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Background and Purpose: The Brain Tumor Reporting and Data System (BT-RADS) is a structured radiology reporting algorithm that was introduced to provide uniformity in posttreatment primary brain tumor follow-up and reporting, but its interrater reliability (IRR) assessment has not been widely studied. Our goal is to evaluate the IRR among neuroradiologists and radiology residents in the use of BT-RADS., Materials and Methods: This retrospective study reviewed 103 consecutive MR studies in 98 adult patients previously diagnosed with and treated for primary brain tumor (January 2019 to February 2019). Six readers with varied experience (4 neuroradiologists and 2 radiology residents) independently evaluated each case and assigned a BT-RADS score. Readers were blinded to the original score reports and the reports from other readers. Cases in which at least 1 neuroradiologist scored differently were subjected to consensus scoring. After the study, a post hoc reference score was also assigned by 2 readers by using future imaging and clinical information previously unavailable to readers. The interrater reliabilities were assessed by using the Gwet AC2 index with ordinal weights and percent agreement., Results: Of the 98 patients evaluated (median age, 53 years; interquartile range, 41-66 years), 53% were men. The most common tumor type was astrocytoma (77%) of which 56% were grade 4 glioblastoma. Gwet index for interrater reliability among all 6 readers was 0.83 (95% CI: 0.78-0.87). The Gwet index for the neuroradiologists' group (0.84 [95% CI: 0.79-0.89]) was not statistically different from that for the residents' group (0.79 [95% CI: 0.72-0.86]) (χ 2 = 0.85; P = .36). All 4 neuroradiologists agreed on the same BT-RADS score in 57 of the 103 studies, 3 neuroradiologists agreed in 21 of the 103 studies, and 2 neuroradiologists agreed in 21 of the 103 studies. Percent agreement between neuroradiologist blinded scores and post hoc reference scores ranged from 41%-52%., Conclusions: A very good interrater agreement was found when tumor reports were interpreted by independent blinded readers by using BT-RADS criteria. Further study is needed to determine if this high overall agreement can translate into greater consistency in clinical care., (© 2024 by American Journal of Neuroradiology.)

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 Aβ 42 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., (© 2024 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

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., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

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., (© 2023 International Society for Magnetic Resonance in Medicine.)

There is no gold standard for the diagnosis of Alzheimer's disease (AD), except from autopsies, which motivates the use of unsupervised learning. A mixture of regressions is an unsupervised method that can simultaneously identify clusters from multiple biomarkers while learning within-cluster demographic effects. Cerebrospinal fluid (CSF) biomarkers for AD have detection limits, which create additional challenges. We apply a mixture of regressions with a multivariate truncated Gaussian distribution (also called a censored multivariate Gaussian mixture of regressions or a mixture of multivariate tobit regressions) to over 3,000 participants from the Emory Goizueta Alzheimer's Disease Research Center and Emory Healthy Brain Study to examine amyloid-beta peptide 1-42 (Abeta42), total tau protein and phosphorylated tau protein in CSF with known detection limits. We address three gaps in the literature on mixture of regressions with a truncated multivariate Gaussian distribution: software availability; inference; and clustering accuracy. We discovered three clusters that tend to align with an AD group, a normal control profile and non-AD pathology. The CSF profiles differed by race, gender and the genetic marker ApoE4, highlighting the importance of considering demographic factors in unsupervised learning with detection limits. Notably, African American participants in the AD-like group had significantly lower tau burden.

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 (R w and R b , 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 (R w  = 0.95) and brain-body temperature differences (R w  = 0.96). Between scans, variability was high for both brain temperature (R b  = 0.30) and brain-body temperature differences (R b  = 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., (© 2022 International Society for Magnetic Resonance in Medicine.)

Background: Deep brain stimulation (DBS) for Parkinson's disease (PD) is generally contraindicated in persons with dementia but it is frequently performed in people with mild cognitive impairment or normal cognition, and current clinical guidelines are primarily based on these cohorts., Objectives: To determine if moderately cognitive impaired individuals including those with mild dementia could meaningfully benefit from DBS in terms of motor and non-motor outcomes., Methods: In this retrospective case-control study, we identified a cohort of 40 patients with PD who exhibited moderate (two or more standard deviations below normative scores) cognitive impairment (CI) during presurgical workup and compared their 1-year clinical outcomes to a cohort of 40 matched patients with normal cognition (NC). The surgery targeted subthalamus, pallidus or motor thalamus, in a unilateral, bilateral or staged approach., Results: At preoperative baseline, the CI cohort had higher Unified Parkinson's Disease Rating Scale (UPDRS) subscores, but similar levodopa responsiveness compared to the NC cohort. The NC and CI cohorts demonstrated comparable degrees of postoperative improvement in the OFF-medication motor scores, motor fluctuations, and medication reduction. There was no difference in adverse event rates between the two cohorts. Outcomes in the CI cohort did not depend on the target, surgical staging, or impaired cognitive domain., Conclusions: Moderately cognitively impaired patients with PD can experience meaningful motor benefit and medication reduction with DBS., (© 2023 International Parkinson and Movement Disorder Society.)

