Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately

Increased NoGo-anteriorisation in first-episode schizophrenia patients during Continuous Performance Test

OBJECTIVE: NoGo-stimuli during a Continuous Performance Test (CPT) activate prefrontal brain structures such as the anterior cingulate gyrus and lead to an anteriorisation of the positive electrical field of the NoGo-P300 relative to the Go-P300, so-called NoGo-anteriorisation (NGA). NGA during CPT is regarded as a neurophysiological standard index for cognitive response control. While it is known that patients with chronic schizophrenia exhibit a significant reduction in NGA, it is unclear whether this also occurs in patients undergoing their first-episode. Thus, the aim of the present study was to determine NGA in a group of patients with first-episode schizophrenia by utilizing a CPT paradigm. METHODS: Eighteen patients with first-episode schizophrenia and 18 matched healthy subjects were investigated electrophysiologically during a cued CPT, and the parameters of the Go- and NoGo-P300 were determined using microstate analysis. Low resolution tomography analysis (LORETA) was used for source determination. RESULTS: Due to a more posterior Go- and a more anterior NoGo-centroid, NGA was greater in patients than in healthy controls. LORETA indicated the same sources for both groups after Go-stimuli, but a more anterior source in patients after NoGo-stimuli. In patients P300-amplitude responses to both Go- and NoGo-stimuli were decreased, and P300-latency to NoGo-stimuli was increased. After the Go-stimuli false reactions and reaction times were increased in patients. CONCLUSIONS: Attention was reduced in patients with first-episode schizophrenia, as indicated by more false reactions, prolongation of reaction time, P300-latencies and by a decrease in P300-amplitude. Significantly however, the NGA and prefrontal LORETA-sources indicate intact prefrontal brain structures in first-episode schizophrenia patients. Previously described changes in this indicator of prefrontal function may be related to a progressive decay in chronic schizophrenia. SIGNIFICANCE: The results support the idea of a possible new biological marker of first episode psychosis, which may be a useful parameter for the longitudinal measurement of changing prefrontal brain function in a single schizophrenia patient.