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Theories of adult brain development, based on neuropsychological test results and structural neuroimaging, suggest differential rates of age-related change in function across cortical and subcortical sub-regions. However, it remains unclear if these trends also extend to the aging dopamine system. Here we examined cross-sectional adult age differences in estimates of D2-like receptor binding potential across several cortical and subcortical brain regions using PET imaging and the radiotracer [ F]Fallypride in two samples of healthy human adults (combined N = 132). After accounting for regional differences in overall radioligand binding, estimated percent difference in receptor binding potential by decade (linear effects) were highest in most temporal and frontal cortical regions (~6-16% per decade), moderate in parahippocampal gyrus, pregenual frontal cortex, fusiform gyrus, caudate, putamen, thalamus, and amygdala (~3-5%), and weakest in subcallosal frontal cortex, ventral striatum, pallidum, and hippocampus (~0-2%). Some regions showed linear effects of age while many showed curvilinear effects such that binding potential declined from young adulthood to middle age and then was relatively stable until old age. Overall, these data indicate that the rate and pattern of decline in D2 receptor availability is regionally heterogeneous. However, the differences across regions were challenging to organize within existing theories of brain development and did not show the same pattern of regional change that has been observed in gray matter volume, white matter integrity, or cognitive performance. This variation suggests that existing theories of adult brain development may need to be modified to better account for the spatial dynamics of dopaminergic system aging.
© 2019 Wiley Periodicals, Inc.
PURPOSE - MR fingerprinting (MRF) sequences permit efficient T and T estimation in cranial and extracranial regions, but these areas may include substantial fat signals that bias T and T estimates. MRI fat signal fraction estimation is also a topic of active research in itself, but may be complicated by B heterogeneity and blurring during spiral k-space acquisitions, which are commonly used for MRF. An MRF method is proposed that separates fat and water signals, estimates water T and T, and accounts for B effects with spiral blurring correction, in a single sequence.
THEORY AND METHODS - A k-space-based fat-water separation method is further extended to unbalanced steady-state free precession MRF with swept echo time. Repeated application of this k-space fat-water separation to demodulated forms of the measured data allows a B map and correction to be approximated. The method is compared with MRF without fat separation across a broad range of fat signal fractions (FSFs), water Ts and Ts, and under heterogeneous static fields in simulations, phantoms, and in vivo.
RESULTS - The proposed method's FSF estimates had a concordance correlation coefficient of 0.990 with conventional measurements, and reduced biases in the T and T estimates due to fat signal relative to other MRF sequences by several hundred ms. The B correction improved the FSF, T, and T estimation compared to those estimates without correction.
CONCLUSION - The proposed method improves MRF water T and T estimation in the presence of fat and provides accurate FSF estimation with inline B correction.
Copyright © 2019 Elsevier Inc. All rights reserved.
PURPOSE - To test the ability of a novel pulse sequence applied in vivo at 3 Tesla to separate the contributions to the water signal from amide proton transfer (APT) and relayed nuclear Overhauser enhancement (rNOE) from background direct water saturation and semisolid magnetization transfer (MT). The lack of such signal source isolation has confounded conventional chemical exchange saturation transfer (CEST) imaging.
METHODS - We quantified APT and rNOE signals using a chemical exchange rotation transfer (CERT) metric, MTR . A range of duty cycles and average irradiation powers were applied, and results were compared with conventional CEST analyses using asymmetry (MTR ) and extrapolated magnetization transfer (EMR).
RESULTS - Our results indicate that MTR is more specific than MTR and, because it requires as few as 3 data points, is more rapid than methods requiring a complete Z-spectrum, such as EMR. In white matter, APT (1.5 ± 0.5%) and rNOE (2.1 ± 0.7%) were quantified by using MTR with a 30% duty cycle and a 0.5-µT average power. In addition, our results suggest that MTR is insensitive to B inhomogeneity, further magnifying its speed advantage over CEST metrics that require a separate B measurement. However, MTR still has nontrivial sensitivity to B inhomogeneities.