Purpose: The presence of orientation-dependent susceptibility artifacts in magnetic resonance chemical shift thermometry (CST) can confound accurate temperature calculations. Here, we quantify the effect of white matter (WM) tract orientation on CST due to tissue-specific susceptibility., Methods: Twenty-nine healthy volunteers (27 ± 4 years old) were scanned on a 3 T MR scanner with a 32-channel head coil. Diffusion tensor imaging (DTI), T1-weighted imaging, and single voxel spectroscopy (SVS) for CST were acquired. Participants were then asked to rotate their head ∼3-5° (yaw or roll) to alter the orientation of WM tracts relative to the external magnetic field. After head rotation, a second SVS scan and T1-weighted imaging were acquired. The WM-fraction-normalized DTI principal eigenvector (V1) images were used to calculate the length of the x-y component of V1, which was used as a surrogate for WM tracts perpendicular to B 0 . A linear regression model was used to determine the relationship between the perpendicular WM tracts and brain temperature., Results: Significant temperature differences between post- and pre-head rotation scans were observed for brain (-0.72 °C ± 1.36 °C, p = 0.01) but not body (0.012 °C ± 0.07 °C, p = 0.37) temperatures. The difference in brain temperature was positively associated with the corresponding change in perpendicular WM tracts after head rotation (R 2  = 0.26, p = 0.005)., Conclusion: Our results indicate WM tract orientation affects temperature calculations, suggesting artifacts from orientation-dependent susceptibility may be present in CST., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Brain temperature is an understudied parameter relevant to brain injury and ischemia. To advance our understanding of thermal dynamics in the human brain, combined with the challenges of routine experimental measurements, a biophysical modeling framework was developed to facilitate individualized brain temperature predictions. Model-predicted brain temperatures using our fully conserved model were compared with whole brain chemical shift thermometry acquired in 30 healthy human subjects (15 male and 15 female, age range 18-36 years old). Magnetic resonance (MR) thermometry, as well as structural imaging, angiography, and venography, were acquired prospectively on a Siemens Prisma whole body 3 T MR scanner. Bland-Altman plots demonstrate agreement between model-predicted and MR-measured brain temperatures at the voxel-level. Regional variations were similar between predicted and measured temperatures (< 0.55 °C for all 10 cortical and 12 subcortical regions of interest), and subcortical white matter temperatures were higher than cortical regions. We anticipate the advancement of brain temperature as a marker of health and injury will be facilitated by a well-validated computational model which can enable predictions when experiments are not feasible., (© 2022. The Author(s).)

Joint and Individual Variation Explained (JIVE) is a model that decomposes multiple datasets obtained on the same subjects into shared structure, structure unique to each dataset, and noise. JIVE is an important tool for multimodal data integration in neuroimaging. The two most common algorithms are R.JIVE, an iterative approach, and AJIVE, which uses principal angle analysis. The joint structure in JIVE is defined by shared subspaces, but interpreting these subspaces can be challenging. In this paper, we reinterpret AJIVE as a canonical correlation analysis of principal component scores. This reformulation, which we call CJIVE, (1) provides an intuitive view of AJIVE; (2) uses a permutation test for the number of joint components; (3) can be used to predict subject scores for out-of-sample observations; and (4) is computationally fast. We conduct simulation studies that show CJIVE and AJIVE are accurate when the total signal ranks are correctly specified but, generally inaccurate when the total ranks are too large. CJIVE and AJIVE can still extract joint signal even when the joint signal variance is relatively small. JIVE methods are applied to integrate functional connectivity (resting-state fMRI) and structural connectivity (diffusion MRI) from the Human Connectome Project. Surprisingly, the edges with largest loadings in the joint component in functional connectivity do not coincide with the same edges in the structural connectivity, indicating more complex patterns than assumed in spatial priors. Using these loadings, we accurately predict joint subject scores in new participants. We also find joint scores are associated with fluid intelligence, highlighting the potential for JIVE to reveal important shared structure., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Murden, Zhang, Guo and Risk.)

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., (Copyright © 2022. Published by Elsevier Inc.)

While hippocampal atrophy and its regional susceptibility to Alzheimer's disease (AD) are well reported at late stages of AD, studies of the asymptomatic stage of AD are limited but could elucidate early stage pathophysiology as well as provide predictive biomarkers. In this study, we performed multi-modal magnetic resonance imaging (MRI) to estimate morphometry, functional connectivity, and tissue microstructure of hippocampal subfields in cognitively normal adults including those with asymptomatic AD. High-resolution resting-state functional, diffusion and structural MRI, cerebral spinal fluid (CSF), and neuropsychological evaluations were performed in healthy young adults (HY: n = 40) and healthy older adults with negative (HO-: n = 47) and positive (HO+ : n = 25) CSF biomarkers of AD. Morphometry, functional connectivity, and tissue microstructure were estimated from the structural, functional, and diffusion MRI images, respectively. Our results indicated that normal aging affected morphometry, connectivity, and microstructure in all hippocampal subfields, while the subiculum and CA1-3 demonstrated the greatest sensitivity to asymptomatic AD pathology. Tau, rather than amyloid-β, was closely associated with imaging-derived synaptic and microstructural measures. Microstructural metrics were significantly associated with neuropsychological assessments. These findings suggest that the subiculum and CA1-3 are the most vulnerable in asymptomatic AD and tau level is driving these early changes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wu, Shahid, Lin, Hone-Blanchet, Smith, Risk, Bisht, Loring, Goldstein, Levey, Lah and Qiu.)

