Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy.
Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy.
Mov Disord. 2024 Feb;39(2):305-317. doi: 10.1002/mds.29678. Epub 2023 Dec 6.
Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting-state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline.
The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD-cognitive states, ranging from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features.
Among 118 PD patients age-, sex-, and education matched with 35 healthy controls, 52 were classified with PD-NC, 46 with PD-MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs-fMRI data and used to train ML models.
FD outperformed fALFF metrics in differentiating between PD-cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity.
Our study indicates that PD-cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD-cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Higuchi 的分形维数（FD）捕捉大脑动力学复杂性，可能是分析静息状态功能磁共振成像（fMRI）数据和检测帕金森病（PD）认知下降下神经元相互作用复杂性的有前途的方法。
本研究旨在比较 FD 与更成熟的自发性神经活动指标——低频波动的分数振幅（fALFF），并通过机器学习（ML）模型确定哪种方法可以最好地区分 PD 的认知状态，从正常认知（PD-NC）、轻度认知障碍（PD-MCI）到痴呆（PDD）。最后，旨在探索 fALFF 和 FD 与 PD 临床和认知特征的相关性。
在 118 名年龄、性别和教育程度与 35 名健康对照匹配的 PD 患者中，根据广泛的认知和临床评估，将 52 名患者分类为 PD-NC，46 名患者分类为 PD-MCI，20 名患者分类为 PDD。在 rs-fMRI 数据上计算 fALFF 和 FD 指标，并用于训练 ML 模型。
FD 在区分 PD 认知状态方面优于 fALFF 指标，总体准确率为 78%（相比之下为 62%）。PD 患者在感觉运动网络、中央执行网络（CEN）和默认模式网络（DMN）内表现出神经元动力学复杂性增加，同时 CEN 和 DMN 内自发性神经元活动减少，其复杂性增加与痴呆的存在密切相关。此外，我们发现一些 DMN 关键枢纽与认知表现和疾病严重程度较差相关。
我们的研究表明，PD 认知下降的特征是自发神经元活动改变和时间复杂性增加，涉及 CEN 和 DMN，可能反映了这些网络的分离增加。因此，我们提出 FD 作为 PD 认知下降的预后生物标志物。
