Citation: | WU Yusi, JIANG Mingqing, LUO Hua, RUAN Jianghai. Research on spontaneous upper limb activity and graph theory of electroencephalogram in patients with acute ischemic stroke[J]. Journal of Clinical Medicine in Practice, 2024, 28(19): 73-78. DOI: 10.7619/jcmp.20242749 |
To evaluate the changes in motor function impairment and brain functional networks of patients with acute ischemic stroke(AIS) through parameters of spontaneous activities of both upper limbs and electroencephalogram graph theory analysis methods.
The data of 34 acute ischemic stroke patients(observation group) with upper limb motor disorders who were treated in the Department of Neurology of the Affiliated Hospital of Southwest Medical University from January 2022 to October 2023, and 40 healthy controls (HC group) were collected. The subjects completed the National Institutes of Health Stroke Scale (NIHSS) and Fugl-Meyer Assessment (FMA) within 7 days, and wore wrist activity recorders (Actiwatch) continuously for 24 hours to collect data on spontaneous activities of upper limbs and analyzed related parameters such as the coordination coefficient of both upper limbs (r), the activity ratio of the affected side to the healthy side upper limb (ULAR), etc. At the same time, all subjects completed approximately 2 hours of 19-channel electroencephalogram examination. After preprocessing the electroencephalogram data, 5 segments of 10-second resting-state electroencephalogram were extracted for graph theory.
① Compared to healthy individuals, AIS patients exhibited decreased functional connectivity edges in the δ and θ bands, with substantial reductions in network connections in the α band. In the β band, connections between the frontal, right parietal, and occipital regions weakened, while connections from the right temporal lobe to the left temporal lobe strengthened. In the γ band, there was a significant increase in connections throughout the brain. ② Graph theory analysis revealed significantly increased shortest path lengths (α band: t=2.228, P < 0.05, d=-0.52; β band: t=-3.641, P < 0.01, d=-0.878) and decreased global efficiency (α band: t=2.535, P < 0.05, d=0.591; β band: t=3.321, P < 0.01, d=0.803) in the observation group compared to the control group. In the γ band, local efficiency (t=3.279, P < 0.01, d=0.765) and clustering coefficients were significantly higher (t=3.358, P < 0.01, d=0.783). ③ In the γ band, the ULAR≤30% group showed significantly reduced shortest path length (t=-2.063, P < 0.05, d=-0.802) and increased global efficiency (t=2.226, P < 0.05, d=0.865), local efficiency (t=2.95, P < 0.05, d=1.147), and clustering coefficient (t=2.962, P < 0.05, d=1.148). ④ In the observation group, the bilateral upper limb coordination coefficient during sleep was negatively correlated with NIHSS scores (r=-0.389, P < 0.05) and ULAR (r=-0.395, P < 0.05), while FMA scores were positively correlated with ULAR (r=0.442, P < 0.05).
The parameters of spontaneous activities of the upper limbs can be used to determine the impairment of motor function in AIS patients. The combination of changes in brain functional networks and motor impairments can provide new ideas for the study of their neural network mechanisms.
[1] |
WU S M, WU B, LIU M, et al. Stroke in China: advances and challenges in epidemiology, prevention, and management[J]. Lancet Neurol, 2019, 18(4): 394-405. doi: 10.1016/S1474-4422(18)30500-3
|
[2] |
江涛, 王引言, 方晟宇. 全面解析运动功能网络的拓扑性质与保护机制[J]. 中华神经外科杂志, 2020, 36(2): 109-111. doi: 10.3760/cma.j.issn.1001-2346.2020.02.001
|
[3] |
D'AGATA F, PEILA E, CICERALE A, et al. Cognitive and neurophysiological effects of non-invasive brain stimulation in stroke patients after motor rehabilitation[J]. Front Behav Neurosci, 2016, 10: 135.
