Objective To investigate the changes in functional connectivity of EEG signals in patients with cerebellar infarction and their relationship with cognitive function. Methods EEG data from 37 cerebellar infarction patients (cerebellar infarction group) and 40 healthy controls (control group) were preprocessed and decomposed into five frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-45 Hz). Through phase synchronization analysis, the phase locking value was calculated to construct the electrode lead connectivity matrices and compute network attribute parameters, including small-world index, clustering coefficient, local efficiency, global efficiency, shortest path length, and to evaluate the relationships between these network attribute parameters and cognitive function scores. Results The cerebellar infarction group showed significantly increased functional connectivity in the delta and gamma bands (P<0.05) and significantly decreased connectivity in the alpha and beta bands (P<0.05). In the delta band, cerebellar infarction group exhibited significantly increased global efficiency, local efficiency, and clustering coefficient (P<0.05), with considerably decreased small-world index and shortest path length (P<0.05). In the alpha and beta bands, cerebellar infarction group showed significantly reduced global efficiency, local efficiency, and clustering coefficient (P<0.05), with a significantly increased shortest path length (P<0.05) .In the gamma band, the local efficiency and clustering coefficient in cerebellar infarction group were significantly higher than those in the control group (P<0.05). Global efficiency, local efficiency, and clustering coefficient were positively correlated with cognitive function scores (P<0.01), while the shortest path length was negatively correlated with cognitive function scores in the cerebellar infarction group (P<0.01). Conclusion Abnormal resting-state brain network connectivity and network attributes in patients with cerebellar infarction are closely related to cognitive dysfunction, providing a basis for exploring more effective diagnostic and therapeutic strategies.
Key words
cerebellar infarction; resting-state brain network; electroencephalogram; phase-locked values; cognitive scale score
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References
[1] Choi KD, Lee H, Kim JS. Vertigo in brainstem and cerebellar strokes[J]. Curr Opin Neurol, 2013, 26(1): 90-95.
[2] Stoodley CJ, Valera EM, Schmahmann JD. Functional topography of the cerebellum for motor and cognitive tasks: an fMRI study[J]. NeuroImage, 2012, 59(2): 1560-1570.
[3] 付秀娟,肖哲曼,尹皓,等. 后循环与认知障碍[J]. 中国卒中杂志, 2017, 12(7): 654-658.
Fu XJ, Xiao ZM, Yin H, et al. Posterior circulation and cognitive dysfunction[J]. Chin J Stroke, 2017, 12(7): 654-658.
[4] Cao J, Zhao Y, Shan X, et al. Brain functional and effective connectivity based on electroencephalography recordings: a review[J]. Hum Brain Mapp, 2021, 43(2).
[5] Noroozian M. The role of the cerebellum in cognition: beyond coordination in the central nervous system[J]. Neurol Clin, 2014, 32(4): 1081-1104.
[6] 周燕,权利,阮江海. 基于静息态脑电图的偏头痛患者脑网络变化[J]. 北京生物医学工程, 2022, 41(1): 17-23.
Zhou Y, Quan L, Ruan JH. Brain network changes of patients with migraine based on resting state electroencephalogram[J]. Beijing Biomed Eng, 2022, 41(1): 17-23.
[7] Wang D, Yao Q, Yu M, et al. Topological disruption of structural brain networks in patients with cognitive impairment following cerebellar infarction[J]. Front Neurol, 2019, 10: 759.
[8] 张丽丽,吴婧,董兰真. 急性小脑梗死患者认知功能变化及其与大脑结构网络的相关性研究[J]. 临床和实验医学杂志, 2024, 23(5): 449-453.
Zhang LL, Wu J, Dong LZ. Cognitive function changes and its correlation with brain structure network in patients with acute cerebellar infarction[J]. J Clin Exp Med, 2024, 23(5): 449-453.
[9] 李玉爽,敖亚雯,赵益林,等. 基于图论分析对自闭症谱系障碍儿童脑功能网络异常拓扑学属性的研究[J]. 磁共振成像, 2022, 13(03): 37-42.
