Impacts of Simultaneous Multislice Acquisition on Sensitivity and Specificity in fMRI

Simultaneous multislice (SMS) imaging can be used to decrease the time between acquisition of fMRI volumes, which can increase sensitivity by facilitating the removal of higher-frequency artifacts and boosting effective sample size. The technique requires an additional processing step in which the slices are separated, or unaliased, to recover the whole brain volume. However, this may result in signal “leakage” between aliased locations, i.e., slice “leakage,” and lead to spurious activation (decreased specificity). SMS can also lead to noise amplification, which can reduce the benefits of decreased repetition time. In this study, we evaluate the original slice-GRAPPA (no leak block) reconstruction algorithmand acceleration factor (AF = 8) used in the fMRI data in the young adult Human Connectome Project (HCP). We also evaluate split slice-GRAPPA (leak block), which can reduce slice leakage. We use simulations to disentangle higher test statistics into true positives (sensitivity) and false positives (decreased specificity). Slice leakage was greatly decreased by split slice-GRAPPA. Noise amplification was decreased by using moderate acceleration factors (AF = 4). We examined slice leakage in unprocessed fMRI motor task data from the HCP. When data were smoothed, we found evidence of slice leakage in some, but not all, subjects. We also found evidence of SMS noise amplification in unprocessed task and processed resting-state HCP data

Spatiotemporal mixed modeling of multi-subject task fMRI via method of moments

Estimating spatiotemporal models for multi-subject fMRI is computationally challenging. We propose a mixed model for localization studies with spatial random effects and time-series errors. We develop method-of-moment estimators that leverage population and spatial information and are scalable to massive datasets. In simulations, subject-specific estimates of activation are considerably more accurate than the standard voxel-wise general linear model. Our mixed model also allows for valid population inference. We apply our model to cortical data from motor and theory of mind tasks from the Human Connectome Project (HCP). The proposed method results in subject-specific predictions that appear smoother and less noisy than those from the popular single-subject univariate approach. In particular, the regions of motor cortex associated with a left-hand finger-tapping task appear to be more clearly delineated. Subject-specific maps of activation from task fMRI are increasingly used in pre-surgical planning for tumor removal and in locating targets for transcranial magnetic stimulation. Our findings suggest that using spatial and population information is a promising avenue for improving clinical neuroimaging

Ecological and Social Factors Constrain Spatial and Temporal Opportunities for Mating in a Migratory Songbird

Many studies of sexual selection assume that individuals have equal mating opportunities and that differences in mating success result from variation in sexual traits. However, the inability of sexual traits to explain variation in male mating success suggests that other factors moderate the strength of sexual selection. Extrapair paternity is common in vertebrates and can contribute to variation in mating success and thus serves as a model for understanding the operation of sexual selection. We developed a spatially explicit, multifactor model of all possible female-male pairings to test the hypothesis that ecological (food availability) and social (breeding density, breeding distance, and the social mate's nest stage) factors influence an individual's opportunity for extrapair paternity in a socially monogamous bird, the black-throated blue warbler, Setophaga caerulescens. A male's probability of siring extrapair young decreased with increasing distance to females, breeding density, and food availability. Males on food-poor territories were more likely to sire extrapair young, and these offspring were produced farther from the male's territory relative to males on food-abundant territories. Moreover, males sired extrapair young mostly during their social mates' incubation stage, especially males on food-abundant territories. This study demonstrates how ecological and social conditions constrain the spatial and temporal opportunities for extrapair paternity that affect variation in mating success and the strength of sexual selection in socially monogamous species

Interpretive JIVE: Connections with CCA and an application to brain connectivity

Joint and Individual Variation Explained (JIVE) is a model that decomposes multiple datasets obtained on the same subjects into shared structure, structure unique to each dataset, and noise. JIVE is an important tool for multimodal data integration in neuroimaging. The two most common algorithms are R.JIVE, an iterative approach, and AJIVE, which uses principal angle analysis. The joint structure in JIVE is defined by shared subspaces, but interpreting these subspaces can be challenging. In this paper, we reinterpret AJIVE as a canonical correlation analysis of principal component scores. This reformulation, which we call CJIVE, (1) provides an intuitive view of AJIVE; (2) uses a permutation test for the number of joint components; (3) can be used to predict subject scores for out-of-sample observations; and (4) is computationally fast. We conduct simulation studies that show CJIVE and AJIVE are accurate when the total signal ranks are correctly specified but, generally inaccurate when the total ranks are too large. CJIVE and AJIVE can still extract joint signal even when the joint signal variance is relatively small. JIVE methods are applied to integrate functional connectivity (resting-state fMRI) and structural connectivity (diffusion MRI) from the Human Connectome Project. Surprisingly, the edges with largest loadings in the joint component in functional connectivity do not coincide with the same edges in the structural connectivity, indicating more complex patterns than assumed in spatial priors. Using these loadings, we accurately predict joint subject scores in new participants. We also find joint scores are associated with fluid intelligence, highlighting the potential for JIVE to reveal important shared structure

