IEEE Trans Biomed Eng. 2023 Aug;70(8):2475-2485. doi: 10.1109/TBME.2023.3250355. Epub 2023 Jul 18.
Inferring causal or effective connectivity between measured timeseries is crucial to understanding directed interactions in complex systems. This task is especially challenging in the brain as the underlying dynamics are not well-understood. This paper aims to introduce a novel causality measure called frequency-domain convergent cross-mapping (FDCCM) that utilizes frequency-domain dynamics through nonlinear state-space reconstruction.
Using synthesized chaotic timeseries, we investigate general applicability of FDCCM at different causal strengths and noise levels. We also apply our method on two resting-state Parkinson's datasets with 31 and 54 subjects, respectively. To this end, we construct causal networks, extract network features, and perform machine learning analysis to distinguish Parkinson's disease patients (PD) from age and gender-matched healthy controls (HC). Specifically, we use the FDCCM networks to compute the betweenness centrality of the network nodes, which act as features for the classification models.
The analysis on simulated data showed that FDCCM is resilient to additive Gaussian noise, making it suitable for real-world applications. Our proposed method also decodes scalp-EEG signals to classify the PD and HC groups with approximately 97% leave-one-subject-out cross-validation accuracy. We compared decoders from six cortical regions to find that features derived from the left temporal lobe lead to a higher classification accuracy of 84.5% compared to other regions. Moreover, when the classifier trained using FDCCM networks from one dataset was tested on an independent out-of-sample dataset, it attained an accuracy of 84%. This accuracy is significantly higher than correlational networks (45.2%) and CCM networks (54.84%).
These findings suggest that our spectral-based causality measure can improve classification performance and reveal useful network biomarkers of Parkinson's disease.
推断测量时间序列之间的因果或有效连通性对于理解复杂系统中的有向相互作用至关重要。由于基础动力学尚未得到很好的理解，因此该任务在大脑中特别具有挑战性。本文旨在引入一种新的因果度量方法，称为频域会聚交叉映射（FDCCM），该方法通过非线性状态空间重建利用频域动态。
使用合成混沌时间序列，我们研究了 FDCCM 在不同因果强度和噪声水平下的一般适用性。我们还分别应用我们的方法对两个具有 31 和 54 个受试者的静息状态帕金森数据集。为此，我们构建因果网络，提取网络特征，并进行机器学习分析，以区分帕金森病患者（PD）和年龄和性别匹配的健康对照组（HC）。具体来说，我们使用 FDCCM 网络计算网络节点的中间中心性，作为分类模型的特征。
对模拟数据的分析表明，FDCCM 对加性高斯噪声具有弹性，使其适用于实际应用。我们提出的方法还使用头皮 EEG 信号进行解码，以 97%的留一受试者交叉验证准确率对 PD 和 HC 组进行分类。我们比较了来自六个皮质区域的解码器，发现与其他区域相比，源自左颞叶的特征导致更高的分类准确率 84.5%。此外，当使用 FDCCM 网络从一个数据集训练的分类器在独立的样本外数据集上进行测试时，它达到了 84%的准确率。这一准确性明显高于相关网络（45.2%）和 CCM 网络（54.84%）。
这些发现表明，我们基于频谱的因果度量可以提高分类性能，并揭示帕金森病的有用网络生物标志物。
