Insular Cortex Modulation by Repetitive Transcranial Magnetic Stimulation with Concurrent Functional Magnetic Resonance Imaging: Preliminary Findings
Bibliographic record
Abstract
Background/Objectives: The insula plays a role in various medical conditions, including eating disorders, addiction, and chronic pain. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising therapeutic avenue, yet few studies have investigated its modulation effects on the insula. Moreover, direct evidence of target engagement remains scarce. This study aimed to stimulate the insula with rTMS and assess BOLD signal modulation through concurrent functional magnetic resonance imaging (fMRI). Methods: Ten participants were recruited and six underwent a single session of 5 Hz high-frequency rTMS over the right insular cortex inside the MRI scanner, using a compatible MRI-B91 TMS coil. Stimulation consisted of 10 trains of 10 seconds, with 50-second interval between trains. Frameless stereotactic neuronavigation ensured precise targeting. Paired t-tests were used to compare the mean BOLD signal obtained between stimulation trains with resting-state fMRI acquired before the rTMS stimulation session (significant cluster threshold of 10 voxels; False Discovery Rate at q < 0.01). Results: Increased activity was observed in the anterior, middle, and middle-inferior insula, while deactivations occurred in the ventral anterior and posterior insula. Two participants reported dysgeusia, providing further evidence of insular modulation. Conclusions: This study provides neuroimaging evidence for rTMS-induced insular modulation. Our results are highly relevant for future clinical applications, with potential therapeutic avenues in individuals with conditions where insular dysfunction plays a key role.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".