Akgüller Ömer, Balcı Mehmet Ali, Cioca Gabriela
Faculty of Science, Department of Mathematics, Mugla Sitki Kocman University, Muğla 48000, Turkey.
Engineering Sciences Department, Engineering and Architecture Faculty, Izmir Katip Celebi University, Izmir 35620, Turkey.
Diagnostics (Basel). 2024 Dec 4;14(23):2728. doi: 10.3390/diagnostics14232728.
This study investigates disruptions in functional brain networks in Parkinson's Disease (PD), using advanced modeling and machine learning. Functional networks were constructed using the Nonlinear Autoregressive Distributed Lag (NARDL) model, which captures nonlinear and asymmetric dependencies between regions of interest (ROIs). Key network metrics and information-theoretic measures were extracted to classify PD patients and healthy controls (HC), using deep learning models, with explainability methods employed to identify influential features. Resting-state fMRI data from the Parkinson's Progression Markers Initiative (PPMI) dataset were used to construct NARDL-based networks. Metrics, such as Degree, Closeness, Betweenness, and Eigenvector Centrality, along with Network Entropy and Complexity, were analyzed. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models, classified PD and HC groups. Explainability techniques, including SHAP and LIME, identified significant features driving the classifications. PD patients showed reduced Closeness (22%) and Betweenness Centrality (18%). CNN achieved 91% accuracy, with Network Entropy and Eigenvector Centrality identified as key features. Increased Network Entropy indicated heightened randomness in PD brain networks. NARDL-based analysis with interpretable deep learning effectively distinguishes PD from HC, offering insights into neural disruptions and potential personalized treatments for PD.
本研究使用先进的建模和机器学习方法，调查帕金森病（PD）患者大脑功能网络的破坏情况。使用非线性自回归分布滞后（NARDL）模型构建功能网络，该模型可捕捉感兴趣区域（ROI）之间的非线性和非对称依赖性。提取关键网络指标和信息论度量，使用深度学习模型对PD患者和健康对照（HC）进行分类，并采用可解释性方法识别有影响力的特征。帕金森病进展标记倡议（PPMI）数据集的静息态功能磁共振成像（fMRI）数据用于构建基于NARDL的网络。分析了度、接近度、中介中心性和特征向量中心性等指标，以及网络熵和复杂性。卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）模型对PD组和HC组进行了分类。包括SHAP和LIME在内的可解释性技术确定了驱动分类的显著特征。PD患者的接近度降低了22%，中介中心性降低了18%。CNN的准确率达到91%，网络熵和特征向量中心性被确定为关键特征。网络熵增加表明PD大脑网络的随机性增强。基于NARDL的分析与可解释的深度学习有效地将PD与HC区分开来，为神经破坏和PD的潜在个性化治疗提供了见解。
