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脑性瘫痪(CP)以运动功能障碍为核心表现,常伴随感知觉和认知缺陷,其病理基础与大脑感觉运动网络异常重塑密切相关。功能性近红外光谱技术(fNIRS)作为一种非侵入式、便携的脑功能成像工具,可实时监测脑血流动力学变化,为脑瘫患者的脑功能评估和康复治疗提供了动态、可重复的监测手段。研究表明,fNIRS能够揭示脑瘫患儿感觉运动皮层和前额叶皮层的异常激活模式(如过度激活、时空特征异常、偏侧化紊乱)及脑网络连接障碍(功能连接减弱、因果连接增强),并可监测康复干预诱导的神经可塑性变化,为临床干预策略的优化及疗效评估提供客观生物标志物。
Abstract:Cerebral palsy (CP) is characterized primarily by motor dysfunction and is often accompanied by sensory and cognitive impairments; its pathological basis is closely associated with abnormal remodeling of the brain’s sensorimotor networks. Functional near-infrared spectroscopy (fNIRS), as a non-invasive and portable brain imaging tool, can monitor changes in cerebral hemodynamics in real time, providing a dynamic and reproducible monitoring method for the assessment of brain function and rehabilitation treatment in patients with cerebral palsy. This article reviews the application of fNIRS in the study of neural mechanisms in children with cerebral palsy from the perspectives of brain activation patterns and brain network connectivity, and further summarizes research progress in the assessment of rehabilitation outcomes. Studies have shown that fNIRS can reveal abnormal activation patterns (such as hyperactivation, spatiotemporal abnormalities, and disrupted lateralization) in the sensorimotor and prefrontal cortices of children with cerebral palsy, as well as impairments in brain network connectivity (weakened functional connectivity and enhanced causal connectivity) . It can also monitor neuroplasticity changes induced by rehabilitation interventions, providing a theoretical basis for understanding the neural mechanisms of cerebral palsy rehabilitation and establishing objective biomarkers for optimizing clinical intervention strategies and evaluating treatment efficacy. Finally, this paper outlines future directions for fNIRS in the early diagnosis of cerebral palsy, the integration of multimodal technologies, and the application of artificial intelligence.
[1] 中国康复医学会儿童康复专业委员会,中国残疾人康复协会小儿脑性瘫痪康复专业委员会,中国医师协会康复医师分会儿童康复专业委员会,等.中国脑性瘫痪康复指南(2022)第一章:概论[J].中华实用儿科临床杂志,2022,37(12):887-892.
[2] 近红外脑功能成像临床应用专家共识[J].中国老年保健医学,2021,19(2):3-9.
[3] 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.
[4] J?BSIS F F. Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters[J]. Science, 1977,198(4323):1264-1267.
[5] 王佳,曾妮,王先斌,等.近红外脑功能成像技术在脑卒中后康复治疗中的应用和研究进展[J].中华物理医学与康复杂志,2025,47(4):371-375.
[6] 刘宝根,周兢,李菲菲.脑功能成像的新方法——功能性近红外光谱技术(fNIRS)[J].心理科学,2011,34(4):943-949.
[7] 戴磊,陈健尔,张婉莹.功能性近红外光谱成像技术在脑卒中运动功能障碍康复中的应用进展[J].中华物理医学与康复杂志,2023,45(5):454-459.
[8] 赵玉宫,史姗姗.功能性近红外光谱成像在孤独症谱系障碍中的应用研究进展[J].中华物理医学与康复杂志,2023,45(3):278-283.
[9] 龚婧仪,种玉飞,段璨,等.近红外脑功能成像在轻度认知障碍诊断与康复中的应用进展[J].中华物理医学与康复杂志,2024,46(6):556-560.
[10] 王颖,蒲昱帆,王辉煌,等.基于功能性近红外光谱成像技术观察左侧脑卒中后吞咽障碍患者的皮质功能变化[J].中华物理医学与康复杂志,2025,47(8):734-739.
[11] 宫双,唐敏.吞咽中枢机制的功能近红外光谱成像研究进展[J].中华物理医学与康复杂志,2023,45(12):1140-1143.
[12] 张耀文,黄文浩,李鑫,等.基于近红外脑功能成像的健康人吞咽热水和冰水过程中脑血流动力学信号的变化[J].中华物理医学与康复杂志,2023,45(12):1104-1109.
[13] 杨朋坤,吴毅,余克威.功能性近红外光谱技术在脑卒中后失语症中的应用和研究进展[J].中华物理医学与康复杂志,2025,47(5):468-471.
[14] 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.
