PET imaging studies of the role of the dopamine D2 receptor family in movement and neuropsychiatric disorders are limited by the use of radioligands that have near‐equal affinities for D2 and D3 receptor subtypes and are susceptible to competition with endogenous dopamine. By contrast, the radioligand [18F]N‐methylbenperidol ([18F]NMB) has high selectivity and affinity for the D2 receptor subtype (D2R) and is not sensitive to endogenous dopamine. Although [18F]NMB has high binding levels in striatum, its utility for measuring D2R in extrastriatal regions is unknown. A composite MR‐PET image was constructed across 14 healthy adult participants representing average NMB uptake 60 to 120 min after [18F]NMB injection. Regional peak radioactivity was identified using a peak‐finding algorithm. FreeSurfer and manual tracing identified a priori regions of interest (ROI) on each individual's MR image and tissue activity curves were extracted from coregistered PET images. [18F]NMB binding potentials (BPNDs) were calculated using the Logan graphical method with cerebellum as reference region. In eight unique participants, extrastriatal BPND estimates were compared between Logan graphical methods and a three‐compartment kinetic tracer model. Radioactivity and BPND levels were highest in striatum, lower in extrastriatal subcortical regions, and lowest in cortical regions relative to cerebellum. Age negatively correlated with striatal BPNDs. BPND estimates for extrastriatal ROIs were highly correlated across kinetic and graphical methods. Our findings indicate that PET with [18F]NMB measures specific binding in extrastriatal regions, making it a viable radioligand to study extrastriatal D2R levels in healthy and diseased states. Synapse 66:770–780, 2012. © 2012 Wiley Periodicals, Inc.

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Date Presented 04/06/19 Identifying subtle motor delays in early childhood is challenging. Accelerometry is a novel way to characterize upper-extremity motor patterns in typically developing children. Differences are identified between typical children and those with unilateral motor deficits. Primary Author and Speaker: Catherine Hoyt Contributing Authors: Andrew Van, Mario Ortega, Jon Koller, Elyse Everett, Annie Nguyen, Catherine Lang, Brad Schlaggar, Nico Dosenbach

e spread of an infectious disease through a population can be modeled using a network or a graph. In digital advertising, internet device graphs are graph data sets that organize identi ers produced by mobile phones, PCs, TVs, and tablets as they access media on the internet. Characterized by immense scale, they have become ubiquitous as they enable targeted advertising, content customization and tracking. is paper posits that internet device graphs, in particular those based on IP colocation, can provide signi cant utility in predicting and modeling the spread of infectious disease. Starting the week of March 16th, 2020, in the United States, many individuals began to 'shelter-in-place' as schools and workplaces across the nation closed because of the COVID-19 pandemic. is paper quanti es the e ect of the shelter-in-place orders on a large scale internet device graph with more than a billion nodes by studying the graph before and a er orders went into e ect. e e ects are clearly visible. e structure of the graph suggests behavior least conducive to transmission of infection occurred in the US between April 12th and 19th, 2020. is paper also discusses the utility of device graphs for i) contact tracing, ii) prediction of 'hot spots', iii) simulation of infectious disease spread, and iv) delivery of advertisement-based warnings to potentially exposed individuals.e paper also posits an overarching question: can systems and datasets amassed by entities in the digital ad ecosystem aid in the ght against COVID-19?