Genome-wide association studies of 27 accelerometry-derived physical activity measurements identified novel loci and genetic mechanisms.

Physical inactivity (PA) is an important risk factor for a wide range of diseases. Previous genome-wide association studies (GWAS), based on self-reported data or a small number of phenotypes derived from accelerometry, have identified a limited number of genetic loci associated with habitual PA and provided evidence for involvement of central nervous system in mediating genetic effects. In this study, we derived 27 PA phenotypes from wrist accelerometry data obtained from 88,411 UK Biobank study participants. Single-variant association analysis based on mixed-effects models and transcriptome-wide association studies (TWAS) together identified 5 novel loci that were not detected by previous studies of PA, sleep duration and self-reported chronotype. For both novel and previously known loci, we discovered associations with novel phenotypes including active-to-sedentary transition probability, light-intensity PA, activity during different times of the day and proxy phenotypes to sleep and circadian patterns. Follow-up studies including TWAS, colocalization, tissue-specific heritability enrichment, gene-set enrichment and genetic correlation analyses indicated the role of the blood and immune system in modulating the genetic effects and a secondary role of the digestive and endocrine systems. Our findings provided important insights into the genetic architecture of PA and its underlying mechanisms.

ModelArray: An R package for statistical analysis of fixel-wise data.

Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data.

ciftiTools: A package for reading, writing, visualizing, and manipulating CIFTI files in R.

There is significant interest in adopting surface- and grayordinate-based analysis of MR data for a number of reasons, including improved whole-cortex visualization, the ability to perform surface smoothing to avoid issues associated with volumetric smoothing, improved inter-subject alignment, and reduced dimensionality. The CIFTI grayordinate file format introduced by the Human Connectome Project further advances grayordinate-based analysis by combining gray matter data from the left and right cortical hemispheres with gray matter data from the subcortex and cerebellum into a single file. Analyses performed in grayordinate space are well-suited to leverage information shared across the brain and across subjects through both traditional analysis techniques and more advanced statistical methods, including Bayesian methods. The R statistical environment facilitates use of advanced statistical techniques, yet little support for grayordinates analysis has been previously available in R. Indeed, few comprehensive programmatic tools for working with CIFTI files have been available in any language. Here, we present the ciftiTools R package, which provides a unified environment for reading, writing, visualizing, and manipulating CIFTI files and related data formats. We illustrate ciftiTools' convenient and user-friendly suite of tools for working with grayordinates and surface geometry data in R, and we describe how ciftiTools is being utilized to advance the statistical analysis of grayordinate-based functional MRI data.

Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19.

BACKGROUND: Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission. OBJECTIVE: To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization. DESIGN: Retrospective observational cohort study. SETTINGS: Five hospitals in Maryland and Washington, D.C. PATIENTS: Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease. MEASUREMENTS: A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization. RESULTS: Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively. LIMITATION: The SCARP tool was developed by using data from a single health system. CONCLUSION: Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information. PRIMARY FUNDING SOURCE: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Patient Trajectories Among Persons Hospitalized for COVID-19 : A Cohort Study.

BACKGROUND: Risk factors for progression of coronavirus disease 2019 (COVID-19) to severe disease or death are underexplored in U.S. cohorts. OBJECTIVE: To determine the factors on hospital admission that are predictive of severe disease or death from COVID-19. DESIGN: Retrospective cohort analysis. SETTING: Five hospitals in the Maryland and Washington, DC, area. PATIENTS: 832 consecutive COVID-19 admissions from 4 March to 24 April 2020, with follow-up through 27 June 2020. MEASUREMENTS: Patient trajectories and outcomes, categorized by using the World Health Organization COVID-19 disease severity scale. Primary outcomes were death and a composite of severe disease or death. RESULTS: Median patient age was 64 years (range, 1 to 108 years); 47% were women, 40% were Black, 16% were Latinx, and 21% were nursing home residents. Among all patients, 131 (16%) died and 694 (83%) were discharged (523 [63%] had mild to moderate disease and 171 [20%] had severe disease). Of deaths, 66 (50%) were nursing home residents. Of 787 patients admitted with mild to moderate disease, 302 (38%) progressed to severe disease or death: 181 (60%) by day 2 and 238 (79%) by day 4. Patients had markedly different probabilities of disease progression on the basis of age, nursing home residence, comorbid conditions, obesity, respiratory symptoms, respiratory rate, fever, absolute lymphocyte count, hypoalbuminemia, troponin level, and C-reactive protein level and the interactions among these factors. Using only factors present on admission, a model to predict in-hospital disease progression had an area under the curve of 0.85, 0.79, and 0.79 at days 2, 4, and 7, respectively. LIMITATION: The study was done in a single health care system. CONCLUSION: A combination of demographic and clinical variables is strongly associated with severe COVID-19 disease or death and their early onset. The COVID-19 Inpatient Risk Calculator (CIRC), using factors present on admission, can inform clinical and resource allocation decisions. PRIMARY FUNDING SOURCE: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Moderated t-tests for group-level fMRI analysis.

