2,142 research outputs found
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
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
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 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
Nilearn for new use cases: Scaling up computational and community efforts
Introduction
Nilearn (https://nilearn.github.io) is a well-established Python package that provides statistical
and machine learning tools for fast and easy analysis of brain images with instructive
documentation and a friendly community. This focus has led to its current position as a crucial
part of the neuroimaging community’s open-source software ecosystem, supporting efficient and
reproducible science [1]. It has been continuously developed over the past 10 years, currently
with 900 stars, 500 forks, and 176 contributors on GitHub. Nilearn leverages and builds upon
other central Python machine learning packages, such as Scikit-Learn [2], that are extensively
used, tested, and optimized by a large scientific and industrial community.
In recent years, efforts in Nilearn have been focused on meeting evolving community needs by
increasing General Linear Model (GLM) support, interfacing with initiatives like fMRIPrep and
BIDS, and improving the user documentation. Here we report on progress regarding our current
priorities.
Methods
Nilearn is developed to be accessible and easy-to-use for researchers and the open-source
community. It features user-focused documentation that includes a user guide and an example
gallery as well as comprehensive contribution guidelines. Nilearn is also presented in tutorials
and workshops throughout the year including the Montreal Artificial Intelligence and
Neuroscience (MAIN) Educational Workshop, the OHBM Brainhack event, and for the Chinese
Open Science Network.
The community is encouraged to ask questions, report bugs, make suggestions for
improvements or new features, and make direct contributions to the source code. We use the
platforms Neurostars, GitHub, and Discord to interact with contributors and users on a daily
basis.
Nilearn adheres to best practices in software development including using version control, unit
testing, and requiring multiple reviews of contributions. We also have a continuous integration
infrastructure set up to automate many aspects of our development process and make sure our
code is continuously tested and up-to-date.
Results
Nilearn supports methods such as image manipulation and processing, decoding, functional
connectivity analysis, GLM, multivariate pattern analysis, along with plotting volumetric and
surface data.
In the latest release, cluster-level and TFCE-based family-wise error rate (FWER) control have
been added to support the mass univariate and GLM analysis modules, expanding from the
already implemented voxel-level correction method (see Fig1). Optimizing Nilearn’s maskers is
also underway such as the recently added classes for handling multi-subject 4D image data.
These also provide the option to use parallelization to speed up computation.
In addition, Nilearn has introduced a new theme to update the documentation making the
website more readable and accessible (https://nilearn.github.io/). This change also sets the
stage for further improvement and modernisation of several aspects of the documentation, like
the user guide.
Development on Nilearn’s interfaces module added a new function to write BIDS-compatible
model results to disk. This and further development of the BIDS interface will facilitate
interaction with other relevant community tools such as FitLins [3]. Finally, several surface
plotting enhancements are in progress including improving the API for background maps (see
Fig2).
Conclusion
Nilearn is extensively used by researchers of the neuroimaging community due to its
implementations of well-founded methods and visualization tools which are often essential in
brain imaging research for quality control and communicating results. Recent work has
highlighted areas where more active work is needed to scale the project both technically and
socially, including: working with large datasets, better supporting analyses on the cortical
surface, and advancing standard practice in neuroimaging statistics through active community
outreach.
References
[1] Poldrack, R., Gorgolewski, K., Varoquaux, G. (2019). Computational and Informatic
Advances for Reproducible Data Analysis in Neuroimaging Annual Review of Biomedical Data
Science 2(1), 119-138. https://dx.doi.org/10.1146/annurev-biodatasci-072018-021237
[2] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M.,
Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher,
M., Perrot, M., Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python, Journal of
Machine Learning Research, 12, 2825-2830.
[3] Markiewicz, C. J., De La Vega, A., Wagner, A., Halchenko, Y. O., Finc, K., Ciric, R.,
Goncalves, M., Nielson, D. M., Kent, J. D., Lee, J. A., Bansal, S., Poldrack, R. A., Gorgolewski,
K. J. (2022). poldracklab/fitlins: 0.11.0 (0.11.0). Zenodo. https://doi.org/10.5281/zenodo.7217447This poster was presented at OHBM 2023
The relationship between negative life events and cortical structural connectivity in adolescents
Adolescence is a crucial period for physical and psychological development. The impact of negative life events represents a risk factor for the onset of neuropsychiatric disorders. This study aims to investigate the relationship between negative life events and structural brain connectivity, considering both graph theory and connectivity strength. A group (n = 487) of adolescents from the IMAGEN Consortium was divided into Low and High Stress groups. Brain networks were extracted at an individual level, based on morphological similarity between grey matter regions with regions defined using an atlas-based region of interest (ROI) approach. Between-group comparisons were performed with global and local graph theory measures in a range of sparsity levels. The analysis was also performed in a larger sample of adolescents (n = 976) to examine linear correlations between stress level and network measures. Connectivity strength differences were investigated with network-based statistics. Negative life events were not found to be a factor influencing global network measures at any sparsity level. At local network level, between-group differences were found in centrality measures of the left somato-motor network (a decrease of betweenness centrality was seen at sparsity 5%), of the bilateral central visual and the left dorsal attention network (increase of degree at sparsity 10% at sparsity 30% respectively). Network-based statistics analysis showed an increase in connectivity strength in the High stress group in edges connecting the dorsal attention, limbic and salience networks. This study suggests negative life events alone do not alter structural connectivity globally, but they are associated to connectivity properties in areas involved in emotion and attention.</p
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
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