Jaroslaw Harezlak

Distinct Helper T Cell Type 1 and 2 Responses Associated With Malaria Protection and Risk in RTS,S/AS01E Vaccinees

Background The RTS,S/AS01E malaria vaccine has moderate efficacy, lower in infants than children. Current efforts to enhance RTS,S/AS01E efficacy would benefit from learning about the vaccine-induced immunity and identifying correlates of malaria protection, which could, for instance, inform the choice of adjuvants. Here, we sought cellular immunity-based correlates of malaria protection and risk associated with RTS,S/AS01E vaccination. Methods We performed a matched case-control study nested within the multicenter African RTS,S/AS01E phase 3 trial. Children and infant samples from 57 clinical malaria cases (32 RTS,S/25 comparator vaccinees) and 152 controls without malaria (106 RTS,S/46 comparator vaccinees) were analyzed. We measured 30 markers by Luminex following RTS,S/AS01E antigen stimulation of cells 1 month postimmunization. Crude concentrations and ratios of antigen to background control were analyzed. Results Interleukin (IL) 2 and IL-5 ratios were associated with RTS,S/AS01E vaccination (adjusted P ≤ .01). IL-5 circumsporozoite protein (CSP) ratios, a helper T cell type 2 cytokine, correlated with higher odds of malaria in RTS,S/AS01E vaccinees (odds ratio, 1.17 per 10% increases of CSP ratios; P value adjusted for multiple testing = .03). In multimarker analysis, the helper T cell type 1 (TH1)-related markers interferon-γ, IL-15, and granulocyte-macrophage colony-stimulating factor protected from subsequent malaria, in contrast to IL-5 and RANTES, which increased the odds of malaria. Conclusions RTS,S/AS01E-induced IL-5 may be a surrogate of lack of protection, whereas TH1-related responses may be involved in protective mechanisms. Efforts to develop second-generation vaccine candidates may concentrate on adjuvants that modulate the immune system to support enhanced TH1 responses and decreased IL-5 responses.

Stability of MRI metrics in the advanced research core of the NCAA-DoD concussion assessment, research and education (CARE) consortium

The NCAA-DoD Concussion Assessment, Research, and Education (CARE) consortium is performing a large-scale, comprehensive study of sport related concussions in college student-athletes and military service academy cadets. The CARE “Advanced Research Core” (ARC), is focused on executing a cutting-edge investigative protocol on a subset of the overall CARE athlete population. Here, we present the details of the CARE ARC MRI acquisition and processing protocol along with preliminary analyzes of within-subject, between-site, and between-subject stability across a variety of MRI biomarkers. Two experimental datasets were utilized for this analysis. First, two “human phantom” subjects were imaged multiple times at each of the four CARE ARC imaging sites, which utilize equipment from two imaging vendors. Additionally, a control cohort of healthy athletes participating in non-contact sports were enrolled in the study at each CARE ARC site and imaged at four time points. Multiple morphological image contrasts were acquired in each MRI exam; along with quantitative diffusion, functional, perfusion, and relaxometry imaging metrics. As expected, the imaging markers were found to have varying levels of stability throughout the brain. Importantly, between-subject variance was generally found to be greater than within-subject and between-site variance. These results lend support to the expectation that cross-site and cross-vendor advanced quantitative MRI metrics can be utilized to improve analytic power in assessing sensitive neurological variations; such as those effects hypothesized to occur in sports-related-concussion. This stability analysis provides a crucial foundation for further work utilizing this expansive dataset, which will ultimately be freely available through the Federal Interagency Traumatic Brain Injury Research Informatics System.

Prediction of sustained harmonic walking in the free-living environment using raw accelerometry data

OBJECTIVE Using raw, sub-second-level accelerometry data, we propose and validate a method for identifying and characterizing walking in the free-living environment. We focus on sustained harmonic walking (SHW), which we define as walking for at least 10 s with low variability of step frequency. APPROACH We utilize the harmonic nature of SHW and quantify the local periodicity of the tri-axial raw accelerometry data. We also estimate the fundamental frequency of the observed signals and link it to the instantaneous walking (step-to-step) frequency (IWF). Next, we report the total time spent in SHW, number and durations of SHW bouts, time of the day when SHW occurred, and IWF for 49 healthy, elderly individuals. MAIN RESULTS The sensitivity of the proposed classification method was found to be 97%, while specificity ranged between 87% and 97% and the prediction accuracy ranged between 94% and 97%. We report the total time in SHW between 140 and 10 min d-1 distributed between 340 and 50 bouts. We estimate the average IWF to be 1.7 steps-per-second. SIGNIFICANCE We propose a simple approach for the detection of SHW and estimation of IWF, based on Fourier decomposition.

Brain Connectivity-Informed Regularization Methods for Regression

One of the challenging problems in brain imaging research is a principled incorporation of information from different imaging modalities. Frequently, each modality is analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method to estimate the association between the brain structure features and a scalar outcome within the linear regression framework. Our regularization technique provides a principled approach to use external information from the structural brain connectivity and inform the estimation of the regression coefficients. Our proposal extends the classical Tikhonov regularization framework by defining a penalty term based on the structural connectivity-derived Laplacian matrix. Here, we address both theoretical and computational issues. The approach is first illustrated using simulated data and compared with other penalized regression methods. We then apply our regularization method to study the associations between the alcoholism phenotypes and brain cortical thickness using a diffusion imaging derived measure of structural connectivity. Using the proposed methodology in 148 young male subjects with a risk for alcoholism, we found a negative associations between cortical thickness and drinks per drinking day in bilateral caudal anterior cingulate cortex, left lateral OFC, and left precentral gyrus.

