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Sea star wasting disease (SSWD) first appeared in Oregon in April 2014, and by June had spread to most of the coast. Although delayed compared to areas to the north and south, SSWD was initially most intense in north and central Oregon and spread southward. Up to 90% of individuals showed signs of disease from June-August 2014. In rocky intertidal habitats, populations of the dominant sea star Pisaster ochraceus were rapidly depleted, with magnitudes of decline in density among sites ranging from -2x to -9x (59 to 84%) and of biomass from -2.6x to -15.8x (60 to 90%) by September 2014. The frequency of symptomatic individuals declined over winter and persisted at a low rate through the spring and summer 2015 (~5–15%, at most sites) and into fall 2015. Disease expression included six symptoms: initially with twisting arms, then deflation and/or lesions, lost arms, losing grip on substrate, and final disintegration. SSWD was disproportionally higher in orange individuals, and higher in tidepools. Although historically P. ochraceus recruitment has been low, from fall 2014 to spring 2015 an unprecedented surge of sea star recruitment occurred at all sites, ranging from ~7x to 300x greater than in 2014. The loss of adult and juvenile individuals in 2014 led to a dramatic decline in predation rate on mussels compared to the previous two decades. A proximate cause of wasting was likely the “Sea Star associated Densovirus” (SSaDV), but the ultimate factors triggering the epidemic, if any, remain unclear. Although warm temperature has been proposed as a possible trigger, SSWD in Oregon populations increased with cool temperatures. Since P. ochraceus is a keystone predator that can strongly influence the biodiversity and community structure of the intertidal community, major community-level responses to the disease are expected. However, predicting the specific impacts and time course of change across west coast meta-communities is difficult, suggesting the need for detailed coast-wide investigation of the effects of this outbreak.

Cigarette smoking is a major public health danger. Women and men smoke for different reasons and cessation treatments, such as the nicotine patch, are preferentially beneficial to men. The biological substrates of these sex differences are unknown. Earlier PET studies reported conflicting findings but were each hampered by experimental and/or analytical limitations. Our new image analysis technique, lp-ntPET (Normandin et al., 2012; Morris et al., 2013; Kim et al., 2014), has been optimized for capturing brief (lasting only minutes) and highly localized dopaminergic events in dynamic PET data. We coupled our analysis technique with high-resolution brain scanning and high-frequency motion correction to create the optimal experiment for capturing and characterizing the effects of smoking on the mesolimbic dopamine system in humans. Our main finding is that male smokers smoking in the PET scanner activate dopamine in the right ventral striatum during smoking but female smokers do not. This finding-men activating more ventrally than women-is consistent with the established notion that men smoke for the reinforcing drug effect of cigarettes whereas women smoke for other reasons, such as mood regulation and cue reactivity. lp-ntPET analysis produces a novel multidimensional endpoint: voxel-level temporal patterns of neurotransmitter release ("DA movies") in individual subjects. By examining these endpoints quantitatively, we demonstrate that the timing of dopaminergic responses to cigarette smoking differs between men and women. Men respond consistently and rapidly in the ventral striatum whereas women respond faster in a discrete subregion of the dorsal putamen.

The lp-ntPET (“linear parametric ntPET”) model estimates time-variation in endogenous neurotransmitter levels from dynamic PET data. The pattern of dopamine change over time may be an important element of the brain’s response to addictive substances such as cigarettes or alcohol. We have extended the lp-ntPET model from the original ROI-based implementation to be able to apply the model at the voxel-level. The resulting endpoint is a dynamic image, or movie, of transient neurotransmitter changes. Simulations were performed to select threshold values to reduce the false positive rate when applied to real 11C-raclopride PET data. We tested the new voxel-wise method on simulated data and finally, we applied it to 11C-raclopride PET data of subjects smoking cigarettes in the PET scanner. In simulation, the temporal precision of neurotransmitter response was shown to be similar to that of ROI-based lp-ntPET (standard deviation ~3 min). False positive rates for the voxel-wise method were well controlled by combining a statistical threshold (the F-test) with a new spatial (cluster-size) thresholding operation. Sensitivity of detection for the new algorithm was greater than 80 % for the case of short-lived dopamine changes that occur in sub-regions of the striatum as might be the case with cigarette smoking. Finally, in 11C-raclopride PET data, dopamine movies reveal for the first time that different temporal patterns of the dopamine response to smoking may exist in different sub-regions of the striatum. These spatio-temporal patterns of neurotransmitter change created by voxel-wise lp-ntPET may serve as novel biomarkers for addiction and/or treatment efficacy.