Effect oF Low Frequency Transcranial Magnetic Stimulation on Non-Motor Symptoms (Cognition, Depression and Excessive Day Time Sleepiness) in Parkinson’s Disease: A Randomized, Case-Controlled, Parallel Clinical Trial
Bibliographic record
Abstract
Background: Parkinson's disease is a complex neurodegenerative disorder characterized by motor and non-motor symptoms. Non-motor issues, such as sleep disorders, depression, and autonomic dysfunction, often appear years before motor symptoms, significantly affecting daily life. However, their overall impact on the disease's burden is still debated. Aim of the study: This study aims to evaluate the effect of Low Frequency Transcranial Magnetic Stimulation (LF-TMS) on non-motor symptoms particularly cognition, depression, and excessive day time sleepiness (EDS) in PD patients. Methods: A randomized, case-controlled trial was conducted on 40 PD patients enrolled from Al- Zahraa University Hospital. Patients were randomly assigned to receive either ten consecutive LF-TMS sessions (n=20) or sham sessions (n=20). Cognitive function, depressive symptoms, sleep quality, and daytime sleepiness were assessed before, immediately after and after one- month of TMS sessions using the Montreal Cognitive Assessment (MOCA), Hamilton Depression Rating Scale (HDRS), Pittsburgh Sleep Quality Index (PSQI), and Epsworth Sleepiness Scale (ESS), respectively. In addition, the motor threshold was determined for the right first dorsal interosseous muscle, and patients received ten consecutive days of LF-TMS over the right dorsolateral prefrontal cortex (DLPFC). Results: Compared to the sham group, the patient group showed significantly higher MOCA scores improvement (p<0.001) and significantly improvement in depressive symptoms evaluated by the HDRS. In addition, a statistically significant higher improvement percentage of PSQI and ESS in the patients group than in the sham group, with a p-value (p<0.001), and this improvement present immediately after LF-TMS sessions and remained for one month later. The improvement in MOCA showed a negative correlation with age in years with a p-value (p<0.05). Conclusion: LF-TMS improved cognition, depression, and EDS in PD patients, supporting its potential as a non-invasive treatment for non-motor symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".