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The Chronic Migraine Brain: What Have We Learned From Neuroimaging?慢性偏头痛的大脑:我们从神经影像学中学到了什么?
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Increased neural connectivity between the hypothalamus and cortical resting-state functional networks in chronic migraine.慢性偏头痛患者下丘脑与皮质静息状态功能网络之间的神经连通性增加。
J Neurol. 2020 Jan;267(1):185-191. doi: 10.1007/s00415-019-09571-y. Epub 2019 Oct 12.
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Aberrant interactions of cortical networks in chronic migraine: A resting-state fMRI study.慢性偏头痛患者皮质网络的异常交互:一项静息态 fMRI 研究。
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Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study.慢性下腰痛患者大脑功能连接的视觉网络改变:静息态功能连接和机器学习研究。
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慢性偏头痛中静态功能连接和动态功能网络连接的异常调制。

Aberrant modulations of static functional connectivity and dynamic functional network connectivity in chronic migraine.

作者信息

Zou Yan, Tang Weijun, Qiao Xiangyang, Li Ji

机构信息

Department of Integrated Traditional and Western Medicine, Huashan Hospital, Fudan University, Shanghai, China.

Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.

出版信息

Quant Imaging Med Surg. 2021 Jun;11(6):2253-2264. doi: 10.21037/qims-20-588.

DOI:10.21037/qims-20-588
PMID:34079699
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8107335/
Abstract

BACKGROUND

Chronic migraine (CM) is a common and disabling neurological disorder that affects 1-2% of the global population. The aim of the present study was to identify the functional characteristics of the CM brain using static functional connectivity (s-FC), static functional network connectivity (s-FNC), and dynamic functional network connectivity (d-FNC) analyses.

METHODS

In the present study, 17 CM patients and 20 sex- and age-matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. We utilized independent component (IC) analysis to identify 13 ICs. These 13 ICs were then classified into the following 6 resting-state networks (RSNs): the default mode network (DMN), executive control network (ECN), dorsal attention network, auditory network (AN), visual network (VN), and cerebellum network. Subsequently, s-FC, s-FNC, and d-FNC analyses of 13 ICs were employed for between-group comparisons. Three temporal metrics (fraction of time spent, mean dwell time, and number of transitions), which were derived from the state-transition vector, were calculated for group comparisons. In addition, correlation analyses were performed between these dynamic metrics and clinical characteristics [mean visual analog scale (VAS) scores, days with headache per month, days with migraine pain feature per month, and disease duration].

RESULTS

In the comparison of s-FC of 13 ICs within RSNs between the CM and HC groups, increased connectivity was observed in the left angular gyrus (Angular_L) of the ECN (IC 2) and the right superior parietal gyrus (Parietal_Sup_R) of the AN (IC 5), and reduced connectivity was found in the left superior frontal gyrus (Frontal_Sup_2_L) of the AN (IC 5) and DMN (IC 19), the right calcarine sulcus (Calcarine_R) of the VN (IC 7), and the left precuneus (Precuneus_L) of the DMN (IC 17) in CM patients. In the comparison of the d-FNC of 13 IC pairs within RSNs between the two groups, the CM group exhibited significantly decreased connections between the DMN (IC 11) and AN (IC 5), and increased connections between the ECN (IC 2, IC 4) and DMN (IC 19), ECN (IC 4) and AN (IC 5), and ECN (IC 4) and VN (IC 13) in state 1. However, no significant differences in s-FNC were observed between the two groups during the s-FNC analysis. Between-group comparisons of three dynamic metrics between the CM and HC groups showed a longer fraction of time spent and mean dwell time in state 2 for CM patients. Furthermore, from the correlation analyses between these metrics and clinical characteristics, we observed a significant positive correlation between the number of transitions and mean VAS scores.

CONCLUSIONS

Our findings suggest that functional features of the CM brain may fluctuate over time instead of remaining static, and provide further evidence that migraine chronification may be related to abnormal pattern connectivity between sensory and cognitive brain networks.

摘要

背景

慢性偏头痛(CM)是一种常见的致残性神经疾病,影响全球1%-2%的人口。本研究的目的是使用静态功能连接(s-FC)、静态功能网络连接(s-FNC)和动态功能网络连接(d-FNC)分析来确定CM大脑的功能特征。

方法

在本研究中,17例CM患者和20例年龄及性别匹配的健康对照者(HCs)接受了静息态功能磁共振成像检查。我们利用独立成分(IC)分析来识别13个IC。然后将这13个IC分为以下6个静息态网络(RSNs):默认模式网络(DMN)、执行控制网络(ECN)、背侧注意网络、听觉网络(AN)、视觉网络(VN)和小脑网络。随后,对13个IC进行s-FC、s-FNC和d-FNC分析以进行组间比较。从状态转换向量导出的三个时间指标(花费时间的比例、平均停留时间和转换次数)用于组间比较。此外,还对这些动态指标与临床特征[平均视觉模拟量表(VAS)评分、每月头痛天数、每月偏头痛疼痛特征天数和病程]进行了相关性分析。

结果

在CM组和HC组之间RSNs内13个IC的s-FC比较中,ECN(IC 2)的左侧角回(Angular_L)和AN(IC 5)的右侧顶上回(Parietal_Sup_R)的连接性增加,而AN(IC 5)和DMN(IC 19)的左侧额上回(Frontal_Sup_2_L)、VN(IC 7)的右侧距状沟(Calcarine_R)以及DMN(IC 17)的左侧楔前叶(Precuneus_L)的连接性降低。在两组之间RSNs内13对IC的d-FNC比较中,CM组在状态1下DMN(IC 11)和AN(IC 5)之间的连接显著减少,而ECN(IC 2、IC 4)与DMN(IC 19)、ECN(IC 4)与AN(IC 5)以及ECN(IC 4)与VN(IC 13)之间的连接增加。然而,在s-FNC分析中,两组之间未观察到s-FNC的显著差异。CM组和HC组之间三个动态指标的组间比较显示,CM患者在状态2下花费时间的比例和平均停留时间更长。此外,从这些指标与临床特征的相关性分析中,我们观察到转换次数与平均VAS评分之间存在显著正相关。

结论

我们的研究结果表明,CM大脑的功能特征可能随时间波动而非保持静态,并进一步证明偏头痛的慢性化可能与感觉和认知脑网络之间的异常模式连接有关。