Abstract
BACKGROUND: 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. METHODS: Surface EEG signals and real-time calcium (Ca(2+)) activities of lateral hypothalamus MCH neurons (Ca(2+)(MCH)) were simultaneously recorded in male and female adult rats. Mathematical modeling and machine learning were then applied to predict Ca(2+)(MCH) using EEG derivatives. The robustness of the predictions was tested across sex and treatment conditions. Finally, features extracted from the EEG-predicted Ca(2+)(MCH) either before or after cocaine experience were used to predict future drug-seeking behaviors. RESULTS: An EEG waveform derivative-a modified theta-delta-theta peak ratio (EEG(TDT) ratio)-accurately tracked real-time Ca(2+)(MCH) 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(TDT) ratio, suggesting shortening and circadian redistribution of synchronous MCH neuron activities. In addition, features of EEG(TDT) ratio indicative of prolonged synchronous MCH neuron activities predicted lower subsequent cocaine seeking. EEG(TDT) ratio also exhibited advantages over conventional REMS measures for the predictions. CONCLUSIONS: The identified EEG(TDT) 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.