Fast and powerful heritability inference for family-based neuroimaging studies.

Heritability estimation has become an important tool for imaging genetics studies. The large number of voxel- and vertex-wise measurements in imaging genetics studies presents a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot estimate heritability, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel-wise or cluster-wise P-values. Moreover, available heritability tools rely on P-values that can be inaccurate with usual parametric inference methods. In this work we develop fast estimation and inference procedures for voxel-wise heritability, drawing on recent methodological results that simplify heritability likelihood computations (Blangero et al., 2013). We review the family of score and Wald tests and propose novel inference methods based on explained sum of squares of an auxiliary linear model. To address problems with inaccuracies with the standard results used to find P-values, we propose four different permutation schemes to allow semi-parametric inference (parametric likelihood-based estimation, non-parametric sampling distribution). In total, we evaluate 5 different significance tests for heritability, with either asymptotic parametric or permutation-based P-value computations. We identify a number of tests that are both computationally efficient and powerful, making them ideal candidates for heritability studies in the massive data setting. We illustrate our method on fractional anisotropy measures in 859 subjects from the Genetics of Brain Structure study

The Psychometric Properties of the Desires for Drug Questionnaire (DDQ) among Iranians Methamphetamine Abusers

Introduction: Drug-craving as a multidimensional subjective experience recently has beenaccepted as an addiction hallmark. Desire for Drug Questionnaire or DDQ is a well-knownquestionnaire for measurement of drug craving severity. This study aimed to investigate thepsychometric properties of the DDQ among Iranian methamphetamine abusers.Method: DDQ was translated from English into Farsi by language experts. The questionnaire wasthen used for evaluation of craving among 50 male methamphetamine abusers. Then, DDQquestionnaire' scores was subjected to an exploratory principal components factor analysis. Toassess construct validity of DDQ, the model was evaluated using confirmatory factor analysis.Internal consistency was examined by calculating cronbach’s alpha.Results: Finally, the Persian version of DDQ was verified with 13 items and three factors. Threefactors with high eigenvalues were identified by (PCA) that accounted for 70.63% of the totalvariance. Given the relative fit of the confirmatory factor model, the construct validity of the DDQwas verified. Cronbach’s alpha coefficient of the total score of the questionnaire was 0.86.Conclusion: The Farsi-translated version of DDQ questionnaires had good psychometric properties.The questionnaire could be considered as a valid and reliable instrument for the assessment of drugcraving level in Iranian methamphetamine abusers

Characterisation of MS phenotypes across the age span using a novel data set integrating 34 clinical trials (NO.MS cohort): age is a key contributor to presentation

Background: The Oxford Big Data Institute, multiple sclerosis (MS) physicians and Novartis aim to address unresolved questions in MS with a novel comprehensive clinical trial data set. Objective: The objective of this study is to describe the Novartis–Oxford MS (NO.MS) data set and to explore the relationships between age, disease activity and disease worsening across MS phenotypes. Methods: We report key characteristics of NO.MS. We modelled MS lesion formation, relapse frequency, brain volume change and disability worsening cross-sectionally, as a function of patients’ baseline age, using phase III study data (≈8000 patients). Results: NO.MS contains data of ≈35,000 patients (>200,000 brain images from ≈10,000 patients), with >10 years follow-up. (1) Focal disease activity is highest in paediatric patients and decreases with age, (2) brain volume loss is similar across age and phenotypes and (3) the youngest patients have the lowest likelihood (<25%) of disability worsening over 2 years while risk is higher (25%–75%) in older, disabled or progressive MS patients. Young patients benefit most from treatment. Conclusion: NO.MS will illuminate questions related to MS characterisation, progression and prognosis. Age modulates relapse frequency and, thus, the phenotypic presentation of MS. Disease worsening across all phenotypes is mediated by age and appears to some extent be independent from new focal inflammatory activity

ENIGMA-Sleep:Challenges, opportunities, and the road map

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine

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

N Fusi et al (2014): Warped linear mixed models for the genetic analysis of transformed phenotypes

Talk given as part of Neuroimaging Statistics Research at Warwick seminars on 22nd January 2015

Computationally efficient mixed effect model for genetic analysis of high dimensional neuroimaging data

A new research direction in the neuroimaging discipline, so called imaging genetic, has emerged recently concerns describing individual differences in imaging phenotypes using genetic and environmental factors. The large number of voxel- and vertex-wise measurements in imaging genetics studies present a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot perform essential genetic analyses including heritability and association estimations and testings, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel- wise or cluster-wise P-values. Moreover, available genetic tools rely on P-values that can be inaccurate with usual parametric inference methods. In this thesis computationally efficient linear mixed effect model for voxel-wise genetic analyses of high-dimensional imaging phenotypes are developed. Specifically, fast estimation and inference procedures for heritability and association analyses are introduced using orthogonal transformations that dramatically simplify the likelihood and restricted likelihood functions of mixed effect model. We review the family of score, likelihood ratio and Wald tests and propose novel inference methods for fixed and random effect terms in the mixed effect models. To address problems with inaccuracies with the standard results used to find P-values, we propose different permutation schemes to allow semi-parametric inference (parametric likelihood-based estimation, non-parametric sampling distribution). In total, we evaluate different significance tests for heritability and association, with either asymptotic parametric or permutation-based P-value computations. We identify a number of tests that are both computationally efficient and powerful, making them ideal candidates for heritability and genome-wide association studies in the massive data setting

Modeling genetically-induced dependence in studies of families and "unrelated" individuals

Talk given at OHBM 201