First-in-human prediction of chronic pain state using intracranial neural biomarkers.
First-in-human prediction of chronic pain state using intracranial neural biomarkers.
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DOI：
10.1038/s41593-023-01338-z
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发表时间：
2023-06
影响因子：
25
通讯作者：
Chang, Edward F.
中科院分区：
文献类型：
--
作者：
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.
慢性疼痛综合征往往难以治疗，并造成巨大的痛苦和残疾。疼痛的严重程度通常是通过主观报告来衡量的，而可以指导诊断和治疗的客观生物标记物缺乏。此外，在临床相关的时间尺度上，哪些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仍不清楚。4例顽固性神经病理性疼痛患者在前扣带回皮质和眶前叶皮质(OFC)植入慢性颅内电极。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。我们使用机器学习方法成功地从神经活动中高灵敏度地预测了个体内慢性疼痛严重程度评分。慢性疼痛解码依赖于OFC持续的能量变化，这往往不同于任务期间与急性诱发疼痛状态相关的短暂活动模式。因此，颅内OFC信号可以用来预测患者的自发性和慢性疼痛状态。
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.
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影响因子：
16.2
作者：
Fries P
通讯作者：
Fries P
DOI：
10.1073/pnas.1517629112
发表时间：
2015-11-03
影响因子：
11.1
作者：
Feingold, Joseph;Gibson, Daniel J.;Graybiel, Ann M.
通讯作者：
Graybiel, Ann M.
影响因子：
25
作者：
Ceko, Marta;Kragel, Philip A.;Wager, Tor D.
通讯作者：
Wager, Tor D.
影响因子：
5.3
作者：
Baliki, Marwan N.;Chialvo, Dante R.;Apkarian, A. Vania
通讯作者：
Apkarian, A. Vania
影响因子：
14.5
作者：
Hashmi, Javeria A.;Baliki, Marwan N.;Apkarian, A. Vania
通讯作者：
Apkarian, A. Vania