Lower Prevalence of Asymptomatic Alzheimer's Disease Among Healthy African Americans.

OBJECTIVE: Alzheimer's disease (AD) is believed to be more common in African Americans (AA), but biomarker studies in AA populations are limited. This report represents the largest study to date examining cerebrospinal fluid AD biomarkers in AA individuals. METHODS: We analyzed 3,006 cerebrospinal fluid samples from controls, AD cases, and non-AD cases, including 495 (16.5%) self-identified black/AA and 2,456 (81.7%) white/European individuals using cutoffs derived from the Alzheimer's Disease Neuroimaging Initiative, and using a data-driven multivariate Gaussian mixture of regressions. RESULTS: Distinct effects of race were found in different groups. Total Tauand phospho181-Tau were lower among AA individuals in all groups (p < 0.0001), and Aß42 was markedly lower in AA controls compared with white controls (p < 0.0001). Gaussian mixture of regressions modeling of cerebrospinal fluid distributions incorporating adjustments for covariates revealed coefficient estimates for AA race comparable with 2-decade change in age. Using Alzheimer's Disease Neuroimaging Initiative cutoffs, fewer AA controls were classified as biomarker-positive asymptomatic AD (8.0% vs 13.4%). After adjusting for covariates, our Gaussian mixture of regressions model reduced this difference, but continued to predict lower prevalence of asymptomatic AD among AA controls (9.3% vs 13.5%). INTERPRETATION: Although the risk of dementia is higher, data-driven modeling indicates lower frequency of asymptomatic AD in AA controls, suggesting that dementia among AA populations may not be driven by higher rates of AD. ANN NEUROL 2024;96:463-475.

Feasibility of Multiparametric Perfusion Assessment in Diabetic Foot Ulcer Using Intravoxel Incoherent Motion and Blood Oxygenation-Level Dependent MRI.

BACKGROUND: Patients with type-2 diabetes (T2DM) are at increased risk of developing diabetic foot ulcers (DFU) and experiencing impaired wound healing related to underlying microvascular disease. PURPOSE: To evaluate the sensitivity of intra-voxel incoherent motion (IVIM) and blood oxygen level dependent (BOLD) MRI to microvascular changes in patients with DFUs. STUDY TYPE: Case-control. POPULATION: 20 volunteers who were age and body mass index matched, including T2DM patients with DFUs (N = 10, mean age = 57.5 years), T2DM patients with controlled glycemia and without DFUs (DC, N = 5, mean age = 57.4 years) and healthy controls (HC, N = 5, mean age = 52.8 years). FIELD STRENGTH/SEQUENCE: 3T/multi-b-value IVIM and dynamic BOLD. ASSESSMENT: Resting IVIM parameters were obtained using a multi-b-value diffusion-weighted imaging sequence and two IVIM models were fit to obtain diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) and microvascular volume fraction (MVF) parameters. Microvascular reactivity was evaluated by inducing an ischemic state in the foot with a blood pressure cuff during dynamic BOLD imaging. Perfusion indices were assessed in two regions of the foot: the medial plantar (MP) and lateral plantar (LP) regions. STATISTICAL TESTS: Effect sizes of group mean differences were assessed using Hedge's g adjusted for small sample sizes. RESULTS: DFU participants exhibited elevated D*, f, and MVF values in both regions (g ≥ 1.10) and increased D (g = 1.07) in the MP region compared to DC participants. DC participants showed reduced f and MVF compared to HC participants in the MP region (g ≥ 1.06). Finally, the DFU group showed reduced tolerance for ischemia in the LP region (g = -1.51) and blunted reperfusion response in both regions (g < -2.32) compared to the DC group during the cuff-occlusion challenge. DATA CONCLUSION: The combined use of IVIM and BOLD MRI shows promise in differentiating perfusion abnormalities in the feet of diabetic patients and suggests hyperperfusion in DFU patients. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 1.

Differences in Achilles tendon mechanical properties between professional ballet dancers and collegiate athletes utilizing shear wave elastography.

PURPOSE: To report normative stiffness parameters obtained using shear wave elastography in dorsiflexion from the Achilles tendons in asymptomatic professional ballet dancers and compare them with college-level athletes. METHODS: An Institutional Review Board (IRB)-approved study consists of 28 professional ballet dancers and 64 asymptomatic collegiate athletes. The athletes were further subdivided into runner and non-runner disciplines. Shear wave elastography (SWE) measurements were made in maximum ankle dorsiflexion position. RESULTS AND DISCUSSION: Forty-eight (52%) males and 44 (48%) females were examined with an overall mean age of 22.2 (± 3.8 years). There were no significant SWE differences between dominant and non-dominant legs in both groups and comparing spin vs. non-spin leg of ballet dancers (p > 0.05). Ballet dancers had significantly higher short-axis velocity values than runners and non-runners (2.34 m/s increase and 2.79 m/s increase, respectively, p < 0.001). Long-axis velocity was significantly higher in ballet dancers compared to non-runners (by 0.80 m/s, p < 0.001), but was not different between ballet dancers and runners (p > 0.05). Short-axis modulus was significantly higher in dancers compared to runners and non-runners (by 135.2 kPa and 159.2 kPa, respectively, p < 0.001). Long-axis modulus (LAM) was not significantly different in ballet dancers when compared to runners. CONCLUSION: Asymptomatic professional ballet dancers exhibit greater short-axis tendon stiffness compared to athletes and greater long-axis tendon stiffness compared to non-runners but similar to runners. The functional benefit from elevated short-axis stiffness in dancers is not clear but may be related to greater axial loading and adaptations of the tendon matrix.

