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Two controversial tenets of metapopulation biology are whether patch quality and the surrounding matrix are more important to turnover (colonisation and extinction) than biogeography (patch area and isolation) and whether factors governing turnover during equilibrium also dominate nonequilibrium dynamics. We tested both tenets using 18 years of surveys for two secretive wetland birds, black and Virginia rails, during (1) a period of equilibrium with stable occupancy and (2) after drought and arrival of West Nile Virus (WNV), which resulted in WNV infections in rails, increased extinction and decreased colonisation probabilities modified by WNV, nonequilibrium dynamics for both species and occupancy decline for black rails. Area (primarily) and isolation (secondarily) drove turnover during both stable and unstable metapopulation dynamics, greatly exceeding the effects of patch quality and matrix conditions. Moreover, slopes between turnover and patch characteristics changed little between equilibrium and nonequilibrium, confirming the overriding influences of biogeographic factors on turnover.

BackgroundCarotid webs (CaWs) are associated with ischemic strokes in younger patients without degrees of stenosis that are traditionally considered clinically significant.ObjectiveTo compare the hemodynamic parameters in the internal carotid artery (ICA) bulbar segment in patients with CaW with those in patients with atherosclerotic lesions using time–density curve (TDC) analysis of digital subtraction angiography (DSA) images.MethodsWe retrospectively assessed DSA images of 47 carotid arteries in 41 adult patients who underwent ICA catheter angiography for evaluation after ischemic stroke. Hemodynamic parameters, including full width at half maximum (FWHM) and area under the time–density curve (AUC) as proxies for increased flow stasis, were calculated using TDC analyses of a region of interest (ROI) in the ICA bulb immediately rostral to the web/atherosclerotic plaque, relative to a standardized ROI in the ipsilateral distal common carotid artery (eg, relative FWHM (rFWHM)). Hemodynamic parameters were compared using non-parametric Kruskal-Wallis tests. Logistic regression was used to predict CaW versus mild/moderate atherosclerosis for each hemodynamic parameter, adjusting for degree of stenosis.ResultsMean age of patients was 56.0±13 years, with 22 (53.7%) women. 17 CaWs, 22 atherosclerotic plaques (15 mild/moderate and 7 severe), and eight normal carotid arteries were assessed. Significant between-group differences were present in the relative total AUC (pConclusionCaW results in larger local hemodynamic disruption, characterized by flow stasis, than mild/moderate carotid atherosclerotic lesions, suggesting that CaWs may produce larger regions of thrombogenic flow stasis.

To compare logistic regression to elastic net for identifying and ranking clinical risk factors for blunt cerebrovascular injury (BCVI). Consecutive trauma patients undergoing screening CTA at a level 1 trauma center over a 2-year period. Each internal carotid artery (ICA) and vertebral artery (VA) was independently graded by 2 neuroradiologists using the Denver grading scale. Unadjusted odds ratios were calculated by univariate and adjusted odds ratios by multiple logistic regression with FDR correction. We applied logistic regression with the elastic net penalty and tenfold cross-validation. Total of 467 patients; 73 patients with BCVI. Maxillofacial fracture, basilar skull fracture, and GCS had significant unadjusted odds ratios (OR) for ICA injury and C-spine fracture, spinal ligamentous injury, and age for VA injury. Only transverse foramen fracture had significant adjusted OR for VA injury, with none for ICA injury, after FDR correction. Using elastic net, ICA injury variables included maxillofacial fracture, basilar skull fracture, GCS, and carotid canal fracture. For VA injury, these included cervical spine transverse foramen fracture, ligamentous injury, C1–C3 fractures, posterior element fracture, and vertebral body fracture. Elastic net statistical learning methods identified additional risk factors and outperformed multiple logistic regression for BCVI. Elastic net allows the study of a large number of variables, and is useful when covariates are correlated.