CONCLUSION - We demonstrated that MTR is an alternative metric to evaluate APT and rNOE, which is fast, robust to B inhomogeneity, and easy to process.
© 2018 International Society for Magnetic Resonance in Medicine.
Background - Trauma-related hospitalizations drive a high percentage of health care expenditure and inpatient resource consumption, which is directly related to length of stay (LOS). Robust and reliable interactions among health care employees can reduce LOS. However, there is little known about whether certain patterns of interactions exist and how they relate to LOS and its variability. The objective of this study is to learn interaction patterns and quantify the relationship to LOS within a mature trauma system and long-standing electronic medical record (EMR).
Methods - We adapted a spectral co-clustering methodology to infer the interaction patterns of health care employees based on the EMR of 5588 hospitalized adult trauma survivors. The relationship between interaction patterns and LOS was assessed via a negative binomial regression model. We further assessed the influence of potential confounders by age, number of health care encounters to date, number of access action types care providers committed to patient EMRs, month of admission, phenome-wide association study codes, procedure codes, and insurance status.
Results - Three types of interaction patterns were discovered. The first pattern exhibited the most collaboration between employees and was associated with the shortest LOS. Compared to this pattern, LOS for the second and third patterns was 0.61 days (P = 0.014) and 0.43 days (P = 0.037) longer, respectively. Although the 3 interaction patterns dealt with different numbers of patients in each admission month, our results suggest that care was provided for similar patients.
Discussion - The results of this study indicate there is an association between LOS and the extent to which health care employees interact in the care of an injured patient. The findings further suggest that there is merit in ascertaining the content of these interactions and the factors that induce these differences in interaction patterns within a trauma system.
Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.
Extent of response to neoadjuvant chemotherapy, tumor size, and patient age are important prognostic variables for patients with osteosarcoma, but applying information from these continuous variables in survival models is difficult. Dichotomization is usually inappropriate and alternative statistical techniques should be considered instead. Nonlinear multivariable regression methods (restricted cubic splines and fractional polynomials) were applied to data from the National Cancer Database to model continuous prognostic factors for overall survival from localized, high-grade osteosarcoma of the appendicular and nonspinal skeleton following neoadjuvant chemotherapy and surgical resection (N=2493). The assumption that log hazard ratios were linear in relation to these continuous prognostic factors was tested using likelihood ratio tests of model deviance and Wald tests of spline coefficients. Log hazard ratios for increasing patient age were linear over the range of 4 to 80 years, but showed evidence for variation in the coefficient over elapsed follow-up time. Tumor size also showed a linear relationship with log hazard over the range of 1 to 30 cm. Hazard ratios for chemotherapy effect profoundly deviated from log-linear (P<0.004), with significantly decreased hazard for death from baseline for patients with ≥90% tumor necrosis (hazard ratio, 0.32; 95% confidence interval, 0.20-0.52; P<0.0001). Important implications of these results include: (1) ≥90% tumor necrosis defines good chemotherapy response in a clinically useful manner; (2) staging osteosarcoma by dichotomizing tumor size is inappropriate; and (3) patient age can be modeled as a linear effect on the log hazard ratio in prognostic models with the caveat that risk may change over duration of the analysis.
Population stratification (PS) is a primary consideration in studies of genetic determinants of human traits. Failure to control for PS may lead to confounding, causing a study to fail for lack of significant results, or resources to be wasted following false-positive signals. Here, historical and current approaches for addressing PS when performing genetic association studies in human populations are reviewed. Methods for detecting the presence of PS, including global and local ancestry methods, are described. Also described are approaches for accounting for PS when calculating association statistics, such that measures of association are not confounded. Many traits are being examined for the first time in minority populations, which may inherently feature PS. © 2017 by John Wiley & Sons, Inc.
