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功能性神经网络对帕金森病患者在认知障碍的整个范围内进行分层。

Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment.

作者信息

Hajebrahimi Farzin, Budak Miray, Saricaoglu Mevhibe, Temel Zeynep, Demir Tugce Kahraman, Hanoglu Lutfu, Yildirim Suleyman, Bayraktaroglu Zubeyir

机构信息

Functional Imaging and Cognitive-Affective Neuroscience Lab (fINCAN), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey.

Department of Physical Therapy and Rehabilitation, School of Health Sciences, Istanbul Medipol University, Istanbul, Turkey.

出版信息

Brain Behav. 2024 Jan;14(1):e3395. doi: 10.1002/brb3.3395.

Abstract

INTRODUCTION

Cognitive impairment (CI) is a significant non-motor symptoms in Parkinson's disease (PD) that often precedes the emergence of motor symptoms by several years. Patients with PD hypothetically progress from stages without CI (PD-normal cognition [NC]) to stages with Mild CI (PD-MCI) and PD dementia (PDD). CI symptoms in PD are linked to different brain regions and neural pathways, in addition to being the result of dysfunctional subcortical regions. However, it is still unknown how functional dysregulation correlates to progression during the CI. Neuroimaging techniques hold promise in discriminating CI stages of PD and further contribute to the biomarker formation of CI in PD. In this study, we explore disparities in the clinical assessments and resting-state functional connectivity (FC) among three CI stages of PD.

METHODS

We enrolled 88 patients with PD and 26 healthy controls (HC) for a cross sectional clinical study and performed intra- and inter-network FC analysis in conjunction with comprehensive clinical cognitive assessment.

RESULTS

Our findings underscore the significance of several neural networks, namely, the default mode network (DMN), frontoparietal network (FPN), dorsal attention network, and visual network (VN) and their inter-intra-network FC in differentiating between PD-MCI and PDD. Additionally, our results showed the importance of sensory motor network, VN, DMN, and salience network (SN) in the discriminating PD-NC from PDD. Finally, in comparison to HC, we found DMN, FPN, VN, and SN as pivotal networks for further differential diagnosis of CI stages of PD.

CONCLUSION

We propose that resting-state networks (RSN) can be a discriminating factor in distinguishing the CI stages of PD and progressing from PD-NC to MCI or PDD. The integration of clinical and neuroimaging data may enhance the early detection of PD in clinical settings and potentially prevent the disease from advancing to more severe stages.

摘要

引言

认知障碍(CI)是帕金森病(PD)中一种重要的非运动症状,通常在运动症状出现前数年就已出现。PD患者理论上会从无CI阶段(PD-正常认知[NC])发展到轻度CI阶段(PD-MCI)和PD痴呆(PDD)。PD中的CI症状除了是皮质下区域功能失调的结果外,还与不同的脑区和神经通路有关。然而,功能失调与CI病程中的进展如何相关仍不清楚。神经影像学技术有望区分PD的CI阶段,并进一步有助于PD中CI的生物标志物形成。在本研究中,我们探讨了PD三个CI阶段在临床评估和静息态功能连接(FC)方面的差异。

方法

我们招募了88例PD患者和26名健康对照(HC)进行横断面临床研究,并结合全面的临床认知评估进行了网络内和网络间FC分析。

结果

我们的研究结果强调了几个神经网络的重要性,即默认模式网络(DMN)、额顶叶网络(FPN)、背侧注意网络和视觉网络(VN)及其网络内和网络间FC在区分PD-MCI和PDD方面的作用。此外,我们的结果显示了感觉运动网络、VN、DMN和突显网络(SN)在区分PD-NC和PDD方面的重要性。最后,与HC相比,我们发现DMN、FPN、VN和SN是进一步鉴别PD的CI阶段的关键网络。

结论

我们提出静息态网络(RSN)可以作为区分PD的CI阶段以及从PD-NC进展到MCI或PDD的一个鉴别因素。临床和神经影像学数据的整合可能会提高临床环境中PD的早期检测,并有可能防止疾病进展到更严重的阶段。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a30/10808882/2fb3b73fc0ec/BRB3-14-e3395-g007.jpg

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