MMN-Indexed Auditory Change Detection in Major Depressive Disorder
Bibliographic record
Abstract
In major depressive disorder (MDD), event-related potentials that are involved in auditory cortex function (i.e. N100 and P300) often have greater latencies and decreased amplitudes. The auditory mismatch negativity (MMN) is thought to be produced by generators in the auditory cortex, as well as the frontal lobes. Reports on differences in MMN in those with MDD have been varied. It was hypothesized that the wide range of results in the literature may be due to the use of different deviant types in eliciting the MMN. To attempt and explain these inconsistencies, the current study employed a multifeature MMN paradigm with 5 deviant tone types in community-dwelling participants with a diagnosis of MDD. We found those with MDD had higher MMN amplitudes following tones that deviated in intensity and location, but no difference in MMNs elicted by the other deivants (relative to unaffected controls). Location MMN deviants were negatively correlated with depression severity scores (i.e. larger MMN with greater severity). We also found longer MMN latencies following the pitch deviant. These results suggest the early auditory change detection process is altered in MDD, but only following certain types of auditory stimuli. Potential explanations for these findings, including high levels of anxiety and the influence of tryptophan are explored. Equally, the current report highlights the importance of using various deviant types when examining the MMN in clinical populations.
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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 | 0.002 |
| 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 | 0.000 | 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".