The Use of DREADDs to Deconstruct Behavior
Bibliographic record
Abstract
A central goal in understanding brain function is to link specific cell populations to behavioral outputs. In recent years, the selective targeting of specific neural circuits has been made possible with the development of new experimental approaches, including chemogenetics. This technique allows for the control of molecularly defined subsets of cells through engineered G protein-coupled receptors (GPCRs), which have the ability to activate or silence neuronal firing. Through chemogenetics, neural circuits are being linked to behavioral outputs at an unprecedented rate. Further, the coupling of chemogenetics with imaging techniques to monitor neural activity in freely moving animals now makes it possible to deconstruct the complex whole-brain networks that are fundamental to behavioral states. In this review, we highlight a specific chemogenetic application known as DREADDs (designer receptors exclusively activated by designer drugs). DREADDs are used ubiquitously to modulate GPCR activity in vivo and have been widely applied in the basic sciences, particularly in the field of behavioral neuroscience. Here, we focus on the impact and utility of DREADD technology in dissecting the neural circuitry of various behaviors including memory, cognition, reward, feeding, anxiety and pain. By using DREADDs to monitor the electrophysiological, biochemical, and behavioral outputs of specific neuronal types, researchers can better understand the links between brain activity and behavior. Additionally, DREADDs are useful in studying the pathogenesis of disease and may ultimately have therapeutic potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".