Recent advances in the neurobiology of anxiety disorders: Implications for novel therapeutics
Bibliographic record
Abstract
Anxiety disorders are a highly prevalent and disabling class of psychiatric disorders. This review focuses on new directions in neurobiological research and implications for the development of novel psychopharmacological treatments. Neuroanatomical and neuroimaging research in anxiety disorders has centered on the role of the amygdala, reciprocal connections between the amygdala and the prefrontal cortex, and, most recently, alterations in interoceptive processing by the anterior insula. Anxiety disorders are characterized by alterations in a diverse range of neurochemical systems, suggesting ample novel targets for drug therapies. Corticotropin-releasing factor (CRF) concentrations are elevated in a subset of anxiety disorders, which suggests the potential utility of CRF receptor antagonists. Pharmacological blockade of the memory-enhancing effects of stress hormones such as glucocorticoids and noradrenaline holds promise as a preventative approach for trauma-related anxiety. The glutamatergic system has been largely overlooked as a potential pharmacological target, although convergent preclinical, neuroimaging, and early clinical findings suggest that glutamate receptor antagonists may have potent anxiolytic effects. Glutamatergic receptor agonists (e.g., D-cycloserine) also have an emerging role in the treatment of anxiety as facilitators of fear extinction during concurrent behavioral interventions. The neuropeptides substance P, neuropeptide Y, oxytocin, orexin, and galanin are each implicated in anxiety pathways, and neuropeptide analogs or antagonists show early promise as anxiolytics in preclinical and/or clinical research. Each of these active areas of research holds promise for expanding and improving evidence-based treatment options for individuals suffering with clinical anxiety.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".