[15] ZIMEO MORAIS G A, SCHOLKMANN F, BALARDIN J B, et al. Non-neuronal evoked and spontaneous hemodynamic changes in the anterior temporal region of the human head may lead to misinterpretations of functional near-infrared spectroscopy signals[J]. Neurophotonics, 2018,5(1):11002.
[16] POLLONINI L, BORTFELD H, OGHALAI J S. PHOEBE: a method for real time mapping of optodes-scalp coupling in functional near-infrared spectroscopy[J]. Biomed Opt Express, 2016,7(12):5104-5119.
[17] COOPER R J, SELB J, GAGNON L, et al. A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy[J]. Front Neurosci, 2012,6:147.
[18] BRIGADOI S, CECCHERINI L, CUTINI S, et al. Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data[J]. Neuroimage, 2014,85 Pt 1:181-191.
[19] HU X S, ARREDONDO M M, GOMBA M, et al. Comparison of motion correction techniques applied to functional near-infrared spectroscopy data from children (Erratum)[J]. J Biomed Opt, 2020,25(5):1.
[20] HUPPERT T J, DIAMOND S G, FRANCESCHINI M A, et al. HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain[J]. Appl Opt, 2009,48(10):D280-D298.
[21] TAK S, YE J C. Statistical analysis of fNIRS data: a comprehensive review[J]. Neuroimage, 2014,85 Pt 1:72-91.
[22] UGA M, DAN I, SANO T, et al. Optimizing the general linear model for functional near-infrared spectroscopy: an adaptive hemodynamic response function approach[J]. Neurophotonics, 2014,1(1):015004.
[23] TIAN F, DELGADO M R, DHAMNE S C, et al. Quantification of functional near infrared spectroscopy to assess cortical reorganization in children with cerebral palsy[J]. Opt Express, 2010,18(25):25973-25986.
[24] KHAN B, TIAN F, BEHBEHANI K, et al. Identification of abnormal motor cortex activation patterns in children with cerebral palsy by functional near-infrared spectroscopy[J]. J Biomed Opt, 2010,15(3):36008.
[25] ALEXANDRAKIS G, KHAN B, TIAN F, et al. Translation of near infrared brain imaging to assess children with cerebral palsy[J]. Proc Spie, 2016,19(3):718005-718005.
[26] 眭有昕,郭川,朱仕哲,等.功能性近红外光谱技术在脑卒中患者运动功能康复中的应用进展[J].中国康复医学杂志,2023,38(9):1315-1320.
[27] SUKAL-MOULTON T, DE CAMPOS A C, ALTER K E, et al. Functional near-infrared spectroscopy to assess sensorimotor cortical activity during hand squeezing and ankle dorsiflexion in individuals with and without bilateral and unilateral cerebral palsy[J]. Neurophotonics, 2020,7(4):45001.
[28] DE CAMPOS A C, SUKAL-MOULTON T, HUPPERT T, et al. Brain activation patterns underlying upper limb bilateral motor coordination in unilateral cerebral palsy: an fNIRS study[J]. Dev Med Child Neurol, 2020,62(5):625-632.
[29] KHAN O A, SINGH T, BARANY D A, et al. Accuracy deficits during robotic time-constrained reaching are related to altered prefrontal cortex activity in children with cerebral palsy[J]. J Neuroeng Rehabil, 2024,21(1):216.
[30] SUKAL-MOULTON T, DE CAMPOS A C, ALTER K E, et al. Relationship between sensorimotor cortical activation as assessed by functional near infrared spectroscopy and lower extremity motor coordination in bilateral cerebral palsy[J]. Neuroimage Clin, 2018,20:275-285.
[31] KURZ M J, WILSON T W, ARPIN D J. An fNIRS exploratory investigation of the cortical activity during gait in children with spastic diplegic cerebral palsy[J]. Brain Dev, 2014,36(10):870-877.
[32] LICEA J, KHAN O A, SINGH T, et al. Prefrontal cortex hemodynamic activity during a test of lower extremity functional muscle strength in children with cerebral palsy: A functional near-infrared spectroscopy study[J]. Eur J Neurosci, 2024,59(2):298-307.
[33] 武英华,高晶.下肢外骨骼机器人在脑性瘫痪儿童康复中的应用进展[J].中华物理医学与康复杂志,2024,46(6):561-564.
[34] ZHANG T, XU G, CHANG Y, et al. Study of the brain function characteristics in children with cerebral palsy during walking using functional near-infrared spectroscopy[J]. Neurophotonics, 2025,12(2):25004.
[35] SURKAR S M, HOFFMAN R M, HARBOURNE R, et al. Neural activation within the prefrontal cortices during the goal-directed motor actions of children with hemiplegic cerebral palsy[J]. Neurophotonics, 2018,5(1):11021.
