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Large-scale functional MRI analysis to accumulate knowledge on brain functions

Yannick Schwartz 1
1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
Abstract : How can we accumulate knowledge on brain functions? How can we leverage years of research in functional MRI to analyse finer-grained psychological constructs, and build a comprehensive model of the brain? Researchers usually rely on single studies to delineate brain regions recruited by mental processes. They relate their findings to previous works in an informal way by defining regions of interest from the literature. Meta-analysis approaches provide a more principled way to build upon the literature. This thesis investigates three ways to assemble knowledge using activation maps from a large amount of studies. First, we present an approach that uses jointly two similar fMRI experiments, to better condition an analysis from a statistical standpoint. We show that it is a valuable data-driven alternative to traditional regions of interest analyses, but fails to provide a systematic way to relate studies, and thus does not permit to integrate knowledge on a large scale. Because of the difficulty to associate multiple studies, we resort to using a single dataset sampling a large number of stimuli for our second contribution. This method estimates functional networks associated with functional profiles, where the functional networks are interacting brain regions and the functional profiles are a weighted set of cognitive descriptors. This work successfully yields known brain networks and automatically associates meaningful descriptions. Its limitations lie in the unsupervised nature of this method, which is more difficult to validate, and the use of a single dataset. It however brings the notion of cognitive labels, which is central to our last contribution. Our last contribution presents a method that learns functional atlases by combining several datasets. [Henson 2006] shows that forward inference, i.e. the probability of an activation given a cognitive process, is often not sufficient to conclude on the engagement of brain regions for a cognitive process. Conversely, [Poldrack 2006] describes reverse inference as the probability of a cognitive process given an activation, but warns of a logical fallacy in concluding on such inference from evoked activity. Avoiding this issue requires to perform reverse inference with a large coverage of the cognitive space. We present a framework that uses a "meta-design" to describe many different tasks with a common vocabulary, and use forward and reverse inference in conjunction to outline functional networks that are consistently represented across the studies. We use a predictive model for reverse inference, and perform prediction on unseen studies to guarantee that we do not learn studies' idiosyncrasies. This final contribution permits to learn functional atlases, i.e. functional networks associated with a cognitive concept. We explored different possibilities to jointly analyse multiple fMRI experiments. We have found that one of the main challenges is to be able to relate the experiments with one another. As a solution, we propose a common vocabulary to describe the tasks. [Henson 2006] advocates the use of forward and reverse inference in conjunction to associate cognitive functions to brain regions, which is only possible in the context of a large scale analysis to overcome the limitations of reverse inference. This framing of the problem therefore makes it possible to establish a large statistical model of the brain, and accumulate knowledge across functional neuroimaging studies.
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Submitted on : Friday, June 5, 2015 - 4:48:05 PM
Last modification on : Monday, February 10, 2020 - 6:13:43 PM


  • HAL Id : tel-01160550, version 1


Yannick Schwartz. Large-scale functional MRI analysis to accumulate knowledge on brain functions. Medical Imaging. Université Paris Sud - Paris XI, 2015. English. ⟨NNT : 2015PA112056⟩. ⟨tel-01160550⟩



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