Accelerated functional Magnetic Resonance Imaging (fMRI) with 'multiband' protocols is now relatively widespread. These protocols can be used to dramatically reduce the repetition time (TR) and produce a time-series sampled at a higher temporal resolution, which may produce benefits in the statistical methods typically used to analyse fMRI data. We tested the effects of higher temporal resolutions for fMRI on statistical outcome measures in a comprehensive manner on two different MRI scanner platforms. Spatial resolution was maintained at a constant of 3 mm isotropic voxels, and an in-plane acceleration factor of 2 was used for all experiments. Experiment 1 tested a range of acceleration factors (1-6) against a standard EPI protocol on a single composite task that mapped a number of basic sensory, motor, and cognitive networks. Experiment 2 compared the standard protocol with acceleration factors of 2 and 3 on both resting-state and two task paradigms (an N-back task, and faces/places task), with a number of different analysis approaches. Results from experiment 1 showed modest but relatively inconsistent effects of the higher sampling rate on statistical outcome measures. Experiment 2 showed strong benefits of the multiband protocols on results derived from resting-state data, but more varied effects on results from the task paradigms. Notably, the multiband protocols were superior when Multi-Voxel Pattern Analysis was used to interrogate the faces/places data, but showed less benefit in conventional General Linear Model analyses of the same data. In general, ROI-derived measures of statistical effects benefitted only modestly from higher sampling resolution, with greater effects seen when using a measure of the top range of statistical values. Across both experiments, results from the two scanner platforms were broadly comparable. The statistical benefits of high temporal resolution fMRI with multiband protocols may therefore depend on a number of factors, including the nature of the investigation (resting-state vs. task-based), the experimental design, the particular statistical outcome measure, and the type of analysis used.

Plasmodium parasites have extensive needs from their host hepatocytes during the obligate liver stage of infection, yet there remains sparse knowledge of specific host regulators. Here we assess 34 host-targeted kinase inhibitors for their capacity to eliminate Plasmodium yoeliiinfected hepatocytes. Using pre-existing activity profiles of each inhibitor, we generate a predictive computational model that identifies host kinases, which facilitate Plasmodium yoelii liver stage infection. We predict 47 kinases, including novel and previously described kinases that impact infection. The impact of a subset of kinases is experimentally validated, including Receptor Tyrosine Kinases, members of the MAP Kinase cascade, and WEE1. Our approach also predicts host-targeted kinase inhibitors of infection, including compounds already used in humans. Three of these compounds, VX-680, Roscovitine and Sunitinib, each eliminate >85% of infection. Our approach is well-suited to uncover key host determinants of infection in difficult model systems, including field-isolated parasites and/or emerging pathogens.

The quantification method described here allows reliable visualization, quantification, and mapping of heterogeneous cell populations in immunolabeled sections across whole mouse brains.