轻型缺血性卒中后认知障碍与脑动态功能连接状态改变的功能磁共振成像研究

doi:10.3969/j.issn.1673-5765.2021.10.004

摘要/Abstract

摘要： 目的探索轻型缺血性卒中后有认知障碍（cognitive impairment，CI）患者和无认知障碍（no cognitive impairment，NCI）患者脑动态功能连接（functional connectivity，FC）状态的变化。方法选择2014年12月1日-2016年5月31日首都医科大学附属北京天坛医院神经病学中心就诊的轻型急性缺血性卒中患者为研究对象，对所有患者进行神经心理学评估和多模态MRI检查，分为CI 组（15例）和NCI组（11例），同时招募年龄、性别均匹配的志愿者作为健康对照（healthy control，HC）组（29例）。基于静息态功能头颅MRI影像，利用动态功能网络连接方法构建一系列随时间变化的FC网络，然后通过聚类方法划分为多个具有代表性的动态FC状态（分别为模块化连接状态、强连接状态、局部连接状态和稀疏连接状态），比较HC组、CI组与NCI组的FC动态特征（各状态的时间比例、驻留时间及各状态间的转换次数）差异，并在两个时间点（基线和3个月随访期）探索CI组与NCI组动态FC状态的变化。结果 HC组、CI组和NCI组在基线和随访期各连接状态的时间比例差异均无统计学意义。基线CI组和NCI组在稀疏连接状态的驻留时间比HC组更低，三组差异有统计学意义（P =0.035），但两两比较的结果均未通过Bonferroni校正；而在随访期，各连接状态的驻留时间差异均无统计学意义。纵向比较中，与基线相比，CI组随访期在模块化连接状态的时间比例明显下降（P =0.035），在稀疏连接状态的时间比例明显上升（P =0.025），在模块化连接状态的驻留时间明显降低（P =0.012）；而NCI 组在两个时间点各连接状态的时间比例和驻留时间差异均无统计学意义。对于转换次数，所有组间的差异均无统计学意义。结论轻型缺血性卒中患者急性期较对照人群有局部连接状态增多而稀疏连接状态减少的趋势，但差异未达统计学意义；对于有认知障碍的轻型缺血性卒中患者，发病3个月时模块化连接状态和稀疏连接状态均较急性期显著恢复；动态功能网络能够客观反映大脑功能的变化。

文章导读： 动态功能网络能更早、更客观地反映卒中患者认知功能的改变，未来将可能成为一种卒中患者认知功能的新的临床辅助诊断工具。

关键词: 卒中; 认知障碍; 动态功能网络连接; 功能磁共振成像

Abstract: Objective To explore the changes of dynamic brain functional connectivity (FC) states in patients with cognitive impairment (CI) and no cognitive impairment (NCI) after minor ischemic stroke (MIS). Methods From December 1, 2014 to May 31, 2016, the consecutive patients with first acute MIS in Department of Neurology of Beijing Tiantan Hospital, Capital Medical University were

enrolled in this study. All patients underwent neuropsychological evaluation and multimodal

MRI, and were divided into the CI group (15 participants) and NCI group (11 participants). At the same time, participants with matched age and gender were recruited as healthy control (HC) (29 participants). Based on resting-state brain functional MRI images, a series of real-time dynamic FC networks were constructed by using the dynamic functional network connectivity method, which were divided into multiple representative dynamic FC states (modular connection state, strong connection state, local connection state and sparse connection state) by clustering method. The differences in dynamic characteristics of FC (fraction time and dwell time for each state, and the times of transition between states) were compared among HC, CI and NCI groups, and the changes of dynamic FC states at two time points (baseline and 3-month follow-up) were compared between CI and NCI groups. Results There was no statistical difference in fraction time of all connection states between HC, CI and NCI groups at baseline and 3-month follow-up. The dwell time of CI and NCI group at the baseline was lower than that of HC group, and the difference among the three groups was statistically significant (P =0.035), and the pairwise comparison by Bonferroni correction showed no statistical differences. During the follow-up, there was no statistical difference in dwell time among all connection states. In longitudinal comparison, compared with those at the baseline, fraction time in modular connection state decreased significantly (P =0.035) and increased significantly in sparse connection state (P =0.025) in CI group, and the dwell time in modular connection state reduced significantly (P =0.012) in CI group during the follow-up. However, there were no statistical differences in the fraction time and the dwell time of each connection state at two time points in NCI group. There were no statistical differences in times of transition between all the groups. Conclusions Compared with the healthy control, patients with MIS showed a trend of increasing local connection state and decreasing sparse connection state in the acute stage, but the differences were not statistically significant. For MIS patients with CI, modular connection state and sparse connection state improved significantly at 3-month follow-up compared with that in acute phase. The dynamic brain functional networks can objectively reflect the changes of brain function.