There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. Previous connectome integration studies almost exclusively required predefined atlases. However, there are many potential atlases to choose from and this choice heavily affects all subsequent analyses. To avoid such an arbitrary choice, we propose a novel atlas‐free approach, named Surface‐Based Connectivity Integration (SBCI), to more accurately study the relationships between SC and FC throughout the intra‐cortical gray matter. SBCI represents both SC and FC in a continuous manner on the white surface, avoiding the need for prespecified atlases. The continuous SC is represented as a probability density function and is smoothed for better facilitation of its integration with FC. To infer the relationship between SC and FC, three novel sets of SC‐FC coupling (SFC) measures are derived. Using data from the Human Connectome Project, we introduce the high‐quality SFC measures produced by SBCI and demonstrate the use of these measures to study sex differences in a cohort of young adults. Compared with atlas‐based methods, this atlas‐free framework produces more reproducible SFC features and shows greater predictive power in distinguishing biological sex. This opens promising new directions for all connectomics studies., There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. In the following article, we propose a novel atlas‐free approach, named Surface‐Based Connectivity Integration (SBCI), to more accurately study the relationships between SC and FC throughout the intra‐cortical gray matter. Compared with atlas‐based methods, the atlas‐free framework produces more reproducible SC‐FC coupling features and shows greater predictive power in distinguishing biological sex. This opens promising new directions for all connectomics studies.

Brain temperature is an important yet understudied medical parameter, and increased brain temperature after injury is associated with worse patient outcomes. The scarcity of methods for measuring brain temperature non-invasively motivates the need for computational models enabling predictions when clinical measurements are challenging. Here, we develop a biophysical model based on the first principles of energy and mass conservation that uses data from magnetic resonance imaging of individual brain tissue and vessel structure to facilitate personalized brain temperature predictions. We compare model-predicted 3D thermal distributions with experimental temperature measured using whole brain magnetic resonance-based thermometry. We find brain thermometry maps predicted by the model capture unique spatial variations for each subject, which are in agreement with experimentally-measured temperatures. As medicine becomes more personalized, this foundational study provides a framework to develop an individualized approach for brain temperature predictions. While progress has been made to formulate models for brain temperature regulation that incorporate non-simplified anatomy and vasculature, accounting for heterogeneity between individuals is still a challenge. Here, the authors propose a fully conserved biophysical model that, starting from an individual subject’s magnetic resonance-derived tissue and vessel structure, predicts individual patterns in local brain temperature in agreement with magnetic resonance thermometry.

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...

Post-concussive visually induced dizziness (VID), in which symptoms are provoked by exposure to complex visual motion, is associated with protracted recovery. Although vestibular rehabilitation therapy (VRT) is recommended to treat post-concussive dizziness, there is sparse literature reporting on specific VRT interventions treating VID.A consecutive series of 26 individuals referred for VRT post-concussion were retrospectively assessed for inclusion in this case series. Each participant underwent a combination of conventional VRT and a technology-enhanced visual desensitization home exercise program (HEP). Self-report and objective measures were recorded from initial and discharge therapy evaluations. Twenty-three individuals (mean age 23.1 ± 12.4) with post-concussive dizziness (mean 109 ± 56 days post-injury) and no evidence for peripheral vestibular dysfunction were included. Treatment duration averaged 6.9 ± 2.5 weeks.There were significant improvements in post-intervention on subjective and objective measures of dizziness and gait (The combination therapy intervention improved outcome measures consistent with VRT treatment outcomes in both concussion and non-traumatic vestibular conditions. Individuals referred for VRT post-concussion warrant assessment for VID and may benefit from the addition of technology-enhanced visual desensitization.

The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder in children without an intellectual disability to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.