In recent years, there has been significant criticism of functional magnetic resonance imaging (fMRI) studies with small sample sizes. The argument is that such studies have low statistical power, as well as reduced likelihood for statistically significant results to be true effects. The prevalence of these studies has led to a situation where a large number of published results are not replicable and likely false. Despite this growing body of evidence, small sample fMRI studies continue to be regularly performed; likely due to the high cost of scanning. In this report we investigate the use of a moderated t-statistic for performing group-level fMRI analysis to help alleviate problems related to small sample sizes. The proposed approach, implemented in the popular R-package LIMMA (linear models for microarray data), has found wide usage in the genomics literature for dealing with similar issues. Utilizing task-based fMRI data from the Human Connectome Project (HCP), we compare the performance of the moderated t-statistic with the standard t-statistic, as well as the pseudo t-statistic commonly used in non-parametric fMRI analysis. We find that the moderated t-test significantly outperforms both alternative approaches for studies with sample sizes less than 40 subjects. Further, we find that the results were consistent both when using voxel-based and cluster-based thresholding. We also introduce an R-package, LIMMI (linear models for medical images), that provides a quick and convenient way to apply the method to fMRI data.

A multi-scanner neuroimaging data harmonization using RAVEL and ComBat.

Modern neuroimaging studies frequently combine data collected from multiple scanners and experimental conditions. Such data often contain substantial technical variability associated with image intensity scale (image intensity scales are not the same in different images) and scanner effects (images obtained from different scanners contain substantial technical biases). Here we evaluate and compare results of data analysis methods without any data transformation (RAW), with intensity normalization using RAVEL, with regional harmonization methods using ComBat, and a combination of RAVEL and ComBat. Methods are evaluated on a unique sample of 16 study participants who were scanned on both 1.5T and 3T scanners a few months apart. Neuroradiological evaluation was conducted for 7 different regions of interest (ROI's) pertinent to Alzheimer's disease (AD). Cortical measures and results indicate that: (1) RAVEL substantially improved the reproducibility of image intensities; (2) ComBat is preferred over RAVEL and the RAVEL-ComBat combination in terms of regional level harmonization due to more consistent harmonization across subjects and image-derived measures; (3) RAVEL and ComBat substantially reduced bias compared to analysis of RAW images, but RAVEL also resulted in larger variance; and (4) the larger root mean square deviation (RMSD) of RAVEL compared to ComBat is due mainly to its larger variance.

Outcome-Stratified Analysis of Biomarker Trajectories for Patients Infected With Severe Acute Respiratory Syndrome Coronavirus 2.

Longitudinal trajectories of vital signs and biomarkers during hospital admission of patients with COVID-19 remain poorly characterized despite their potential to provide critical insights about disease progression. We studied 1884 patients with severe acute respiratory syndrome coronavirus 2 infection from April 3, 2020, to June 25, 2020, within 1 Maryland hospital system and used a retrospective longitudinal framework with linear mixed-effects models to investigate relevant biomarker trajectories leading up to 3 critical outcomes: mechanical ventilation, discharge, and death. Trajectories of 4 vital signs (respiratory rate, ratio of oxygen saturation (Spo2) to fraction of inspired oxygen (Fio2), pulse, and temperature) and 4 laboratory values (C-reactive protein (CRP), absolute lymphocyte count (ALC), estimated glomerular filtration rate, and D-dimer) clearly distinguished the trajectories of patients with COVID-19. Before any ventilation, log(CRP), log(ALC), respiratory rate, and Spo2-to-Fio2 ratio trajectories diverge approximately 8-10 days before discharge or death. After ventilation, log(CRP), log(ALC), respiratory rate, Spo2-to-Fio2 ratio, and estimated glomerular filtration rate trajectories again diverge 10-20 days before death or discharge. Trajectories improved until discharge and remained unchanged or worsened until death. Our approach characterizes the distribution of biomarker trajectories leading up to competing outcomes of discharge versus death. Moving forward, this model can contribute to quantifying the joint probability of biomarkers and outcomes when provided clinical data up to a given moment.

Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study.

The ability of individuals to engage in physical activity is a critical component of overall health and quality of life. However, there is a natural decline in physical activity associated with the aging process. Establishing normative trends of physical activity in aging populations is essential to developing public health guidelines and informing clinical perspectives regarding individuals' levels of physical activity. Beyond overall quantity of physical activity, patterns regarding the timing of activity provide additional insights into latent health status. Wearable accelerometers, paired with statistical methods from functional data analysis, provide the means to estimate diurnal patterns in physical activity. To date, these methods have been only applied to study aging trends in populations based in the United States. Here, we apply curve registration and functional regression to 24 h activity profiles for 88,793 men (N = 39,255) and women (N = 49,538) ages 42-78 from the UK Biobank accelerometer study to understand how physical activity patterns vary across ages and by gender. Our analysis finds that daily patterns in both the volume of physical activity and probability of being active change with age, and that there are marked gender differences in these trends. This work represents the largest-ever population analyzed using tools of this kind, and suggest that aging trends in physical activity are reproducible in different populations across countries.

Neuroconductor: an R platform for medical imaging analysis.

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.

Relationship of White Matter Lesions with Intracerebral Hemorrhage Expansion and Functional Outcome: MISTIE II and CLEAR III.

BACKGROUND/OBJECTIVE: Intracerebral hemorrhage (ICH) patients commonly have concomitant white matter lesions (WML) which may be associated with poor outcome. We studied if WML affects hematoma expansion (HE) and post-stroke functional outcome in a post hoc analysis of patients from randomized controlled trials. METHODS: In ICH patients from the clinical trials MISTIE II and CLEAR III, WML grade on diagnostic computed tomography (dCT) scan (dCT, < 24 h after ictus) was assessed using the van Swieten scale (vSS, range 0-4). The primary outcome for HE was > 33% or > 6 mL ICH volume increase from dCT to the last pre-randomization CT (< 72 h of dCT). Secondary HE outcomes were: absolute ICH expansion, > 10.4 mL total clot volume increase, and a subgroup analysis including patients with dCT < 6 h after ictus using the primary HE definition of > 33% or > 6 mL ICH volume increase. Poor functional outcome was assessed at 180 days and defined as modified Rankin Scale (mRS) ≥ 4, with ordinal mRS as a secondary endpoint. RESULTS: Of 635 patients, 55% had WML grade 1-4 at dCT (median 2.2 h from ictus) and 13% had subsequent HE. WML at dCT did not increase the odds for primary or secondary HE endpoints (P ≥ 0.05) after adjustment for ICH volume, intraventricular hemorrhage volume, warfarin/INR > 1.5, ictus to dCT time in hours, age, diabetes mellitus, and thalamic ICH location. WML increased the odds for having poor functional outcome (mRS ≥ 4) in univariate analyses (vSS 4; OR 4.16; 95% CI 2.54-6.83; P < 0.001) which persisted in multivariable analyses after adjustment for HE and other outcome risk factors. CONCLUSIONS: Concomitant WML does not increase the odds for HE in patients with ICH but increases the odds for poor functional outcome. CLINICAL TRIAL REGISTRATION: http://www.clinicaltrials.gov trial-identifiers: NCT00224770 and NCT00784134.

Thrombolytic removal of intraventricular haemorrhage in treatment of severe stroke: results of the randomised, multicentre, multiregion, placebo-controlled CLEAR III trial.

