功能性近红外光谱应用于康复领域的可视化分析

doi:10.3969/j.issn.1006-9771.2023.10.008

《中国康复理论与实践》 ›› 2023, Vol. 29 ›› Issue (10): 1171-1178.doi: 10.3969/j.issn.1006-9771.2023.10.008

功能性近红外光谱应用于康复领域的可视化分析

张宁¹^,², 杨远滨¹^,²(), 田浩林¹, 万梦莹¹^,²

1.中国中医科学院望京医院康复一科，北京市 100102
2.北京体育大学运动医学与康复学院，北京市 100084

收稿日期:2023-07-25 修回日期:2023-09-11 出版日期:2023-10-25 发布日期:2023-11-16
通讯作者: 杨远滨(1967-)，女，博士，教授、主任医师，主要研究方向：中西医结合神经康复、骨科康复。E-mail： duyiran0506@126.com
作者简介:张宁(1999-)，女，汉族，山东泰安市人，硕士研究生，主要研究方向：神经康复。
基金资助:
中国中医科学院创新工程项目(C12021A03010)

Application of functional near-infrared spectroscopy in rehabilitation: a visualized analysis

ZHANG Ning¹^,², YANG Yuanbin¹^,²(), TIAN Haolin¹, WAN Mengying¹^,²

1. Department 1 of Rehabilitation Medicine, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China
2. School of Sports Medicine and Rehabilitation, Beijing Sport University, Beijing 100084, China

Received:2023-07-25 Revised:2023-09-11 Published:2023-10-25 Online:2023-11-16
Contact: YANG Yuanbin, E-mail: duyiran0506@126.com
Supported by:
China Academy of Chinese Medical Sciences Innovation Project(C12021A03010)

摘要/Abstract

摘要：

目的对近10年功能性近红外光谱(fNIRS)应用于康复领域相关研究的现状、热点及前沿进行可视化分析。
方法检索2003年1月至2022年12月Web of Science核心合集数据库中fNIRS应用于康复领域的相关文献，采用CiteSpace 6.1.R6进行可视化分析。
结果共纳入828篇文献，年发文量总体呈上升趋势，发文量最多的作者是李增勇，发文量最多的国家是美国，发文量最多的机构是卡罗林斯卡学院。热点关键词包括儿童、脑卒中、激活、生活质量、脑瘫等。突现强度排在前列的关键词包括早期干预、言语知觉、脑瘫、可塑性、脊髓损伤、物理治疗、视觉反馈、帕金森病等。聚类分析显示，近10年fNIRS于康复领域的应用涉及物理治疗、言语治疗、作业治疗以及手术的预康复和早期康复，主要集中于6大主题：脑卒中患者运动功能康复、认知功能康复、听力及言语功能康复、脑瘫儿童功能康复、重症康复心肺功能监测、慢性疾病长期康复护理。fNIRS应用于脑卒中患者神经康复的研究占据较大的比重。
结论 fNIRS应用于康复领域相关研究的热度总体呈上升趋势，未来可持续关注fNIRS在研究神经疾患损伤和恢复机制中的应用，尤其是各种康复干预手段对皮质可塑性的影响以及病因学研究。

关键词: 功能性近红外光谱, 康复, 可视化分析

Abstract:

Objective To summarize the current situation, hot spots and frontiers of the application of functional near-infrared spectroscopy (fNIRS) in rehabilitation in recent ten years.
Methods Literature related to the application of fNIRS in rehabilitation was retrieved from the core collection of Web of Science from January, 2003 to December, 2022, and the visualized analysis was performed by CiteSpace 6.1.R6 software.
Results A total of 828 literatures were included. The annual publication volume was on the rise. The most prolific author was LI Zengyong. The most published country was the United States. And the most published institution was Karolinska Institute. Hot keywords included children, stroke, activation, quality of life, cerebral palsy, etc. The hot keywords of bursting intensity included early intervention, speech perception, cerebral palsy, plasticity, spinal cord injury, physical therapy, visual feedback, Parkinson's disease, etc. Cluster analysis suggested that in the past decade, the application of fNIRS in rehabilitation involved physical therapy, speech therapy, occupational therapy, pre-rehabilitation and early rehabilitation of surgery, mainly focusing on six themes: the rehabilitation of motor dysfunction after stroke, cognitive impairment, hearing and speech dysfunction, children with cerebral palsy, cardiopulmonary function monitoring in severe diseases and long-term nursing of chronic diseases. Among them, the application of fNIRS in neurological rehabilitation of stroke patients occupied a large proportion, which might be the current research hot spot and trend.
Conclusion The application of fNIRS in rehabilitation is on the rise. In the future, more attention should be paid to the application of fNIRS in the study of the injury and recovery mechanism of neurological disorders, especially the effects of various rehabilitation interventions on cortical plasticity and the etiology of neurological disorders.

