加州大学Martin M. Monti小组的一项最新研究开发出了对抗性人工智能揭示了意识障碍的机制和治疗方法。该项研究成果发表在2026年3月24日出版的《自然—神经科学》上。

为了解决这个问题，该课题组人员引入了一个生成对抗人工智能（AI）框架，该框架将深度神经网络与可解释的机器学习驱动的神经场模型进行对比。深度神经网络经过训练，可以在超过68万个10秒的神经电生理样本中检测意识，并在565名患者、健康志愿者和动物身上进行了验证。这种对抗性结构产生了清醒和昏迷大脑的生物学逼真模拟，重现了人类、猴子、老鼠和蝙蝠的经验神经生理特征。

在没有明确编程的情况下，人工智能模型可以回溯已知的DOC对大脑刺激的反应，并生成关于无意识机制的可测试预测。两个这样的预测在这里得到了验证：51例DOC患者的扩散磁共振成像支持的基底节区间接通路的选择性破坏，以及6例昏迷患者和大鼠中风模型切除的脑组织的RNA测序支持的皮质抑制-抑制性突触偶联增加。该模型还确定了丘脑下核的高频刺激是一种有希望的DOC干预措施，这得到了人类患者电生理数据的支持。这项工作引入了一个人工智能框架，用于意识研究中的因果推理和治疗发现，以及更广泛的复杂系统。

据悉，由于缺乏探索机制或测试干预措施的实验模型，理解意识障碍（DOC）仍然是神经科学中最具挑战性的问题之一。

附：英文原文

Title: Adversarial AI reveals mechanisms and treatments for disorders of consciousness

Author: Toker, Daniel, Zheng, Zhong Sheng, Thum, Jasmine A., Guang, Jing, Annen, Jitka, Miyamoto, Hiroyuki, Yamakawa, Kazuhiro, Vespa, Paul M., Laureys, Steven, Schnakers, Caroline, Bari, Ausaf A., Hudson, Andrew, Pouratian, Nader, Monti, Martin M.

Issue&Volume: 2026-03-24

Abstract: Understanding disorders of consciousness (DOC) remains one of the most challenging problems in neuroscience, hindered by the lack of experimental models for probing mechanisms or testing interventions. Here, to address this, we introduce a generative adversarial artificial intelligence (AI) framework that pits deep neural networks—trained to detect consciousness across more than 680,000 ten-second neuroelectrophysiology samples and validated on 565 patients, healthy volunteers and animals—against interpretable, machine learning-driven neural field models. This adversarial architecture produces biologically realistic simulations of both conscious and comatose brains that recapitulate empirical neurophysiological features across humans, monkeys, rats and bats. Without explicit programming, the AI model retrodicts known DOC responses to brain stimulation and generates testable predictions about the mechanisms of unconsciousness. Two such predictions are validated here: selective disruption of the basal ganglia indirect pathway, supported by diffusion magnetic resonance imaging in 51 patients with DOC, and increased cortical inhibitory-to-inhibitory synaptic coupling, supported by RNA sequencing of resected brain tissue from 6 human patients with coma and a rat stroke model. The model also identifies high-frequency stimulation of the subthalamic nucleus as a promising intervention for DOC, supported by electrophysiological data from human patients. This work introduces an AI framework for causal inference and therapeutic discovery in consciousness research, as well as in complex systems more broadly.

DOI: 10.1038/s41593-026-02220-4

Source: https://www.nature.com/articles/s41593-026-02220-4