Purpose To evaluate the interrater reliability among radiologists examining posttreatment head and neck squamous cell carcinoma (HNSCC) fluorodeoxyglucose PET/contrast-enhanced CT (CECT) scans using Neck Imaging Reporting and Data System (NI-RADS). Materials and Methods In this retrospective study, images in 80 patients with HNSCC who underwent posttreatment surveillance PET/CECT and immediate prior comparison CECT or PET/CECT (from June 2014 to July 2016) were uploaded to the American College of Radiology's cloud-based website, Cortex. Eight radiologists from seven institutions with variable NI-RADS experience independently evaluated each case and assigned an appropriate prose description and NI-RADS category for the primary site and the neck site. Five of these individuals were experienced readers (> 5 years of experience), and three were novices (< 5 years of experience). In total, 640 lexicon-based and NI-RADS categories were assigned to lesions among the 80 included patients by the eight radiologists. Light generalization of Cohen κ for interrater reliability was performed. Results Of the 80 included patients (mean age, 63 years ± 10 [standard deviation]), there were 58 men (73%); 60 patients had stage IV HNSCC (75%), and the most common tumor location was oropharynx (n = 32; 40%). Light κ for lexicon was 0.30 (95% CI: 0.23, 0.36) at the primary site and 0.31 (95% CI: 0.24, 0.37) at the neck site. Light κ for NI-RADS category was 0.55 (95% CI: 0.46, 0.63) at the primary site and 0.60 (95% CI: 0.48, 0.69) at the neck site. Percent agreement between lexicon and correlative NI-RADS category was 84.4% (540 of 640) at the primary site and 92.6% (593 of 640) at the neck site. There was no significant difference in interobserver agreement among the experienced versus novice raters. Conclusion Moderate agreement was achieved among eight radiologists using NI-RADS at posttreatment HNSCC surveillance imaging. Keywords: CT, PET/CT, Head/Neck, Neck, Neoplasms-Primary, Observer Performance Supplemental material is available for this article. © RSNA, 2021.

Independent component analysis (ICA) is an unsupervised learning method popular in functional magnetic resonance imaging (fMRI). Group ICA has been used to search for biomarkers in neurological disorders including autism spectrum disorder and dementia. However, current methods use a principal component analysis (PCA) step that may remove low-variance features. Linear non-Gaussian component analysis (LNGCA) enables simultaneous dimension reduction and feature estimation including low-variance features in single-subject fMRI. A group LNGCA model is proposed to extract group components shared by more than one subject. Unlike group ICA methods, this novel approach also estimates individual (subject-specific) components orthogonal to the group components. To determine the total number of components in each subject, a parametric resampling test is proposed that samples spatially correlated Gaussian noise to match the spatial dependence observed in data. In simulations, estimated group components achieve higher accuracy compared to group ICA. The method is applied to a resting-state fMRI study on autism spectrum disorder in 342 children (252 typically developing, 90 with autism), where the group signals include resting-state networks. The discovered group components appear to exhibit different levels of temporal engagement in autism versus typically developing children, as revealed using group LNGCA. This novel approach to matrix decomposition is a promising direction for feature detection in neuroimaging.

Wastewater based epidemiology (WBE) is increasingly used to provide decision makers with actionable data about community health. WBE efforts to date have primarily focused on sewer-transported wastewater in high-income countries, but at least 1.8 billion people in low- and middle-income countries (LMIC) use onsite sanitation systems such as pit latrines and septic tanks. Like wastewater, fecal sludges from such systems offer similar advantages in community pathogen monitoring and other epidemiological applications. To evaluate the distribution of enteric pathogens inside pit latrines–which could inform sampling methods for WBE in LMIC settings unserved by sewers–we collected fecal sludges from the surface, mid-point, and maximum-depth of 33 pit latrines in urban and peri-urban Malawi and analyzed the 99 samples for 20 common enteric pathogens via multiplex quantitative reverse transcription PCR. Using logistic regression adjusted for household population, latrine sharing, the presence of a concrete floor or slab, water source, and anal cleansing materials, we found no significant difference in the odds of detecting the 20 pathogens from the mid-point (adjusted odds ratio, aOR = 1.1; 95% confidence interval = 0.73, 1.6) and surface samples (aOR = 0.80, 95% CI = 0.54, 1.2) compared with those samples taken from the maximum depth. Our results suggest that, for the purposes of routine pathogen monitoring, pit latrine sampling depth does not strongly influence the odds of detecting enteric pathogens by molecular methods. A single sample from the pit latrines’ surface, or a composite of surface samples, may be preferred as the most recent material contributed to the pit and may be easiest to collect., PLoS Neglected Tropical Diseases, 15 (3), ISSN:1935-2727, ISSN:1935-2735

