Characterizing the cortical pathways underlying visual trigger induced urinary urgency incontinence by functional MRI
Bibliographic record
Abstract
AIMS: Visual triggers have long been recognized clinically to stimulate urgency urinary incontinence (UUI). Current pathophysiology recognizes the importance of cortical control over micturition but there is no standardized methodology for clinicians to study the impact of visual triggers. Our aim was to develop an imaging protocol able to characterize the brain's response to personalized visual triggers, providing a methodology for evaluation on connectivity within the brain in patients with visually triggered urinary urgency. METHODS: A magnetic resonance imaging (MRI) methodology specific for urologic use was developed. A 3T-Elition Scanner was first used to acquire static structural images. These images were then used to define approximately 200 brain regions of interest (ROI) using a validated brain atlas. Then, real-time functional MRI (fMRI) scans were conducted during natural bladder filling, where study subjects were shown randomized block sequences of visual stimuli comprised of both subject-specific trigger images and neutral images. The fMRI scan data were merged to identify key ROI underlying UUI. RESULTS: Dynamic fMRI scans were conducted in 10 subjects, 4 with trigger-induced UUI, 2 with trigger-induced urgency, and 4 with no urgency or leakage to visual triggers. No subjects with UUI history lost continence during imaging, but all four subjects reported sensations of urgency in response to their own subject-specific trigger images. The ROI identified were the periaqueductal gray, anterior cingulate gyrus, pons, and prefrontal cortex. We found increased activity in the prefrontal cortex and limbic system ROI in response to subject-specific visual triggers of UUI. CONCLUSIONS: This information provides proof of principle for further exploration of subject-specific trigger image evaluation using fMRI to explore causation in patients with UUI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".