Benjamin B. Risk

Experimental food supplementation reveals habitat-dependent male reproductive investment in a migratory bird

Environmental factors can shape reproductive investment strategies and influence the variance in male mating success. Environmental effects on extrapair paternity have traditionally been ascribed to aspects of the social environment, such as breeding density and synchrony. However, social factors are often confounded with habitat quality and are challenging to disentangle. We used both natural variation in habitat quality and a food supplementation experiment to separate the effects of food availability—one key aspect of habitat quality—on extrapair paternity (EPP) and reproductive success in the black-throated blue warbler, Setophaga caerulescens. High natural food availability was associated with higher within-pair paternity (WPP) and fledging two broods late in the breeding season, but lower EPP. Food-supplemented males had higher WPP leading to higher reproductive success relative to controls, and when in low-quality habitat, food-supplemented males were more likely to fledge two broods but less likely to gain EPP. Our results demonstrate that food availability affects trade-offs in reproductive activities. When food constraints are reduced, males invest in WPP at the expense of EPP. These findings imply that environmental change could alter how individuals allocate their resources and affect the selective environment that drives variation in male mating success.

Impacts of simultaneous multislice acquisition on sensitivity and specificity in fMRI

&NA; 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 algorithm and 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. HighlightsIn simulations, SMS improves sensitivity in motor cortex.Split slice‐GRAPPA reduces slice leakage to improve specificity.Moderate acceleration factors (AF = 4) reduce spatially structured measurement error.Slice leakage in smoothed unprocessed HCP data (AF = 8) in some, not all, subjects.Evidence of noise amplification in unprocessed and processed HCP data.

Linear Non-Gaussian Component Analysis Via Maximum Likelihood

ABSTRACT Independent component analysis (ICA) is popular in many applications, including cognitive neuroscience and signal processing. Due to computational constraints, principal component analysis (PCA) is used for dimension reduction prior to ICA (PCA+ICA), which could remove important information. The problem is that interesting independent components (ICs) could be mixed in several principal components that are discarded and then these ICs cannot be recovered. We formulate a linear non-Gaussian component model with Gaussian noise components. To estimate the model parameters, we propose likelihood component analysis (LCA), in which dimension reduction and latent variable estimation are achieved simultaneously. Our method orders components by their marginal likelihood rather than ordering components by variance as in PCA. We present a parametric LCA using the logistic density and a semiparametric LCA using tilted Gaussians with cubic B-splines. Our algorithm is scalable to datasets common in applications (e.g., hundreds of thousands of observations across hundreds of variables with dozens of latent components). In simulations, latent components are recovered that are discarded by PCA+ICA methods. We apply our method to multivariate data and demonstrate that LCA is a useful data visualization and dimension reduction tool that reveals features not apparent from PCA or PCA+ICA. We also apply our method to a functional magnetic resonance imaging experiment from the Human Connectome Project and identify artifacts missed by PCA+ICA. We present theoretical results on identifiability of the linear non-Gaussian component model and consistency of LCA. Supplementary materials for this article are available online.

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.

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.

Note on bias from averaging repeated measurements in heritability studies

Ge et al. (1) consider the extension of Fisher’s classic model for heritability to the case where there are repeated measurements on subjects. One approach to analyzing repeated measurements is to average observations. The authors show empirically and via simulations that estimates of heritability derived from averaging repeated measurements lead to underestimates of heritability. Some may find the bias revealed by Ge et al. (1) to be surprising because averaging is commonly justified in other settings such as repeated measures ANOVA. In this letter we detail the bias that arises from conflating measurement error and unique environmental variance. This elucidates the authors’ empirical findings, which represent a case with large measurement error exacerbated by only two measurements per subject. Consider the model for additive genetic, common environmental, and unique environmental components. We use the mixed-model formulation as … [↵][1]1To whom correspondence should be addressed. Email: brisk{at}emory.edu. [1]: #xref-corresp-1-1

Diagnostic radiology resident perspectives on fellowship training and career interest in emergency radiology

Purpose(1) Evaluate radiology resident perception of emergency radiology (ER). (2) Identify potential barriers to pursuing fellowship training or a career in ER among radiology residents.Materials and methodsA 9-question digital survey was designed using Qualtrics Experience Management software (Qualtrics Inc., Provo, UT) and distributed to all US radiology residents via a multi-pronged distribution approach.ResultsFour hundred fifty-one residents responded out of an estimated national total of 4432 residents (10.2%). Gender proportion was nationally representative (female = 24.5%; p = 0.57), with a slight R1 predominance (p = 0.034). Of the residents, 88.8% were aware that an ER subspecialty exists, 82.0% were aware that ER fellowships exist, but only 51.7% were aware that the American Society of Emergency Radiology (ASER) exists. Nearly a quarter reported no ER division or ER resident rotation. Residents in a program without an ER division or rotation were nearly twice as likely to be unaware of the existence of ER subspecialty, ER fellowships, and ASER compared to others (p = 0.017). The presence of an ER division and rotation significantly increases the knowledge of ASER (65.5% vs. 40.7%, p < 0.001) and increases residents’ ratings of their ER training (p < 0.001). The following factors were ranked as the most important for fellowship choice: (1) personal interest, (2) intellectually stimulating, and (3) work hours. When asked if ER had an appealing work schedule, the mean response was 56 out of 100 (0 = disagree, 100 = agree).ConclusionUS radiology residents with the greatest exposure to ER during residency are more familiar with ER training, ER career opportunities, and ASER and had a more favorable perception of the field. Subspecialty leaders should focus on ER’s inherent intellectual appeal and reframe its nontraditional schedule as positive (flexible).