神经影像在卒中后脑可塑性机制中的应用进展

doi:10.3969/j.issn.1006-9771.2021.01.007

《中国康复理论与实践》 ›› 2021, Vol. 27 ›› Issue (1): 48-53.doi: 10.3969/j.issn.1006-9771.2021.01.007

神经影像在卒中后脑可塑性机制中的应用进展

张豪杰^1,2, 李芳^1,2, 李晁金子^1,2, 米海霞^1,2, 刘旭^1,2, 白晨^1,2, 李冰洁^1,2, 杜晓霞^1,2, 张通^1,2

1.首都医科大学康复医学院，北京市 100068
2.中国康复研究中心北京博爱医院神经康复中心，北京市 100068

收稿日期:2019-11-27 修回日期:2020-03-30 出版日期:2021-01-25 发布日期:2021-01-27
通讯作者: 张通，E-mail： tom611@126.com
作者简介:张豪杰(1983-)，男，汉族，河南平顶山市人，博士研究生，主要研究方向：神经康复；张通(1961-)，男，汉族，北京市人，主任医师、教授，博士生导师，主要研究方向：神经康复、神经病学。
基金资助:
1.中国康复研究中心项目(No. 2018ZX-10);2.中央级公益性科研院所基本科研业务费专项基金项目(No. 2018CZ-2)

Advance in Application of Neuroimaging in Plasticity Mechanism after Stroke (review)

ZHANG Hao-jie^1,2, LI Fang^1,2, LI Chao-jin-zi^1,2, MI Hai-xia^1,2, LIU Xu^1,2, BAI Chen^1,2, LI Bing-jie^1,2, DU Xiao-xia^1,2, ZHANG Tong^1,2

1. Capital Medical University School of Rehabilitation Medicine, Beijing 100068, China
2. Department of Neurology, Beijing Bo'ai Hospital, China Rehabilitation Research Centre, Beijing 100068, China

Received:2019-11-27 Revised:2020-03-30 Published:2021-01-25 Online:2021-01-27
Contact: ZHANG Tong, E-mail: tom611@126.com
Supported by:
China Rehabilitation Research Centre Project (No. 2018ZX-10) and National Special Fund of Basic Research of Public Benefits for Institutes at Central Governmental Level (No. 2018CZ-2)

摘要/Abstract

摘要： 神经影像技术是进行卒中后脑可塑性机制研究的重要手段。弥散张量成像可用于描述白质纤维束结构，评估受损程度，但不能反映不同脑区之间的功能联系；任务态功能磁共振成像(fMRI)可检测特定任务引起对应的脑区激活情况，但试验设计复杂，对受试者要求高；静息态fMRI可进行复杂脑网络分析，反映不同脑区功能联系，但数据分析方法复杂；功能近红外光谱技术(fNIRS)用非侵入性方法反映脑区功能激活情况，相比fMRI，fNIRS有更好的时间分辨率，但空间分辨率稍差。多种检测手段结合可能是将来研究的重要方向。

关键词: 脑卒中, 康复, 可塑性, 神经影像, 磁共振成像, 近红外光谱, 综述

Abstract: Neuroimaging technique is a kind of significant means to explore the mechanism of cerebral plasticity after stroke. Diffusion tensor imaging can be used to describe the structure of white matter fiber bundles and evaluate the degree of damage, but it cannot reflect the functional connections between different brain regions. Task-state functional magnetic resonance (fMRI) can detect the activation of corresponding brain regions caused by specific tasks, but the test design is complex and demanding for subjects. Resting-state fMRI can analyze complex brain networks and reflect functional connections in different brain regions, but the method of data analysis is complex. Functional near-infrared spectroscopy (fNIRS) is another non-invasive method to reflect the functional activation of brain regions, in which temporal resolution is better than fMRI, but the spatial resolution is slightly lower. The combination of multiple detection methods may be an important research direction in the future.