Background Diabetes is the leading cause of lower extremity nontraumatic amputation globally, and diabetic foot osteomyelitis (DFO) is usually the terminal event before limb loss. Although guidelines recommend percutaneous bone biopsy (PBB) for microbiological diagnosis of DFO in several common scenarios, it is unclear how frequently PBBs yield positive cultures and whether they cause harm or improve outcomes. Methods We searched the PubMed, EMBASE, and Cochrane Trials databases for articles in any language published up to December 31, 2019, reporting the frequency of culture-positive PBBs. We calculated the pooled proportion of culture-positive PBBs using a random-effects meta-analysis model and reported on PBB-related adverse events, DFO outcomes, and antibiotic adjustment based on PBB culture results where available. Results Among 861 articles, 11 studies met inclusion criteria and included 780 patients with 837 PBBs. Mean age ranged between 56.6 and 71.0 years old. The proportion of males ranged from 62% to 86%. All studies were longitudinal observational cohorts, and 10 were from Europe. The range of culture-positive PBBs was 56%–99%, and the pooled proportion of PBBs with a positive culture was 84% (95% confidence interval, 73%–91%). There was heterogeneity between studies and no consistency in definitions used to define adverse events. Impact of PBB on DFO outcomes or antibiotic management were seldom reported. Conclusions This meta-analysis suggests PBBs have a high yield of culture-positive results. However, this is an understudied topic, especially in low- and middle-income countries, and the current literature provides very limited data regarding procedure safety and impact on clinical outcomes or antibiotic management., The proportion of culture-positive percutaneous bone biopsies among patients with suspected diabetic foot osteomyelitis in this meta-analysis was high (84%) suggesting this could be a useful diagnostic tool. However, there are several limitations to the literature.

PURPOSE To quantify changes in the management of pediatric patients with isolated splenic injury from 2007 to 2015. MATERIALS AND METHODS Patients under 18 years old with registered splenic injury in the National Trauma Data Bank (2007-2015) were identified. Splenic injuries were categorized into 5 management types: nonoperative management (NOM), embolization, splenic repair, splenectomy, or a combination therapy. Linear mixed models accounting for confounding variables were used to examine the direct impact of management on length of stay (LOS), intensive care unit (ICU) days, and ventilator days. RESULTS Of included patients (n = 24,128), 90.3% (n = 21,789), 5.6% (n = 1,361), and 2.7% (n = 640) had NOM, splenectomy, and embolization, respectively. From 2007 to 2015, the rate of embolization increased from 1.5% to 3.5%, and the rate of splenectomy decreased from 6.9% to 4.4%. Combining injury grades, NOM was associated with the shortest LOS (5.1 days), ICU days (1.9 days), and ventilator days (0.5 day). Moreover, splenectomy was associated with longer LOS (10.1 days), ICU days (4.5 days), and ventilator days (2.1 days) than NOM. The average failure rate of NOM was 1.5% (180 failures/12,378 cases). Average embolization failure was 1.3% (6 failures/456 cases). Splenic artery embolization was associated with lower mortality than splenectomy (OR: 0.10, P

To evaluate the utilization and efficacy of various treatments for management of adult patients with splenic trauma, highlighting the evolving role of splenic artery embolization.The National Trauma Data Bank (NTDB) was queried for patients who sustained splenic trauma between 2007 and 2015, excluding those with death on arrival and selected nonsplenic high-grade injuries. Patients were categorized into (1) nonoperative management (NOM), (2) embolization, (3) splenectomy, (4) splenic repair, and (5) combined treatment groups. Evaluated outcomes included hospital length of stay (LOS), intensive care unit LOS, mortality, and NOM and embolization failures.Overall, 117,743 patients with splenic predominant trauma were included in this study. Over the 9-year study period, 85,793 (72.9%) were treated with NOM, 21,999 (18.9%) with splenectomy, 3895 (3.3%) with embolization, and 2131 (1.8%) with splenic repair. From 2007 to 2015, mortality rates declined from 7.6% to 4.7%. The rate of NOM did not significantly change over time, while embolization increased 369% (1.3%-4.8%). Failure of NOM was 4.4% in 2007 and decreased to 3.4% in 2015. Across all injury grades, NOM had the shortest LOS (8.3 days), followed by splenic repair (12.3), embolization (12.6), and splenectomy (13.8) (p0.001). When adjusted for various clinical factors including severity of splenic injury, mortality rates were 7.1% for splenectomy, 3.2% for embolization, and 2.5% for NOM.Most patients with splenic-dominant blunt trauma are managed with NOM. Over time, the use of embolization has increased while open surgery has declined, and mortality has improved for all treatment methods. Compared to splenectomy, embolization is associated with shorter hospital LOS but is still used relatively infrequently.