Key words: functional near-infrared spectroscopy, rehabilitation, visualized analysis

中图分类号:

R493

张宁, 杨远滨, 田浩林, 万梦莹. 功能性近红外光谱应用于康复领域的可视化分析[J]. 《中国康复理论与实践》, 2023, 29(10): 1171-1178.

ZHANG Ning, YANG Yuanbin, TIAN Haolin, WAN Mengying. Application of functional near-infrared spectroscopy in rehabilitation: a visualized analysis[J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2023, 29(10): 1171-1178.

图/表 11

图1

图2

图3

表1

表2

图4

表3

表4

图5

表5

图6

参考文献 53

[1]	IZZETOGLU K, AYAZ H, MERZAGORA A, et al. The evolution of field deployable fNIR spectroscopy from bench to clinical settings[J]. J Innov Opt Health Sci, 2011, 4(3): 239-250. doi: 10.1142/S1793545811001587
[2]	PINTI P, TACHTSIDIS I, HAMILTON A, et al. The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience[J]. Ann N Y Acad Sci, 2020, 1464(1): 5-29. doi: 10.1111/nyas.v1464.1
[3]	RAHMAN M A, SIDDIK A B, GHOSH T K, et al. A narrative review on clinical applications of fNIRS[J]. J Digit Imaging, 2020, 33(5): 1167-1184. doi: 10.1007/s10278-020-00387-1
[4]	FERRARI M, QUARESIMA V. A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application[J]. Neuroimage, 2012, 63(2): 921-935. doi: 10.1016/j.neuroimage.2012.03.049 pmid: 22510258
[5]	FOX P T, RAICHLE M E. Stimulus rate dependence of regional cerebral blood flow in human striate cortex, demonstrated by positron emission tomography[J]. J Neurophysiol, 1984, 51(5): 1109-1120. doi: 10.1152/jn.1984.51.5.1109 pmid: 6610024
[6]	CHEN W L, WAGNER J, HEUGEL N, et al. Functional near-infrared spectroscopy and its clinical application in the field of neuroscience: advances and future directions[J]. Front Neurosci, 2020, 14: 724. doi: 10.3389/fnins.2020.00724
[7]	LEE FRIESEN C, LAWRENCE M, INGRAM T G J, et al. Home-based portable fNIRS-derived cortical laterality correlates with impairment and function in chronic stroke[J]. Front Hum Neurosci, 2022, 16: 1023246. doi: 10.3389/fnhum.2022.1023246
[8]	CHEN C. Searching for intellectual turning points: progressive knowledge domain visualization[J]. Proc Natl Acad Sci USA, 2004, 101 (Suppl 1): 5303-5310. doi: 10.1073/pnas.0307513100
[9]	LUO H, CAI Z, HUANG Y, et al. Study on pain catastrophizing from 2010 to 2020: a bibliometric analysis via CiteSpace[J]. Front Psychol, 2021, 12: 759347. doi: 10.3389/fpsyg.2021.759347
[10]	LIU S, SUN Y P, GAO X L, et al. Knowledge domain and emerging trends in Alzheimer's disease: a scientometric review based on CiteSpace analysis[J]. Neural Regen Res, 2019, 14(9): 1643-1650. doi: 10.4103/1673-5374.255995 pmid: 31089065
[11]	SABE M, CHEN C, PEREZ N, et al. Thirty years of research on negative symptoms of schizophrenia: a scientometric analysis of hotspots, bursts, and research trends[J]. Neurosci Biobehav Rev, 2023, 144: 104979. doi: 10.1016/j.neubiorev.2022.104979
[12]	ZHONG D, LI Y, HUANG Y, et al. Molecular mechanisms of exercise on cancer: a bibliometrics study and visualization analysis via CiteSpace[J]. Front Mol Biosci, 2022, 8: 797902. doi: 10.3389/fmolb.2021.797902
[13]	TAŞKIN Z, AYDINOGLU A U. Collaborative interdisciplinary astrobiology research: a bibliometric study of the NASA Astrobiology Institute[J]. Scientometrics, 2015, 103(3): 1003-1022. doi: 10.1007/s11192-015-1576-8
[14]	CHEN C M. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature[J]. J Am Soc Inf Sci Technol, 2006, 57(3): 359-377. doi: 10.1002/asi.v57:3
[15]	YANG M, YANG Z, YUAN T, et al. A systemic review of functional near-infrared spectroscopy for stroke: current application and future directions[J]. Front Neurol, 2019, 10: 58. doi: 10.3389/fneur.2019.00058 pmid: 30804877
[16]	NASEER N, HONG K S. fNIRS-based brain-computer interfaces: a review[J]. Front Hum Neurosci, 2015, 9: 3. Erratum in: Front Hum Neurosci, 2015, 9: 172. doi: 10.3389/fnhum.2015.00003 pmid: 25674060
