Anti-cytokine agents for anhedonia: targeting inflammation and the immune system to treat dimensional disturbances in depression
Bibliographic record
Abstract
The etiology of mood disorders is mechanistically heterogeneous, underscoring the need for a dimensional approach to identify and develop targeted treatments in psychiatry. Accumulating evidence implicates inflammation as an important contributor to the pathophysiology of depression and presents the immune system as a viable therapeutic target that may be more proximate to the pathogenic nexus of brain-based disorders in specific subpopulations. Anhedonia is a transdiagnostic (e.g. Parkinson's disease, diabetes mellitus, rheumatic diseases), yet specific, and clinically relevant symptom dimension subserved by well-characterized neurobiological and neurophysiological substrates of the positive valence systems (PVS). Brain circuits, nodes, and networks, as well as cellular and molecular pathways (e.g. dopaminergic transmission; excitotoxicity; synaptic plasticity), subserving anhedonia are preferentially affected by inflammatory processes. To our knowledge, no published randomized, controlled clinical trial in populations with mood disorders has, to date, primarily sought to determine the effects of an anti-inflammatory agent on PVS functions or pathophysiology. Three ongoing clinical trials aim to investigate the effects of anti-TNF-alpha biologic infliximab on measures of anhedonia [ClinicalTrials.gov identifier: NCT02363738], motivational behavior and circuitry [ClinicalTrials.gov identifier: NCT03006393], and glutamatergic changes in the basal ganglia [ClinicalTrials.gov identifier: NCT03004443] in clinical populations with unipolar or bipolar depression. Positive results would further instantiate the relevance of inflammatory processes and the immune system in the pathophysiology of mood disorders and provide the impetus to develop scalable treatments targeting inflammation and the immune system to mitigate transdiagnostic, dimensional disturbances in brain-based disorders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".