Resting-State Brain Temperature: Dynamic Fluctuations in Brain Temperature and the Brain-Body Temperature Gradient.

BACKGROUND: While fluctuations in healthy brain temperature have been investigated over time periods of weeks to months, dynamics over shorter time periods are less clear. PURPOSE: To identify physiological fluctuations in brain temperature in healthy volunteers over time scales of approximately 1 hour. STUDY TYPE: Prospective. SUBJECTS: A total of 30 healthy volunteers (15 female; 26 ± 4 years old). SEQUENCE AND FIELD STRENGTH: 3 T; T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) and semi-localized by adiabatic selective refocusing (sLASER) single-voxel spectroscopy. ASSESSMENTS: Brain temperature was calculated from the chemical shift difference between N-acetylaspartate and water. To evaluate within-scan repeatability of brain temperature and the brain-body temperature difference, 128 spectral transients were divided into two sets of 64-spectra. Between-scan repeatability was evaluated using two time periods, ~1-1.5 hours apart. STATISTICAL TESTS: A hierarchical linear mixed model was used to calculate within-scan and between-scan correlations (Rw and Rb , respectively). Significance was determined at P ≤ .05. Values are reported as the mean ± standard deviation. RESULTS: A significant difference in brain temperature was observed between scans (-0.4 °C) but body temperature was stable (P = .59). Brain temperature (37.9 ± 0.7 °C) was higher than body temperature (36.5 ± 0.5 °C) for all but one subject. Within-scan correlation was high for brain temperature (Rw  = 0.95) and brain-body temperature differences (Rw  = 0.96). Between scans, variability was high for both brain temperature (Rb  = 0.30) and brain-body temperature differences (Rb  = 0.41). DATA CONCLUSION: Significant changes in brain temperature over time scales of ~1 hour were observed. High short-term repeatability suggests temperature changes appear to be due to physiology rather than measurement error. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?

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.

Stability of patch-turnover relationships under equilibrium and nonequilibrium metapopulation dynamics driven by biogeography.

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.

ACE of space: estimating genetic components of high-dimensional imaging data.

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.

Utilization and Comparative Effectiveness of Uterine Artery Embolization versus Hysterectomy for Severe Postpartum Hemorrhage: A National Inpatient Sample Study.

PURPOSE: To identify differences in mortality or length of hospital stay for mothers treated with uterine artery embolization (UAE) or hysterectomy for severe postpartum hemorrhage (PPH), as well as to analyze whether geographic or clinical determinants affected the type of therapy received. MATERIALS AND METHODS: This National Inpatient Sample study from 2005 to 2017 included all patients with live-birth deliveries. Severe PPH was defined as PPH that required transfusion, hysterectomy, or UAE. Propensity score weighting-adjusted demographic, maternal, and delivery risk factors were used to assess mortality and prolonged hospital stay. RESULTS: Of 9.8 million identified live births, PPH occurred in 31.0 per 1,000 cases. The most common intervention for PPH was transfusion (116.4 per 1,000 cases of PPH). Hysterectomy was used more frequently than UAE (20.4 vs 12.9 per 1,000 cases). The following factors predicted that hysterectomy would be used more commonly than UAE: previous cesarean delivery, breech fetal position, placenta previa, transient hypertension during pregnancy without pre-eclampsia, pre-existing hypertension without pre-eclampsia, pre-existing hypertension with pre-eclampsia, unspecified maternal hypertension, and gestational diabetes (all P < .001). Delivery risk factors associated with greater utilization of hysterectomy over UAE included postterm pregnancy, premature rupture of membranes, cervical laceration, forceps vaginal delivery, and shock (all P < .001). There was no difference in mortality between hysterectomy and UAE. After balancing demographic, maternal, and delivery risk factors, the odds of prolonged hospital stay were 0.38 times lower with UAE than hysterectomy (P < .001). CONCLUSIONS: Despite similar mortality and shorter hospital stays, UAE is used far less than hysterectomy in the management of severe PPH.

Group linear non-Gaussian component analysis with applications to neuroimaging.

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.

Which multiband factor should you choose for your resting-state fMRI study?

