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• W2531044265 abstract "Event Abstract Back to Event Mapping cognitive ontologies to and from the brain Yannick Schwartz1, 2*, Bertrand Thirion1, 2 and Gael Varoquaux1, 2 1 Parietal Team, Inria Saclay, France 2 Neurospin, CEA, France Introduction Large-scale mapping of cognitive brain functions using fMRI relies on the accumulation of individual studies. fMRI meta-analyses combine several studies and open the opportunity to invert the statistical inference of individual studies standard analysis [2]. Our goal is to find a bidirectional link between brain activity patterns and cognitive functions. To that end, we propose a methodology that leverages the Cognitive Paradigm Ontology (CogPO) [5] to perform image-based meta analyses. We use two models: forward inference to find regions activated for a given label, and reverse inference to find regions predictive of a given label. Method In order to find correspondences between studies, we choose to use activation maps and label them with terms drawn from CogPO. CogPO describes paradigms by defining categories of terms such as the explicit stimulus, or the instructions. Those terms are likely to be shared across studies regardless of the original topic of the study. In this work, we use 19 studies, mainly from OpenfMRI [3], comprising 131 different conditions and labeled with 19 terms. Forward inference: we use the standard fMRI analysis framework and, for each voxel of the subject-level activation maps, tests its significance relative to a term using a General Linear Model. The design matrix models the presence of the terms. Reverse inference: We use a One-vs-All (OvA) approach to predict the presence of CogPO terms. The classifiers are trained within a leave-one-study-out or leave-one-laboratory-out cross validation scheme. Predicting terms on new experiments ensures we are not building a study detector. Results Forward inference: The resulting regions (Figure 1) lack functional specificity, but are relevant to the corresponding terms. Reverse inference: The prediction problem is highly multi-class and imbalanced, as seen in the long-tailed distribution of the terms representation (Figure 2). Figure 2 also shows the corresponding precision and recall scores of several classifiers, as well as the chance levels for both cross validations. Conclusion We present a methodology to accumulate knowledge across studies. We use ontology terms to find commonalities between studies and a careful cross validation to avoid learning idiosyncrasies. The main remaining roadblock is the class imbalance problem, which goes together with a lack of data in general. This is a known egg and chicken problem, as few datasets are available online, few meta-analysis methods are developed and further limit the incentive for sharing new data [1, 4]. In the future, we plan to apply this methodology to more datasets, and extend it to make zero-shot learning of tasks. Figure 1 Figure 2 Acknowledgements This work was supported by the ANR grants BrainPedia ANR-10-JCJC 1408-01 and IRMGroup ANR-10-BLAN-0126-02, as well as the NSF grant NSF OCI-1131441 for the OpenfMRI project. References [1] S.G. Costafreda (2009), ‘Pooling fMRI data: meta-analysis, mega-analysis and multi-center studies’, Frontiers in Neuroinformatics. [2] R. Poldrack, et al. (2009), ‘Decoding the large-scale structure of brain function by classifying mental states across individuals’, Psychological Science. [3] R. Poldrack, et al. (2013), ‘Towards open sharing of task-based fMRI data: The openfMRI project’, Frontiers in Neuroinformatics. [4] JB. Poline, et al (2012). ‘Data sharing in neuroimaging research’, Frontiers in Neuroinformatics. [5] J. Turner, et al. (2012), ‘The cognitive paradigm ontology: design and application’, Neuroinformatics. Keywords: fMRI meta-analysis, forward inference, reverse inference, cognitive ontology, large database Conference: Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014. Presentation Type: Poster, to be considered for oral presentation Topic: Neuroimaging Citation: Schwartz Y, Thirion B and Varoquaux G (2014). Mapping cognitive ontologies to and from the brain. Front. Neuroinform. Conference Abstract: Neuroinformatics 2014. doi: 10.3389/conf.fninf.2014.18.00084 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 27 Apr 2014; Published Online: 04 Jun 2014. * Correspondence: Mr. Yannick Schwartz, Parietal Team, Inria Saclay, Saclay, France, yannick.schwartz@inria.fr Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Yannick Schwartz Bertrand Thirion Gael Varoquaux Google Yannick Schwartz Bertrand Thirion Gael Varoquaux Google Scholar Yannick Schwartz Bertrand Thirion Gael Varoquaux PubMed Yannick Schwartz Bertrand Thirion Gael Varoquaux Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page." @default.
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