Exploration of neural mechanisms and classification models of post-stroke visuospatial neglect_West China Medical Journal

Authors：

ZHAO Wanying , YE Linlin , ZHANG Yichen , SONG Weiqun ,  CAO Lei

Department of Rehabilitation, Xuanwu Hospital, Capital Medical University, Beijing 100053, P. R. China;

Corresponding author：

CAO Lei, Email: caolei_xw@163.com

Keywords：

Electroencephalography microstates; graph theory analysis; visuospatial neglect; machine learning

DOI：

10.7507/1002-0179.202504239

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To investigate the network reorganization and dynamic brain activity in visuospatial neglect (VSN) patients using resting-state electroencephalography (rEEG), and to develop classification models to facilitate its identification. Methods In this retrospective study, stroke patients admitted to the Department of Rehabilitation, Xuanwu Hospital, Capital Medical University between August 2022 and December 2024 were included and divided into VSN (n=22) and non-VSN (n=21) groups based on paper-and-pencil assessments. A healthy control group (n=20) was also recruited. Microstate segmentation and graph-theoretical analysis were applied to rEEG data to extract microstate parameters and topological network features. Four machine learning models (logistic regression, naïve Bayes, k-nearest neighbors, and decision tree) were built for classification. Results Compared with the non-VSN group, the VSN group showed significantly increased mean duration and time coverage in microstate C, and significantly decreased coverage and occurrence in microstate D. Graph-theoretical analysis revealed higher average clustering coefficients in the VSN group. Degree centrality in the frontal-central regions (C1, CZ) was significantly lower, while that in the parietal-occipital regions (P5, P3, PO7, PO5) was significantly higher than in the non-VSN group. Among the classification models, logistic regression and naïve Bayes models performed best, with the mean duration of microstate C contributing most to classification performance. Conclusions Patients with VSN exhibit distinct alterations in electroencephalography microstate dynamics and functional network topology. Microstate parameters play a crucial role in distinguishing VSN from non-VSN stroke cases, and combining these features with machine learning offers a promising approach for early identification and personalized intervention of VSN.

Citation： ZHAO Wanying, YE Linlin, ZHANG Yichen, SONG Weiqun, CAO Lei. Exploration of neural mechanisms and classification models of post-stroke visuospatial neglect. West China Medical Journal, 2025, 40(6): 881-888. doi: 10.7507/1002-0179.202504239 Copy

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33.	Shen D, Yang B, Li J, et al. The potential associations between acupuncture sensation and brain functional network: a EEG study. Cogn Neurodyn, 2025, 19(1): 49.
34.	Vossel S, Geng JJ, Fink GR. Dorsal and ventral attention systems: distinct neural circuits but collaborative roles. Neuroscientist, 2014, 20(2): 150-159.
35.	Corbetta M, Kincade MJ, Lewis C, et al. Neural basis and recovery of spatial attention deficits in spatial neglect. Nat Neurosci, 2005, 8(11): 1603-1610.
36.	He BJ, Snyder AZ, Vincent JL, et al. Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect. Neuron, 2007, 53(6): 905-918.
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38.	Guerrero MC, Parada JS, Espitia HE. EEG signal analysis using classification techniques: logistic regression, artificial neural networks, support vector machines, and convolutional neural networks. Heliyon, 2021, 7(6): e07258.
39.	Nguyen T, Khosravi A, Creighton D, et al. EEG data classification using wavelet features selected by Wilcoxon statistics. Neural Comput Appl, 2015, 26(5): 1193-1202.
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41.	Snyder SM, Hall JR, Cornwell SL, et al. Addition of EEG improves accuracy of a logistic model that uses neuropsychological and cardiovascular factors to identify dementia and MCI. Psychiatry Res, 2011, 186(1): 97-102.