There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. However, several fundamental problems are still not well considered when conducting such connectome integration analyses, e.g., “Which structure (e.g., gray matter, white matter, white surface or pial surface) should be used for defining SC and FC and exploring their relationships”, “Which brain parcellation should be used”, and “How do the SC and FC correlate with each other and how do such correlations vary in different locations of the brain?”. In this work, we develop a new framework calledsurface-based connectivity integration(SBCI) to facilitate the integrative analysis of SC and FC with a re-thinking of these problems. We propose to use the white surface (the interface of white matter and gray matter) to build both SC and FC since diffusion signals are in the white matter while functional signals are more present in the gray matter. SBCI also represents both SC and FC in a continuous manner at very high spatial resolution on the white surface, avoiding the need of pre-specified atlases which may bias the comparison of SC and FC. Using data from the Human Connectome Project, we show that SBCI can create reproducible, high quality SC and FC, in addition to three novel imaging biomarkers reflective of the similarity between SC and FC throughout the brain, called global, local, and discreteSC-FC coupling. Further, we demonstrate the usefulness of these biomarkers in finding group effects due to biological sex throughout the brain.

Multi‐channel phased receive arrays have been widely adopted for magnetic resonance imaging (MRI) and spectroscopy (MRS). An important step in the use of receive arrays for MRS is the combination of spectra collected from individual coil channels. The goal of this work was to implement an improved strategy termed OpTIMUS (i.e., optimized truncation to integrate multi‐channel MRS data using rank‐R singular value decomposition) for combining data from individual channels. OpTIMUS relies on spectral windowing coupled with a rank‐R decomposition to calculate the optimal coil channel weights. MRS data acquired from a brain spectroscopy phantom and 11 healthy volunteers were first processed using a whitening transformation to remove correlated noise. Whitened spectra were then iteratively windowed or truncated, followed by a rank‐R singular value decomposition (SVD) to empirically determine the coil channel weights. Spectra combined using the vendor‐supplied method, signal/noise2 weighting, previously reported whitened SVD (rank‐1), and OpTIMUS were evaluated using the signal‐to‐noise ratio (SNR). Significant increases in SNR ranging from 6% to 33% (P ≤ 0.05) were observed for brain MRS data combined with OpTIMUS compared with the three other combination algorithms. The assumption that a rank‐1 SVD maximizes SNR was tested empirically, and a higher rank‐R decomposition, combined with spectral windowing prior to SVD, resulted in increased SNR., Optimized truncation to integrate multi‐channel MRS data using rank‐R singular value decomposition (OpTIMUS) is an improved strategy for combining MRS data collected from multi‐channel phased receive arrays that uses a rank‐R decomposition and spectral windowing prior to singular value decomposition (SVD). Spectra combined using OpTIMUS, whitened singular value decomposition (WSVD), signal/noise2 (S/N2) weighting, and the vendor‐supplied method were evaluated using SNR. In vivo spectra combined with OpTIMUS had significantly higher SNR compared with the other three methods.

Independent component analysis (ICA) is popular in many applications, including cognitive neuroscience and signal processing. Due to computational constraints, principal component analysis 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 this model, 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 semi-parametric 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 an fMRI 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.

As advances in technology allow the acquisition of complementary information, it is increasingly common for scientific studies to collect multiple datasets. Large-scale neuroimaging studies often include multiple modalities (e.g., task functional MRI, resting-state fMRI, diffusion MRI, and/or structural MRI), with the aim to understand the relationships between datasets. In this study, we seek to understand whether regions of the brain activated in a working memory task relate to resting-state correlations. In neuroimaging, a popular approach uses principal component analysis for dimension reduction prior to canonical correlation analysis with joint independent component analysis, but this may discard biological features with low variance and/or spuriously associate structure unique to a dataset with joint structure. We introduce Simultaneous Non-Gaussian component analysis (SING) in which dimension reduction and feature extraction are achieved simultaneously, and shared information is captured via subject scores. We apply our method to a working memory task and resting-state correlations from the Human Connectome Project. We find joint structure as evident from joint scores whose loadings highlight resting-state correlations involving regions associated with working memory. Moreover, some of the subject scores are related to fluid intelligence.