Multiband acquisition, also called simultaneous multislice, has become a popular technique in resting-state functional connectivity studies. Multiband (MB) acceleration leads to a higher temporal resolution but also leads to spatially heterogeneous noise amplification, suggesting the costs may be greater in areas such as the subcortex. We evaluate MB factors of 2, 3, 4, 6, 8, 9, and 12 with 2 mm isotropic voxels, and additionally 2 mm and 3.3 mm single-band acquisitions, on a 32-channel head coil. Noise amplification was greater in deeper brain regions, including subcortical regions. Correlations were attenuated by noise amplification, which resulted in spatially varying biases that were more severe at higher MB factors. Temporal filtering decreased spatial biases in correlations due to noise amplification, but also tended to decrease effect sizes. In seed-based correlation maps, left-right putamen connectivity and thalamo-motor connectivity were highest in the single-band 3.3 mm protocol. In correlation matrices, MB 4, 6, and 8 had a greater number of significant correlations than the other acquisitions (both with and without temporal filtering). We recommend single-band 3.3 mm for seed-based subcortical analyses, and MB 4 provides a reasonable balance for studies analyzing both seed-based correlation maps and connectivity matrices. In multiband studies including secondary analyses of large-scale datasets, we recommend reporting effect sizes or test statistics instead of correlations. If correlations are reported, temporal filtering (or another method for thermal noise removal) should be used. The Emory Multiband Dataset is available on OpenNeuro.

Surface-Based Connectivity Integration: An atlas-free approach to jointly study functional and structural connectivity.

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.

Scalable Bayesian matrix normal graphical models for brain functional networks.

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.

Contemporary Management of Pediatric Blunt Splenic Trauma: A National Trauma Databank Analysis.

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 <.001). No statistically significant difference was observed in mortality between embolization and NOM (OR: 0.96, P = 1.0). CONCLUSIONS: In pediatric splenic injury, NOM is the most utilized and associated with favorable outcomes, most notably in grades III to V pediatric splenic injury. If intervention is needed, embolization is effective and increasingly utilized most significantly in lower grade injuries.

Impact of an Urban Sanitation Intervention on Enteric Pathogen Detection in Soils.

Environmental fecal contamination is common in many low-income cities, contributing to a high burden of enteric infections and associated negative sequelae. To evaluate the impact of a shared onsite sanitation intervention in Maputo, Mozambique on enteric pathogens in the domestic environment, we collected 179 soil samples at shared latrine entrances from intervention (n = 49) and control (n = 51) compounds during baseline (preintervention) and after 24 months (postintervention) as part of the Maputo Sanitation Trial. We tested soils for the presence of nucleic acids associated with 18 enteric pathogens using a multiplex reverse transcription qPCR platform. We detected at least one pathogen-associated gene target in 91% (163/179) of soils and a median of 3 (IQR = 1, 5) pathogens. Using a difference-in-difference analysis and adjusting for compound population, visibly wet soil, sun exposure, wealth, temperature, animal presence, and visible feces, we estimate the intervention reduced the probability of detecting ≥1 pathogen gene by 15% (adjusted prevalence ratio, aPR = 0.85; 95% CI: 0.70, 1.0) and the total number of pathogens by 35% (aPR = 0.65; 0.44, 0.95) in soil 24 months following the intervention. These results suggest that the intervention reduced the presence of some fecal contamination in the domestic environment, but pathogen detection remained prevalent 24 months following the introduction of new latrines.

Statistical learning of blunt cerebrovascular injury risk factors using the elastic net.

PURPOSE: To compare logistic regression to elastic net for identifying and ranking clinical risk factors for blunt cerebrovascular injury (BCVI). MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Optimized truncation to integrate multi-channel MRS data using rank-R singular value decomposition.

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.

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 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.

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 METHODS: 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. RESULTS: 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 < 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). CONCLUSION: US 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).

JIVE integration of imaging and behavioral data.

A major goal in neuroscience is to understand the neural pathways underlying human behavior. We introduce the recently developed Joint and Individual Variation Explained (JIVE) method to the neuroscience community to simultaneously analyze imaging and behavioral data from the Human Connectome Project. Motivated by recent computational and theoretical improvements in the JIVE approach, we simultaneously explore the joint and individual variation between and within imaging and behavioral data. In particular, we demonstrate that JIVE is an effective and efficient approach for integrating task fMRI and behavioral variables using three examples: one example where task variation is strong, one where task variation is weak and a reference case where the behavior is not directly related to the image. These examples are provided to visualize the different levels of signal found in the joint variation including working memory regions in the image data and accuracy and response time from the in-task behavioral variables. Joint analysis provides insights not available from conventional single block decomposition methods such as Singular Value Decomposition. Additionally, the joint variation estimated by JIVE appears to more clearly identify the working memory regions than Partial Least Squares (PLS), while Canonical Correlation Analysis (CCA) gives grossly overfit results. The individual variation in JIVE captures the behavior unrelated signals such as a background activation that is spatially homogeneous and activation in the default mode network. The information revealed by this individual variation is not examined in traditional methods such as CCA and PLS. We suggest that JIVE can be used as an alternative to PLS and CCA to improve estimation of the signal common to two or more datasets and reveal novel insights into the signal unique to each dataset.

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.