1. Corbetta M, Shulman GL. Spatial neglect and attention networks. Annu Rev Neurosci, 2011, 34: 569-599.
2. Cao L, Ye L, Xie H, et al. Neural substrates in patients with visual-spatial neglect recovering from right-hemispheric stroke. Front Neurosci, 2022, 16: 974653.
3. Ros T, Michela A, Mayer A, et al. Disruption of large-scale electrophysiological networks in stroke patients with visuospatial neglect. Netw Neurosci, 2022, 6(1): 69-89.
4. Wei Z, Li H, Ma L, et al. Emotion recognition based on microstate analysis from temporal and spatial patterns of electroencephalogram. Front Neurosci, 2024, 18: 1355512.
5. Hu W, Zhang Z, Zhao H, et al. EEG microstate correlates of emotion dynamics and stimulation content during video watching. Cereb Cortex, 2023, 33(3): 523-542.
6. Liu Y, Yu Y, Ye Z, et al. Fusion of spatial, temporal, and spectral eeg signatures improves multilevel cognitive load prediction. IEEE Trans Hum Mach Syst, 2023, 53(2): 357-366.
7. Zappasodi F, Croce P, Giordani A, et al. Prognostic value of EEG microstates in acute stroke. Brain Topogr, 2017, 30(5): 698-710.
8. Wang T, Tang J, Wang C, et al. Effect of music stimuli on corticomuscular coupling and the brain functional connectivity network. Biomed Signal Process Control, 2023, 79: 104264.
9. Liu Z, Han F, Wang Q. Task-relevant brain dynamics among cognitive subsystems induced by regional stimulation in a whole-brain computational model. Phys Rev E, 2023, 108(4-1): 044402.
10. Xu F, Zhao J, Liu M, et al. Exploration of sleep function connection and classification strategies based on sub-period sleep stages. Front Neurosci, 2023, 16: 1088116.
11. Zhang Y, Ye L, Cao L, et al. Resting-state electroencephalography changes in poststroke patients with visuospatial neglect. Front Neurosci, 2022, 16: 974712.
12. 李宜轩, 李颖, 肖倩, 等. 不同情绪错误记忆的脑电微状态功能网络分析. 浙江大学学报(工学版), 2025, 59(1): 49-61.
13. Carbone GA, Michel CM, Farina B, et al. Altered EEG patterns in individuals with disorganized attachment: an EEG microstates study. Brain Topogr, 2024, 37(3): 420-431.
14. Damborská A, Tomescu MI, Honzírková E, et al. EEG resting-state large-scale brain network dynamics are related to depressive symptoms. Front Psychiatry, 2019, 10: 548.
15. Liu J, Hu X, Shen X, et al. The EEG microstate representation of discrete emotions. Int J Psychophysiol, 2023, 186: 33-41.
16. Michel CM, Koenig T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review. Neuroimage, 2018, 180(Pt B): 577-593.
17. Xu J, Pan Y, Zhou S, et al. EEG microstates are correlated with brain functional networks during slow-wave sleep. Neuroimage, 2020, 215: 116786.
18. Chokron S, Colliot P, Bartolomeo P, et al. Visual, proprioceptive and tactile performance in left neglect patients. Neuropsychologia, 2002, 40(12): 1965-1976.
19. Gainotti G. The role of automatic orienting of attention towards ipsilesional stimuli in non-visual (tactile and auditory) neglect: a critical review. Cortex, 2010, 46(2): 150-160.
20. Bolognini N, Convento S, Rossetti A, et al. Multisensory processing after a brain damage: clues on post-injury crossmodal plasticity from neuropsychology. Neurosci Biobehav Rev, 2013, 37(3): 269-278.
21. Bellmann A, Meuli R, Clarke S. Two types of auditory neglect. Brain, 2001, 124(Pt 4): 676-687.
22. Pavani F, Husain M, Ládavas E, et al. Auditory deficits in visuospatial neglect patients. Cortex, 2004, 40(2): 347-365.
23. Si X, Han S, Zhang K, et al. The temporal dynamics of EEG microstate reveals the neuromodulation effect of acupuncture with Deqi. Front Neurosci, 2021, 15: 715512.
24. Raichle ME. The brain’s default mode network. Annu Rev Neurosci, 2015, 38: 433-447.
25. Zhou X, Zhang Z, Yu L, et al. Disturbance of functional and effective connectivity of the salience network involved in attention deficits in right temporal lobe epilepsy. Epilepsy Behav, 2021, 124: 108308.
26. Di Giuliano M, Schumann A, de la Cruz F, et al. Effective connectivity analysis of response inhibition functional network. Front Neurosci, 2025, 19: 1525038.
27. Milz P, Faber PL, Lehmann D, et al. The functional significance of EEG microstates--associations with modalities of thinking. Neuroimage, 2016, 125: 643-656.
28. Britz J, Van De Ville D, Michel CM. BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage, 2010, 52(4): 1162-1170.
29. Seitzman BA, Abell M, Bartley SC, et al. Cognitive manipulation of brain electric microstates. Neuroimage, 2017, 146: 533-543.
30. Hao Z, Zhai X, Cheng D, et al. EEG microstate-specific functional connectivity and stroke-related alterations in brain dynamics. Front Neurosci, 2022, 16: 848737.
31. Wang G, Yang Y, Liu X, et al. Neural connectivity and balance control in aging: insights from directed cortical networks during sensory conflict. Neuroimage, 2025, 312: 121218.
32. Zhang JJ, Bai Z, Fong KNK. Resting-state cortical electroencephalogram rhythms and network in patients after chronic stroke. J Neuroeng Rehabil, 2024, 21(1): 32.
33. Shen D, Yang B, Li J, et al. The potential associations between acupuncture sensation and brain functional network: a EEG study. Cogn Neurodyn, 2025, 19(1): 49.
34. Vossel S, Geng JJ, Fink GR. Dorsal and ventral attention systems: distinct neural circuits but collaborative roles. Neuroscientist, 2014, 20(2): 150-159.
35. Corbetta M, Kincade MJ, Lewis C, et al. Neural basis and recovery of spatial attention deficits in spatial neglect. Nat Neurosci, 2005, 8(11): 1603-1610.
36. He BJ, Snyder AZ, Vincent JL, et al. Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect. Neuron, 2007, 53(6): 905-918.
37. Spadone S, de Pasquale F, Digiovanni A, et al. Dynamic brain states in spatial neglect after stroke. Front Syst Neurosci, 2023, 17: 1163147.
38. Guerrero MC, Parada JS, Espitia HE. EEG signal analysis using classification techniques: logistic regression, artificial neural networks, support vector machines, and convolutional neural networks. Heliyon, 2021, 7(6): e07258.
39. Nguyen T, Khosravi A, Creighton D, et al. EEG data classification using wavelet features selected by Wilcoxon statistics. Neural Comput Appl, 2015, 26(5): 1193-1202.
40. Kocanaogullari D, Mak J, Kersey J, et al. EEG-based neglect detection for stroke patients. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020: 264-267.
41. Snyder SM, Hall JR, Cornwell SL, et al. Addition of EEG improves accuracy of a logistic model that uses neuropsychological and cardiovascular factors to identify dementia and MCI. Psychiatry Res, 2011, 186(1): 97-102.

West China Medical Journal

Exploration of neural mechanisms and classification models of post-stroke visuospatial neglect

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