EEG-ERP Correlates of Cognitive Dysfunction in Polycystic Ovarian Syndrome
Bibliographic record
Abstract
Background Polycystic ovarian syndrome (PCOS) has been shown to affect the psychological and cognitive status of a woman. However, amidst various conflicting reports in this regard, very few studies attempted to assess these aspects objectively using electroencephalography (EEG) and event-related potential (ERP). Purpose To assess changes in neurocognitive and psychological parameters of PCOS women without any other comorbidities. Methods PCOS women aged 18 years to 35 years, diagnosed from obstetrics and gynecology OPD who are otherwise free of any other comorbidities, were assessed for psychological status (anxiety and depression using the State-Trait Anxiety Inventory and Beck Depression Inventory, respectively). Thereafter, a cognitive assessment was done subjectively by the Montreal Cognitive Assessment (MoCA) questionnaire and objectively by using EEG [absolute and relative power of alpha, beta, and theta waves along with theta/beta ratios (TBR) and theta/alpha ratio (TAR)] and P300 amplitude and latency of ERP during a visual oddball paradigm task in control ( n = 30) and PCOS ( n = 37) subjects. Results PCOS women showed significantly higher anxiety and depression scores along with low MoCA scores. Significantly reduced absolute alpha, increased frontal beta, and markedly increased theta (relative) power with increased TAR in the PCOS group were seen. Also, a significant reduction in P300 amplitude with prolonged latency during the visual oddball paradigm task was evident in them. Conclusion Reduced alpha and higher theta activity with increased TAR are indicative of poor neural processing ability. Reduced P300 amplitude with more latency also suggests a cognitive decline, which is corroborated by reduced MoCA scores. Our study objectively indicates the presence of subclinical cognitive impairment in PCOS patients even without any comorbidities.
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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".