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.

The genetic architecture of changes in adiposity during adulthood.

Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 1.5 million primary-care health records in over 177,000 individuals in UK Biobank to study the genetic architecture of weight-change. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (a missense variant in APOE). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI, and higher in women than in men. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology driving quantitative trait values in adulthood.

Application of a convolutional neural network to the quality control of MRI defacing.

Large-scale neuroimaging datasets present unique challenges for automated processing pipelines. Motivated by a large clinical trials dataset with over 235,000 MRI scans, we consider the challenge of defacing - anonymisation to remove identifying facial features. The defacing process must undergo quality control (QC) checks to ensure that the facial features have been removed and that the brain tissue is left intact. Visual QC checks are time-consuming and can cause delays in preparing data. We have developed a convolutional neural network (CNN) that can assist with the QC of the application of MRI defacing; our CNN is able to distinguish between scans that are correctly defaced and can classify defacing failures into three sub-types to facilitate parameter tuning during remedial re-defacing. Since integrating the CNN into our anonymisation pipeline, over 75,000 scans have been processed. Strict thresholds have been applied so that ambiguous classifications are referred for visual QC checks, however all scans still undergo an efficient verification check before being marked as passed. After applying the thresholds, our network is 92% accurate and can classify nearly half of the scans without the need for protracted manual checks. Our model can generalise across MRI modalities and has comparable performance when tested on an independent dataset. Even with the introduction of the verification checks, incorporation of the CNN has reduced the time spent undertaking QC checks by 42% during initial defacing, and by 35% overall. With the help of the CNN, we have been able to successfully deface 96% of the scans in the project whilst maintaining high QC standards. In a similarly sized new project, we would expect the model to reduce the time spent on manual QC checks by 125 h. Our approach is applicable to other projects with the potential to greatly improve the efficiency of imaging anonymisation pipelines.

Multivariate phenotype analysis enables genome-wide inference of mammalian gene function.

The function of the majority of genes in the human and mouse genomes is unknown. Investigating and illuminating this dark genome is a major challenge for the biomedical sciences. The International Mouse Phenotyping Consortium (IMPC) is addressing this through the generation and broad-based phenotyping of a knockout (KO) mouse line for every protein-coding gene, producing a multidimensional data set that underlies a genome-wide annotation map from genes to phenotypes. Here, we develop a multivariate (MV) statistical approach and apply it to IMPC data comprising 148 phenotypes measured across 4,548 KO lines. There are 4,256 (1.4% of 302,997 observed data measurements) hits called by the univariate (UV) model analysing each phenotype separately, compared to 31,843 (10.5%) hits in the observed data results of the MV model, corresponding to an estimated 7.5-fold increase in power of the MV model relative to the UV model. One key property of the data set is its 55.0% rate of missingness, resulting from quality control filters and incomplete measurement of some KO lines. This raises the question of whether it is possible to infer perturbations at phenotype-gene pairs at which data are not available, i.e., to infer some in vivo effects using statistical analysis rather than experimentation. We demonstrate that, even at missing phenotypes, the MV model can detect perturbations with power comparable to the single-phenotype analysis, thereby filling in the complete gene-phenotype map with good sensitivity. A factor analysis of the MV model's fitted covariance structure identifies 20 clusters of phenotypes, with each cluster tending to be perturbed collectively. These factors cumulatively explain 75% of the KO-induced variation in the data and facilitate biological interpretation of perturbations. We also demonstrate that the MV approach strengthens the correspondence between IMPC phenotypes and existing gene annotation databases. Analysis of a subset of KO lines measured in replicate across multiple laboratories confirms that the MV model increases power with high replicability.

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: 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 < 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. 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).

How patients with multiple sclerosis acquire disability.

