Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania.
Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania.
Biol Psychiatry. 2024 Nov 1;96(9):739-751. doi: 10.1016/j.biopsych.2024.04.009. Epub 2024 Apr 26.
Identifying biomarkers that predict substance use disorder propensity may better strategize antiaddiction treatment. Melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus critically mediate interactions between sleep and substance use; however, their activities are largely obscured in surface electroencephalogram (EEG) measures, hindering the development of biomarkers.
Surface EEG signals and real-time calcium (Ca) activities of lateral hypothalamus MCH neurons (Ca) were simultaneously recorded in male and female adult rats. Mathematical modeling and machine learning were then applied to predict Ca using EEG derivatives. The robustness of the predictions was tested across sex and treatment conditions. Finally, features extracted from the EEG-predicted Ca either before or after cocaine experience were used to predict future drug-seeking behaviors.
An EEG waveform derivative-a modified theta-delta-theta peak ratio (EEG ratio)-accurately tracked real-time Ca in rats. The prediction was robust during rapid eye movement sleep (REMS), persisted through vigilance states, sleep manipulations, and circadian phases, and was consistent across sex. Moreover, cocaine self-administration and long-term withdrawal altered EEG ratio, suggesting shortening and circadian redistribution of synchronous MCH neuron activities. In addition, features of EEG ratio indicative of prolonged synchronous MCH neuron activities predicted lower subsequent cocaine seeking. EEG ratio also exhibited advantages over conventional REMS measures for the predictions.
The identified EEG ratio may serve as a noninvasive measure for assessing MCH neuron activities in vivo and evaluating REMS; it may also serve as a potential biomarker for predicting drug use propensity.
识别预测物质使用障碍倾向的生物标志物,可以更好地制定抗成瘾治疗策略。外侧下丘脑的黑素浓缩激素(MCH)神经元在睡眠和物质使用之间的相互作用中起着关键作用;然而,它们的活动在表面脑电图(EEG)测量中很大程度上被掩盖了,这阻碍了生物标志物的发展。
同时记录雄性和雌性成年大鼠的外侧下丘脑 MCH 神经元的表面 EEG 信号和实时钙(Ca)活动。然后应用数学建模和机器学习来预测使用 EEG 导数的 Ca。测试了这些预测在性别和治疗条件下的稳健性。最后,从 EEG 预测的 Ca 中提取的特征无论是在可卡因体验之前还是之后,都用于预测未来的觅药行为。
一个 EEG 波形导数——修正的θ-δ-θ 峰比(EEG 比)——准确地跟踪了大鼠的实时 Ca。该预测在快速眼动睡眠(REMS)期间是稳健的,在警觉状态、睡眠操作和昼夜节律阶段持续存在,并且在性别上是一致的。此外,可卡因自我给药和长期戒断改变了 EEG 比,表明同步 MCH 神经元活动的缩短和昼夜节律重新分布。此外,提示延长同步 MCH 神经元活动的 EEG 比特征预测了随后可卡因寻求的减少。EEG 比在预测方面也优于传统的 REMS 措施。
所确定的 EEG 比可作为评估体内 MCH 神经元活动和评估 REMS 的非侵入性测量方法;它也可以作为预测药物使用倾向的潜在生物标志物。