[36] SURKAR S M, HOFFMAN R M, HARBOURNE R, et al. Cognitive-motor interference heightens the prefrontal cortical activation and deteriorates the task performance in children with hemiplegic cerebral palsy[J]. Arch Phys Med Rehabil, 2021,102(2):225-232.
[37] 邹正,柯晓婷,宋建飞,等.基于近红外脑功能成像的静息态功能连接研究在脑卒中后功能障碍患者中的应用进展[J].中华物理医学与康复杂志,2024,46(9):848-851.
[38] ZHANG T, XU G, HUO C, et al. Cortical hemodynamic response and networks in children with cerebral palsy during upper limb bilateral motor training[J]. J Biophotonics, 2023,16(5):e202200326.
[39] XIE P, NIE Z, ZHANG T, et al. FNIRS based study of brain network characteristics in children with cerebral palsy during bilateral lower limb movement[J]. Med Phys, 2024,51(6):4434-4446.
[40] KHAN O A, RAHMAN S, BADUNI K, et al. Assessment of cortical activity, functional connectivity, and neuroplasticity in cerebral palsy using functional near-infrared spectroscopy: A scoping review[J]. Dev Med Child Neurol, 2025,67(7):875-891.
[41] CAO J, KHAN B, HERVEY N, et al. Evaluation of cortical plasticity in children with cerebral palsy undergoing constraint-induced movement therapy based on functional near-infrared spectroscopy[J]. J Biomed Opt, 2015,20(4):46009.
[42] PERPETUINI D, RUSSO E F, CARDONE D, et al. Identification of functional cortical plasticity in children with cerebral palsy associated to robotic-assisted gait training: an fNIRS study[J]. J Clin Med, 2022,11(22):6790.
[43] SURKAR S M, HOFFMAN R M, WILLETT S, et al. Hand-arm bimanual intensive therapy improves prefrontal cortex activation in children with hemiplegic cerebral palsy[J]. Pediatr Phys Ther, 2018,30(2):93-100.
[44] DE BRITO BRAND?O M, GORDON A M, MANCINI M C. Functional impact of constraint therapy and bimanual training in children with cerebral palsy: a randomized controlled trial[J]. Am J Occup Ther, 2012,66(6):672-681.
[45] CHEN S, ZHANG X, CHEN X, et al. The assessment of interhemispheric imbalance using functional near-infrared spectroscopic and transcranial magnetic stimulation for predicting motor outcome after stroke[J]. Front Neurosci, 2023,17:1231693.
[46] LIN J, JIN S, YOU Y, et al. EEG-fNIRS multilayer brain network analysis revealed functional neural reorganization of rTMS with motor training in stroke[J]. IEEE Trans Biomed Eng, 2026,73(1):269-280.
[47] STRUCKMANN W, BODéN R, GINGNELL M, et al. Modulation of dorsolateral prefrontal cortex functional connectivity after intermittent theta-burst stimulation in depression: combining findings from fNIRS and fMRI[J]. Neuroimage Clin, 2022,34:103028.
[48] YI L, XIE G, LI Z, et al. Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine[J]. Front Neurosci, 2023,17:1205931.
[49] KAWABATA Y, IMAZU S I, MATSUMOTO K, et al. rTMS therapy reduces hypofrontality in patients with depression as measured by fNIRS[J]. Front Psychiatry, 2022,13:814611.
[50] LIM S, DONG S Y, MCINTYRE R S, et al. Exploring functional connectivity in attention deficit/hyperactivity disorder: a functional near-infrared spectroscopy study with machine learning analysis[J]. IEEE J Biomed Health Inform, 2025,29(10):7473-7485.
[51] GUEVARA E, SOLANA-LAVALLE G, ROSAS-ROMERO R. Integrating fNIRS and machine learning: shedding light on Parkinson's disease detection[J]. EXCLI J, 2024,23:763-771.
[52] HUANG Z, LIU M, YANG H, et al. Functional near-infrared spectroscopy-based diagnosis support system for distinguishing between mild and severe depression using machine learning approaches[J]. Neurophotonics, 2024,11(2):25001.
[53] SHI C, DU X, REN Z. Prediction of health anxiety using resting-state functional near-infrared spectroscopy and machine learning[J]. J Affect Disord, 2025,374:39-45.
基本信息:
中图分类号:R742.3;R493
引用信息:
[1]李夏薇,周宇,何璐娜,等.功能性近红外光谱技术用于脑性瘫痪患儿功能障碍神经机制及康复效果评估研究进展[J].山东医药().
基金信息:
江苏省科技项目(BK20241850); 江苏省卫生健康委科研项目(M2022022)
2026-08-05
2026-08-05
2026-08-05