Functional brain imaging of swallowing: An activation likelihood estimation meta‐analysis
Bibliographic record
Abstract
A quantitative, voxel-wise meta-analysis was performed to investigate the cortical control of water and saliva swallowing. Studies that were included in the meta-analysis (1) examined water swallowing, saliva swallowing, or both, and (2) reported brain activation as coordinates in standard space. Using these criteria, a systematic literature search identified seven studies that examined water swallowing and five studies of saliva swallowing. An activation likelihood estimation (ALE) meta-analysis of these studies was performed with GingerALE. For water swallowing, clusters with high activation likelihood were found in the bilateral sensorimotor cortex, right inferior parietal lobule, and right anterior insula. For saliva swallowing, clusters with high activation likelihood were found in the left sensorimotor cortex, right motor cortex, and bilateral cingulate gyrus. A between-condition meta-analysis revealed clusters with higher activation likelihood for water than for saliva swallowing in the right inferior parietal lobule, right postcentral gyrus, and right anterior insula. Clusters with higher activation likelihood for saliva than for water swallowing were found in the bilateral supplementary motor area, bilateral anterior cingulate gyrus, and bilateral precentral gyrus. This meta-analysis emphasizes the distributed and partly overlapping cortical networks involved in the control of water and saliva swallowing. Water swallowing is associated with right inferior parietal activation, likely reflecting the sensory processing of intraoral water stimulation. Saliva swallowing more strongly involves premotor areas, which are crucial for the initiation and control of movements.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.038 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".