Chronic pain is characterized by discrete pain episodes of unpredictable frequency and duration. This hinders the study of pain mechanisms and contributes to the use of pharmacological treatments associated with side effects, addiction and drug tolerance. Here, we show that a closed-loop brain–machine interface (BMI) can modulate sensory-affective experiences in real time in freely behaving rats by coupling neural codes for nociception directly with therapeutic cortical stimulation. The BMI decodes the onset of nociception via a state-space model on the basis of the analysis of online-sorted spikes recorded from the anterior cingulate cortex (which is critical for pain processing) and couples real-time pain detection with optogenetic activation of the prelimbic prefrontal cortex (which exerts top–down nociceptive regulation). In rats, the BMI effectively inhibited sensory and affective behaviours caused by acute mechanical or thermal pain, and by chronic inflammatory or neuropathic pain. The approach provides a blueprint for demand-based neuromodulation to treat sensory-affective disorders, and could be further leveraged for nociceptive control and to study pain mechanisms.

慢性疼痛的特征是频率和持续时间不可预测的离散疼痛发作。这阻碍了对疼痛机制的研究，并有助于使用与副作用、成瘾和药物耐受性相关的药物治疗。在这里，我们表明闭环脑机接口 (BMI) 可以通过将伤害感受的神经代码与治疗性皮层刺激直接耦合，实时调节自由行为大鼠的感官情感体验。BMI 基于对前扣带皮层（对疼痛处理至关重要）记录的在线分类尖峰的分析，通过状态空间模型解码伤害感受的开始，并将实时疼痛检测与光遗传学激活相结合前边缘前额叶皮层（施加自上而下的伤害性调节）。在大鼠中，BMI 有效地抑制了由急性机械或热痛以及慢性炎症或神经性疼痛引起的感觉和情感行为。该方法为基于需求的神经调节治疗感觉情感障碍提供了蓝图，并可进一步用于伤害性控制和研究疼痛机制。