[17]	LIN Q, ZHANG Y, ZHANG Y, et al. The frequency effect of the motor imagery brain computer interface training on cortical response in healthy subjects: a randomized clinical trial of functional near-infrared spectroscopy study[J]. Front Neurosci, 2022, 16: 810553. doi: 10.3389/fnins.2022.810553
[18]	FLOREANI E D, ORLANDI S, CHAU T. A pediatric near-infrared spectroscopy brain-computer interface based on the detection of emotional valence[J]. Front Hum Neurosci, 2022, 16: 938708. doi: 10.3389/fnhum.2022.938708
[19]	KHAN H, NASEER N, YAZIDI A, et al. Analysis of human gait using hybrid EEG-fNIRS-based BCI system: a review[J]. Front Hum Neurosci, 2021, 14: 613254. doi: 10.3389/fnhum.2020.613254
[20]	WANG Z, CAO C, CHEN L, et al. Multimodal neural response and effect assessment during a BCI-based neurofeedback training after stroke[J]. Front Neurosci, 2022, 16: 884420. doi: 10.3389/fnins.2022.884420
[21]	LIU L, JIN M, ZHANG L, et al. Brain-computer interface-robot training enhances upper extremity performance and changes the cortical activation in stroke patients: a functional near-infrared spectroscopy study[J]. Front Neurosci, 2022, 16: 809657. doi: 10.3389/fnins.2022.809657
[22]	WANG Y, YANG Z, JI H, et al. Cross-modal transfer learning from EEG to functional near-infrared spectroscopy for classification task in brain-computer interface system[J]. Front Psychol, 2022, 13: 833007. doi: 10.3389/fpsyg.2022.833007
[23]	DALY J J, WOLPAW J R. Brain-computer interfaces in neurological rehabilitation[J]. Lancet Neurol, 2008, 7(11): 1032-1043. doi: 10.1016/S1474-4422(08)70223-0 pmid: 18835541
[24]	HEROLD F, WIEGEL P, SCHOLKMANN F, et al. Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks[J]. Neurophotonics, 2017, 4(4): 041403. doi: 10.1117/1.NPh.4.4.041403
[25]	SCHOLKMANN F, KLEISER S, METZ A J, et al. A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology[J]. Neuroimage, 2014, 85: 6-27. doi: 10.1016/j.neuroimage.2013.05.004
[26]	HEROLD F, WIEGEL P, SCHOLKMANN F, et al. Applications of functional near-infrared spectroscopy (fNIRS) neuroimaging in exercise-cognition science: a systematic, methodology-focused review[J]. J Clin Med, 2018, 7(12): 466. doi: 10.3390/jcm7120466
[27]	VITORIO R, STUART S, ROCHESTER L, et al. fNIRS response during walking: artefact or cortical activity? A systematic review[J]. Neurosci Biobehav Rev, 2017, 83: 160-172. doi: 10.1016/j.neubiorev.2017.10.002
[28]	HOCKE L M, ONI I K, DUSZYNSKI C C, et al. Automated processing of fNIRS data: a visual guide to the pitfalls and consequences[J]. Algorithms, 2018, 11(5): 67. doi: 10.3390/a11050067
[29]	AL-YAHYA E, JOHANSEN-BERG H, KISCHKA U, et al. Prefrontal cortex activation while walking under dual-task conditions in stroke: a multimodal imaging study[J]. Neurorehabil Neural Repair, 2016, 30(6): 591-599. doi: 10.1177/1545968315613864
[30]	BATULA A M, MARK J A, KIM Y E, et al. Comparison of brain activation during motor imagery and motor movement using fNIRS[J]. Comput Intell Neurosci, 2017, 2017: 5491296.
[31]	VON LÜHMANN A, HERFF C, HEGER D, et al. Toward a wireless open source instrument: functional near-infrared spectroscopy in mobile neuroergonomics and BCI applications[J]. Front Hum Neurosci, 2015, 9: 617. doi: 10.3389/fnhum.2015.00617 pmid: 26617510
[32]	DOBKIN B H. Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation[J]. J Physiol, 2007, 579(Pt 3): 637-642. doi: 10.1113/jphysiol.2006.123067
[33]	CORTESE S, SABÉ M, CHEN C, et al. Half a century of research on attention-deficit/hyperactivity disorder: a scientometric study[J]. Neurosci Biobehav Rev, 2022, 140: 104769. doi: 10.1016/j.neubiorev.2022.104769
[34]	谢良玉, 曹盛楠, 王佳颖, 等. 基于CiteSpace下肢康复机器人的研究可视化分析[J]. 中国医疗设备, 2023, 38(5): 143-148, 172.
