Contingent negative variation: a biomarker of abnormal attention in functional movement disorders
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Contingent negative variation (CNV) is a negative cortical wave that precedes a pre-cued imperative stimulus requiring a quick motor response. It has been related to motor preparation and anticipatory attention. The aim was to ascertain whether the clinical improvement of functional movement disorders after physiotherapy would be associated with faster reaction times and modulation of CNV. METHODS: Motor performance and CNV were analysed during a pre-cued choice reaction time task with varying cue validity. Twenty-one patients with functional movement disorders and 13 healthy controls at baseline were compared. Patients then underwent physiotherapy. At follow-up after physiotherapy, patients were categorized as clinically improved (responders) or not improved (non-responders) and retested. RESULTS: At baseline, patients did not generate CNV, contrary to controls [mean amplitude (µV) at the end of preparation to move: patients -0.47 (95% CI -1.94, 1.00) versus controls -2.59 (95% CI -4.46, -0.72)]. Responders performed faster after physiotherapy [mean natural logarithm (ln) reaction time (RT) (ms): follow-up 6.112 (95% CI 5.923, 6.301) versus baseline 6.206 (95% CI 6.019, 6.394), P = 0.010], contrary to non-responders. Simultaneously, responders showed a recovery of CNV after physiotherapy [follow-up -1.95 (95% CI -3.49, -0.41) versus baseline -0.19 (95% CI -1.73, 1.35), P < 0.001], contrary to non-responders [follow-up -0.32 (95% CI -1.79, 1.14) versus baseline -0.72 (95% CI -2.19, 0.75), P = 0.381]. CONCLUSIONS: Clinical improvement of functional movement disorders after physiotherapy was associated with faster reaction times and normalization of CNV, which was absent at baseline. These findings suggest that CNV may constitute a useful neurophysiological biomarker related to abnormal attention in functional movement disorders.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".