An Electroencephalogram Signature of Melanin-Concentrating Hormone Neuron Activities Predicts Cocaine Seeking
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".