Exploring the use of accelerated intermittent theta burst stimulation combined with biofeedback based balance training in individuals with dementia and Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: Individuals with cognitive impairment are at an elevated risk of falls compared to those without. Impaired balance is a significant risk factor for falls, and emerging evidence suggests that balance control may serve as a marker of cognitive decline. METHOD: Repetitive transcranial magnetic stimulation (rTMS), a non-invasive brain stimulation technique, has been shown to enhance synaptic plasticity, thereby improving motor and cognitive function. This study aimed to investigate whether an accelerated protocol of rTMS delivered over 14 days could improve cognition and balance in individuals with dementia and Alzheimer's disease. Participants were randomized into three groups: rTMS targeting the primary motor cortex (M1), the dorsolateral prefrontal cortex (DLPFC), or a placebo stimulation group. Accelerated intermittent theta burst stimulation (aiTBS) was applied daily, followed by 10 minutes of biofeedback-based balance training. Balance training focused on improving center-of-pressure control, targeting left/right, front/back, and diagonal weight-shifting abilities. RESULT: Preliminary results indicate that the M1-targeted group showed significant improvements in balance, as measured by Limits of Stability and Balance and Fall Risk assessments and an improvement in cognition, as measured by the MoCA, compared to the DLPFC and placebo group. CONCLUSION: This study demonstrates the first application of aiTBS combined with balance training to enhance balance and cognition. These findings suggest the potential clinical utility of this combined approach for managing symptoms of dementia and Alzheimer's disease.
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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) | 0.001 | 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".