Key words: stroke, rehabilitation, plasticity, neuroimaging, magnetic resonance imaging, near-infrared spectroscopy, review

中图分类号:

R743.3

张豪杰, 李芳, 李晁金子, 米海霞, 刘旭, 白晨, 李冰洁, 杜晓霞, 张通. 神经影像在卒中后脑可塑性机制中的应用进展[J]. 《中国康复理论与实践》, 2021, 27(1): 48-53.

ZHANG Hao-jie, LI Fang, LI Chao-jin-zi, MI Hai-xia, LIU Xu, BAI Chen, LI Bing-jie, DU Xiao-xia, ZHANG Tong. Advance in Application of Neuroimaging in Plasticity Mechanism after Stroke (review)[J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2021, 27(1): 48-53.

参考文献

1 GBD 2016 Neurology Collaborators. Global, regional, and national burden of stroke, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016 [J]. Lancet Neurol, 2019, 18(5): 439-458.
2 Zhou M, Wang H, Zhu J, et al. Cause-specific mortality for 240 causes in China during 1990-2013: a systematic subnational analysis for the Global Burden of Disease Study 2013 [J]. Lancet, 2016, 387(10015): 251-272.
3 Okabe N, Shiromoto T, Himi N, et al. Neural network remodeling underlying motor map reorganization induced by rehabilitative training after ischemic stroke [J]. Neuroscience, 2016, 339: 338-362.
4 Xing Y, Yang S D, Dong F, et al. The beneficial role of early exercise training following stroke and possible mechanisms [J]. Life Sci, 2018, 198: 32-37.
5 Ostwald S, Davis S G, Kelley C, et al. Evidence-based educational guidelines for stroke survivors after discharge home [J]. J Neurosci Nurs, 2008, 40(3): 173-179.
6 Adams H P, Adams R J, Brott T, et al. Guidelines for the early management of patients with ischemic stroke: a scientific statement from the Stroke Council of the American Stroke Association [J]. Stroke, 2003, 34(4): 1056-1083.
7 Nudo R J, Wise B M, SiFuentes F, et al. Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct [J]. Science, 1996, 272(5269): 1791-1794.
8 Gresham G E. Stroke outcome research [J]. Stroke, 1986, 17(3): 358-360.
9 Indredavik B, Slordahl S A, Bakke F, et al. Stroke unit treatment. Long-term effects [J]. Stroke, 1997, 28(10): 1861-1866.
10 Maulden S A, Gassaway J, Horn S D, et al. Timing of initiation of rehabilitation after stroke [J]. Arch Phys Med Rehabil, 2005, 86(12 Suppl 2): S34-S40.
11 Hylin M J, Kerr A L, Holden R. Understanding the mechanisms of recovery and/or compensation following injury [J]. Neural Plast, 2017, 2017: 7125057.
12 Dombovy M L. Understanding stroke recovery and rehabilitation: current and emerging approaches [J]. Curr Neurol Neurosci Rep, 2004, 4(1): 31-35.
13 Laffont I, Bakhti K, Coroian F, et al. Innovative technologies applied to sensorimotor rehabilitation after stroke [J]. Ann Phys Rehabil Med, 2014, 57(8): 543-551.
14 Johansson B B. Brain plasticity and stroke rehabilitation. The Willis lecture [J]. Stroke, 2000, 31(1): 223-230.
15 Buma F, Kwakkel G, Ramsey N. Understanding upper limb recovery after stroke [J]. Restor Neurol Neurosci, 2013, 31(6): 707-722.
16 Bönstrup M, Krawinkel L, Schulz R, et al. Low-frequency brain oscillations track motor recovery in human stroke [J]. Ann Neurol, 2019, 86(6): 853-865.
17 Bethe A, Woitas E. Studien über die Plastizität des Nervensystems [J]. Pflüger's Archiv für die gesamte Physiologie des Menschen und der Tiere, 1930, 224(1): 821-835.
18 Cassidy J M, Cramer S C. Spontaneous and therapeutic-induced mechanisms of functional recovery after stroke [J]. Transl Stroke Res, 2017, 8(1): 33-46.