Patients with multiple sclerosis acquire disability either through relapse-associated worsening (RAW) or progression independent of relapse activity (PIRA). This study addresses the relative contribution of relapses to disability worsening over the course of the disease, how early progression begins and the extent to which multiple sclerosis therapies delay disability accumulation. Using the Novartis-Oxford multiple sclerosis (NO.MS) data pool spanning all multiple sclerosis phenotypes and paediatric multiple sclerosis, we evaluated ∼200 000 Expanded Disability Status Scale (EDSS) transitions from >27 000 patients with ≤15 years follow-up. We analysed three datasets: (i) A full analysis dataset containing all observational and randomized controlled clinical trials in which disability and relapses were assessed (n = 27 328); (ii) all phase 3 clinical trials (n = 8346); and (iii) all placebo-controlled phase 3 clinical trials (n = 4970). We determined the relative importance of RAW and PIRA, investigated the role of relapses on all-cause disability worsening using Andersen-Gill models and observed the impact of the mechanism of worsening and disease-modifying therapies on the time to reach milestone disability levels using time continuous Markov models. PIRA started early in the disease process, occurred in all phenotypes and became the principal driver of disability accumulation in the progressive phase of the disease. Relapses significantly increased the hazard of all-cause disability worsening events; following a year in which relapses occurred (versus a year without relapses), the hazard increased by 31-48% (all P < 0.001). Pre-existing disability and older age were the principal risk factors for incomplete relapse recovery. For placebo-treated patients with minimal disability (EDSS 1), it took 8.95 years until increased limitation in walking ability (EDSS 4) and 18.48 years to require walking assistance (EDSS 6). Treating patients with disease-modifying therapies delayed these times significantly by 3.51 years (95% confidence limit: 3.19, 3.96) and 3.09 years (2.60, 3.72), respectively. In patients with relapsing-remitting multiple sclerosis, those who worsened exclusively due to RAW events took a similar length of time to reach milestone EDSS values compared with those with PIRA events; the fastest transitions were observed in patients with PIRA and superimposed relapses. Our data confirm that relapses contribute to the accumulation of disability, primarily early in multiple sclerosis. PIRA begins in relapsing-remitting multiple sclerosis and becomes the dominant driver of disability accumulation as the disease evolves. Pre-existing disability and older age are the principal risk factors for further disability accumulation. The use of disease-modifying therapies delays disability accrual by years, with the potential to gain time being highest in the earliest stages of multiple sclerosis.

Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project.

Imaging genetics analyses use neuroimaging traits as intermediate phenotypes to infer the degree of genetic contribution to brain structure and function in health and/or illness. Coefficients of relatedness (CR) summarize the degree of genetic similarity among subjects and are used to estimate the heritability - the proportion of phenotypic variance explained by genetic factors. The CR can be inferred directly from genome-wide genotype data to explain the degree of shared variation in common genetic polymorphisms (SNP-heritability) among related or unrelated subjects. We developed a central processing and graphics processing unit (CPU and GPU) accelerated Fast and Powerful Heritability Inference (FPHI) approach that linearizes likelihood calculations to overcome the ∼N2-3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 × 105 voxel-wise traits in N = 1,206 twin and sibling participants from the Human Connectome Project (HCP) (550 M/656 F, age = 28.8 ± 3.7 years) and N = 37,432 (17,531 M/19,901 F; age = 63.7 ± 7.5 years) participants from the UK Biobank (UKBB). The FPHI estimates were in excellent agreement with heritability values calculated using Genome-wide Complex Trait Analysis software (r = 0.96 and 0.98 in HCP and UKBB sample) while significantly reducing computational (102-4 times). The regional and voxel-wise traits heritability estimates for the HCP and UKBB were likewise in excellent agreement (r = 0.63-0.76, p < 10-10). In summary, the hardware-accelerated FPHI made it practical to calculate heritability values for voxel-wise neuroimaging traits, even in very large samples such as the UKBB. The patterns of additive genetic variance in neuroimaging traits measured in a large sample of related and unrelated individuals showed excellent agreement regardless of the estimation method. The code and instruction to execute these analyses are available at www.solar-eclipse-genetics.org.

Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis.

BACKGROUND: Novartis and the University of Oxford's Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression. METHOD: The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2200 centres worldwide. For the "IL-17" project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA)). RESULTS: A fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project. CONCLUSIONS: An informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools.

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.

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.

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 SAD compared 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.

Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data.

Imaging genetic analyses use heritability calculations to measure the fraction of phenotypic variance attributable to additive genetic factors. We tested the agreement between heritability estimates provided by four methods that are used for heritability estimates in neuroimaging traits. SOLAR-Eclipse and OpenMx use iterative maximum likelihood estimation (MLE) methods. Accelerated Permutation inference for ACE (APACE) and fast permutation heritability inference (FPHI), employ fast, non-iterative approximation-based methods. We performed this evaluation in a simulated twin-sibling pedigree and phenotypes and in diffusion tensor imaging (DTI) data from three twin-sibling cohorts, the human connectome project (HCP), netherlands twin register (NTR) and BrainSCALE projects provided as a part of the enhancing neuro imaging genetics analysis (ENIGMA) consortium. We observed that heritability estimate may differ depending on the underlying method and dataset. The heritability estimates from the two MLE approaches provided excellent agreement in both simulated and imaging data. The heritability estimates for two approximation approaches showed reduced heritability estimates in datasets with deviations from data normality. We propose a data homogenization approach (implemented in solar-eclipse; www.solar-eclipse-genetics.org) to improve the convergence of heritability estimates across different methods. The homogenization steps include consistent regression of any nuisance covariates and enforcing normality on the trait data using inverse Gaussian transformation. Under these conditions, the heritability estimates for simulated and DTI phenotypes produced converging heritability estimates regardless of the method. Thus, using these simple suggestions may help new heritability studies to provide outcomes that are comparable regardless of software package.

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).

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.

ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide.

In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) - a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date - of schizophrenia and major depression - ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens - now numbering over 30,000 MRI scans - have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants - and genetic variants in general - may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures - from tens of thousands of people - that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.

Validation for Persian Versions of "Desire for Drug Questionnaire" and "Obsessive Compulsive Drug Use Scale" in Heroin Dependents.

OBJECTIVE: Drug Craving could be defined as a subjective motivational state associated with a strong desire to consume drugs. Craving is a subjective phenomenon; therefore, self-report (subjective) craving measures are usually referenced. Two well-known questionnaires for measurement of drug craving severity are Desire for Drug Questionnaire or DDQ (for instant craving) and Obsessive Compulsive Drug Use Scale or OCDUS [for craving in a period of time (periodic craving), usually one week]. In this study, we evaluated the content-related validity of these questionnaires for Persian-language speaking crystalline-heroin abusers. METHODS: After translation by two different groups, back translation and retranslation process of the DDQ and OCDUS questionnaires were achieved by an expert team in English language; we used them for evaluation of instant and periodic craving among 131 male crystalline-heroin abusers. Then, both DDQ and OCDUS questionnaire's scores were subjected to an exploratory principal components factor analysis (PCA). The criterion for factor extraction was an eigenvalue equal to or more than 1. RESULTS: The factor analysis of DDQ and OCDUS led to three factors for DDQ and four factors for OCDUS; each group of factors together explained 62% and 65% of the common factor variance, respectively. There was no significant correlation between different DDQ and OCDUS components and demographic factors. Nevertheless, approximately all of the seven OCDUS and DDQ components were significantly correlated to each other. CONCLUSION: The Persian version of DDQ and OCDUS questionnaires could be considered as valid and reliable instruments for evaluation of drug craving in male crystalline-heroin Persian-language speaking abusers.

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 with controls. Tract-specific heterochronicity measures, that is, abnormal rates of adolescent maturation and aging explained approximately 50% of the regional variance effects of diagnosis and age-by-diagnosis interaction in patients. Interactive, three-dimensional 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. Hum Brain Mapp 37:4673-4688, 2016. © 2016 Wiley Periodicals, Inc.