Key words: Stroke; Cognitive impairment; Dynamic functional network connectivity;Functional magnetic resonance imaging

肖瑞珠, 左丽君, 周怡君, 陈姚静, 刘艳亭, 刘涛. 轻型缺血性卒中后认知障碍与脑动态功能连接状态改变的功能磁共振成像研究[J]. 中国卒中杂志, 2021, 16(10): 996-1005.

XIAO Rui-Zhu, ZUO Li-Jun, ZHOU Yi-Jun, CHEN Yao-Jing, LIU Yan-Ting, LIU Tao. Functional Magnetic Resonance Imaging Study on the Changes of Dynamic Brain Functional Connectivity States#br# in Patients with Cognitive Impairment after Mild Ischemic Stroke[J]. Chinese Journal of Stroke, 2021, 16(10): 996-1005.

参考文献

[1] ZUO L J，DONG Y H，ZHU R Y，et al. Screening
for cognitive impairment with the montreal
cognitive assessment in Chinese patients with
acute mild stroke and transient ischaemic attack：a
validation study[J/OL]. BMJ Open，2016，6（7）：
e011310[2021-08-30]. http：//dx.doi.org/10.1136/
bmjopen-2016-011310.
[2] VAN ROOIJ F G，SCHAAPSMEERDERS P，
MAAIJWEE N A M，et al. Persistent cognitive
impairment after transient ischemic attack[J]. Stroke，
2014，45（8）：2270-2274.
[3] GODEFROY O，FICKL A，ROUSSEL M，et
al. Is the montreal cognitive assessment superior
to the mini-mental state examination to detect
poststroke cognitive impairment? A study with
neuropsychological evaluation[J]. Stroke，2011，42
（6）：1712-1716.
[4] SMITHA K A，AKHIL RAJA K，ARUN K M，et
al. Resting state fMRI：a review on methods in
resting state connectivity analysis and resting state
networks[J]. Neuroradiol J，2017，30（4）：305-317.
[5] GUERRA-CARRILLO B，MACKEY A P，BUNGE
S A. Resting-state fMRI：a window into human
brain plasticity[J]. Neuroscientist，2014，20（5）：
522-533.
[6] GOLESTANI A M，TYMCHUK S，DEMCHUK A，
et al. Longitudinal evaluation of resting-state FMRI
after acute stroke with hemiparesis[J]. Neurorehabil
Neural Repair，2013，27（2）：153-163.
[7] REHME A K，VOLZ L J，FEIS D L，et al.