	XIE L Y, CAO S N, WANG J Y, et al. Visual analysis of lower limb rehabilitation robot based on CiteSpace[J]. Chin Med Dev, 2023, 38(5): 143-148, 172.
[35]	CHEN C, SONG I Y, YUAN X, et al. The thematic and citation landscape of data and knowledge engineering (1985-2007)[J]. Data Knowl Eng, 2008, 67(2): 234-259. doi: 10.1016/j.datak.2008.05.004
[36]	CHEN C. An information-theoretic view of visual analytics[J]. IEEE Comput Graph Appl, 2008, 28(1): 18-23.
[37]	SHIBATA N, KAJIKAWA Y, TAKEDA Y, et al. Detecting emerging research fronts based on topological measures in citation networks of scientific publications[J]. Technovation, 2008, 28(11): 758-775. doi: 10.1016/j.technovation.2008.03.009
[38]	RIEKE J D, MATARASSO A K, YUSUFALI M M, et al. Development of a combined, sequential real-time fMRI and fNIRS neurofeedback system to enhance motor learning after stroke[J]. J Neurosci Methods, 2020, 341: 108719. doi: 10.1016/j.jneumeth.2020.108719
[39]	ARUN K M, SMITHA K A, SYLAJA P N, et al. Identifying resting-state functional connectivity changes in the motor cortex using fNIRS during recovery from stroke[J]. Brain Topogr, 2020, 33(6): 710-719. doi: 10.1007/s10548-020-00785-2
[40]	CHEN Z, SONG X H, QIAO Y J, et al. Increased inertia triggers linear responses in motor cortices during large-extent movements: a fNIRS study[J]. Brain Sci, 2022, 12(11): 1539. doi: 10.3390/brainsci12111539
[41]	顾雨薇, 孙莉敏. 功能性近红外光谱在脑卒中偏瘫康复中的应用进展[J]. 中国康复医学杂志, 2023, 38(2): 257-262.
[42]	DELORME M, VERGOTTE G, PERREY S, et al. Time course of sensorimotor cortex reorganization during upper extremity task accompanying motor recovery early after stroke: an fNIRS study[J]. Restor Neurol Neurosci, 2019, 37(3): 207-218.
[43]	FUSTER J M. Prefrontal neurons in networks of executive memory[J]. Brain Res Bull, 2000, 52(5): 331-336. pmid: 10922510
[44]	LIM S B, YANG C L, PETERS S, et al. Phase-dependent brain activation of the frontal and parietal regions during walking after stroke: an fNIRS study[J]. Front Neurol, 2022, 13: 904722. doi: 10.3389/fneur.2022.904722
[45]	SONG Y, SUN Z, SUN W, et al. Neuroplasticity following stroke from a functional laterality perspective: a fNIRS study[J]. Brain Topogr, 2023, 36(3): 283-293. doi: 10.1007/s10548-023-00946-z pmid: 36856917
[46]	DECETY J, GRÈZES J. Neural mechanisms subserving the perception of human actions[J]. Trends Cog Sci, 1999: 3: 172-178.
[47]	MALOUIN F, RICHARDS C L, JACKSON P L, et al. Motor imagery for optimizing the reacquisition of locomotor skills after cerebral damage[M]. London: Oxford University Press, 2010: 161-176.
[48]	MALOUIN F, RICHARDS C L. Mental practice for relearning locomotor skills[J]. Phys Ther, 2010, 90(2): 240-251. doi: 10.2522/ptj.20090029 pmid: 20022993
[49]	DAI Y, HUANG F, ZHU Y. Clinical efficacy of motor imagery therapy based on fNIRs technology in rehabilitation of upper limb function after acute cerebral infarction[J]. Pak J Med Sci, 2022, 38(7): 1980-1985. doi: 10.12669/pjms.38.7.5344 pmid: 36246721
[50]	KAISER V, BAUERNFEIND G, KREILINGER A, et al. Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG[J]. Neuroimage, 2014, 85(Pt 1): 432-444. doi: 10.1016/j.neuroimage.2013.04.097
[51]	HUO C, ZHANG S, XU G, et al. Cortical activation response during acupuncture intervention for hemiplegia limbs in stroke patients: a preliminary fNIRS study[C]. New York: IEEE, 2022: 1-2.
[52]	LIANG J, SONG Y, BELKACEM A N, et al. Prediction of balance function for stroke based on EEG and fNIRS features during ankle dorsiflexion[J]. Front Neurosci, 2022, 16: 968928. doi: 10.3389/fnins.2022.968928
[53]	COLLETT J, FLEMING M K, MEESTER D, et al. Dual-task walking and automaticity after stroke: insights from a secondary analysis and imaging sub-study of a randomised controlled trial[J]. Clin Rehabil, 2021, 35(11): 1599-1610. doi: 10.1177/02692155211017360

