神经科学中的深度学习方法：从分子机制建模到意识状态分类
Deep Learning Methods in Neuroscience: From Modeling Molecular Mechanisms to Classifying States of Consciousness
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中文总结 AI 辅助
本文综述了深度学习方法在意识状态研究中的应用，涵盖分类、建模与标记识别，指出其有效性及局限性，并强调发展混合、可解释且生理学基础的架构以提升临床转化潜力。
中文摘要 AI 辅助
对当代意识状态研究方法的关键分析。本综述聚焦于分类、聚类、麻醉下脑状态建模以及识别脑功能的可测量神经生物学特征等方法。在以下三个主要领域进行了比较分析：基于EEG和fMRI数据使用神经网络自动检测意识状态；麻醉剂作用下脑结构-功能动力学的建模；以及检测与意识水平相关的神经生理学指标。所得结论表明，深度神经模型在脑状态的分类与预测以及动态结构-功能连接性分析中展现出日益增强的有效性。尽管如此，也发现了显著局限性，包括模型的可解释性有限、缺乏标准化指标，以及意识标记的特异性问题。我们的研究结果支持开发混合型、可泛化的、具有生理学基础的架构的必要性。此外，此类方法可能提高计算模型在临床神经科学中的转化潜力。多种机器和计算建模方法已在脑状态的自动聚类和分类、多层级模型的开发以及与意识水平相关的连接模式识别等任务中展现出其有效性。对于所分析模型的实际应用，需要进行更大规模的分析和更大的数据集，以及实施模型可解释性方法。基于EEG和LFP的模型因其可用性和实时监测的可能性，在临床应用中前景最为广阔。
英文摘要
A critical analysis of contemporary approaches to the study of conscious states. The review focuses on methods of classification, clustering, modeling of brain states under anesthesia and identification of measurable neurobiological characteristics of brain function. A comparative analysis was conducted in the following three major areas: automatic detection of states of consciousness using neural networks based on EEG and fMRI data; modeling of the structural-functional dynamics of the brain under the effects of anesthetics; and detection of neurophysiological indicators which correlate with the level of consciousness. The obtained conclusions demonstrate the growing effectiveness of deep neural models in the classification and prediction of brain states and the analysis of dynamic structural-functional connectivity. Nonetheless, significant limitations were also identified, including the limited interpretability of the models, the lack of standardized metrics, and the problem of the specificity of consciousness markers. Our findings support the need for developing hybrid, generalizible, physiologically grounded architectures. Furthermore, such approaches may improve the translational potential of computational models in clinical neuroscience. Diverse methods of machine and computational modeling have demonstrated their effectiveness in tasks of automatic clustering and classification of brain states, the development of multilevel models and the identification of connectivity patterns correlated with levels of consciousness. A larger-scale analysis and a larger dataset, as well as the implementation of model interpretability approaches are required for the practical application of the analyzed models. The models based on EEG and LFP are the most promising for clinical application due to their availability and the possibility of real-time monitoring.
发表机构
- Peter the Great St. Petersburg Polytechnic University（彼得大帝圣彼得堡理工大学）
机构由 AI 辅助整理，请以论文原文为准。
