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Brain-sur-Allonnes, France

Daunizeau J.,Brain and Spine Institute | Daunizeau J.,University College London | Adam V.,University College London | Rigoux L.,Brain and Spine Institute
PLoS Computational Biology

This work is in line with an on-going effort tending toward a computational (quantitative and refutable) understanding of human neuro-cognitive processes. Many sophisticated models for behavioural and neurobiological data have flourished during the past decade. Most of these models are partly unspecified (i.e. they have unknown parameters) and nonlinear. This makes them difficult to peer with a formal statistical data analysis framework. In turn, this compromises the reproducibility of model-based empirical studies. This work exposes a software toolbox that provides generic, efficient and robust probabilistic solutions to the three problems of model-based analysis of empirical data: (i) data simulation, (ii) parameter estimation/model selection, and (iii) experimental design optimization. © 2014 Daunizeau et al. Source

Rojkova K.,French Institute of Health and Medical Research | Rojkova K.,Brain and Spine Institute | Volle E.,French Institute of Health and Medical Research | Urbanski M.,French Institute of Health and Medical Research | And 4 more authors.
Brain Structure and Function

In neuroscience, there is a growing consensus that higher cognitive functions may be supported by distributed networks involving different cerebral regions, rather than by single brain areas. Communication within these networks is mediated by white matter tracts and is particularly prominent in the frontal lobes for the control and integration of information. However, the detailed mapping of frontal connections remains incomplete, albeit crucial to an increased understanding of these cognitive functions. Based on 47 high-resolution diffusion-weighted imaging datasets (age range 22–71 years), we built a statistical normative atlas of the frontal lobe connections in stereotaxic space, using state-of-the-art spherical deconvolution tractography. We dissected 55 tracts including U-shaped fibers. We further characterized these tracts by measuring their correlation with age and education level. We reported age-related differences in the microstructural organization of several, specific frontal fiber tracts, but found no correlation with education level. Future voxel-based analyses, such as voxel-based morphometry or tract-based spatial statistics studies, may benefit from our atlas by identifying the tracts and networks involved in frontal functions. Our atlas will also build the capacity of clinicians to further understand the mechanisms involved in brain recovery and plasticity, as well as assist clinicians in the diagnosis of disconnection or abnormality within specific tracts of individual patients with various brain diseases. © 2015, Springer-Verlag Berlin Heidelberg. Source

Noll K.R.,University of Houston | Sullaway C.,University of Houston | Ziu M.,Brain and Spine Institute | Weinberg J.S.,University of Houston | Wefel J.S.,University of Houston

Background: Various tumor characteristics have been associated with neurocognitive functioning (NCF), though the role of tumor grade has not been adequately examined. Methods: Seventy-two patients with histologically confirmed grade IV glioma (n = 37), grade III glioma (n = 20), and grade II glioma (n = 15) in the left temporal lobe completed preoperative neuropsychological assessment. Rates of impairment and mean test performances were compared by tumor grade with follow-up analysis of the influence of other tumor- and patient-related characteristics on NCF. Results: NCF impairment was more frequent in patients with grade IV tumor compared with patients with lower-grade tumors in verbal learning, executive functioning, as well as language abilities. Mean performances significantly differed by tumor grade on measures of verbal learning, processing speed, executive functioning, and language, with the grade IV group exhibiting worse performances than patients with lower-grade tumors. Group differences in mean performances remained significant when controlling for T1-weighted and fluid attenuated inversion recovery MRI-based lesion volume. Performances did not differ by seizure status or antiepileptic and steroid use. Conclusions: Compared with patients with grade II or III left temporal lobe glioma, patients with grade IV tumors exhibit greater difficulty with verbal learning, processing speed, executive functioning, and language. Differences in NCF associated with glioma grade were independent of lesion volume, seizure status, and antiepileptic or steroid use, lending support to the concept of "lesion momentum" as a primary contributor to deficits in NCF of newly diagnosed patients prior to surgery. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. Source

Devaine M.,Brain and Spine Institute | Devaine M.,University Pierre and Marie Curie | Hollard G.,Maison des science Economiques | Daunizeau J.,Brain and Spine Institute | And 2 more authors.

Theory of Mind (ToM) is the ability to attribute mental states (e.g., beliefs and desires) to other people in order to understand and predict their behaviour. If others are rewarded to compete or cooperate with you, then what they will do depends upon what they believe about you. This is the reason why social interaction induces recursive ToM, of the sort "I think that you think that I think, etc.". Critically, recursion is the common notion behind the definition of sophistication of human language, strategic thinking in games, and, arguably, ToM. Although sophisticated ToM is believed to have high adaptive fitness, broad experimental evidence from behavioural economics, experimental psychology and linguistics point towards limited recursivity in representing other's beliefs. In this work, we test whether such apparent limitation may not in fact be proven to be adaptive, i.e. optimal in an evolutionary sense. First, we propose a meta-Bayesian approach that can predict the behaviour of ToM sophistication phenotypes who engage in social interactions. Second, we measure their adaptive fitness using evolutionary game theory. Our main contribution is to show that one does not have to appeal to biological costs to explain our limited ToM sophistication. In fact, the evolutionary cost/benefit ratio of ToM sophistication is non trivial. This is partly because an informational cost prevents highly sophisticated ToM phenotypes to fully exploit less sophisticated ones (in a competitive context). In addition, cooperation surprisingly favours lower levels of ToM sophistication. Taken together, these quantitative corollaries of the "social Bayesian brain" hypothesis provide an evolutionary account for both the limitation of ToM sophistication in humans as well as the persistence of low ToM sophistication levels. © 2014 Devaine et al. Source

Daunizeau J.,University College London | Daunizeau J.,University of Zurich | den Ouden H.E.M.,Donders Institute for Brain | Pessiglione M.,Brain and Spine Institute | And 4 more authors.

In a companion paper [1], we have presented a generic approach for inferring how subjects make optimal decisions under uncertainty. From a Bayesian decision theoretic perspective, uncertain representations correspond to "posterior" beliefs, which result from integrating (sensory) information with subjective "prior" beliefs. Preferences and goals are encoded through a "loss" (or "utility") function, which measures the cost incurred by making any admissible decision for any given (hidden or unknown) state of the world. By assuming that subjects make optimal decisions on the basis of updated (posterior) beliefs and utility (loss) functions, one can evaluate the likelihood of observed behaviour. In this paper, we describe a concrete implementation of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions) and demonstrate its utility by applying it to both simulated and empirical reaction time data from an associative learning task. Here, inter-trial variability in reaction times is modelled as reflecting the dynamics of the subjects' internal recognition process, i.e. the updating of representations (posterior densities) of hidden states over trials while subjects learn probabilistic audio-visual associations. We use this paradigm to demonstrate that our meta-Bayesian framework allows for (i) probabilistic inference on the dynamics of the subject's representation of environmental states, and for (ii) model selection to disambiguate between alternative preferences (loss functions) human subjects could employ when dealing with trade-offs, such as between speed and accuracy. Finally, we illustrate how our approach can be used to quantify subjective beliefs and preferences that underlie inter-individual differences in behaviour. © 2010 Daunizeau et al. Source

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