Copyright © 2017 John Wiley and Sons, Inc.
There is evidence that models of psychopathology specifying a general factor and specific second-order factors fit better than competing structural models. Nonetheless, additional tests are needed to examine the generality and boundaries of the general factor model. In a selected second wave of a cohort study, first-order dimensions of psychopathology symptoms in 499 23- to 31-year-old twins were analyzed. Using confirmatory factor analysis, a bifactor model specifying a general factor and specific internalizing and externalizing factors fit better than competing models. Factor loadings in this model were sex invariant despite greater variances in the specific internalizing factor among females and greater variances in the general and specific externalizing factors among males. The bifactor structure was robust to the exclusion of any single first-order dimension of psychopathology. Furthermore, the results were essentially unchanged when all overlapping symptoms that define multiple disorders were excluded from symptom dimensions. Furthermore, the best-fitting bifactor model also emerged in exploratory structural equation modeling with freely estimated cross-loadings. The general factor of psychopathology was robust across variations in measurement and analysis.
Copyright © 2017 John Wiley & Sons, Ltd.
Age-related changes in cognition are partially mediated by the presence of neuropathology and neurodegeneration. This manuscript evaluates the degree to which biomarkers of Alzheimer's disease, (AD) neuropathology and longitudinal changes in brain structure, account for age-related differences in cognition. Data from the AD Neuroimaging Initiative (n = 1012) were analyzed, including individuals with normal cognition and mild cognitive impairment. Parallel process mixed effects regression models characterized longitudinal trajectories of cognitive variables and time-varying changes in brain volumes. Baseline age was associated with both memory and executive function at baseline (p's < 0.001) and change in memory and executive function performances over time (p's < 0.05). After adjusting for clinical diagnosis, baseline, and longitudinal changes in brain volume, and baseline levels of cerebrospinal fluid biomarkers, age effects on change in episodic memory and executive function were fully attenuated, age effects on baseline memory were substantially attenuated, but an association remained between age and baseline executive function. Results support previous studies that show that age effects on cognitive decline are fully mediated by disease and neurodegeneration variables but also show domain-specific age effects on baseline cognition, specifically an age pathway to executive function that is independent of brain and disease pathways.
Copyright © 2017 Elsevier Inc. All rights reserved.
BACKGROUND - Pediatric oncology patients are at an increased risk of invasive bacterial infection due to immunosuppression. The risk of such infection in the absence of severe neutropenia (absolute neutrophil count ≥ 500/μL) is not well established and a validated prediction model for blood stream infection (BSI) risk offers clinical usefulness.
METHODS - A 6-site retrospective external validation was conducted using a previously published risk prediction model for BSI in febrile pediatric oncology patients without severe neutropenia: the Esbenshade/Vanderbilt (EsVan) model. A reduced model (EsVan2) excluding 2 less clinically reliable variables also was created using the initial EsVan model derivative cohort, and was validated using all 5 external validation cohorts. One data set was used only in sensitivity analyses due to missing some variables.
RESULTS - From the 5 primary data sets, there were a total of 1197 febrile episodes and 76 episodes of bacteremia. The overall C statistic for predicting bacteremia was 0.695, with a calibration slope of 0.50 for the original model and a calibration slope of 1.0 when recalibration was applied to the model. The model performed better in predicting high-risk bacteremia (gram-negative or Staphylococcus aureus infection) versus BSI alone, with a C statistic of 0.801 and a calibration slope of 0.65. The EsVan2 model outperformed the EsVan model across data sets with a C statistic of 0.733 for predicting BSI and a C statistic of 0.841 for high-risk BSI.
CONCLUSIONS - The results of this external validation demonstrated that the EsVan and EsVan2 models are able to predict BSI across multiple performance sites and, once validated and implemented prospectively, could assist in decision making in clinical practice. Cancer 2017;123:3781-3790. © 2017 American Cancer Society.
© 2017 American Cancer Society.