国家/地区	发文量/n	中心性
美国	194	0.28
德国	80	0.19
中国	76	0.08
英国	57	0.21
瑞典	54	0.13
意大利	53	0.08
加拿大	47	0.17
日本	39	0.14
印度	31	0.09
法国	31	0.03

机构	发文量/n	中心性
卡罗林斯卡学院	20	0.02
林雪平大学	12	0.01
山东大学	12	0.01
北京航空航天大学	11	0.01
民政部重点实验室	11	0.01

研究	研究类型	引用次数
Yang等^[15](2019)	综述论文	16
Herold等^[24](2017)	综述论文	16
Scholkmann等^[25](2014)	综述论文	16
Herold等^[26](2018)	综述论文	13
Ferrari等^[4](2012)	综述论文	13
Vitorio等^[27](2017)	综述论文	13

研究	中心性	排序
Naseer等^[16](2015)	0.13	1
Hocke等^[28](2018)	0.07	2
Al-Yahya等^[29](2016)	0.07	2
Batula等^[30](2017)	0.07	2
von Lühmann等^[31](2015)	0.07	2

编号	大小	Silhouette	标签	关键词
#0	121	0.763	神经康复	脑卒中、帕金森病、前额叶皮质、初级运动皮质、顶叶皮质、皮质激活、功能连接、上肢、神经反馈、脑机接口、虚拟现实、步态、平衡扰动任务、双任务、抗阻训练、肌力、镜像疗法、外骨骼、动作控制、神经可塑性、神经再生、神经代偿、针灸等
#1	72	0.745	言语治疗	言语发育障碍、语音清晰度、语音加工能力、自闭症谱系障碍、感音神经性听力损失等
#2	50	0.706	运动疗法	脊髓损伤、多发性硬化症、机器人辅助步态训练、可穿戴设备、神经可塑性、运动处方等
#3	47	0.717	心脏康复	预康复、有氧运动、心血管疾病、6分钟步行测试、活动监测、认知功能等
#4	37	0.754	日常生活活动	日常生活电子辅助设备、康复机器人、健康计划执行情况、生活质量、环境辅助生活等
#5	20	0.890	长期护理	职业、体力活动、适应能力、照顾者教育、心理教育干预等
#6	7	0.974	儿童	儿童言语、智力障碍、视觉反馈、人工耳蜗、新生儿听力筛查等