19 Hermann D M, Chopp M. Promoting brain remodelling and plasticity for stroke recovery: therapeutic promise and potential pitfalls of clinical translation [J]. Lancet Neurol, 2012, 11(4): 369-380.
20 Quinlan E B, Dodakian L, See J, et al. Biomarkers of rehabilitation therapy vary according to stroke severity [J]. Neural Plast, 2018, 2018: 9867196.
21 Seitz R J, Donnan G A. Role of neuroimaging in promoting long-term recovery from ischemic stroke [J]. J Magn Reson Imaging, 2010, 32(4): 756-772.
22 Squire L R, Dronkers N, Baldo J. Encyclopedia of Neuroscience [M]. Amsterdam: Elsevier, 2009.
23 Bremner J D. Brain Imaging Handbook [M]. New York: WW Norton & Co., 2005.
24 Feys H, Hetebrij J, Wilms G, et al. Predicting arm recovery following stroke: value of site of lesion [J]. Acta Neurol Scand, 2000, 102(6): 371-377.
25 Schiemanck S K, Kwakkel G, Post M W, et al. Predicting long-term independency in activities of daily living after middle cerebral artery stroke: does information from MRI have added predictive value compared with clinical information? [J]. Stroke, 2006, 37(4): 1050-1054.
26 Adhikari M H, Hacker C D, Siegel J S, et al. Decreased integration and information capacity in stroke measured by whole brain models of resting state activity [J]. Brain, 2017, 140(4): 1068-1085.
27 Mirzaei G, Adeli H. Resting state functional magnetic resonance imaging processing techniques in stroke studies [J]. Rev Neurosci, 2016, 27(8): 871-885.
28 Raffin E, Dyrby T B. Diagnostic approach to functional recovery: diffusion-weighted imaging and tractography [J]. Front Neurol Neurosci, 2013, 32: 26-35.
29 Puig J, Blasco G, Schlaug G, et al. Diffusion tensor imaging as a prognostic biomarker for motor recovery and rehabilitation after stroke [J]. Neuroradiology, 2017, 59(4): 343-351.
30 Mukherjee P, Berman J I, Chung S W, et al. Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings [J]. Am J Neuroradiol, 2008, 29(4): 632-641.
31 Mukherjee P, Chung S W, Berman J I, et al. Diffusion tensor MR imaging and fiber tractography: technical considerations [J]. Am J Neuroradiol, 2008, 29(5): 843-852.
32 Yamada N, Ueda R, Kakuda W, et al. Diffusion tensor imaging evaluation of neural network development in patients undergoing therapeutic repetitive transcranial magnetic stimulation following stroke [J]. Neural Plast, 2018, 2018: 3901016.
33 Puig J, Blasco G, Daunis-I-Estadella J, et al. Decreased corticospinal tract fractional anisotropy predicts long-term motor outcome after stroke [J]. Stroke, 2013, 44(7): 2016-2018.
34 Song J, Nair V A, Young B M, et al. DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology [J]. Front Hum Neurosci, 2015, 9: 195.
35 Puig J, Pedraza S, Blasco G, et al. Wallerian degeneration in the corticospinal tract evaluated by diffusion tensor imaging correlates with motor deficit 30 days after middle cerebral artery ischemic stroke [J]. Am J Neuroradiol, 2010, 31(7): 1324-1330.
36 Ogawa S, Lee T M, Nayak A S, et al. Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields [J]. Magn Reson Med, 1990, 14(1): 68-78.
37 Friston K J, Holmes A P, Worsley K J, et al. Statistical parametric maps in functional imaging: a general linear approach [J]. Hum Brain Mapp, 1994, 2(4): 189-210.
38 Carey J R, Kimberley T J, Lewis S M, et al. Analysis of fMRI and finger tracking training in subjects with chronic stroke [J]. Brain, 2002, 125(Pt 4): 773-788.
