Corticomuscular coherence during upright standing in unilateral transfemoral amputees
Bibliographic record
Abstract
Patients with a lower limb amputation suffer from an impaired balance control and thereby are at a higher risk to fall. To cope with this deficit, they adapt their neuromuscular system by modifying biomechanical and neuromuscular structures. In this study, we investigated changes in corticomuscular coherence between the motor cortex and muscles of the trunk and the intact lower leg. We recorded electroencephalogram (EEG) and electromyogram (EMG) data from 10 unilateral transfemoral amputees and 10 age-matched able-bodied controls during quiet upright stance with eyes open, eyes closed and during dual tasking. To analyse afferent and efferent corticomuscular coherence, directional wavelet coherence between EEG and EMG signals was computed. The corticomuscular coherence analysis showed significant differences between amputees and controls in the afferent and efferent direction and across visual conditions, suggesting differences in the processing of sensory feedback. A power spectral density analysis of the motor cortex contralateral to the amputated leg of amputees showed increased power, as well as a pronounced decrease in alpha frequencies indicating an increased cognitive load. This exploratory study stimulates further hypotheses on how coordination of brain and muscle activity is modulated after a lower limb amputation.
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 | 0.000 | 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".