Double-wavelet transform for multi-subject resting state functional magnetic resonance imaging data.

Conventional regions of interest (ROIs)-level resting state fMRI (functional magnetic resonance imaging) response analyses do not rigorously model the underlying spatial correlation within each ROI. This can result in misleading inference. Moreover, they tend to estimate the temporal covariance matrix with the assumption of stationary time series, which may not always be valid. To overcome these limitations, we propose a double-wavelet approach that simplifies temporal and spatial covariance structure because wavelet coefficients are approximately uncorrelated under mild regularity conditions. This property allows us to analyze much larger dimensions of spatial and temporal resting-state fMRI data with reasonable computational burden. Another advantage of our double-wavelet approach is that it does not require the stationarity assumption. Simulation studies show that our method reduced false positive and false negative rates by properly taking into account spatial and temporal correlations in data. We also demonstrate advantages of our method by using resting-state fMRI data to study the difference in resting-state functional connectivity between healthy subjects and patients with major depressive disorder.

Double-wavelet transform for multisubject task-induced functional magnetic resonance imaging data.

The goal of this article is to model multisubject task-induced functional magnetic resonance imaging (fMRI) response among predefined regions of interest (ROIs) of the human brain. Conventional approaches to fMRI analysis only take into account temporal correlations, but do not rigorously model the underlying spatial correlation due to the complexity of estimating and inverting the high dimensional spatio-temporal covariance matrix. Other spatio-temporal model approaches estimate the covariance matrix with the assumption of stationary time series, which is not always feasible. To address these limitations, we propose a double-wavelet approach for modeling the spatio-temporal brain process. Working with wavelet coefficients simplifies temporal and spatial covariance structure because under regularity conditions, wavelet coefficients are approximately uncorrelated. Different wavelet functions were used to capture different correlation structures in the spatio-temporal model. The main advantages of the wavelet approach are that it is scalable and that it deals with nonstationarity in brain signals. Simulation studies showed that our method could reduce false-positive and false-negative rates by taking into account spatial and temporal correlations simultaneously. We also applied our method to fMRI data to study activation in prespecified ROIs in the prefontal cortex. Data analysis showed that the result using the double-wavelet approach was more consistent than the conventional approach when sample size decreased.

Optimal design of perturbations for individual two-compartment pharmacokinetic analysis.

We consider the optimal design of pharmacokinetic studies in patients that receive intermittent hemodialysis and intravenous antibiotic. Hemodialysis perturbs the pharmacokinetic system, providing additional opportunity for study. Designs that allocate measurements to occur exclusively during hemodialysis are shown to be viable alternatives to conventional designs, where all measurements occur outside of hemodialysis. Furthermore, hybrid designs with both conventional and intradialytic measurements have nearly double the efficiency of conventional designs. Convex optimal design and Monte Carlo techniques were used to simultaneously optimize hemodialysis event characteristics and sampling times, accounting for population pharmacokinetic heterogeneity. We also present several related methodological innovations.

An insulin resistance associated neural correlate of impulsivity in type 2 diabetes mellitus.

Central insulin resistance (IR) influences striatal dopamine (DA) tone, an important determinant of behavioral self-regulation. We hypothesized that an association exists between the degree of peripheral IR and impulse control, mediated by the impact of IR on brain circuits controlling the speed of executing "go" and/or "stop" responses. We measured brain activation and associated performance on a stop signal task (SST) in obese adults with type 2 diabetes (age, 48.1 ± 6.9 yrs (mean ± SD); BMI, 36.5 ± 4.0 kg/m2; HOMA-IR, 7.2 ± 4.1; 12 male, 18 female). Increasing IR, but not BMI, was a predictor of shorter critical stop signal delay (cSSD), a measure of the time window during which a go response can be successfully countermanded (R2 = 0.12). This decline was explained by an IR-associated increase in go speed (R2 = 0.13) with little impact of IR or BMI on stop speed. Greater striatal fMRI activation contrast in stop error (SE) compared with stop success (SS) trials (CONSE>SS) was a significant predictor of faster go speeds (R2 = 0.33, p = 0.002), and was itself predicted by greater IR (CONSE>SS vs HOMA-IR: R2 = 0.10, p = 0.04). Furthermore, this impact of IR on striatal activation was a significant mediator of the faster go speeds and greater impulsivity observed with greater IR. These findings suggest a neural mechanism by which IR may increase impulsivity and degrade behavioral self-regulation.