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Theses

Statistical models for comprehensive meta-analysis of neuroimaging studies

Abstract : Thousands of neuroimaging studies are published every year. Exploiting this huge amount of results is difficult. Indeed, individual studies lack statistical power and report many spurious findings. Even genuine effects are often specific to particular experimental settings and difficult to reproduce. Meta- analysis aggregates studies to identify consistent trends in reported associations between brain structure and behavior. The standard approach to meta-analysis starts by gathering a sample of studies that investigate a same mental process or disease.Then, a statistical test delineates brain regions where there is a significant agreement among reported findings. In this thesis, we develop a different kind of metaanalysis that focuses on prediction rather than hypothesis testing. We build predictive models that map textual descriptions of experiments, mental processes or diseases to anatomical regions in the brain. Our supervised learning approach comes with a natural quantitative evaluation framework, and we conduct extensive experiments to validate and compare statistical models. We collect and share the largest existing dataset of neuroimaging studies and stereotactic coordinates. This dataset contains the full text and locations of neurological observations for over 13 000 publications. In the last part, we turn to decoding: inferring mental states from brain activity.We perform this task through meta-analysis of fMRI statistical maps collected from an online data repository. We use fMRI data to distinguish a wide range of mental conditions. Standard meta-analysis is an essential tool to distinguish true discoveries from noise and artifacts. This thesis introduces methods for predictive metaanalysis, which complement the standard approach and help interpret neuroimaging results and formulate hypotheses or formal statistical priors.
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  • HAL Id : tel-02495783, version 1

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Jérôme Dockes. Statistical models for comprehensive meta-analysis of neuroimaging studies. Machine Learning [stat.ML]. Université Paris-Saclay, 2019. English. ⟨NNT : 2019SACLT048⟩. ⟨tel-02495783⟩

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