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![Cortical maps showing the difference in posterior probabilities of FPCN regions to be active given topic segregation and when given no topic segregation queries. We mask out brain voxels that are not part of the FPCN. The difference between posterior probabilities is defined as Δ=P[VoxelReported(x,y,z)|SingleTopicAssociation(t)]-P[VoxelReported(x,y,z)|TopicAssociation(t)]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta = \mathbf{P} [ \text {VoxelReported}(x, y, z) | \text {SingleTopicAssociation}(t) ] - \mathbf{P} [ \text {VoxelReported}(x, y, z) | \text {TopicAssociation}(t)]$$\end{document}.](publication/365345836/figure/fig3/AS:11431281096829294@1668311773117/Cortical-maps-showing-the-difference-in-posterior-probabilities-of-FPCN-regions-to-be.png)
Cortical maps showing the difference in posterior probabilities of FPCN regions to be active given topic segregation and when given no topic segregation queries. We mask out brain voxels that are not part of the FPCN. The difference between posterior probabilities is defined as Δ=P[VoxelReported(x,y,z)|SingleTopicAssociation(t)]-P[VoxelReported(x,y,z)|TopicAssociation(t)]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta = \mathbf{P} [ \text {VoxelReported}(x, y, z) | \text {SingleTopicAssociation}(t) ] - \mathbf{P} [ \text {VoxelReported}(x, y, z) | \text {TopicAssociation}(t)]$$\end{document}.
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Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis. However, existing meta-analysis tools are limited to investigating simple neuroscience concepts and expressing a restricted range of questions. Here, we expand the scope of neuroimaging meta-analysis...
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... In this study, we pushed the limits of meta-analysis, as we leveraged the full power of NeuroLang's representation and query evaluation engines. These contributions have been reviewed at Science Advances, are still under revision by our team [Iov+21], and were presented in chapter 5. ...
This thesis contributes to the development of a probabilistic logic programming language specific to the domain of cognitive neuroscience, coined NeuroLang, and presents some of its applications to the meta-analysis of the functional brain mapping literature. By relying on logic formalisms such as datalog, and their probabilistic extensions, we show how NeuroLang makes it possible to combine uncertain and heterogeneous data to formulate rich meta-analytic hypotheses. We encode the Neurosynth database into a NeuroLang program and formulate probabilistic logic queries resulting in term-association brain maps and coactivation brain maps similar to those obtained with existing tools, and highlighting existing brain networks. We prove the correctness of our model by using the joint probability distribution defined by the Bayesian network translation of probabilistic logic programs, showing that queries lead to the same estimations as Neurosynth. Then, we show that modeling term-to-study associations probabilistically based on term frequency-document inverse frequency (TF-IDF) measures results in better accuracy on simulated data, and a better consistency on real data, for two-term conjunctive queries on smaller sample sizes. Finally, we use NeuroLang to formulate and test concrete functional brain mapping hypotheses, reproducing past results. By solving segregation logic queries combining the Neurosynth database, topic models, and the data-driven functional atlas DiFuMo, we find supporting evidence of the existence of an heterogeneous organisation of the frontoparietal control network (FPCN), and find supporting evidence that the subregion of the fusiform gyrus called visual word form area (VWFA) is recruited within attentional tasks, on top of language-related cognitive tasks.
