Although a number of recent studies have examined functional connectivity at rest, few have assessed differences between connectivity both during rest and across active task paradigms. Therefore, the question of whether cortical connectivity patterns remain stable or change with task engagement continues to be unaddressed. We collected multi-scan fMRI data on healthy controls (N = 53) and schizophrenia patients (N = 42) during rest and across paradigms arranged hierarchically by sensory load. We measured functional network connectivity among 45 non-artifactual distinct brain networks. Then, we applied a novel analysis to assess cross paradigm connectivity patterns applied to healthy controls and patients with schizophrenia. To detect these patterns, we fit a group by task full factorial ANOVA model to the group average functional network connectivity values. Our approach identified both stable (static effects) and state-based differences (dynamic effects) in brain connectivity providing a better understanding of how individuals’ reactions to simple sensory stimuli are conditioned by the context within which they are presented. Our findings suggest that not all group differences observed during rest are detectable in other cognitive states. In addition, the stable differences of heightened connectivity between multiple brain areas with thalamus across tasks underscore the importance of the thalamus as a gateway to sensory input and provide new insight into schizophrenia.

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Essentially all women are exposed to polycyclic aromatic hydrocarbons (PAHs), formed during incomplete combustion of organic materials, including fossil fuels, wood, foods, and tobacco. PAHs are ovarian toxicants in rodents, and cigarette smoking is associated with reproductive abnormalities in women. Biomonitoring of hydroxylated PAH (OH-PAH) metabolites in urine provides an integrated measure of exposure to PAHs via multiple routes and has been used to characterize exposure to PAHs in humans. We hypothesized that concentrations of OH-PAHs in urine are associated with reproductive function in women. We recruited women 18–44 years old, living in Orange County, California to conduct daily measurement of urinary luteinizing hormone (LH) and estrone 3-glucuronide (E13G) using a microelectronic fertility monitor for multiple menstrual cycles; these data were used to calculate endocrine endpoints. Participants also collected urine samples on cycle day 10 for measurement of nine OH-PAHs. Models were constructed for eight endpoints using a Bayesian mixed modeling approach with subject-specific random effects allowing each participant to act as a baseline for her set of measurements. We observed associations between individual OH-PAH concentrations and follicular phase length, follicular phase LH and E13G concentrations, preovulatory LH surge concentrations, and periovulatory E13G slope and concentration. We have demonstrated the feasibility of using urinary reproductive hormone data obtained via fertility monitors to calculate endocrine endpoints for epidemiological studies of ovarian function during multiple menstrual cycles. The results show that environmental exposure to PAHs is associated with changes in endocrine markers of ovarian function in women in a PAH-specific manner.

In this article, we extend smoothing splines to model the regression mean structure when data are sampled through a complex survey. Smoothing splines are evaluated both with and without sample weights, and are compared with local linear estimator. Simulation studies find that nonparametric estimators perform better when sample weights are incorporated, rather than being treated as if iid. They also find that smoothing splines perform better than local linear estimator through completely data-driven bandwidth selection methods.

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