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Spectral dynamic causal modelling in healthy women reveals brain connectivity changes along the menstrual cycle.

ABSTRACT: Longitudinal menstrual cycle studies allow to investigate the effects of ovarian hormones on brain organization. Here, we use spectral dynamic causal modelling (spDCM) in a triple network model to assess effective connectivity changes along the menstrual cycle within and between the default mode, salience and executive control networks (DMN, SN, and ECN). Sixty healthy young women were scanned three times along their menstrual cycle, during early follicular, pre-ovulatory and mid-luteal phase. Related to estradiol, right before ovulation the left insula recruits the ECN, while the right middle frontal gyrus decreases its connectivity to the precuneus and the DMN decouples into anterior/posterior parts. Related to progesterone during the mid-luteal phase, the insulae (SN) engage to each other, while decreasing their connectivity to parietal ECN, which in turn engages the posterior DMN. When including the most confident connections in a leave-one out cross-validation, we find an above-chance prediction of the left-out subjects' cycle phase. These findings corroborate the plasticity of the female brain in response to acute hormone fluctuations and may help to further understand the neuroendocrine interactions underlying cognitive changes along the menstrual cycle.

SUBMITTER: Hidalgo-Lopez E

PROVIDER: S-EPMC8355156 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Spectral dynamic causal modelling in healthy women reveals brain connectivity changes along the menstrual cycle.

Hidalgo-Lopez Esmeralda E Zeidman Peter P Harris TiAnni T Razi Adeel A Pletzer Belinda B

Communications biology 20210810 1

Longitudinal menstrual cycle studies allow to investigate the effects of ovarian hormones on brain organization. Here, we use spectral dynamic causal modelling (spDCM) in a triple network model to assess effective connectivity changes along the menstrual cycle within and between the default mode, salience and executive control networks (DMN, SN, and ECN). Sixty healthy young women were scanned three times along their menstrual cycle, during early follicular, pre-ovulatory and mid-luteal phase. R ...[more]

PMID: 34376799

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Project description:Functional and effective connectivity are known to change systematically over time. These changes might be explained by several factors, including intrinsic fluctuations in activity-dependent neuronal coupling and contextual factors, like experimental condition and time. Furthermore, contextual effects may be subject-specific or conserved over subjects. To characterize fluctuations in effective connectivity, we used dynamic causal modelling (DCM) of cross spectral responses over 1- min of electroencephalogram (EEG) recordings during rest, divided into 1-sec windows. We focused on two intrinsic networks: the default mode and the saliency network. DCM was applied to estimate connectivity in each time-window for both networks. Fluctuations in DCM connectivity parameters were assessed using hierarchical parametric empirical Bayes (PEB). Within-subject, between-window effects were modelled with a second-level linear model with temporal basis functions as regressors. This procedure was conducted for every subject separately. Bayesian model reduction was then used to assess which (combination of) temporal basis functions best explain dynamic connectivity over windows. A third (between-subject) level model was used to infer which dynamic connectivity parameters are conserved over subjects. Our results indicate that connectivity fluctuations in the default mode network and to a lesser extent the saliency network comprised both subject-specific components and a common component. For both networks, connections to higher order regions appear to monotonically increase during the 1- min period. These results not only establish the predictive validity of dynamic connectivity estimates - in virtue of detecting systematic changes over subjects - they also suggest a network-specific dissociation in the relative contribution of fluctuations in connectivity that depend upon experimental context. We envisage these procedures could be useful for characterizing brain state transitions that may be explained by their cognitive or neuropathological underpinnings.

Project description:BackgroundThe vaginal microbial community plays a vital role in maintaining women's health. Understanding the precise bacterial composition is challenging because of the diverse and difficult-to-culture nature of many bacterial constituents, necessitating culture-independent methodology. During a natural menstrual cycle, physiological changes could have an impact on bacterial growth, colonization, and community structure. The objective of this study was to assess the stability of the vaginal microbiome of healthy Canadian women throughout a menstrual cycle by using cpn60-based microbiota analysis. Vaginal swabs from 27 naturally cycling reproductive-age women were collected weekly through a single menstrual cycle. Polymerase chain reaction (PCR) was performed to amplify the universal target region of the cpn60 gene and generate amplicons representative of the microbial community. Amplicons were pyrosequenced, assembled into operational taxonomic units, and analyzed. Samples were also assayed for total 16S rRNA gene content and Gardnerella vaginalis by quantitative PCR and screened for the presence of Mollicutes by using family and genus-specific PCR.ResultsOverall, the vaginal microbiome of most women remained relatively stable throughout the menstrual cycle, with little variation in diversity and only modest fluctuations in species richness. Microbiomes between women were more different than were those collected consecutively from individual women. Clustering of microbial profiles revealed the expected groupings dominated by Lactobacillus crispatus, Lactobacillus iners, and Lactobacillus jensenii. Interestingly, two additional clusters were dominated by either Bifidobacterium breve or a heterogeneous mixture of nonlactobacilli. Direct G. vaginalis quantification correlated strongly with its pyrosequencing-read abundance, and Mollicutes, including Mycoplasma hominis, Ureaplasma parvum, and Ureaplasma urealyticum, were detected in most samples.ConclusionsOur cpn60-based investigation of the vaginal microbiome demonstrated that in healthy women most vaginal microbiomes remained stable through their menstrual cycle. Of interest in these findings was the presence of Bifidobacteriales beyond just Gardnerella species. Bifidobacteriales are frequently underrepresented in 16S rRNA gene-based studies, and their detection by cpn60-based investigation suggests that their significance in the vaginal community may be underappreciated.

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Spectral dynamic causal modelling in healthy women reveals brain connectivity changes along the menstrual cycle.

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Spectral dynamic causal modelling in healthy women reveals brain connectivity changes along the menstrual cycle.

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