A scalable approach for continuous time Markov models with covariates

Existing methods for fitting continuous time Markov models (CTMM) in the presence of covariates suffer from scalability issues due to high computational cost of matrix exponentials calculated for each observation. In this article, we propose an optimization technique for CTMM which uses a stochastic gradient descent algorithm combined with differentiation of the matrix exponential using a Padé approximation. This approach makes fitting large scale data feasible. We present two methods for computing standard errors, one novel approach using the Padé expansion and the other using power series expansion of the matrix exponential. Through simulations, we find improved performance relative to existing CTMM methods, and we demonstrate the method on the large-scale multiple sclerosis NO.MS data set

Heritability Estimation of Reliable Connectomic Features*

Brain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed ~5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%

Current commands for high-efficiency torque control of DC shunt motor

The current commands for a high-efficiency torque control of a DC shunt motor are described. In the proposed control method, the effect of a magnetic saturation and an armature reaction are taken into account by representing the coefficients of an electromotive force and a torque as a function of the field current, the armature current and the revolving speed. The current commands at which the loss of the motor drive system becomes a minimum are calculated as an optimal problem. The proposed control technique of a motor is implemented on the microprocessor-based control system. The effect of the consideration of the magnetic saturation and the armature reaction on the produced torque and the minimisation of the loss are discussed analytically and experimentally </p

Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes

Genome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors, such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes presents enormous challenges in terms of computational intensity and the need to account multiple testing in both the imaging and genetic domain. Here we present a method that makes mixed models practical with high-dimensional traits by a combination of a transformation applied to the data and model, and the use of a non-iterative variance component estimator. With such speed enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic

Investigating microstructure of white matter tracts as candidate endophenotypes of Social Anxiety Disorder – findings from the Leiden Family Lab study on Social Anxiety Disorder (LFLSAD)

Background: Social anxiety disorder (SAD) is a mental illness with a complex, partially genetic background.Differences in characteristics of white matter (WM) microstructure have been reported in patients with SADcompared to healthy controls. Also, WM characteristics are moderately to highly heritable. Endophenotypes are measurable characteristics on the road from genotype to phenotype, putatively reflective of genetically based disease mechanisms. In search of candidate endophenotypes of SAD we used a unique sample of SAD patients and their family members of two generations to explore microstructure of WM tracts as candidate endophenotypes. We focused on two endophenotype criteria: co-segregation with social anxiety within the families, and heritability.Methods: Participants (n = 94 from 8 families genetically vulnerable for SAD) took part in the Leiden Family Lab Study on Social Anxiety Disorder (LFLSAD). We employed tract-based spatial statistics to examine structural WM characteristics, being fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD) and radial diffusivity (RD), in three a-priori defined tracts of interest: uncinate fasciculus (UF), superior longitudinal fasciculus (SLF) and inferior longitudinal fasciculus (ILF). Associations with social anxiety symptoms and heritability were estimated.Results: Increased FA in the left and right SLF co-segregated with symptoms of social anxiety. These findings were coupled with decreased RD and MD. All characteristics of WM microstructure were estimated to be at least moderately heritable. Conclusion: These findings suggest that alterations in WM microstructure in the SLF could be candidate endophenotypes of SAD, as they co-segregated within families genetically vulnerable for SAD and are heritable. These findings further elucidate the genetic susceptibility to SAD and improve our understanding of the overall etiology.Pathways through Adolescenc

Heterochronicity of white matter development and aging explains regional patient control differences in schizophrenia

Background Altered brain connectivity is implicated in the development and clinical burden of schizophrenia. Relative to matched controls, schizophrenia patients show (1) a global and regional reduction in the integrity of the brain’s white matter (WM), assessed using diffusion tensor imaging (DTI) fractional anisotropy (FA), and (2) accelerated age-related decline in FA values. In the largest mega-analysis to date, we tested if differences in the trajectories of WM tract development influenced patient-control differences in FA. We also assessed if specific tracts showed exacerbated decline with aging. Methods Three cohorts of schizophrenia patients (total n=177) and controls (total n=249; age=18–61 years) were ascertained with three 3T Siemens MRI scanners. Whole-brain and regional FA values were extracted using ENIGMA-DTI protocols. Statistics were evaluated using mega- and meta-analyses to detect effects of diagnosis and age-by-diagnosis interactions. Results In mega-analysis of whole-brain averaged FA, schizophrenia patients had lower FA (p=10−11) and faster age-related decline in FA (p=0.02) compared to controls. Tract-specific heterochronicity measures, i.e., abnormal rates of adolescent maturation and aging explained ~50% of the regional variance effects of diagnosis and age-by-diagnosis interaction in patients. Interactive, 3D visualization of the results is available at www.enigma-viewer.org. Conclusion WM tracts that mature later in life appeared more sensitive to the pathophysiology of schizophrenia and were more susceptible to faster age-related decline in FA values.</p