Li YS, Ao YW, Zhao YL, et al. The abnormal topological properties of brain functional network in children with autism spectrum disorders based on graph theory analysis[J]. Chin J Magn Reson Imaging, 2022, 13(3): 37-42.
[10] Abbasi N, Fereshtehnejad SM, Zeighami Y, et al. Predicting severity and prognosis in Parkinson’s disease from brain microstructure and connectivity[J]. Neuroimage Clin, 2020, 25: 102111.
[11] 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.
Chinese Society of Neurology, Chinese Stroke Society. Chinese guidelines for diagnosis and treatment of acute ischemic stroke 2018[J]. Chin J Neurol, 2018, 51(9): 666-682.
[12] Tombaugh TN, McIntyre NJ. The mini-mental state examination: a comprehensive review[J]. J Am Geriatr Soc, 2015, 40(9): 922-935.
[13] Kang JM, Cho YS, Park S, et al. Montreal cognitive assessment reflects cognitive reserve[J]. BMC Geriatr, 2018, 18(1): 261.
[14] Cui G, Li X, Touyama H. Emotion recognition based on group phase locking value using convolutional neural network[J]. Sci Rep, 2023, 13(1): 3769.
[15] Rodríguez-Méndez DA, San-Juan D, Hallett M, et al. A new model for freedom of movement using connectomic analysis[J]. PeerJ, 2022, 10: e13602.
[16] 邢阳阳,马旭峰,尹鸿峰,等. 基于图论的脑电数据脑功能网络构建研究[J]. 黑龙江科学, 2023, 14(6): 36-39.
Xing YY, Ma XF, Yin HF, et al. Research on the construction of brain function network of EEG data based on graph theory[J]. Heilongjiang Sci, 2023, 14(6): 36-39.
[17] 沈潇童,王玥,毕卉,等. 基于脑网络参数优化的青少年抑郁症患者与健康人群分类识别研究[J]. 生物医学工程学杂志, 2020, 37(6): 1037-1044.
Shen XT, Wang Y, Bi H, et al. Study on classification and identification of depressed patients and healthy people among adolescents based on optimization of brain characteristics of network[J]. J Biomed Eng, 2020, 37(6): 1037-1044.
[18] Tan L, Tang H, Luo H, et al. Exploring brain network oscillations during seizures in drug-na?ve patients with juvenile absence epilepsy[J]. Front Neurol, 2024, 15: 1340959.
[19] Li Z, Liu C, Wang Q, et al. Abnormal functional brain network in Parkinson’s disease and the effect of acute deep brain stimulation[J]. Front Neurol, 2021, 12: 715455.
[20] Hao Z, Zhai X, Cheng D, et al. EEG microstate-specific functional connectivity and stroke-related alterations in brain dynamics[J]. Front Neurosci, 2022, 16: 848737.
[21] Li X, Kang Y, Chen W, et al. Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment[J]. Front Neurosci, 2024, 18: 1323190.
[22] Casula EP, Pellicciari MC, Ponzo V, et al. Cerebellar theta burst stimulation modulates the neural activity of interconnected parietal and motor areas[J]. Sci Rep, 2016, 6: 36191.
[23] Sridharan D, Knudsen EI. Gamma oscillations in the midbrain spatial attention network: linking circuits to function[J]. Curr Opin Neurobiol, 2015, 31: 189-198.
[24] Garcia-Rill E, D’Onofrio S, Mahaffey SC, et al. Bottom-up gamma and bipolar disorder, clinical and neuroepigenetic implications[J]. Bipolar Disord, 2019, 21(2): 108-116.
[25] Cardin JA, Carlén M, Meletis K, et al. Driving fast-spiking cells induces gamma rhythm and controls sensory responses[J]. Nature, 2009, 459(7247): 663-667.
[26] 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.
[27] Liu Q, Liu C, Zhang Y. Characteristics of cognitive function in patients with cerebellar infarction and its association with lesion location[J]. Front Aging Neurosci, 2024, 16: 1397905.
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