T141. EFFECTS OF MENSTRUAL CYCLE ON RESTING STATE FUNCTIONAL CONNECTIVITY IN SCHIZOPHRENIA: A FUNCTIONAL MRI STUDY
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».