Genomic kinship construction to enhance genetic analyses in the human connectome project data

Imaging genetic analyses quantify genetic control over quantitative measurements of brain structure and function using coefficients of relationship (CR) that code the degree of shared genetics between subjects. CR can be inferred through self‐reported relatedness or calculated empirically using genome‐wide SNP scans. We hypothesized that empirical CR provides a more accurate assessment of shared genetics than self‐reported relatedness. We tested this in 1,046 participants of the Human Connectome Project (HCP) (480 M/566 F) recruited from the Missouri twin registry. We calculated the heritability for 17 quantitative traits drawn from four categories (brain diffusion and structure, cognition, and body physiology) documented by the HCP. We compared the heritability and genetic correlation estimates calculated using self‐reported and empirical CR methods Kinship‐based INference for GWAS (KING) and weighted allelic correlation (WAC). The polygenetic nature of traits was assessed by calculating the empirical CR from chromosomal SNP sets. The heritability estimates based on whole‐genome empirical CR were higher but remained significantly correlated (r ∼0.9) with those obtained using self‐reported values. Population stratification in the HCP sample has likely influenced the empirical CR calculations and biased heritability estimates. Heritability values calculated using empirical CR for chromosomal SNP sets were significantly correlated with the chromosomal length (r 0.7) suggesting a polygenic nature for these traits. The chromosomal heritability patterns were correlated among traits from the same knowledge domains; among traits with significant genetic correlations; and among traits sharing biological processes, without being genetically related. The pedigree structures generated in our analyses are available online as a web‐based calculator (www.solar-eclipse-genetics.org/HCP)

Efficacy and safety of ofatumumab in recently diagnosed, treatment-naive patients with multiple sclerosis: Results from ASCLEPIOS I and II.

BackgroundIn the phase III ASCLEPIOS I and II trials, participants with relapsing multiple sclerosis receiving ofatumumab had significantly better clinical and magnetic resonance imaging (MRI) outcomes than those receiving teriflunomide.ObjectivesTo assess the efficacy and safety of ofatumumab versus teriflunomide in recently diagnosed, treatment-naive (RDTN) participants from ASCLEPIOS.MethodsParticipants were randomized to receive ofatumumab (20 mg subcutaneously every 4 weeks) or teriflunomide (14 mg orally once daily) for up to 30 months. Endpoints analysed post hoc in the protocol-defined RDTN population included annualized relapse rate (ARR), confirmed disability worsening (CDW), progression independent of relapse activity (PIRA) and adverse events.ResultsData were analysed from 615 RDTN participants (ofatumumab: n = 314; teriflunomide: n = 301). Compared with teriflunomide, ofatumumab reduced ARR by 50% (rate ratio (95% confidence interval (CI)): 0.50 (0.33, 0.74); p &lt; 0.001), and delayed 6-month CDW by 46% (hazard ratio (HR; 95% CI): 0.54 (0.30, 0.98); p = 0.044) and 6-month PIRA by 56% (HR: 0.44 (0.20, 1.00); p = 0.049). Safety findings were manageable and consistent with those of the overall ASCLEPIOS population.ConclusionThe favourable benefit-risk profile of ofatumumab versus teriflunomide supports its consideration as a first-line therapy in RDTN patients.ASCLEPIOS I and II are registered at ClinicalTrials.gov (NCT02792218 and NCT02792231)

Efficacy and safety of ofatumumab in recently diagnosed, treatment-naive patients with multiple sclerosis: Results from ASCLEPIOS I and II

BACKGROUND: In the phase III ASCLEPIOS I and II trials, participants with relapsing multiple sclerosis receiving ofatumumab had significantly better clinical and magnetic resonance imaging (MRI) outcomes than those receiving teriflunomide. OBJECTIVES: To assess the efficacy and safety of ofatumumab versus teriflunomide in recently diagnosed, treatment-naive (RDTN) participants from ASCLEPIOS. METHODS: Participants were randomized to receive ofatumumab (20 mg subcutaneously every 4 weeks) or teriflunomide (14 mg orally once daily) for up to 30 months. Endpoints analysed post hoc in the protocol-defined RDTN population included annualized relapse rate (ARR), confirmed disability worsening (CDW), progression independent of relapse activity (PIRA) and adverse events. RESULTS: Data were analysed from 615 RDTN participants (ofatumumab: CONCLUSION: The favourable benefit-risk profile of ofatumumab versus teriflunomide supports its consideration as a first-line therapy in RDTN patients.ASCLEPIOS I and II are registered at ClinicalTrials.gov (NCT02792218 and NCT02792231)