Optogenetics in preclinical neuroscience and psychiatry research: recent insights and potential applications
Bibliographic record
Abstract
Ross A McDevitt,1 Sean J Reed,2 Jonathan P Britt2,3 1Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA; 2Integrated Program in Neuroscience, Montreal Neurological Institute, 3Department of Psychology, McGill University, Montreal, QC, Canada Abstract: There have been significant advances in the treatment of psychiatric disease in the last half century, but it is still unclear which neural circuits are ultimately responsible for specific disease states. Fortunately, technical limitations that have constrained this research have recently been mitigated by advances in research tools that facilitate circuit-based analyses. The most prominent of these tools is optogenetics, which refers to the use of genetically encoded, light-sensitive proteins that can be used to manipulate discrete neural circuits with temporal precision. Optogenetics has recently been used to examine the neural underpinnings of both psychiatric disease and symptom relief, and this research has rapidly identified novel therapeutic targets for what could be a new generation of rational drug development. As these and related methodologies for controlling neurons ultimately make their way into the clinic, circuit-based strategies for alleviating psychiatric symptoms could become a remarkably refined approach to disease treatment. Keywords: optogenetics, depression, anxiety, addiction, obsessive-compulsive disorder
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".