The Bed Nucleus of the Stria Terminalis, Homeostatic Satiety, and Compulsions: What Can We Learn From Polydipsia?
Bibliographic record
Abstract
A compulsive phenotype characterizes several neuropsychiatric illnesses - including but not limited to - schizophrenia and obsessive compulsive disorder. Because of its perceived etiological heterogeneity, it is challenging to disentangle the specific neurophysiology that precipitates compulsive behaving. Using polydipsia (or non-regulatory water drinking), we describe candidate neural substrates of compulsivity. We further postulate that aberrant neuroplasticity within cortically projecting structures [i.e., the bed nucleus of the stria terminalis (BNST)] and circuits that encode homeostatic emotions (thirst, hunger, satiety, etc.) underlie compulsive drinking. By transducing an inaccurate signal that fails to represent true homeostatic state, cortical structures cannot select appropriate and adaptive actions. Additionally, augmented dopamine (DA) reactivity in striatal projections to and from the frontal cortex contribute to aberrant homeostatic signal propagation that ultimately biases cortex-dependent behavioral selection. Responding becomes rigid and corresponds with both erroneous, inflexible encoding in both bottom-up structures and in top-down pathways. How aberrant neuroplasticity in circuits that encode homeostatic emotion result in the genesis and maintenance of compulsive behaviors needs further investigation.
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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 | 0.001 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".