T141. EFFECTS OF MENSTRUAL CYCLE ON RESTING STATE FUNCTIONAL CONNECTIVITY IN SCHIZOPHRENIA: A FUNCTIONAL MRI STUDY
Bibliographic record
Abstract
Abstract Background The menstrual cycle is a favorable model for examining the influence of ovarian hormones on cognition, emotion and brain functions. Ovarian hormones have substantial effects on task-related brain activity, but their impacts on functional connectivity at rest have been investigated by a few studies conducted with healthy individuals and these pre-existing findings are inconsistent. As for schizophrenia, different influences of ovarian hormones were reported. For instance, the estrogen hypothesis of schizophrenia has suggested that estrogen plays a neuroprotective role in the pathophysiology of this disorder. The present study investigates resting state functional connectivity (RS-FC) alterations related to menstrual cycle phase and/or hormone levels in patients with schizophrenia and healthy controls and aims to contribute to the understanding of the effects of ovarian hormones on the pathogenesis of schizophrenia and brain functions. Methods The study was conducted with 13 women with schizophrenia (the mean of age: 32 ± 7.67) and 13 healthy women (the mean of age: 30.08 ± 7.27). Resting state functional Magnetic Resonance Imaging (fMRI) scanning, as well as hormonal and clinical assessments, were applied to each participant twice, during two menstrual cycle phases: early follicular (Days 2–6; low estrogen/progesterone) and mid-luteal (Days 20–22; high estrogen/progesterone). The serum hormone levels of estradiol, progesterone, prolactin (only in the patients), follicle-stimulating and luteinizing were assessed. The clinical assessment interviews included the Brief Psychiatric Rating Scales, the Clinical Global Impression (only for the patients), the Global Assessment of Functioning and the Calgary Depression Rating Scale for Schizophrenia (for both groups). Results Our findings revealed that no cycle phase-related alterations existed in RS-FC in both groups. However, specific correlations between each hormone and RS-FC were found in both cycle phases for two groups. In the patients, estrogen was positively correlated with the auditory network (AN) connectivity in the left amygdala at the early follicular phase. In the controls, the positive correlations to progesterone were found in the precuneus for the connectivity of the posterior default mode network (DMN) and the left-frontoparietal network (FPN) during the early follicular phase. Also, progesterone was negatively correlated with the executive control network (ECN) connectivity in the right superior frontal gyrus at the mid-luteal phase in the controls. Furthermore, the patients had lower progesterone levels during the mid-luteal phase compared to the controls (p<0.05). The severity of the psychotic symptoms of the patients didn’t change between the cycle phases. Discussion To our knowledge, this is the first study to examine the influence of the menstrual cycle on the brain’s RS-FC in schizophrenia. Our results indicated that the effects of the menstrual cycle on RS-FC were more prominent in the controls rather than the patients with schizophrenia; and that the findings for the patient group may be associated with the additional mechanisms responsible from schizophrenia. This might imply that differentiating the menstrual cycle effects is difficult in a complex disorder such as schizophrenia. The results obtained from the controls are consistent with the previous findings indicating that the menstrual cycle effects might be associated with the connectivity of the DMN and cognition-related networks. Moreover, our results suggested that estrogen might have a modulating effect on the intrinsic functional connectivity changes in the patients, while progesterone, might mediate to that in the controls.
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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) | 0.003 | 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".