Real world evidence of improved attention and cognition during physical therapy paired with neuromodulation: a brain vital signs study
Bibliographic record
Abstract
Background: Non-invasive neuromodulation using translingual neurostimulation (TLNS) has been shown to advance rehabilitation outcomes, particularly when paired with physical therapy (PT). Together with motor gains, patient-reported observations of incidental improvements in cognitive function have been noted. Both studies in healthy individuals and case reports in clinical populations have linked TLNS to improvements in attention-related cognitive processes. We investigated if the use of combined TLNS/PT would translate to changes in objective neurophysiological cognitive measures in a real-world clinical sample of patients from two separate rehabilitation clinics. Methods: Brain vital signs were derived from event-related potentials (ERPs), specifically auditory sensation (N100), basic attention (P300), and cognitive processing (N400). Additional analyses explored the attention-related N200 response given prior evidence of attention effects from TLNS/PT. The real-world patient sample included a diverse clinical group spanning from mild-to-moderate traumatic brain injury (TBI), stroke, Multiple Sclerosis (MS), Parkinson's Disease (PD), and other neurological conditions. Patient data were also acquired from a standard clinical measure of cognition for comparison. Results: Results showed significant N100 variation between baseline and endpoint following TLNS/PT treatment, with further examination showing condition-specific significant improvements in attention processing (i.e., N100 and N200). Additionally, CogBAT composite scores increased significantly from baseline to endpoint. Discussion: The current study highlighted real-world neuromodulation improvements in neurophysiological correlates of attention. Overall, the real-world findings support the concept of neuromodulation-related improvements extending beyond physical therapy to include potential attention benefits for cognitive rehabilitation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".