In response to reports of inflated false positive rate (FPR) in FMRI group analysis tools, a series of replications, investigations, and software modifications were made to address this issue. While these investigations continue, significant progress has been made to adapt AFNI to fix such problems. Two separate lines of changes have been made. First, a longtailed model for the spatial correlation of the FMRI noise characterized by autocorrelation function (ACF) was developed and implemented into the 3dClustSim tool for determining the clustersize threshold to use for a given voxelwise threshold. Second, the 3dttest++ program was modified to do randomization of the voxelwise t tests and then to feed those randomized t statistic maps into 3dClustSim directly for clustersize threshold determination-without any spatial model for the ACF. These approaches were tested with the Beijing subset of the FCON1000 data collection. The first approach shows markedly improved (reduced) FPR, but in many cases is still above the nominal 5%. The second approach shows FPRs clustered tightly about 5% across all pervoxel p value thresholds ≤ 0.01. If t tests from a univariate GLM are adequate for the group analysis in question, the second approach is what the AFNI group currently recommends for thresholding. If more complex pervoxel statistical analyses are required (where permutation/randomization is impracticable), then our current recommendation is to use the new ACF modeling approach coupled with a pervoxel p threshold of 0.001 or below. Simulations were also repeated with the now infamously "buggy" version of 3dClustSim: the effect of the bug on FPRs was minimal (of order a few percent).
A recent study posted on bioRxiv by Bowring, Maumet and Nichols aimed to compare results of FMRI data that had been processed with three commonly used software packages (AFNI, FSL and SPM). Their stated purpose was to use "default" settings of each software's pipeline for task-based FMRI, and then to quantify overlaps in final clustering results and to measure similarity/dissimilarity in the final outcomes of packages. While in theory the setup sounds simple (implement each package's defaults and compare results), practical realities make this difficult. For example, different softwares would recommend different spatial resolutions of the final data, but for the sake of comparisons, the same value must be used across all. Moreover, we would say that AFNI does not have an explicit default pipeline available: a wide diversity of datasets and study designs are acquired across the neuroimaging community, often requiring bespoke tailoring of basic processing rather than a "one-size-fits-all" pipeline. However, we do have strong recommendations for certain steps, and we are also aware that the choice of a given step might place requirements on other processing steps. Given the very clear reporting of the AFNI pipeline used in Bowring et al. paper, we take this opportunity to comment on some of these aspects of processing with AFNI here, clarifying a few mistakes therein and also offering recommendations. We provide point-by-point considerations of using AFNI's processing pipeline design tool at the individual level, afni_proc.py, along with supplementary programs; while specifically discussed in the context of the present usage, many of these choices may serve as useful starting points for broader processing. It is our intention/hope that the user should examine data quality at every step, and we demonstrate how this is facilitated in AFNI, as well.
Microbial arteritis, an entity often considered under the category of mycotic aneurysms, is an uncommon infectious process which generally results from bacteremic seeding of a preexisting aortic lesion. This report describes a fatal case of microbial arteritis involving a 51-year-old man who presented as an outpatient with diffuse myalgias and abdominal pain of approximately two weeks' duration. Necropsy finding revealed an exsanguinating hemorrhage from an infected nonaneurysmal abdominal aortic plaque caused by Streptococcus pneumoniae. Documented cases of microbial aortitis due to S. pneumoniae are quite rare in present times and were not often observed in the preantibiotic era even in the setting of bacterial endocarditis. The pathology, pathogenesis, and incidence of aneurysmal and nonaneurysmal aortic infections, with special reference to the pneumococcus, are reviewed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.