Characterization of Early Stage Parkinson's Disease From Resting-State fMRI Data Using a Long Short-Term Memory Network.
Characterization of Early Stage Parkinson's Disease From Resting-State fMRI Data Using a Long Short-Term Memory Network.
复制标题
使用长短期记忆网络从静息状态功能磁共振数据表征早期帕金森氏病。
DOI：
10.3389/fnimg.2022.952084
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Dvornek, Nicha C
中科院分区：
文献类型：
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作者：
Guo, Xueqi;Tinaz, Sule;Dvornek, Nicha C
帕金森病（PD）是一种常见而复杂的神经退行性疾病，根据Hoehn-Yahr评分标准分为5个阶段。表征早期疾病进展的脑功能改变将支持准确的疾病分期、新疗法的开发以及疾病进展或治疗反应的客观监测。功能磁共振成像（fMRI）是一种很有前途的工具，在揭示功能连接（FC）的差异和发展生物标志物在PD。虽然fMRI和FC数据已被用于诊断PD通过应用机器学习方法，如支持向量机和逻辑回归，FC的变化在早期PD的表征尚未进行研究。鉴于fMRI数据的复杂性和非线性，我们建议使用长短期记忆（LSTM）网络来区分PD的早期阶段并了解相关的脑功能变化。该研究包括来自帕金森病进展标志物倡议（PPMI）的84名受试者（第2阶段56名，第1阶段28名），这是最大的公共PD数据集。在重复的10倍分层交叉验证下，LSTM模型达到了71.63%的准确率，比最好的传统机器学习方法高出13.52%，比CNN模型高出11.56%，这表明与其他机器学习分类器相比，LSTM模型具有更好的鲁棒性和准确性。最后，我们使用学习的LSTM模型权重来选择有助于模型预测的顶级大脑区域，并进行FC分析来表征疾病阶段和运动障碍的功能变化，以更好地了解PD的大脑机制。
Parkinson's disease (PD) is a common and complex neurodegenerative disorder with five stages on the Hoehn and Yahr scaling. Characterizing brain function alterations with progression of early stage disease would support accurate disease staging, development of new therapies, and objective monitoring of disease progression or treatment response. Functional magnetic resonance imaging (fMRI) is a promising tool in revealing functional connectivity (FC) differences and developing biomarkers in PD. While fMRI and FC data have been utilized for diagnosis of PD through application of machine learning approaches such as support vector machine and logistic regression, the characterization of FC changes in early-stage PD has not been investigated. Given the complexity and non-linearity of fMRI data, we propose the use of a long short-term memory (LSTM) network to distinguish the early stages of PD and understand related functional brain changes. The study included 84 subjects (56 in stage 2 and 28 in stage 1) from the Parkinson's Progression Markers Initiative (PPMI), the largest-available public PD dataset. Under a repeated 10-fold stratified cross-validation, the LSTM model reached an accuracy of 71.63%, 13.52% higher than the best traditional machine learning method and 11.56% higher than a CNN model, indicating significantly better robustness and accuracy compared with other machine learning classifiers. Finally, we used the learned LSTM model weights to select the top brain regions that contributed to model prediction and performed FC analyses to characterize functional changes with disease stage and motor impairment to gain better insight into the brain mechanisms of PD.