睡眠脑电连接组多模态融合的意识障碍预后预测价值
Prognostic Predictive Value of Multimodal Fusion Based on Sleep EEG Connectomics in Disorders of Consciousness
摘要: 意识障碍(DoC)是严重脑损伤后常见的临床结局，其精准评估与预后预测是神经重症领域的核心挑战。睡眠脑电连接组通过分析脑电图(EEG)信号中不同脑区的功能耦合，可揭示大脑网络的动态重构特征，已成为DoC病理机制解析和预后评估的重要工具。近年来，多模态融合技术通过整合EEG、fNIRS、PET和fMRI等多维度数据，显著提高了DoC意识水平分类和预后预测的准确性。本文系统综述睡眠脑电连接组的基础理论及其在DoC研究中的应用进展，重点阐述多模态融合技术的创新价值，分析方法学争议、伦理挑战及未来发展方向。
Abstract: Disorders of consciousness (DoC) are a prevalent clinical outcome after severe brain injury. Accurate assessment and prognostic prediction of DoC remain core challenges in neurocritical care. Sleep electroencephalography (EEG) connectomics characterizes the functional coupling across brain regions based on EEG signals, revealing dynamic remodeling patterns of cerebral neural networks, and has become a powerful tool for exploring the pathological mechanisms and prognostic evaluation of DoC. In recent years, multimodal fusion techniques that integrate multidimensional data from EEG, functional near-infrared spectroscopy (fNIRS), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI) have substantially improved the accuracy of consciousness stratification and prognostic prediction for DoC patients. This paper systematically reviews the fundamental principles of sleep EEG connectomics and its research progress in DoC studies, emphasizes the innovative value of multimodal fusion strategies, and discusses existing methodological controversies, ethical challenges, and future research prospects.
文章引用：白康康, 申峤. 睡眠脑电连接组多模态融合的意识障碍预后预测价值[J]. 临床医学进展, 2026, 16(9): 1297-1304. https://doi.org/10.12677/acm.2026.1693165

参考文献

[1] Zhang, P., Zhou, Y., Ni, H., Huang, Z., Tang, C., Zhuge, Q., et al. (2025) Altered Functional Connectivity of Brainstem ARAS Nuclei Unveils the Mechanisms of Disorders of Consciousness in sTBI: An Exploratory Study. NeuroImage: Clinical, 46, Article ID: 103787.
https://doi.org/10.1016/j.nicl.2025.103787
[2] Mouthon, A., van Hedel, H.J.A., Meyer-Heim, A., Kurth, S., Ringli, M., Pugin, F., et al. (2016) High-Density Electroencephalographic Recordings during Sleep in Children with Disorders of Consciousness. NeuroImage: Clinical, 11, 468-475.
https://doi.org/10.1016/j.nicl.2016.03.012
[3] Zieleniewska, M., Duszyk, A., Różański, P., Pietrzak, M., Bogotko, M. and Durka, P. (2019) Parametric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness. International Journal of Neural Systems, 29, Article ID: 1850049.
https://doi.org/10.1142/s0129065718500491
[4] Wang, N., Chai, X., Song, J., He, Y., He, Q., Zhang, T., et al. (2025) Motor Intention Quantization for Patients with Disorders of Consciousness by Multimodal BCI Combining Electroencephalography and Functional Near‐Infrared Spectroscopy. CNS Neuroscience & Therapeutics, 31, e70012.
https://doi.org/10.1002/cns.70679
[5] Sun, N., Liu, Y., Lin, H., Xiong, J., He, Y., Yang, H., et al. (2026) Multimodal PET/MR Imaging of Prolonged Disorders of Consciousness: A Pilot Feasibility Study. Frontiers in Neuroscience, 20, Article 1761097.
https://doi.org/10.3389/fnins.2026.1761097
[6] Kazazian, K., Kolisnyk, M., Gupta, G., de Jeu, J., Abdalmalak, A. and Owen, A.M. (2026) Towards the Use of Functional Near-Infrared Spectroscopy as an Assessment Tool in Disorders of Consciousness. Imaging Neuroscience, 4, IMAG.a.1217.
https://doi.org/10.1162/imag.a.1217
[7] Toedt, I., Hermann, G., Tagliazucchi, E., Todtenhaupt, I.K., Laufs, H. and von Wegner, F. (2025) EEG Connectivity Is an Objective Signature of Reduced Consciousness and Sleep Depth. Brain Topography, 38, Article No. 63.
