集在预测Aβ42方面产生了最佳的测试性能,而ALL子集由于测试误差最低而最准确地预测了p-tau和t-tau.
■多模式ML可以通过利用非侵入性和经济上可行的变量来帮助预测早期AD中CSF生物标志物的结果。将计算模型集成到医疗实践中，为筛查有AD风险的患者提供了有希望的工具。潜在的指导临床决策。
■轻度-中度AD患者前瞻性招募PSG，然后检测CSF和血液样本的生物标志物。PSG信号经过预处理以提取非线性，时域和频域统计量化特征。使用四个输入特征子集训练多个ML算法：临床变量(CLINVAR)，常规PSG参数(SLEEPVAR)，定量PSG信号特征(PSGVAR)和所有子集的组合(ALL)。采用交叉验证技术来评估模型性能并确保泛化性。开发回归模型以确定用于解释生物标志物中的方差的最有效的变量组合。
■关于49个科目，梯度提升回归因子在估计生物标志物水平方面取得了最好的结果，对每个生物标志物使用不同的损失函数:Aβ42的最小绝对偏差(LAD),p-tau的最小二乘(LS)和t-tau的Huber。ALL子集显示了所有三种生物标志物的最低训练误差，尽管具有不同的测试性能。具体来说,SLEEPVAR子集在预测Aβ42方面产生了最佳的测试性能,而ALL子集由于测试误差最低而最准确地预测了p-tau和t-tau.
■多模式ML可以通过利用非侵入性和经济上可行的变量来帮助预测早期AD中CSF生物标志物的结果。将计算模型集成到医疗实践中，为筛查有AD风险的患者提供了有希望的工具。潜在的指导临床决策。