Microbial community function and bacterial pathogen composition in pit latrines in peri-urban Malaw

Despite the widespread global reliance on pit latrines as improved sanitation systems, the decomposition of waste within pit latrines is poorly understood. One area needing elucidation is the characterization and function of microbial communities within pit latrines. To address this gap, we characterized the microbial communities of 55 lined pit latrines at three sampling layers from two communities in peri-urban Malawi. The microbial communities of the fecal sludge samples were analyzed for beta diversity, pathogen presence, and functional profiling. Household surveys were conducted and used to compare microbial community patterns to household characteristics and pit latrine use patterns. Compared to activated sludge, anaerobic digestion in municipal wastewater systems, and human gut microbiomes, pit latrines were found to contain unique microbial communities. While the microbial community composition as a whole did not vary by sampling depth, pathogen composition varied by sampling depth, location, and household water source. The inferred microbial function also varied by depth (e.g., increase in methanogens and decrease in aerobes with depth). The richness of lined pit latrines determined from surface samples from eight latrines was found to be representative for a given area. Samples from middle and lower depths collected using a Gulper pump did not provide more information on richness, a result that informs future sampling designs. These findings are important for improving waste-based epidemiology (WBE) approaches to understand community health and waste degradation characterization of lined pit latrines

Analysis of Fecal Sludges Reveals Common Enteric Pathogens in Urban Maputo, Mozambique

Sewage surveillance is increasingly used in public health applications; metabolites, biomarkers, and pathogens are detectable in wastewater and can provide useful information about community health. Work on this topic has been limited to wastewaters in mainly high-income settings, however. In low-income countries, where the burden of enteric infection is high, nonsewered sanitation predominates. In order to assess the utility of fecal sludge surveillance as a tool to identify the most prevalent enteric pathogens circulating among at-risk children, we collected 95 matched child stool and fecal sludge samples from household clusters sharing latrines in urban Maputo, Mozambique. We analyzed samples for 20 common enteric pathogens via multiplex real-time quantitative PCR. Among the 95 stools matched to fecal sludges, we detected the six most prevalent bacterial pathogens (Enteroaggregative E. coli, Shigella/Enteroinvasive E. coli, Enterotoxigenic E. coli, Enteropathogenic E. coli, shiga-toxin producing E. coli, Salmonella), and all three protozoan pathogens (Giardia duodenalis, Cryptosporidium parvum, Entamoeba histolytica) in the same rank order in both matrices. We did not observe the same trend for viral pathogens or soil-transmitted helminths, however. Our results suggest that sampling fecal sludges from onsite sanitation offers potential for localized pathogen surveillance in low-income settings where enteric pathogen prevalence is high

Microbial community function and bacterial pathogen composition in pit latrines in peri-urban Malawi

Despite the widespread global reliance on pit latrines as improved sanitation systems, the decomposition of waste within pit latrines is poorly understood. One area needing elucidation is the characterization and function of microbial communities within pit latrines. To address this gap, we characterized the microbial communities of 55 lined pit latrines at three sampling layers from two communities in peri-urban Malawi. The microbial communities of the fecal sludge samples were analyzed for beta diversity, pathogen presence, and functional profiling. Household surveys were conducted and used to compare microbial community patterns to household characteristics and pit latrine use patterns. Compared to activated sludge, anaerobic digestion in municipal wastewater systems, and human gut microbiomes, pit latrines were found to contain unique microbial communities. While the microbial community composition as a whole did not vary by sampling depth, pathogen composition varied by sampling depth, location, and household water source. The inferred microbial function also varied by depth (e.g., increase in methanogens and decrease in aerobes with depth). The richness of lined pit latrines determined from surface samples from eight latrines was found to be representative for a given area. Samples from middle and lower depths collected using a Gulper pump did not provide more information on richness, a result that informs future sampling designs. These findings are important for improving waste-based epidemiology (WBE) approaches to understand community health and waste degradation characterization of lined pit latrines

Non-Gaussian component analysis: testing the dimension of the signal subspace

Dimension reduction is a common strategy in multivariate data analysis which seeks a subspace which contains all interesting features needed for the subsequent analysis. Non-Gaussian component analysis attempts for this purpose to divide the data into a non-Gaussian part, the signal, and a Gaussian part, the noise. We will show that the simultaneous use of two scatter functionals can be used for this purpose and suggest a bootstrap test to test the dimension of the non-Gaussian subspace. Sequential application of the test can then for example be used to estimate the signal dimension