|
[4] |
SCHLEMM E, CHENG B, THOMALLA G, et al. Functional lesion network mapping of sensory deficits after ischemic stroke[J]. Stroke, 2023, 54(11): 2918-2922. doi: 10.1161/STROKEAHA.123.044470
|
[5] |
PAPO D, BULDU J M. Does the brain behave like a (complex) networkI. Dynamics[J]. Phys Life Rev, 2024, 48: 47-98. doi: 10.1016/j.plrev.2023.12.006
|
[6] |
CHI N F, KU H L, CHEN D Y, et al. Cerebral motor functional connectivity at the acute stage: an outcome predictor of ischemic stroke[J]. Sci Rep, 2018, 8(1): 16803. doi: 10.1038/s41598-018-35192-y
|
[7] |
LEE S, LEE Y S, KIM J. Automated evaluation of upper-limb motor function impairment using Fugl-Meyer assessment[J]. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(1): 125-134. doi: 10.1109/TNSRE.2017.2755667
|
[8] |
DE NIET M, BUSSMANN J B, RIBBERS G M, et al. The stroke upper-limb activity monitor: its sensitivity to measure hemiplegic upper-limb activity during daily life[J]. Arch Phys Med Rehabil, 2007, 88(9): 1121-1126. doi: 10.1016/j.apmr.2007.06.005
|
[9] |
KEMP C, PIENAAR P R, HENST R H P, et al. Assessing the validity and reliability and determining cut-points of the Actiwatch 2 in measuring physical activity[J]. Physiol Meas, 2020, 41(8): 085001. doi: 10.1088/1361-6579/aba80f
|
[10] |
中华医学会神经病学分会, 中华医学会神经病学分会脑血管病学组. 中国急性缺血性脑卒中诊治指南2018[J]. 中华神经科杂志, 2018, 51(9): 666-682. doi: 10.3760/cma.j.issn.1006-7876.2018.09.004
|
[11] |
BOLOGNA M, PAPARELLA G. Neurodegeneration and sensorimotor function[J]. Brain Sci, 2020, 10(11): 808. doi: 10.3390/brainsci10110808
|
[12] |
DELAVILLE C, MCCOY A J, GERBER C M, et al. Subthalamic nucleus activity in the awake hemiparkinsonian rat: relationships with motor and cognitive networks[J]. J Neurosci, 2015, 35(17): 6918-6930. doi: 10.1523/JNEUROSCI.0587-15.2015
|
[13] |
GORDON P C, JOVELLAR D B, SONG Y F, et al. Recording brain responses to TMS of primary motor cortex by EEG-utility of an optimized sham procedure[J]. Neuroimage, 2021, 245: 118708. doi: 10.1016/j.neuroimage.2021.118708
|
[14] |
GUAN A, WANG S S, HUANG A L, et al. The role of gamma oscillations in central nervous system diseases: Mechanism and treatment[J]. Front Cell Neurosci, 2022, 16: 962957. doi: 10.3389/fncel.2022.962957
|
[15] |
KATO K, SAWADA M, NISHIMURA Y. Bypassing stroke-damaged neural pathways via a neural interface induces targeted cortical adaptation[J]. Nat Commun, 2019, 10(1): 4699. doi: 10.1038/s41467-019-12647-y
|
[16] |
VÖLKER M, FIEDERER L D J, BERBERICH S, et al. The dynamics of error processing in the human brain as reflected by high-gamma activity in noninvasive and intracranial EEG[J]. Neuroimage, 2018, 173: 564-579. doi: 10.1016/j.neuroimage.2018.01.059
|
[17] |
REN B, YANG K, ZHU L, et al. Multi-granularity analysis of brain networks assembled with intra-frequency and cross-frequency phase coupling for human EEG after stroke[J]. Front Comput Neurosci, 2022, 16: 785397. doi: 10.3389/fncom.2022.785397
|
[18] |
FINNIGAN S, WONG A, READ S. Defining abnormal slow EEG activity in acute ischaemic stroke: Delta/alpha ratio as an optimal QEEG index[J]. Clin Neurophysiol, 2016, 127(2): 1452-1459. doi: 10.1016/j.clinph.2015.07.014
|
[19] |
COLOMBO R, PISANO F, MICERA S, et al. Robotic techniques for upper limb evaluation and rehabilitation of stroke patients[J]. IEEE Trans Neural Syst Rehabil Eng, 2005, 13(3): 311-324. doi: 10.1109/TNSRE.2005.848352
|
[20] |
SHREVE L, KAUR A, VO C, et al. Electroencephalography measures are useful for identifying large acute ischemic stroke in the emergency department[J]. J Stroke Cerebrovasc Dis, 2019, 28(8): 2280-2286. doi: 10.1016/j.jstrokecerebrovasdis.2019.05.019
|
[21] |
DUBOVIK S, PIGNAT J M, PTAK R, et al. The behavioral significance of coherent resting-state oscillations after stroke[J]. Neuroimage, 2012, 61(1): 249-257. doi: 10.1016/j.neuroimage.2012.03.024
|
[22] |
PELLEGRINO G, ARCARA G, CORTESE A M, et al. Cortical gamma-synchrony measured with magnetoencephalography is a marker of clinical status and predicts clinical outcome in stroke survivors[J]. Neuroimage Clin, 2019, 24: 102092. doi: 10.1016/j.nicl.2019.102092
|
[23] |
VECCHIO F, CALIANDRO P, REALE G, et al. Acute cerebellar stroke and middle cerebral artery stroke exert distinctive modifications on functional cortical connectivity: a comparative study via EEG graph theory[J]. Clin Neurophysiol, 2019, 130(6): 997-1007. doi: 10.1016/j.clinph.2019.03.017
|