Identifying neuroimaging markers of motor disability

in acute stroke by machine learning techniques[J].
Cereb Cortex，2015，25（9）：3046-3056.
[8] 朱瑞瑞，张平，闫海清，等. 卒中后抑郁患者静息态
局部脑活动与默认网络功能连接改变的磁共振成像
研究[J]. 中国卒中杂志，2020，15（4）：382-388.
[9] CHANG C，GLOVER G H. Time-frequency
dynamics of resting-state brain connectivity
measured with fMRI[J]. Neuroimage，2010，50（1）：
81-98.
[10] ALLEN E A，DAMARAJU E，PLIS S M，et al.
Tracking whole-brain connectivity dynamics in the
resting state[J]. Cereb Cortex，2014，24（3）：663-
676.
[11] CALHOUN V D，MILLER R，PEARLSON G，et
al. The chronnectome：time-varying connectivity
networks as the next frontier in fMRI data
discovery[J]. Neuron，2014，84（2）：262-274.
[12] DÍEZ-CIRARDA M，STRAFELLA A P，KIM J，et
al. Dynamic functional connectivity in Parkinson's
disease patients with mild cognitive impairment and
normal cognition[J/OL]. Neuroimage Clin，2018，
17：847-855[2021-08-30]. https：//doi.org/10.1016/
j.nicl.2017.12.013.
[13] 王帅文，雷军强，郭顺林. 帕金森病感觉运动控制与
调节网络的动态功能连接及临床运动功能评价[J].
中国医学影像学杂志，2020，28（5）：333-338.
[14] WEBER S，JOHNSEN E，KROKEN R A，et
al. Dynamic functional connectivity patterns
in schizophrenia and the relationship with
hallucinations[J/OL]. Front Psychiatry，2020，
11：227[2021-08-30]. https：//doi.org/10.3389/
fpsyt.2020.00227.
[15] CAI L H，WEI X L，LIU J，et al. Functional
integration and segregation in multiplex brain
networks for Alzheimer's disease[J/OL]. Front
Neurosci，2020，14：51[2021-08-30]. https：//doi.
org/10.3389/fnins.2020.00051.
[16] FISCHER U，BAUMGARTNER A，ARNOLD M，
et al. What is a minor stroke?[J]. Stroke，2010，41（4）：
661-666.
[17] HAMILTON M. The Hamilton rating scale for
depression[M]//Assessment of depression. Berlin：
Springer，1986：143-152.
[18] JORM A F，JACOMB P A. The informant
questionnaire on cognitive decline in the elderly
（IQCODE）：socio-demographic correlates，
reliability，validity and some norms[J]. Psychol Med，
1989，19（4）：1015-1022.
[19] CORDES D，HAUGHTON V M，ARFANAKIS K，
et al. Mapping functionally related regions of brain
with functional connectivity MR imaging[J]. AJNR

Am J Neuroradiol，2000，21（9）：1636-1644.
[20] BAJAJ S，BUTLER A J，DRAKE D，et al.
Functional organization and restoration of the
brain motor-execution network after stroke and
rehabilitation[J/OL]. Front Hum Neurosci，2015，
9：173[2021-08-30]. https：//doi.org/10.3389/
fnhum.2015.00173.
[21] HU J P，DU J，XU Q，et al. Dynamic network
analysis reveals altered temporal variability in
brain regions after stroke：a longitudinal restingstate
fMRI study[J/OL]. Neural Plast，2018，
2018：9394156[2021-08-30]. https：//doi.org/10.
1155/2018/9394156.
[22] CHEN J，SUN D L，SHI Y H，et al. Alterations
of static functional connectivity and dynamic
functional connectivity in motor execution
regions after stroke[J/OL]. Neurosci Lett，2018，
686：112-121[2021-08-30]. https：//doi.org/10.1016/
j.neulet.2018.09.008.
[23] BROWN C E，LI P，BOYD J D，et al. Extensive
turnover of dendritic spines and vascular remodeling
in cortical tissues recovering from stroke[J]. J
Neurosci，2007，27（15）：4101-4109.
[24] DESOWSKA A，TURNER D L. Dynamics of brain
connectivity after stroke[J]. Rev Neurosci，2019，30
（6）：605-623.
[25] RIVA D，TADDEI M，BULGHERONI S. The
neuropsychology of basal ganglia[J]. Eur J Paediatr
Neurol，2018，22（2）：321-326.
[26] XU L，HUANG L，CUI W，et al. Reorganized
functional connectivity of language centers as a
possible compensatory mechanism for basal ganglia
aphasia[J]. Brain Inj，2020，34（3）：430-437.
[27] ZHANG J，LI Z X，CAO X X，et al. Altered
prefrontal-basal ganglia effective connectivity in
patients with poststroke cognitive impairment[J/OL].
Front Neurol，2020，11：577482[2021-08-30].
https：//doi.org/10.3389/fneur.2020.577482.
[28] ZHAO H C，CHENG J，JIANG J Y，et al.
Geometric microstructural damage of white matter
with functional compensation in post-stroke[J/
OL]. Neuropsychologia，2021，160：107980[2021-
08-30]. https：//doi.org/10.1016/j.neuropsychologia.
2021.107980.
[29] AMRHEIN V，KORNER-NIEVERGELT F，ROTH
T. The earth is flat（P >0. 05）：significance thresholds
and the crisis of unreplicable research[J/OL]. PeerJ，
2017，5：e3544[2021-08-30]. https：//doi.org/10.7717/
peerj.3544.