Purpose: 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., Methods: 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., Results: There were significant improvements in post-intervention on subjective and objective measures of dizziness and gait ( p  < .05). The response to intervention was independent of pre-injury migraine history but pre-injury depression/anxiety increased self-report of post-treatment anxiety. Concurrent treatment with medications did not influence response to treatment., Conclusions: 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.

Background: Carotid webs (CaWs) are associated with ischemic strokes in younger patients without degrees of stenosis that are traditionally considered clinically significant., Objective: To 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., Methods: We 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., Results: Mean 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 (p<0.001), relative AUC at wash out (p=0.031), and relative FWHM (p=0.001). Logistic regression to predict CaW versus mild/moderate atherosclerosis showed that rAUC total had the highest predictive value (pAUC=0.96, 95% CI 0.90 to 1.00), followed by rFWHM (0.87, 95% CI 0.74 to 1.00), and rAUC WO (0.74, 95% CI (0.57 to 0.91)., Conclusion: CaW 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., Competing Interests: Competing interests: JNO receives research project funding from Siemens Medical Solutions., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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.

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.

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., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

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., (© 2020 The International Biometric Society.)

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., Competing Interests: The authors have declared that no competing interests exist.

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., (© The Author 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Gliomas are one of the most common types of brain tumors. Given low survival and high treatment resistance rates, particularly for high grade gliomas, there is a need for specific biomarkers that can be used to stratify patients for therapy and monitor treatment response. Recent work has demonstrated that metabolic reprogramming, often mediated by inflammation, can lead to an upregulation of glutamine as an energy source for cancer cells. As a result, glutamine pathways are an emerging pharmacologic target. The goal of this pilot study was to characterize changes in glutamine metabolism and inflammation in human glioma samples and explore the use of glutamine as a potential biomarker. 1 H high-resolution magic angle spinning nuclear magnetic resonance spectra were acquired from ex vivo glioma tissue (n = 16, grades II-IV) to quantify metabolite concentrations. Tumor inflammatory markers were quantified using electrochemiluminescence assays. Glutamate, glutathione, lactate, and alanine, as well as interleukin (IL)-1β and IL-8, increased significantly in samples from grade IV gliomas compared to grades II and III (p ≤ .05). Following dimension reduction of the inflammatory markers using probabilistic principal component analysis, we observed that glutamine, alanine, glutathione, and lactate were positively associated with the first inflammatory marker principal component. Our findings support the hypothesis that glutamine may be a key marker for glioma progression and indicate that inflammation is associated with changes in glutamine metabolism. These results motivate further in vivo investigation of glutamine as a biomarker for tumor progression and treatment response.

Background and Purpose: Interview selection of candidates for academic radiology faculty positions is variable and subject to unconscious biases. The purpose of this study was to retrospectively apply a quantitative curriculum vitae (CV) rubric as a screening tool to identify qualified candidates for further consideration in the hiring process., Materials and Methods: Archived CVs submitted by applicants between 2012 and 2017 for neuroradiology faculty positions at our institution were anonymized. One blinded reviewer scored resumes based on categories that included education, work experience, extracurricular/teaching experience, and research. Logistic regression and receiver operating characteristics analysis were performed. This study was IRB exempted., Results: Of the total 102 applicants, 17 interviews were conducted and 10 candidates were offered a position. Maximum score of the model was 24 points. Mean score was 14 ± 4 (n = 102, range 5-22). Higher total CV score (P = 0.01), medical school ranking (P = 0.03), and number of published manuscripts (P = 0.03) were significantly associated with interview selection. The area under the curve in the ROC analysis for differentiating interview selection based on total CV scoring was 0.69 (95% confidence interval 0.56-0.82). At a cutoff of 14, the model is 82.4% sensitive, and 54.1% specific., Conclusion: Standardized CV scoring is feasible with a cut-off score of 14 points providing high sensitivity in identifying candidates eligible for interview. This tool can potentially be applied in the future to the hiring process as it is neutral to factors such as gender and race and provides an opportunity to address diversity in academic medicine., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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 Author(s) 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

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/noise 2 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., (© 2020 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)

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

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., (Copyright © 2018 Elsevier Inc. All rights reserved.)

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., (Copyright © 2017. Published by Elsevier Inc.)

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.

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., (Copyright © 2016 Elsevier Inc. All rights reserved.)

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., (© 2015 The Author(s) Published by the Royal Society. All rights reserved.)

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., (© 2013, The International Biometric Society.)

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.