Preliminary Assessment of Intrahemispheric QEEG Measures in Bipolar Mood Disorders
Bibliographic record
Abstract
OBJECTIVE: This study assessed the quantitative electroenchephalographic (QEEG) absolute power and coherence differences between a group of patients with bipolar I mood disorder (BMD I) and a group of patients with schizophrenia. We also examined the correlation between QEEG measures and family history of BMD. METHOD: Using the National Institutes of Mental Health (NIMH) Global Rating Scale, we rated 18 adult inpatients with a DSM-III-R diagnosis of BMD I for the severity of the current episode. We also collected data on the family history of the illness. This group was then matched for age, sex, and handedness with an equal number of inpatients with a DSM-III-R diagnosis of schizophrenia. QEEG absolute power and coherence was calculated for the alpha bandwidth (8.0 to 12.0 Hz), assessed at 18 pairs of electrodes in both hemispheres during resting, eyes-closed condition in all the patients. RESULTS: The patients with schizophrenia showed significantly higher coherence (P = 0.047) at 6 pairs of electrodes on the right side. The group with BMD showed significantly higher power (P = 0.042) at 2 pairs of electrodes on the right side. Correlational analysis showed that QEEG measures were significantly correlated (P = 0.01) with positive family history of BMD. CONCLUSION: These findings suggest that the patients with BMD are more disorganized in the right anterior hemisphere and that there is a significant positive correlation between the QEEG measures and the presence of family history of BMD. Further studies in a larger sample are required to confirm these preliminary findings.
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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 | 0.000 | 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) | 0.002 | 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".