BACKGROUND: Intraventricular haemorrhage is a subtype of intracerebral haemorrhage, with 50% mortality and serious disability for survivors. We aimed to test whether attempting to remove intraventricular haemorrhage with alteplase versus saline irrigation improved functional outcome. METHODS: In this randomised, double-blinded, placebo-controlled, multiregional trial (CLEAR III), participants with a routinely placed extraventricular drain, in the intensive care unit with stable, non-traumatic intracerebral haemorrhage volume less than 30 mL, intraventricular haemorrhage obstructing the 3rd or 4th ventricles, and no underlying pathology were adaptively randomly assigned (1:1), via a web-based system to receive up to 12 doses, 8 h apart of 1 mg of alteplase or 0·9% saline via the extraventricular drain. The treating physician, clinical research staff, and participants were masked to treatment assignment. CT scans were obtained every 24 h throughout dosing. The primary efficacy outcome was good functional outcome, defined as a modified Rankin Scale score (mRS) of 3 or less at 180 days per central adjudication by blinded evaluators. This study is registered with ClinicalTrials.gov, NCT00784134. FINDINGS: Between Sept 18, 2009, and Jan 13, 2015, 500 patients were randomised: 249 to the alteplase group and 251 to the saline group. 180-day follow-up data were available for analysis from 246 of 249 participants in the alteplase group and 245 of 251 participants in the placebo group. The primary efficacy outcome was similar in each group (good outcome in alteplase group 48% vs saline 45%; risk ratio [RR] 1·06 [95% CI 0·88-1·28; p=0·554]). A difference of 3·5% (RR 1·08 [95% CI 0·90-1·29], p=0·420) was found after adjustment for intraventricular haemorrhage size and thalamic intracerebral haemorrhage. At 180 days, the treatment group had lower case fatality (46 [18%] vs saline 73 [29%], hazard ratio 0·60 [95% CI 0·41-0·86], p=0·006), but a greater proportion with mRS 5 (42 [17%] vs 21 [9%]; RR 1·99 [95% CI 1·22-3·26], p=0·007). Ventriculitis (17 [7%] alteplase vs 31 [12%] saline; RR 0·55 [95% CI 0·31-0·97], p=0·048) and serious adverse events (114 [46%] alteplase vs 151 [60%] saline; RR 0·76 [95% CI 0·64-0·90], p=0·002) were less frequent with alteplase treatment. Symptomatic bleeding (six [2%] in the alteplase group vs five [2%] in the saline group; RR 1·21 [95% CI 0·37-3·91], p=0·771) was similar. INTERPRETATION: In patients with intraventricular haemorrhage and a routine extraventricular drain, irrigation with alteplase did not substantially improve functional outcomes at the mRS 3 cutoff compared with irrigation with saline. Protocol-based use of alteplase with extraventricular drain seems safe. Future investigation is needed to determine whether a greater frequency of complete intraventricular haemorrhage removal via alteplase produces gains in functional status. FUNDING: National Institute of Neurological Disorders and Stroke.

Feasibility of Coping Effectiveness Training for Caregivers of Children with Autism Spectrum Disorder: a Genetic Counseling Intervention.

Caregivers of children with autism spectrum disorder (ASD) may find it difficult to feel a sense of control and to cope with the overall physical and emotional demands of caring for their child. While caregivers are able to successfully cope with a high level of stress, there are limits to their resources and abilities to cope over time. Genetic counselors working with affected families may be able to help parents more effectively manage stress related to the disorder. Few short-term interventions have been reported in genetic counseling yet implementation of evidence-based examples may be achievable. This study aimed to assess the feasibility of a coping effectiveness training (CET) intervention designed to enhance coping self-efficacy (CSE) among caregivers of children with ASD, with the eventual goal of translating this intervention into genetic counseling practice. A randomized treatment-control design was used to investigate the feasibility of an intervention using CET among caregivers of children with ASD. The primary outcome was the feasibility of the intervention; the secondary outcome was improvements in CSE in the intervention group as compared to the control group. Caregivers were recruited and randomized into the treatment (n=15) or control (n=13) groups. Of these, 22 completed the study (retention: 78.6%). The intervention was highly feasible; most caregivers found the CET helpful, practical, useful, and relatively easy to attend. The treatment group demonstrated significantly increased CSE from pre-intervention to post-intervention (p=0.02). Between group differences were not significant when comparing the pre-post changes. We provide preliminary evidence that CET may be beneficial to caregivers of children with ASD. The results of this feasibility study support development of a phase II study of this intervention in a larger cohort, aimed to be implemented into a genetic counseling setting.

