Exploring the effects of an insulin challenge on neuroimaging outcomes: A scoping review
Bibliographic record
Abstract
• Neuroimaging paradigms with an intranasal insulin (INI) challenge can be used to study insulin action in the human brain. • Comprehensive overview of all relevant studies that employed an INI-based neuroimaging assay of brain insulin signaling. • INI significantly modulates activity and blood flow in regions related to control of food intake, as well as cognition. • Response to INI may be moderated by age, sex, body mass index (BMI), and peripheral insulin sensitivity. • There is merit in exploring brain insulin signaling and the potential therapeutic value of INI in other clinical populations. Emerging evidence demonstrates that insulin has a modulating effect on metabolic and cognitive function in the brain, highlighting the potential role of aberrant brain insulin signaling in the pathogenesis of various neuropsychiatric illnesses. Neuroimaging paradigms using intranasal insulin (INI) as a pharmacological challenge have allowed us to study the effects of insulin in the human brain. In this scoping review, we conducted a systematic database search to identify relevant research studies that employed an INI-based neuroimaging assay of brain insulin signaling. Thirty-six studies met inclusion criteria for this review. INI was found to significantly modulate activity and cerebral blood flow in brain regions related to homeostatic/hedonic control of food intake, as well as cognition. This review highlights the putative role of insulin signaling in the brain and the potential therapeutic value of INI in patients with mental health, addiction, and co-morbid metabolic 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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".