SUMMARY It is of great interest to quantify the contributions of genetic variation to brain structure and function, which are usually measured by high-dimensional imaging data (e.g., magnetic resonance imaging). In addition to the variance, the covariance patterns in the genetic effects of a functional phenotype are of biological importance, and covariance patterns have been linked to psychiatric disorders. The aim of this article is to develop a scalable method to estimate heritability and the nonstationary covariance components in high-dimensional imaging data from twin studies. Our motivating example is from the Human Connectome Project (HCP). Several major big-data challenges arise from estimating the genetic and environmental covariance functions of functional phenotypes extracted from imaging data, such as cortical thickness with 60 000 vertices. Notably, truncating to positive eigenvalues and their eigenfunctions from unconstrained estimators can result in large bias. This motivated our development of a novel estimator ensuring positive semidefiniteness. Simulation studies demonstrate large improvements over existing approaches, both with respect to heritability estimates and covariance estimation. We applied the proposed method to cortical thickness data from the HCP. Our analysis suggests fine-scale differences in covariance patterns, identifying locations in which genetic control is correlated with large areas of the brain and locations where it is highly localized.

Recently, there has been an explosive growth in graphical modeling approaches for estimating brain functional networks. In a detailed study, we show that surprisingly, standard graphical modeling approaches for fMRI data may not yield accurate estimates of the brain network due to the inability to suitably account for temporal correlations. We propose a novel Bayesian matrix normal graphical model that jointly models the temporal covariance and the brain network under a separable structure for the covariance to obtain improved estimates. The approach is implemented via an efficient optimization algorithm that computes the maximum-a-posteriori network estimates having desirable theoretical properties and which is scalable to high dimensions. The proposed method leads to substantial gains in network estimation accuracy compared to standard brain network modeling approaches as illustrated via extensive simulations. We apply the method to resting state fMRI data from the Human Connectome Project involving a large number of time scans and brain regions, to study the relationships between fluid intelligence and functional connectivity, where it is not computationally feasible to apply existing matrix normal graphical models. Our proposed approach led to the detection of differences in connectivity between high and low fluid intelligence groups, whereas these differences were less pronounced or absent using the graphical lasso.

(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. A 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. Four 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

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

Simultaneous multislice (SMS) acquisition 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 acquisition scenarios used in the young adult Human Connectome Project (HCP), as well as split slice-GRAPPA (leak block). In addition to slice leakage, signal leakage can result from spatial smoothing, i.e., smoothing leakage, which leads to inflated regions of activation. Previous studies have generally found that SMS acquisition results in higher test statistics and/or a greater number of activated voxels. Here, we use simulations to disentangle this phenomenon 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, which used the original slice-GRAPPA. 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.

Summary We examine differences between independent component analyses (ICAs) arising from different assumptions, measures of dependence, and starting points of the algorithms. ICA is a popular method with diverse applications including artifact removal in electrophysiology data, feature extraction in microarray data, and identifying brain networks in functional magnetic resonance imaging (fMRI). ICA can be viewed as a generalization of principal component analysis (PCA) that takes into account higher-order cross-correlations. Whereas the PCA solution is unique, there are many ICA methods–whose solutions may differ. Infomax, FastICA, and JADE are commonly applied to fMRI studies, with FastICA being arguably the most popular. Hastie and Tibshirani (2003) demonstrated that ProDenICA outperformed FastICA in simulations with two components. We introduce the application of ProDenICA to simulations with more components and to fMRI data. ProDenICA was more accurate in simulations, and we identified differences between biologically meaningful ICs from ProDenICA versus other methods in the fMRI analysis. ICA methods require nonconvex optimization, yet current practices do not recognize the importance of, nor adequately address sensitivity to, initial values. We found that local optima led to dramatically different estimates in both simulations and group ICA of fMRI, and we provide evidence that the global optimum from ProDenICA is the best estimate. We applied a modification of the Hungarian (Kuhn-Munkres) algorithm to match ICs from multiple estimates, thereby gaining novel insights into how brain networks vary in their sensitivity to initial values and ICA method.