Removing inter-subject technical variability in magnetic resonance imaging studies.

Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect, and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, we decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compare RAVEL to two intensity-normalization-only methods: histogram matching and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area, and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is promising for many other imaging modalities.

Preventive Care Delivery to Young Children With Sickle Cell Disease.

Preventive services can reduce the morbidity of sickle cell disease (SCD) in children but are delivered unreliably. We conducted a retrospective cohort study of children aged 2 to 5 years with SCD, evaluating each child for 14 months and expecting that he/she should receive ≥75% of days covered by antibiotic prophylaxis, ≥1 influenza immunization, and ≥1 transcranial Doppler ultrasound (TCD). We used logistic regression to quantify the relationship between ambulatory generalist and hematologist visits and preventive services delivery. Of 266 children meeting the inclusion criteria, 30% consistently filled prophylactic antibiotic prescriptions. Having ≥2 generalist, non-well child care visits or ≥2 hematologist visits was associated with more reliable antibiotic prophylaxis. Forty-one percent of children received ≥1 influenza immunizations. Children with ≥2 hematologist visits were most likely to be immunized (62% vs. 35% among children without a hematologist visit). Only 25% of children received ≥1 TCD. Children most likely to receive a TCD (42%) were those with ≥2 hematologist visits. One in 20 children received all 3 preventive services. Preventive services delivery to young children with SCD was inconsistent but associated with multiple visits to ambulatory providers. Better connecting children with SCD to hematologists and strengthening preventive care delivery by generalists are both essential.

Quantitative Intracerebral Hemorrhage Localization.

BACKGROUND AND PURPOSE: The location of intracerebral hemorrhage (ICH) is currently described in a qualitative way; we provide a quantitative framework for estimating ICH engagement and its relevance to stroke outcomes. METHODS: We analyzed 111 patients with ICH from the Minimally Invasive Surgery Plus Recombinant-Tissue Plasminogen Activator for Intracerebral Evacuation (MISTIE) II clinical trial. We estimated ICH engagement at a population level using image registration of computed tomographic scans to a template and a previously labeled atlas. Predictive regions of National Institutes of Health Stroke Scale and Glasgow Coma Scale stroke severity scores, collected at enrollment, were estimated. RESULTS: The percent coverage of the ICH by these regions strongly outperformed the reader-labeled locations. The adjusted R(2) almost doubled from 0.129 (reader-labeled model) to 0.254 (quantitative location model) for National Institutes of Health Stroke Scale and more than tripled from 0.069 (reader-labeled model) to 0.214 (quantitative location model). A permutation test confirmed that the new predictive regions are more predictive than chance: P<0.001 for National Institutes of Health Stroke Scale and P<0.01 for Glasgow Coma Scale. CONCLUSIONS: Objective measures of ICH location and engagement using advanced computed tomographic imaging processing provide finer, objective, and more quantitative anatomic information than that provided by human readers. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00224770.

Accuracy of the ABC/2 Score for Intracerebral Hemorrhage: Systematic Review and Analysis of MISTIE, CLEAR-IVH, and CLEAR III.

BACKGROUND AND PURPOSE: The ABC/2 score estimates intracerebral hemorrhage (ICH) volume, yet validations have been limited by small samples and inappropriate outcome measures. We determined accuracy of the ABC/2 score calculated at a specialized reading center (RC-ABC) or local site (site-ABC) versus the reference-standard computed tomography-based planimetry (CTP). METHODS: In Minimally Invasive Surgery Plus Recombinant Tissue-Type Plasminogen Activator for Intracerebral Hemorrhage Evacuation-II (MISTIE-II), Clot Lysis Evaluation of Accelerated Resolution of Intraventricular Hemorrhage (CLEAR-IVH) and CLEAR-III trials. ICH volume was prospectively calculated by CTP, RC-ABC, and site-ABC. Agreement between CTP and ABC/2 was defined as an absolute difference up to 5 mL and relative difference within 20%. Determinants of ABC/2 accuracy were assessed by logistic regression. RESULTS: In 4369 scans from 507 patients, CTP was more strongly correlated with RC-ABC (r(2)=0.93) than with site-ABC (r(2)=0.87). Although RC-ABC overestimated CTP-based volume on average (RC-ABC, 15.2 cm(3); CTP, 12.7 cm3), agreement was reasonable when categorized into mild, moderate, and severe ICH (κ=0.75; P<0.001). This was consistent with overestimation of ICH volume in 6 of 8 previous studies. Agreement with CTP was greater for RC-ABC (84% within 5 mL; 48% of scans within 20%) than for site-ABC (81% within 5 mL; 41% within 20%). RC-ABC had moderate accuracy for detecting ≥5 mL change in CTP volume between consecutive scans (sensitivity, 0.76; specificity, 0.86) and was more accurate with smaller ICH, thalamic hemorrhage, and homogeneous clots. CONCLUSIONS: ABC/2 scores at local or central sites are sufficiently accurate to categorize ICH volume and assess eligibility for the CLEAR-III and MISTIE III studies and moderately accurate for change in ICH volume. However, accuracy decreases with large, irregular, or lobar clots. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: MISTIE-II NCT00224770; CLEAR-III NCT00784134.

