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情绪、认知和皮质下运动网络相互作用是导致步态冻结的基础。

Interactions across emotional, cognitive and subcortical motor networks underlying freezing of gait.

机构信息

Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Yoshida-Konoe, Sakyo-ku, Kyoto 606-8501, Japan; Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry (NCNP), 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan.

Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry (NCNP), 4-1-1, Ogawa-Higashi, Kodaira, Tokyo 187-8551, Japan.

出版信息

Neuroimage Clin. 2023;37:103342. doi: 10.1016/j.nicl.2023.103342. Epub 2023 Feb 2.

Abstract

Freezing of gait (FOG) is a gait disorder affecting patients with Parkinson's disease (PD) and related disorders. The pathophysiology of FOG is unclear because of its phenomenological complexity involving motor, cognitive, and emotional aspects of behavior. Here we used resting-state functional MRI to retrieve functional connectivity (FC) correlated with the New FOG questionnaire (NFOGQ) reflecting severity of FOG in 67 patients with PD. NFOGQ scores were correlated with FCs in the extended basal ganglia network (BGN) involving the striatum and amygdala, and in the extra-cerebellum network (CBLN) involving the frontoparietal network (FPN). These FCs represented interactions across the emotional (amygdala), subcortical motor (BGN and CBLN), and cognitive networks (FPN). Using these FCs as features, we constructed statistical models that explained 40% of the inter-individual variances of FOG severity and that discriminated between PD patients with and without FOG. The amygdala, which connects to the subcortical motor (BGN and CBLN) and cognitive (FPN) networks, may have a pivotal role in interactions across the emotional, cognitive, and subcortical motor networks. Future refinement of the machine learning-based classifier using FCs may clarify the complex pathophysiology of FOG further and help diagnose and evaluate FOG in clinical settings.

摘要

冻结步态(FOG)是一种影响帕金森病(PD)及相关疾病患者的步态障碍。由于其涉及运动、认知和情感行为等方面的现象学复杂性,FOG 的病理生理学尚不清楚。在这里,我们使用静息态功能磁共振成像(rs-fMRI)来检索与反映 FOG 严重程度的新 FOG 问卷(NFOGQ)相关的功能连接(FC),共涉及 67 名 PD 患者。NFOGQ 评分与扩展基底节网络(BGN,包括纹状体和杏仁核)和小脑外网络(CBLN,包括额顶网络(FPN))中的 FC 相关。这些 FC 代表了情感(杏仁核)、皮质下运动(BGN 和 CBLN)和认知网络(FPN)之间的相互作用。我们使用这些 FC 作为特征,构建了统计模型,该模型可以解释 40%的 FOG 严重程度的个体间差异,并可以区分有无 FOG 的 PD 患者。杏仁核与皮质下运动(BGN 和 CBLN)和认知(FPN)网络相连,可能在情感、认知和皮质下运动网络之间的相互作用中起着关键作用。使用 FC 进一步细化基于机器学习的分类器可能会进一步阐明 FOG 的复杂病理生理学,并有助于在临床环境中诊断和评估 FOG。

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