Multiple Electrophysiological Markers of Visual-Attentional Processing in a Novel Task Directed toward Clinical Use
Bibliographic record
Abstract
Individuals who have sustained a mild brain injury (e.g., mild traumatic brain injury or mild cerebrovascular stroke) are at risk to show persistent cognitive symptoms (attention and memory) after the acute postinjury phase. Although studies have shown that those patients perform normally on neuropsychological tests, cognitive symptoms remain present, and there is a need for more precise diagnostic tools. The aim of this study was to develop precise and sensitive markers for the diagnosis of post brain injury deficits in visual and attentional functions which could be easily translated in a clinical setting. Using electrophysiology, we have developed a task that allows the tracking of the processes involved in the deployment of visual spatial attention from early stages of visual treatment (N1, P1, N2, and P2) to higher levels of cognitive processing (no-go N2, P3a, P3b, N2pc, SPCN). This study presents a description of this protocol and its validation in 19 normal participants. Results indicated the statistically significant presence of all ERPs aimed to be elicited by this novel task. This task could allow clinicians to track the recovery of the mechanisms involved in the deployment of visual-attentional processing, contributing to better diagnosis and treatment management for persons who suffer a brain injury.
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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) | 0.000 | 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 | 0.001 | 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".