https://doi.org/10.1007/s10548-025-01144-9
[8] Nekrasova, J., Kanarskii, M., Yankevich, D., et al. (2020) Retrospective Analysis of Sleep Patterns in Patients with Chronic Disorders of Consciousness. Sleep Medicine: X, 2, Article ID: 100024.
https://doi.org/10.1016/j.sleepx.2020.100024
[9] Yu, J., Wu, Y., Wu, B., Xu, C., Cai, J., Wen, X., et al. (2022) Sleep Patterns Correlates with the Efficacy of TDCs on Post-Stroke Patients with Prolonged Disorders of Consciousness. Journal of Translational Medicine, 20, Article No. 601.
https://doi.org/10.1186/s12967-022-03710-2
[10] Raveendran, S., S, K., A G, R., Kenchaiah, R., Sahoo, J., Kumar, S., et al. (2025) Functional Connectivity in EEG: A Multiclass Classification Approach for Disorders of Consciousness. Frontiers in Neuroscience, 19, Article 1550581.
https://doi.org/10.3389/fnins.2025.1550581
[11] He, Z., Lu, R., Guan, Y., Wu, Y., Ge, J., Liu, G., et al. (2022) Brain Metabolic Connectivity Patterns in Patients with Prolonged Disorder of Consciousness after Hypoxic-Ischemic Injury: A Preliminary Study. Brain Sciences, 12, Article 892.
https://doi.org/10.3390/brainsci12070892
[12] Estraneo, A., Loreto, V., Guarino, I., Boemia, V., Paone, G., Moretta, P., et al. (2016) Standard EEG in Diagnostic Process of Prolonged Disorders of Consciousness. Clinical Neurophysiology, 127, 2379-2385.
https://doi.org/10.1016/j.clinph.2016.03.021
[13] Naro, A., Leo, A., Cannavò, A., Buda, A., Bramanti, P. and Calabrò, R.S. (2016) Do Unresponsive Wakefulness Syndrome Patients Feel Pain? Role of Laser-Evoked Potential-Induced γ-Band Oscillations in Detecting Cortical Pain Processing. Neuroscience, 317, 141-148.
https://doi.org/10.1016/j.neuroscience.2016.01.009
[14] Schorr, B., Schlee, W., Arndt, M. and Bender, A. (2016) Coherence in Resting-State EEG as a Predictor for the Recovery from Unresponsive Wakefulness Syndrome. Journal of Neurology, 263, 937-953.
https://doi.org/10.1007/s00415-016-8084-5
[15] Toplutaş, E., Aydın, F. and Hanoğlu, L. (2024) EEG Microstate Analysis in Patients with Disorders of Consciousness and Its Clinical Significance. Brain Topography, 37, 377-387.
https://doi.org/10.1007/s10548-023-00939-y
[16] Erlbeck, H., Real, R.G.L., Kotchoubey, B., Mattia, D., Bargak, J. and Kübler, A. (2017) Basic Discriminative and Semantic Processing in Patients in the Vegetative and Minimally Conscious State. International Journal of Psychophysiology, 113, 8-16.
https://doi.org/10.1016/j.ijpsycho.2016.12.012
[17] Niu, Y., Chen, X., Fan, J., Liu, C., Fang, M., Liu, Z., et al. (2025) Explainable Machine Learning Model Based on EEG, ECG, and Clinical Features for Predicting Neurological Outcomes in Cardiac Arrest Patient. Scientific Reports, 15, Article No. 11498.
https://doi.org/10.1038/s41598-025-93579-0
[18] Wu, H., Huang, X., Lin, D., Liao, Z., Chen, Z., Zhong, H., et al. (2025) A New Multimodal Neuroprognostic Model for Chronic Disorders of Consciousness: Integrating Behavioral, Hormonal, and Imaging Features. NeuroImage, 317, Article ID: 121329.
https://doi.org/10.1016/j.neuroimage.2025.121329
[19] Aihemaitiniyazi, A., Li, T., Zhang, H., Wei, D., Cai, P., Wang, W., et al. (2026) Spinal Cord Stimulation Improves Brain Connectivity and Consciousness Level in Patients with Disorders of Consciousness. NeuroImage, 327, Article ID: 121694.
https://doi.org/10.1016/j.neuroimage.2026.121694
[20] Tong, J., Sun, F., Min, T., Chen, Z. and Yang, Y. (2026) Effects of Multi-Sensory Stimulation on Brain Functional Connectivity in Patients with Disorders of Consciousness. Brain Sciences, 16, Article 299.