Stride variability measures derived from wrist- and hip-worn accelerometers.

Many epidemiological and clinical studies use accelerometry to objectively measure physical activity using the activity counts, vector magnitude, or number of steps. These measures use just a fraction of the information in the raw accelerometry data as they are typically summarized at the minute level. To address this problem, we define and estimate two measures of temporal stride-to-stride gait variability based on raw accelerometry data: Amplitude Deviation (AD) and Phase Deviation (PD). We explore the sensitivity of our approach to on-body placement of the accelerometer by comparing hip, left and right wrist placements. We illustrate the approach by estimating AD and PD in 46 elderly participants in the Developmental Epidemiologic Cohort Study (DECOS) who worn accelerometers during a 400m walk test. We also show that AD and PD have a statistically significant association with the gait speed and sit-to-stand test performance.

Accelerometry data in health research: challenges and opportunities. Review and examples

Wearable accelerometers provide detailed, objective, and continu-ous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popula-rity of wearable technology in health research. An ever increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper we discuss problems related to the collection and analysis of raw acce-lerometry data and provide insights into potential solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability and the effects of sensor location on the body. We also provide a short tutorial for dealing with sampling frequency, device calibration, data labeling and multiple PA monitors synchronization. We illustrate these po-ints using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment.

Connectivity-Informed Adaptive Regularization for Generalized Outcomes

One of the challenging problems in the brain imaging research is a principled incorporation of information from different imaging modalities in association studies. Frequently, data from each modality is analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, griPEER (generalized ridgified Partially Empirical Eigenvectors for Regression) to estimate the association between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER provides a principled approach to use external information from the structural brain connectivity to improve the regression coefficient estimation. Our proposal incorporates a penalty term, derived from the structural connectivity Laplacian matrix, in the penalized generalized linear regression. We address both theoretical and computational issues and show that our method is robust to the incomplete information about the structural brain connectivity. We also provide a significance testing procedure for performing inference on the estimated coefficients in this model. griPEER is evaluated in extensive simulation studies and it is applied in classification of the HIV+ and HIV- individuals.

Hybrid Diffusion Imaging in Mild Traumatic Brain Injury

Abstract Mild traumatic brain injury (mTBI) is an important public health problem. Although conventional medical imaging techniques can detect moderate-to-severe injuries, they are relatively insensitive to mTBI. In this study, we used hybrid diffusion imaging (HYDI) to detect white matter alterations in 19 patients with mTBI and 23 other trauma control patients. Within 15 days (standard deviation = 10) of brain injury, all subjects underwent magnetic resonance HYDI and were assessed with a battery of neuropsychological tests of sustained attention, memory, and executive function. Tract-based spatial statistics (TBSS) was used for voxel-wise statistical analyses within the white matter skeleton to study between-group differences in diffusion metrics, within-group correlations between diffusion metrics and clinical outcomes, and between-group interaction effects. The advanced diffusion imaging techniques, including neurite orientation dispersion and density imaging (NODDI) and q-space analyses, appeared to be more sensitive then classic diffusion tensor imaging. Only NODDI-derived intra-axonal volume fraction (Vic) demonstrated significant group differences (i.e., 5–9% lower in the injured brain). Within the mTBI group, Vic and a q-space measure, P0, correlated with 6 of 10 neuropsychological tests, including measures of attention, memory, and executive function. In addition, the direction of correlations differed significantly between groups (R2 > 0.71 and pinteration < 0.03). Specifically, in the control group, higher Vic and P0 were associated with better performances on clinical assessments, whereas in the mTBI group, higher Vic and P0 were associated with worse performances with correlation coefficients >0.83. In summary, the NODDI-derived axonal density index and q-space measure for tissue restriction demonstrated superior sensitivity to white matter changes shortly after mTBI. These techniques hold promise as a neuroimaging biomarker for mTBI.

Autoregressive and cross-lagged model for bivariate non-commensurate outcomes

Autoregressive and cross-lagged models have been widely used to understand the relationship between bivariate commensurate outcomes in social and behavioral sciences, but not much work has been carried out in modeling bivariate non-commensurate (e.g., mixed binary and continuous) outcomes simultaneously. We develop a likelihood-based methodology combining ordinary autoregressive and cross-lagged models with a shared subject-specific random effect in the mixed-model framework to model two correlated longitudinal non-commensurate outcomes. The estimates of the cross-lagged and the autoregressive effects from our model are shown to be consistent with smaller mean-squared error than the estimates from the univariate generalized linear models. Inclusion of the subject-specific random effects in the proposed model accounts for between-subject variability arising from the omitted and/or unobservable, but possibly explanatory, subject-level predictors. Our model is not restricted to the case with equal number of events per subject, and it can be extended to different types of bivariate outcomes. We apply our model to an ecological momentary assessment study with complex dependence and sampling data structures. Specifically, we study the dependence between the condom use and sexual satisfaction based on the data reported in a longitudinal study of sexually transmitted infections. We find negative cross-lagged effect between these two outcomes and positive autoregressive effect within each outcome. Copyright