Validated automatic brain extraction of head CT images.

BACKGROUND: X-ray computed tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing step in brain imaging research is brain extraction - the process of separating the brain tissue from all other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated and informally proposed, but never formally validated. AIM: To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard with the results of several versions of automatic brain extraction and by estimating the reliability of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data smoothing is studied and reported. METHODS: All images were thresholded using a 0-100 Hounsfield unit (HU) range. In one variant of the pipeline, data were smoothed using a 3-dimensional Gaussian kernel (σ=1mm(3)) and re-thresholded to 0-100HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity (FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were filled. For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually created by one expert CT reader. The resulting brain tissue masks were quantitatively compared to the manual segmentations using sensitivity, specificity, accuracy, and the Dice Similarity Index (DSI). Brain extraction performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing manual and automated segmentation: ICVautomated/ICVmanual. To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV of each scan was estimated and an intraclass correlation (ICC) was estimated using a one-way ANOVA. RESULTS: Smoothing images improves brain extraction results using BET for all measures except specificity (all p<0.01, uncorrected), irrespective of the FI threshold. Using an FI of 0.01 or 0.1 performed better than 0.35. Thus, all reported results refer only to smoothed data using an FI of 0.01 or 0.1. Using an FI of 0.01 had a higher median sensitivity (0.9901) than an FI of 0.1 (0.9884, median difference: 0.0014, p<0.001), accuracy (0.9971 vs. 0.9971; median difference: 0.0001, p<0.001), and DSI (0.9895 vs. 0.9894; median difference: 0.0004, p<0.001) and lower specificity (0.9981 vs. 0.9982; median difference: -0.0001, p<0.001). These measures are all very high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using smoothed data and an FI of 0.01, the mean (SD) ICV ratio was 1.002 (0.008); the mean being close to 1 indicates the ICV estimates are similar for automated and manual segmentation. In the 1095 longitudinal scans, this pipeline had a low failure rate (5.2%) and the ICC estimate was high (0.929, 95% CI: 0.91, 0.945) for successfully extracted brains. CONCLUSION: BET performs well at brain extraction on thresholded, 1mm(3) smoothed CT images with an FI of 0.01 or 0.1. Smoothing before applying BET is an important step not previously discussed in the literature. Analysis code is provided.

Reduction of motion-related artifacts in resting state fMRI using aCompCor.

Recent studies have illustrated that motion-related artifacts remain in resting-state fMRI (rs-fMRI) data even after common corrective processing procedures have been applied, but the extent to which head motion distorts the data may be modulated by the corrective approach taken. We compare two different methods for estimating nuisance signals from tissues not expected to exhibit BOLD fMRI signals of neuronal origin: 1) the more commonly used mean signal method and 2) the principal components analysis approach (aCompCor: Behzadi et al., 2007). Further, we investigate the added benefit of "scrubbing" (Power et al., 2012) following both methods. We demonstrate that the use of aCompCor removes motion artifacts more effectively than tissue-mean signal regression. In addition, inclusion of more components from anatomically defined regions of no interest better mitigates motion-related artifacts and improves the specificity of functional connectivity estimates. While scrubbing further attenuates motion-related artifacts when mean signals are used, scrubbing provides no additional benefit in terms of motion artifact reduction or connectivity specificity when using aCompCor.