39 Stagg C J, Bachtiar V, O'Shea J, et al. Cortical activation changes underlying stimulation-induced behavioural gains in chronic stroke [J]. Brain, 2012, 135(Pt 1): 276-284.
40 Carter A R, Shulman G L, Corbetta M. Why use a connectivity-based approach to study stroke and recovery of function? [J]. Neuroimage, 2012, 62(4): 2271-2280.
41 Biswal B, Yetkin F Z, Haughton V M, et al. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI [J]. Magn Reson Med, 1995, 34(4): 537-541.
42 Greicius M D, Krasnow B, Reiss A L, et al. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis [J]. Proc Natl Acad Sci U S A, 2003, 100(1): 253-258.
43 Grefkes C, Fink G R. Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches [J]. Brain, 2011, 134(Pt 5): 1264-1276.
44 Zhao Z, Wu J, Fan M, et al. Altered intra- and inter-network functional coupling of resting-state networks associated with motor dysfunction in stroke [J]. Hum Brain Mapp, 2018, 39(8): 3388-3397.
45 Bell C S, Mohd Khairi N, Ding Z, et al. Bayesian framework for robust seed-based correlation analysis [J]. Med Phys, 2019, 46(7): 3055-3066.
46 Smitha K A, Arun K M, Rajesh P G, et al. Resting-state seed-based analysis: an alternative to task-based language fMRI and its laterality index [J]. Am J Neuroradiol, 2017, 38(6): 1187-1192.
47 McKeown M J, Makeig S, Brown G G, et al. Analysis of fMRI data by blind separation into independent spatial components [J]. Hum Brain Mapp, 1998, 6(3): 160-188.
48 Golay X, Kollias S, Stoll G, et al. A new correlation-based fuzzy logic clustering algorithm for fMRI [J]. Magn Resonan Med, 1998, 40(2): 249-260.
49 Filippi M, van den Heuvel M P, Fornito A, et al. Assessment of system dysfunction in the brain through MRI-based connectomics [J]. Lancet Neurol, 2013, 12(12): 1189-1199.
50 Salvador R, Suckling J, Schwarzbauer C, et al. Undirected graphs of frequency-dependent functional connectivity in whole brain networks [J]. Philos Trans R Soc Lond B Biol Sci, 2005, 360(1457): 937-946.
51 Kaminski J, Gleich T, Fukuda Y, et al. Association of cortical glutamate and working memory activation in patients with schizophrenia: a multimodal proton magnetic resonance spectroscopy and functional magnetic resonance imaging study [J]. Biol Psychiatry, 2020, 87(3): 225-233.
52 Pornpattananangkul N, Leibenluft E, Pine D S, et al. Association between childhood anhedonia and alterations in large-scale resting-state networks and task-evoked activation [J]. JAMA Psychiatry, 2019, 76(6): 624-633.
53 Etkin A, Maron-Katz A, Wu W, et al. Using fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder [J]. Sci Transl Med, 2019, 11(486): eaal3236.
54 Zunhammer M, Bingel U, Wager T D. Placebo effects on the neurologic pain signature: a meta-analysis of individual participant functional magnetic resonance imaging data [J]. JAMA Neurol, 2018, 75(11): 1321-1330.
55 Backner Y, Kuchling J, Massarwa S, et al. Anatomical wiring and functional networking changes in the visual system following optic neuritis [J]. JAMA Neurol, 2018, 75(3): 287-295.
56 Nardo D, Holland R, Leff AP, et al. Less is more: neural mechanisms underlying anomia treatment in chronic aphasic patients [J]. Brain, 2017, 140(11): 3039-3054.
57 Sheffield J M, Kandala S, Tamminga C A, et al. Transdiagnostic associations between functional brain network integrity and cognition [J]. JAMA Psychiatry, 2017, 74(6): 605-613.
58 Saur D, Ronneberger O, Kummerer D, et al. Early functional magnetic resonance imaging activations predict language outcome after stroke [J]. Brain, 2010, 133(Pt 4): 1252-1264.