https://doi.org/10.3390/brainsci16030299
[21] Willacker, L., Raiser, T.M., Bassi, M., Bender, A., Comanducci, A., Rosanova, M., et al. (2022) Perbrain: A Multimodal Approach to Personalized Tracking of Evolving State-of-Consciousness in Brain-Injured Patients: Protocol of an International, Multicentric, Observational Study. BMC Neurology, 22, Article No. 468.
https://doi.org/10.1186/s12883-022-02958-x
[22] Bodien, Y.G., Fecchio, M., Gilmore, N., et al. (2025) Acute Biomarkers of Consciousness Are Associated with Recovery after Severe Traumatic Brain Injury.
https://doi.org/10.1101/2025.03.02.25322248
[23] Patriota, J., Moreni, G., Mejias, J.F., Talamini, L. and Olcese, U. (2025) Functional Connectivity Drifts during Sleep as a Marker of Fluctuations in the Level of Consciousness. Neuroscience of Consciousness, 2025, niaf061.
https://doi.org/10.1093/nc/niaf061
[24] Manasova, D., Belloli, L.M.L., Rosenfelder, M.J., Willacker, L., Fló Rama, E., Valota, C., et al. (2026) Multimodal Multicentre Investigation of Diagnostic and Prognostic Markers in Disorders of Consciousness. Brain, 149, 1381-1395.
https://doi.org/10.1093/brain/awaf412
[25] Ping, A., Guan, L., Wang, Y., Yang, S., Yang, C., Hu, X., et al. (2025) A Methodological Guideline for Consciousness Assessment via Neural Electrophysiological Activity. Military Medical Research, 12, Article No. 90.
https://doi.org/10.1186/s40779-025-00682-4
[26] Bai, Y., Lin, Y. and Ziemann, U. (2020) Managing Disorders of Consciousness: The Role of Electroencephalography. Journal of Neurology, 268, 4033-4065.
https://doi.org/10.1007/s00415-020-10095-z
[27] Adama, S. and Bogdan, M. (2026) Computational Predictive Processing Models of Consciousness: A Systematic Review of Non-Invasive Brain Signal Analysis in Disorders of Consciousness. Frontiers in Computational Neuroscience, 20, Article 1797090.
https://doi.org/10.3389/fncom.2026.1797090
[28] Bhardwaj, T., Edlow, B.L. and Young, M.J. (2025) Ethically Translating Advanced Neurotechnologies for Disorders of Consciousness: A Survey of Clinicians’ Perspectives. Neurocritical Care, 42, 757-771.
https://doi.org/10.1007/s12028-024-02147-3
[29] Ahmed, Z. (2020) Practicing Precision Medicine with Intelligently Integrative Clinical and Multi-Omics Data Analysis. Human Genomics, 14, Article No. 35.
https://doi.org/10.1186/s40246-020-00287-z
[30] Das, S., Abou-Haidar, R., Rabalais, H., et al. (2022) The C-BIG Repository: An Institution-Level Open Science Platform. Neuroinformatics, 20, 139-153.
https://doi.org/10.1007/s12021-021-09516-9
[31] Segkouli, S., Giakoumis, D., Votis, K., Triantafyllidis, A., Paliokas, I. and Tzovaras, D. (2023) Smart Workplaces for Older Adults: Coping ‘Ethically’ with Technology Pervasiveness. Universal Access in the Information Society, 22, 37-49.
https://doi.org/10.1007/s10209-021-00829-9
[32] Makkar, A. and Santosh, K. (2023) SecureFed: Federated Learning Empowered Medical Imaging Technique to Analyze Lung Abnormalities in Chest X-Rays. International Journal of Machine Learning and Cybernetics, 14, 2659-2670.
https://doi.org/10.1007/s13042-023-01789-7
[33] Curley, W.H., Comanducci, A. and Fecchio, M. (2022) Conventional and Investigational Approaches Leveraging Clinical EEG for Prognosis in Acute Disorders of Consciousness. Seminars in Neurology, 42, 309-324.
https://doi.org/10.1055/s-0042-1755220
[34] Della Bella, G.A., Zang, D., Gui, P., Mateos, D.M., Sitt, J.D., Bekinschtein, T.A., et al. (2025) Detection of EEG Dynamic Complex Patterns in Disorders of Consciousness. Communications Biology, 8, Article No. 1204.
https://doi.org/10.1038/s42003-025-08666-9

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