功能性近红外光谱应用于康复领域的可视化分析

Application of functional near-infrared spectroscopy in rehabilitation: a visualized analysis

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 53

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	罗丽华, 王雨生, 李剑锋, 董继革. 术后早期综合康复对儿童青少年肱骨髁上骨折伴尺神经损伤的效果[J]. 《中国康复理论与实践》, 2024, 30(1): 105-110.
[2]	王子豪, 李昕华, 蒋慧萍, 郭赛男, 梁秋曼, 史婷奇. 全膝关节置换术后短期膝关节功能及其影响因素[J]. 《中国康复理论与实践》, 2024, 30(1): 111-118.
[3]	陈珺雯, 陈谦, 陈程, 李淑月, 刘玲玲, 吴存书, 龚翔, 鲁俊, 许光旭. 改良八段锦身体活动对脑卒中患者心肺功能、运动功能和日常生活活动能力的效果[J]. 《中国康复理论与实践》, 2024, 30(1): 74-80.
[4]	史佳伟, 李凌宇, 杨浩杰, 王琴潞, 邹海欧. 预康复对全膝关节置换术后患者的有效性：系统综述的系统综述[J]. 《中国康复理论与实践》, 2023, 29(9): 1057-1064.
[5]	邹瑜聪, 周静, 林炜明, 李冬霞, 王娟, 王昱棋, 王玉龙. 近5年慢性意识障碍治疗的可视化分析[J]. 《中国康复理论与实践》, 2023, 29(9): 1065-1071.
[6]	蔡华年, 费思先, 张忆晨, 孙青, 郭帅, 宋韬. 基于导纳控制的双边康复机器人运动辅助分析[J]. 《中国康复理论与实践》, 2023, 29(9): 1104-1109.
[7]	刘洋, 张鹏, 黄英, 陈翰, 许辰, 李敏. 知觉压力影响创伤康复期患者事件影响程度的中介效应路径分析[J]. 《中国康复理论与实践》, 2023, 29(8): 954-960.
[8]	易琦峰, 黄卓尔, 杨国莉, 谢丽华, 谢胜锋, 吴小霞, 严谨. 在职医护人员呼吸康复培训知识需求问卷的编制及信度、效度检验[J]. 《中国康复理论与实践》, 2023, 29(8): 985-992.
[9]	李子怡, 宋为群, 杜巨豹, 曹光磊, 张艳明, 李冉. 运动表象训练对膝关节单髁置换术后膝关节功能的效果[J]. 《中国康复理论与实践》, 2023, 29(7): 745-749.
[10]	任艺, 王蕊, 章耀华. 本体感觉神经肌肉促进技术联合神经肌肉电刺激对慢性踝关节不稳的效果[J]. 《中国康复理论与实践》, 2023, 29(7): 750-755.
[11]	李芳, 霍速, 杜巨豹, 刘秀贞, 李小爽, 宋为群. 经颅直流电刺激联合任务导向性康复训练对脊髓损伤大鼠前肢运动障碍的效果[J]. 《中国康复理论与实践》, 2023, 29(7): 777-781.
[12]	崔尧, 丛芳, 黄富表, 曾明, 颜如秀. 不同镜像神经元训练策略下脑与肌肉的活动特征：基于近红外光谱与表面肌电图技术[J]. 《中国康复理论与实践》, 2023, 29(7): 782-790.
[13]	王靖萱, 吕迪阳, 方伯言. 帕金森病步态异常非药物康复循证研究：基于ClinicalTrials.gov数据库分析[J]. 《中国康复理论与实践》, 2023, 29(7): 816-821.
[14]	马甜甜, 于子夫, 秦芳, 冷晓轩, 刘西花. 强制性运动疗法在康复领域应用的可视化分析[J]. 《中国康复理论与实践》, 2023, 29(7): 822-832.
[15]	唐强, 郑爽, 王蕾, 王艳, 李保龙, 刘贵军, 朱路文. 基于世界卫生组织康复胜任力架构的中医康复学专业课程开发研究[J]. 《中国康复理论与实践》, 2023, 29(7): 862-868.