美国加州大学旧金山分校Prasad Shirvalkar团队近期取得重要工作进展。他们首次使用颅内神经生物标志物预测慢性疼痛状态。相关研究成果2023年5月22日在线发表于《自然—神经科学》杂志上。

据介绍，慢性疼痛综合征通常难以治疗，并导致严重的痛苦和残疾。疼痛严重程度通常通过主观报告来衡量，而缺乏可指导诊断和治疗的客观生物标志物。此外，在临床相关的时间尺度上，哪种大脑活动是慢性疼痛的基础，或者这与急性疼痛之间的关系尚不清楚。

研究人员在四名患有顽固性神经性疼痛的患者在前扣带皮层和眶额皮层（OFC）植入了慢性颅内电极。参与者报告的疼痛指标与几个月来每天多次获得的动态直接神经记录一致。研究人员使用机器学习方法以高灵敏度成功预测了神经活动的个体内慢性疼痛严重程度评分。慢性疼痛解码依赖于OFC的持续功率变化，这往往与任务期间与急性诱发疼痛状态相关的短暂活动模式不同。

因此，颅内OFC信号可用于预测患者的自发性慢性疼痛状态。

附：英文原文

Title: First-in-human prediction of chronic pain state using intracranial neural biomarkers

Author: Shirvalkar, Prasad, Prosky, Jordan, Chin, Gregory, Ahmadipour, Parima, Sani, Omid G., Desai, Maansi, Schmitgen, Ashlyn, Dawes, Heather, Shanechi, Maryam M., Starr, Philip A., Chang, Edward F.

Issue&Volume: 2023-05-22

Abstract: Chronic pain syndromes are often refractory to treatment and cause substantial suffering and disability. Pain severity is often measured through subjective report, while objective biomarkers that may guide diagnosis and treatment are lacking. Also, which brain activity underlies chronic pain on clinically relevant timescales, or how this relates to acute pain, remains unclear. Here four individuals with refractory neuropathic pain were implanted with chronic intracranial electrodes in the anterior cingulate cortex and orbitofrontal cortex (OFC). Participants reported pain metrics coincident with ambulatory, direct neural recordings obtained multiple times daily over months. We successfully predicted intraindividual chronic pain severity scores from neural activity with high sensitivity using machine learning methods. Chronic pain decoding relied on sustained power changes from the OFC, which tended to differ from transient patterns of activity associated with acute, evoked pain states during a task. Thus, intracranial OFC signals can be used to predict spontaneous, chronic pain state in patients.

DOI: 10.1038/s41593-023-01338-z

Source: https://www.nature.com/articles/s41593-023-01338-z