Neural correlates of audiotactile phonetic processing in early-blind readers: an fMRI study.

Reading is a multisensory function that relies on arbitrary associations between auditory speech sounds and symbols from a second modality. Studies of bimodal phonetic perception have mostly investigated the integration of visual letters and speech sounds. Blind readers perform an analogous task by using tactile Braille letters instead of visual letters. The neural underpinnings of audiotactile phonetic processing have not been studied before. We used functional magnetic resonance imaging to reveal the neural correlates of audiotactile phonetic processing in 16 early-blind Braille readers. Braille letters and corresponding speech sounds were presented in unimodal, and congruent/incongruent bimodal configurations. We also used a behavioral task to measure the speed of blind readers in identifying letters presented via tactile and/or auditory modalities. Reaction times for tactile stimuli were faster. The reaction times for bimodal stimuli were equal to those for the slower auditory-only stimuli. fMRI analyses revealed the convergence of unimodal auditory and unimodal tactile responses in areas of the right precentral gyrus and bilateral crus I of the cerebellum. The left and right planum temporale fulfilled the 'max criterion' for bimodal integration, but activities of these areas were not sensitive to the phonetical congruency between sounds and Braille letters. Nevertheless, congruency effects were found in regions of frontal lobe and cerebellum. Our findings suggest that, unlike sighted readers who are assumed to have amodal phonetic representations, blind readers probably process letters and sounds separately. We discuss that this distinction might be due to mal-development of multisensory neural circuits in early blinds or it might be due to inherent differences between Braille and print reading mechanisms.

Exploring Neural Correlates of Different Dimensions in Drug Craving Self-Reports among Heroin Dependents.

INTRODUCTION: Drug craving could be described as a motivational state which drives drug dependents towards drug seeking and use. Different types of self-reports such as craving feeling, desire and intention, wanting and need, imagery of use, and negative affect have been attributed to this motivational state. By using subjective self-reports for different correlates of drug craving along with functional neuroimaging with cue exposure paradigm, we investigated the brain regions that could correspond to different dimensions of subjective reports for heroin craving. METHODS: A total of 25 crystalline-heroin smokers underwent functional magnetic resonance imaging (fMRI), while viewing heroin-related and neutral cues presented in a block-design task. During trial intervals, subjects verbally reported their subjective feeling of cue induced craving (CIC). After fMRI procedure, participants reported the intensity of their "need for drug use" and "drug use imagination" on a 0-100 visual analog scale (VAS). Afterwards, they completed positive and negative affect scale (PANAS) and desire for drug questionnaire (DDQ) with 3 components of "desire and intention to drug use," "negative reinforcement," and "loss of control." RESULTS: The study showed significant correlation between "subjective feeling of craving" and activation of the left and right anterior cingulate cortex, as well as right medial frontal gyrus. Furthermore, the "desire and intention to drug use" was correlated with activation of the left precentral gyrus, left superior frontal gyrus, and left middle frontal gyrus. Subjects also exhibited significant correlation between the "need for drug use" and activation of the right inferior temporal gyrus, right middle temporal gyrus, and right parahippocampal gyrus. Correlation between subjective report of "heroin use imagination" and activation of the cerebellar vermis was also observed. Another significant correlation was between the "negative affect" and activation of the left precuneus, right putamen, and right middle temporal gyrus. DISCUSSION: This preliminary study proposes different neural correlates for various dimensions of subjective craving self-reports. It could reflect multidimensionality of cognitive functions corresponding with drug craving. These cognitive functions could represent their motivational and affective outcomes in a single item "subjective craving feeling" or in self-reports with multiple dissociable items, such as intention, need, imagination, or negative feeling. The new psychological models of drug craving for covering various dimensions of subjective craving self-reports based on their neurocognitive correspondence could potentially modify craving assessments in addiction medicine.

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.