Protocol for a randomized comparative effectiveness trial comparing a very low-carbohydrate diet to DASH diet for polycystic ovary syndrome: the SUPER (Supporting Understanding of PCOS Education and Research) trial.

Background: Polycystic ovary syndrome (PCOS), the most common endocrine disorder for women of reproductive age, is associated with increased risk for insulin resistance and type 2 diabetes. Current PCOS treatments insufficiently address the spectrum and severity of the disorder, and there is little evidence-based guidance available for lifestyle management of PCOS, especially through nutritional approaches. Some evidence shows that a very low-carbohydrate diet can improve glucose control compared to low-fat or moderate-carbohydrate diets, leading to improved glucose control and insulin levels that may help to treat symptoms of PCOS.

High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle.

Many studies have shown that the human endocrine system modulates brain function, reporting associations between fluctuations in hormone concentrations and brain connectivity. However, how hormonal fluctuations impact fast changes in brain network organization over short timescales remains unknown. Here, we leverage a recently proposed framework for modeling co-fluctuations between the activity of pairs of brain regions at a framewise timescale.

Hierarchical organization of spontaneous co-fluctuations in densely sampled individuals using fMRI.

Edge time series decompose functional connectivity into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames (time points when the global co-fluctuation amplitude takes on its largest value), including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations (peaks in co-fluctuation time series but of lower amplitude).

Individualized event structure drives individual differences in whole-brain functional connectivity.

Resting-state functional connectivity is typically modeled as the correlation structure of whole-brain regional activity. It is studied widely, both to gain insight into the brain's intrinsic organization but also to develop markers sensitive to changes in an individual's cognitive, clinical, and developmental state. Despite this, the origins and drivers of functional connectivity, especially at the level of densely sampled individuals, remain elusive.

Modularity maximization as a flexible and generic framework for brain network exploratory analysis.

The modular structure of brain networks supports specialized information processing, complex dynamics, and cost-efficient spatial embedding. Inter-individual variation in modular structure has been linked to differences in performance, disease, and development. There exist many data-driven methods for detecting and comparing modular structure, the most popular of which is modularity maximization.