Relationships between auditory event‐related potentials and mood state, medication, and comorbid psychiatric illness in patients with bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Patients with bipolar disorder (BD) exhibit aberrations in auditory event-related potentials (ERPs), although the relationships between these measures and mood state at testing, comorbid psychiatric illness, presence of psychotic features, and medication usage are unclear. The purpose of this study was to investigate the relationships between these factors and auditory ERP measures in BD patients. METHODS: An auditory 'oddball' discrimination task was used to elicit ERPs from 69 patients with type I BD and 52 healthy controls. Patients were placed into subgroups based upon their mood state at testing (euthymic or symptomatic), and ANOVA was used to compare amplitude and peak latency measures from the N100, P200, N200, and P300 ERP components across subgroups. Multiple regression was used to investigate relationships between ERP measures and comorbid psychiatric diagnosis, history of psychotic features, and medication status. RESULTS: Relative to healthy control participants, euthymic and symptomatic BD patients exhibited reduced P300 and P200 amplitude, but ERP measures did not differ among BD patients on the basis of mood status. A history of a comorbid anxiety disorder was associated with reduced N200 peak latency, but prolonged P300 peak latency among BD patients. No other relationships between clinical variables and ERP measures were significant. CONCLUSIONS: The results suggest that disrupted auditory attention may be observed in BD patients regardless of their mood state at testing, medication status, or history of psychosis. These results extend previous findings, and provide further evidence for aberrations in the P300 ERP as an endophenotype for BD.
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 | 0.001 |
| 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".