Demián Wassermann, “Specificity in Cognitive Neuroimaging: Pushing the Envelope in Meta- Analyses by Harnessing Rich Probabilistic Logical Models”
“Specificity in Cognitive Neuroimaging: Pushing the Envelope in Meta- Analyses by Harnessing Rich Probabilistic Logical Models”
Abstract: Which neuroimaging-based cognitive hypotheses have made it to theses? Is there latent knowledge in the neuroimaging community that can be inferred from current literature? This question can be answered through meta-analytic studies. That is, studies which mine and infer knowledge from literature. Nonetheless, the expressivity of current tools to perform literature-based analyses is limited. One can not ask, soundly, wether agreement in the literature links specifically a cognitive function with cortical regions or networks. In this talk, I will introduce a set of new methodologies to infer knowledge from meta-analytic databases, NeuroLang. By breaching the gap between probabilistic logic databases and machine learning, Neurolang infers, through controlled human discourse, probabilistic knowledge from neuroimaging literature, structured human knowledge represented as ontologies, and neuroimaging data. I will introduce the formalism, its complexities and possibilities and how we have harnessed its power to shed novel light into the organisation of the lateral prefrontal cortex, its specificity and lateralization. Furthermore, I will speak about on-going extensions leading to ease the implementation and inference of individualised models in neuroimaging.
Biography: Demian Wassermann is a tenured full research professor (DR2) in the Mind team, part of Inria research institute, Saclay, France, that develops statistics and machine learning techniques for brain imaging. Demian works on probabilistic knowledge representation in neuroimaging as well as in diffusion MRI contributing both methodological advancements as well as software. In the past 5 years Demian has been organizer and program chair of several of the top conferences in the neuroimaging community (ISMRM, MICCAI, IPMI). Since 2018 Demian Wassermann leads an ERC-funded project called NeuroLang with the objective of harnessing probabilistic knowledge representation approaches to facilitate neuroimaging analyses. In the past years Demian’s research has been featured in top venues in machine learning and machine learning in medical imaging (ICLR, NeurIPS, AAAI, IPMI, MICCAI) as well as in top venues in life and general sciences (eLife, Nature Communications).
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Meeting ID: 986 2441 3365