The change of MRI indexes of brain glymphatic function and sleep status before and after repeated transcranial magnetic stimulation in insomnia disorder patients
Bibliographic record
Abstract
Introduction: We aim to explore the associations between brain glymphatic function and sleep status in insomnia disorder patients (IDs), and to investigate whether repeated transcranial magnetic stimulation (rTMS) can improve sleep and glymphatic function. Methods: In this prospective study, consecutive IDs were enrolled and randomly classified into true rTMS group and sham rTMS group. Age- and sex-matched healthy control participants (HCs) were enrolled and examined between January 2023 and December 2023. Neuropsychological test included the Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), the Montreal Cognitive Assessment (MOCA), and the Minimum Mental State Examination (MMSE). Sleep status was accessed using questionnaires and polysomnography (PSG) in all participants. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index of MRI was used to evaluate brain glymphatic function. Results: 28 ID patients of true rTMS group, 9 ID patients of sham rTMS group, and 20 control participants were included. Before rTMS, both of the true/sham rTMS groups had lower DTI-ALPS than HCs. Multivariate linear regression models indicated that N2 sleep duration and the arousal index were independently associated with DTI-ALPS in all IDs. After rTMS, compared with the sham rTMS group, the sleep questionnaires, total sleep time, N2 sleep duration, the arousal index of PSG, and DTI-ALPS reflected that the treatment improved the sleep status and glymphatic function of IDs. Conclusion: Our research implies that N2 sleep duration and the arousal index were independently associated with lymphatic function. The rTMS therapy can improve glymphatic function and sleep status in IDs.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".