Forecasting future species distributions under climate change scenarios using Ecological Niche Models (ENM) is common practice. Typically, these projections do not account for landscape connectivity and species dispersal abilities. When they do account for these factors, they are based on either rather simplistic or overly complex and data-hungry approaches. Here we apply a new approach for predicting species range shifts under different climate change and landscape connectivity scenarios that balances data requirements and output quality. The approach builds on the metapopulation concept to produce a dispersal model based on repeated simulations of stochastic extinction-colonization dynamics across multiple landscapes of variable connectivity. The model is then combined with an ENM to produce more realistic predictions of species range shifts under environmental change. Using the near-threatened Cabrera vole ( Microtus cabrerae ) as a model species and considering two contrasting climate change scenarios (B2 and A1b) and three scenarios of increasing landscape connectivity, we confirmed that model predictions based solely on ENM overestimated future range sizes (2050 and 2080) in relation to predictions incorporating both future climates and landscape connectivity constraints. This supports the idea that landscape change critically affects species range shifts in addition to climate change, and that models disregarding landscape connectivity tend to produce overly optimistic predictions, particularly for species with low dispersal abilities. We suggest that our empirically-based simulation modelling approach provides a useful framework to improve range shift predictions for a broad range of species, which is essential for the conservation planning of metapopulations under climate and landscape change.

The incidence function model (IFM) uses area and connectivity to predict metapopulation dynamics. However, false absences and missing data can lead to underestimates of the number of sites contributing to connectivity, resulting in overestimates of dispersal ability and turnovers (extinctions plus colonizations). We extend estimation methods for the IFM by using a hierarchical Bayesian model to account both for false absences due to imperfect detection and for missing data due to sites not surveyed in some years. We compare parameter estimates, measures of metapopulation dynamics, and forecasts using stochastic patch occupancy models (SPOMs) among three IFM models: (1) a Bayesian formulation assuming no false absences and omitting site-year combinations with missing data; (2) a hierarchical Bayesian formulation assuming no false absences but incorporating missing data; and (3) a hierarchical Bayesian formulation allowing for imperfect detection and incorporating missing data. We fit the models to multiyear data sets of occupancy for two bird species that differ in body size and presumed dispersal ability but inhabit the same network of sites: the small Black Rail (Laterallus jamaicensis) and the medium-sized Virginia Rail (Rallus limicola). Incorporating missing data affected colonization parameters and led to lower estimates of dispersal ability for the Black Rail. Detection rates were high for the Black Rail in most years but moderate for the Virginia Rail. Incorporating imperfect detection resulted in higher occupancy and lower turnover rates for both species, with largest effects for the Virginia Rail. Forecasts using SPOMs were sensitive to both missing data and false absences; persistence in models assuming no false absences was more optimistic than from robust models. Our results suggest that incorporating false absences and missing data into the IFM can improve (1) estimates of dispersal ability and the effect of connectivity on colonization, (2) the scaling of extinction risk with patch area, and (3) forecasts of occupancy and turnover rates.

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.

We examine differences between independent component analyses (ICAs) arising from different as-sumptions, measures of dependence, and starting points of the algorithms. ICA is a popular method with diverse applications including artifact removal in electrophysiology data, feature extraction in microarray data, and identifying brain networks in functional magnetic resonance imaging (fMRI). ICA can be viewed as a generalization of principal component analysis (PCA) that takes into account higher-order cross-correlations. Whereas the PCA solution is unique, there are many ICA methods–whose solutions may differ. Infomax, FastICA, and JADE are commonly applied to fMRI studies, with FastICA being arguably the most popular. Hastie and Tibshirani (2003) demonstrated that ProDenICA outperformed FastICA in simulations with two components. We introduce the application of ProDenICA to simulations with more components and to fMRI data. ProDenICA was more accurate in simulations, and we identified differences between biologically meaningful ICs from ProDenICA versus other methods in the fMRI analysis. ICA methods require nonconvex optimization, yet current practices do not recognize the importance of, nor adequately address sensitivity to, initial values. We found that local optima led to dramatically different estimates in both simulations and group ICA of fMRI, and we provide evidence that the global optimum from ProDenICA is the best estimate. We applied a modification of the Hungarian (Kuhn-Munkres) algorithm to match ICs from multiple estimates, thereby gaining novel insights into how brain networks vary in their sensitivity to initial values and ICA method.

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.