59 Johansen-Berg H, Dawes H, Guy C, et al. Correlation between motor improvements and altered fMRI activity after rehabilitative therapy [J]. Brain, 2002, 125(Pt 12): 2731-2742.
60 Puig J, Blasco G, Alberich-Bayarri A, et al. Resting-state functional connectivity magnetic resonance imaging and outcome after acute stroke [J]. Stroke, 2018, 49(10): 2353-2360.
61 Wang L, Yu C, Chen H, et al. Dynamic functional reorganization of the motor execution network after stroke [J]. Brain, 2010, 133(Pt 4): 1224-1238.
62 Lee J, Park E, Lee A, et al. Alteration and role of interhemispheric and intrahemispheric connectivity in motor network after stroke [J]. Brain Topogr, 2018, 31(4): 708-719.
63 Greicius M. Resting-state functional connectivity in neuropsychiatric disorders [J]. Curr Opin Neurol, 2008, 21(4): 424-430.
64 Jiang D, Du Y, Cheng H, et al. Groupwise spatial normalization of fMRI data based on multi-range functional connectivity patterns [J]. Neuroimage, 2013, 82: 355-372.
65 Jiang Q, Zhang Z G, Chopp M. MRI of stroke recovery [J]. Stroke, 2010, 41(2): 410-414.
66 Stinear C M, Ward N S. How useful is imaging in predicting outcomes in stroke rehabilitation? [J]. Int J Stroke, 2013, 8(1): 33-37.
67 Jobsis F F. Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters [J]. Science, 1977, 198(4323): 1264-1267.
68 Edwards A D, Wyatt J S, Richardson C, et al. Cotside measurement of cerebral blood flow in ill newborn infants by near infrared spectroscopy [J]. Lancet, 1988, 2(8614): 770-771.
69 Mihara M, Miyai I. Review of functional near-infrared spectroscopy in neurorehabilitation [J]. Neurophotonics, 2016, 3(3): 031414.
70 Sun P P, Tan F L, Zhang Z, et al. Feasibility of functional near-infrared spectroscopy (fNIRS) to investigate the mirror neuron system: an experimental study in a real-life situation [J]. Front Hum Neurosci, 2018, 12: 86.
71 Kato H, Izumiyama M, Koizumi H, et al. Near-infrared spectroscopic topography as a tool to monitor motor reorganization after hemiparetic stroke: a comparison with functional MRI [J]. Stroke, 2002, 33(8): 2032-2036.
72 Liu Y C, Yang Y R, Tsai Y A, et al. Brain activation and gait alteration during cognitive and motor dual task walking in stroke-a functional near-infrared spectroscopy study [J]. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(12): 2416-2423.
73 Rea M, Rana M, Lugato N, et al. Lower limb movement preparation in chronic stroke: a pilot study toward an fNIRS-BCI for gait rehabilitation [J]. Neurorehabil Neural Repair, 2014, 28(6): 564-575.
74 Horaguchi T, Ogata Y, Watanabe N, et al. Behavioral and near-infrared spectroscopy study of the effects of distance and choice in a number comparison task [J]. Neurosci Res, 2008, 61(3): 294-301.
75 Lin P Y, Chen J J, Lin S I. The cortical control of cycling exercise in stroke patients: an fNIRS study [J]. Hum Brain Mapp, 2013, 34(10): 2381-2390.
76 Mihara M, Hattori N, Hatakenaka M, et al. Near-infrared spectroscopy-mediated neurofeedback enhances efficacy of motor imagery-based training in poststroke victims: a pilot study [J]. Stroke, 2013, 44(4): 1091-1098.

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神经影像在卒中后脑可塑性机制中的应用进展

Advance in Application of Neuroimaging in Plasticity Mechanism after Stroke (review)

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