Interhemispheric differences in P1 and N1 amplitude in EEG and MEG differ across older individuals with a concussion compared with age‐matched controls
Bibliographic record
Abstract
We studied the effects of mild traumatic brain injury (mTBI) in an aging population. We examined visual search with event-related potentials (ERPs) and event-related fields (ERF) for a lateral color singleton focusing on the P1 and N1 in each hemisphere. Forty participants (19 mTBI and 21 controls) aged 50 to 72 performed a visual search task, while we recorded their magnetoencephalogram (MEG) with simultaneous electroencephalogram (EEG). We compared visual ERPs and ERFs and associated cortical activity estimated using MEG source localization. Relative to matched controls, participants with an mTBI had a smaller P1 in the left hemisphere and a smaller N1 in the right hemisphere. Also, mTBI participants showed inversed activation patterns across the hemispheres during the N1 in MEG compared with controls. This is the first study to investigate the impact of mTBI on neuronal source activations during early visual processing in an aging population. Results showed that when aging individuals suffer from an mTBI, there are perturbations in the amplitude and hemispheric dominance patterns in the visual P1 and N1 responses that are visible for months to years following the injury. Our findings indicate that mTBI can lead to modifications of sensory and/or perceptual responses, suggesting possible adaptive functional reorganization following mTBI.
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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 | 0.001 | 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) | 0.002 | 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".