Brainomics: Harnessing the CubicWeb semantic framework to manage large neuromaging genetics shared resources

National audienceIn neurosciences or psychiatry, large mul-ticentric population studies are being acquired and the corresponding data are made available to the acquisition partners or the scientific community. The massive, heterogeneous and complex data from genetics, imaging , demographics or scores rely on ontologies for their definition, sharing and access. These data must be efficiently queriable by the end user and the database operator. We present the tools based on the CubicWeb open-source framework that serve the data of the european projects IMAGEN and EU-AIMS

Vers une réutilisabilité totale des algorithmes de traitement d'images

Cet article présente l'évolution des techniques de programmation d'algorithmes de traitement d'images et discute des limites de la réutilisabilité de ces algorithmes. En particulier, nous montrons qu'en C++ un algorithme peut s'écrire sous une forme générale, indépendante aussi bien du type des données que du type des structures de données sur lesquelles il peut s'appliquer. Une réutilisabilité totale des algorithmes peut donc être obtenue ; mieux, leur écriture est plus naturelle et elle n'introduit pas de surcoût significatif en temps d'exécution

The Brainomics/Localizer database

International audienceThe Brainomics/Localizer database exposes part of the data collected by the in house Localizer project, which planned to acquire four types of data from volunteer research subjects: anatomical MRI scans, functional MRI data, behavioral and demographic data, and DNA sampling. Over the years, this local project has been collecting such data from hundreds of subjects. We had selected 94 of these subjects for their complete datasets, including all four types of data, as the basis for a prior publication; the Brainomics/Localizer database publishes the data associated with these 94 subjects. Since regulatory rules prevent us from making genetic data available for download, the database serves only anatomical MRI scans, functional MRI data, behavioral and demographic data. To publish this set of heterogeneous data, we use dedicated software based on the open-source CubicWeb semantic web framework. Through genericity in the data model and flexibility in the display of data (web pages, CSV, JSON, XML), CubicWeb helps us expose these complex datasets in original and efficient ways

Brainomics: A management system for exploring and merging heterogeneous brain mapping data

International audienceWe propose an open source solution to manage brain imaging datasets and associated meta data. This framework is a powerful querying and reporting tool, customized for the needs of the emerging imaging-genetics field. A demonstration website and more details are available at http:/brainomics.cea.fr

The empirical replicability of task-based fMRI as a function of sample size

Replicating results (i.e. obtaining consistent results using a new independent dataset) is an essential part of good science. As replicability has consequences for theories derived from empirical studies, it is of utmost importance to better understand the underlying mechanisms influencing it. A popular tool for non-invasive neuroimaging studies is functional magnetic resonance imaging (fMRI). While the effect of underpowered studies is well documented, the empirical assessment of the interplay between sample size and replicability of results for task-based fMRI studies remains limited. In this work, we extend existing work on this assessment in two ways. Firstly, we use a large database of 1400 subjects performing four types of tasks from the IMAGEN project to subsample a series of independent samples of increasing size. Secondly, replicability is evaluated using a multi-dimensional framework consisting of 3 different measures: (un)conditional test-retest reliability, coherence and stability. We demonstrate not only a positive effect of sample size, but also a trade-off between spatial resolution and replicability. When replicability is assessed voxelwise or when observing small areas of activation, a larger sample size than typically used in fMRI is required to replicate results. On the other hand, when focussing on clusters of voxels, we observe a higher replicability. In addition, we observe variability in the size of clusters of activation between experimental paradigms or contrasts of parameter estimates within these

Ventral striatum connectivity during reward anticipation in adolescent smokers

Substance misusers, including adolescent smokers, often have reduced reward system activity during processing of non-drug rewards. Using a psychophysiological interaction approach, we examined functional connectivity with the ventral striatum during reward anticipation in a large (N = 206) sample of adolescent smokers. Increased smoking frequency was associated with (1) increased connectivity with regions involved in saliency and valuation, including the orbitofrontal cortex and (2) reduced connectivity between the ventral striatum and regions associated with inhibition and risk aversion, including the right inferior frontal gyrus. These results demonstrate that functional connectivity during reward processing is relevant to adolescent addiction

Differential predictors for alcohol use in adolescents as a function of familial risk.

Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Drinking Motives, Personality Traits, Life Stressors - Identifying Pathways to Harmful Alcohol Use in Adolescence Using a Panel Network Approach

BACKGROUND AND AIMS: Models of alcohol use risk suggest that drinking motives represent the most proximal risk factors on which more distal factors converge. However, little is known about how distinct risk factors influence each other and alcohol use on different temporal scales (within a given moment vs. over time). We aimed to estimate the dynamic associations of distal (personality and life stressors) and proximal (drinking motives) risk factors, and their relationship to alcohol use in adolescence and early adulthood using a novel graphical vector autoregressive (GVAR) panel network approach.DESIGN, SETTING, AND CASES: We estimated panel networks on data from the IMAGEN study, a longitudinal European cohort study following adolescents across three waves (ages 16, 19, 22). Our sample consisted of 1829 adolescents (51% females) who reported alcohol use on at least one assessment wave.MEASUREMENTS: Risk factors included personality traits (NEO-FFI: neuroticism, extraversion, openness, agreeableness, and conscientiousness; SURPS: impulsivity and sensation seeking), stressful life events (LEQ: sum scores of stressful life events), and drinking motives (DMQ: social, enhancement, conformity, coping anxiety, coping depression). We assessed alcohol use (AUDIT: quantity and frequency) and alcohol-related problems (AUDIT: related problems).FINDINGS: Within a given moment, social (partial correlation (pcor) =0.17) and enhancement motives (pcor=0.15) co-occurred most strongly with drinking quantity and frequency, while coping depression motives (pcor=0.13), openness (pcor=0.05), and impulsivity (pcor=0.09) were related to alcohol-related problems. The temporal network showed no predictive associations between distal risk factors and drinking motives. Social motives (beta=0.21), previous alcohol use (beta=0.11), and openness (beta=0.10) predicted alcohol-related problems over time (all p&lt;0.01).CONCLUSIONS: Heavy and frequent alcohol use, along with social drinking motives, appear to be key targets for preventing the development of alcohol-related problems throughout late adolescence. We found no evidence for personality traits and life stressors predisposing towards distinct drinking motives over time.</p