Neural auditory (dis)integration underpins phoneme change detection in Wernicke’s Aphasia
Bibliographic record
Abstract
Speech perception impairments are a universal feature of Wernicke’s aphasia (WA) and are systematically related to the Wernicke’s-type language comprehension impairment. However, speech perception is not absent; phonological changes can be identified with sufficient acoustic difference between stimuli. This study used a measure of non-instantaneous oscillatory power to explore the neural mechanism associated with impaired and accurate phoneme change detection. Method: Electroencephalography was measured during a multiple deviant mismatch negativity (MMN) paradigm using consonant-vowel-consonant nonword stimuli in participants with WA and neurotypical participants. Oddball stimuli consisted of phonemic changes that could and could not be behaviourally perceived. Sensor level non-zero phase lagged (NZPL) power change, a measure of activity associated with neural interactions, was analysed over the MMN window. Results: Perceptible and non-perceptible phoneme changes were distinguished by NZPL power. Neurotypical and WA participants displayed increased NZPL power to perceptible phoneme changes. Non-perceptible phoneme changes resulted in limited NZPL power change in the neurotypical participants and an unexpected decreased in NZPL power over anterior and central midline and right hemisphere regions in the WA participants. Conclusions: The behavioural percept of phoneme change is associated with wide-spread integrated neural responses. Reduced NZPL power to non-perceived phoneme changes in the WA group indicates reduced neural integration in response to novel phonological information. It is proposed that disrupted ability to form integrated neural responses to auditory-phonological information is a biomarker of the language comprehension impairment in WA.
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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) | 0.001 | 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".