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Functional and Effective Connectivity: A Review
"... Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain’s functional architecture and operational princip ..."
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Cited by 36 (2 self)
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Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain’s functional architecture and operational principles. The inception of this journal has been foreshadowed by an everincreasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead. Key words: causal modeling; brain connectivity; effective connectivity; functional connectivity
Comparing Families of Dynamic Causal Models: Supplementary Material
, 2009
"... VB for RFX inference In previous work we have proposed a VB algorithm for RFX inference over models. This algorithm (see equation 9 in [2]) makes use of the relation q(rm) log rmdrm = ψ(αm) − ψ ( ∑ αj) (1) where q(r) = Dir(α) and ψ() is the digamma function [1]. We can also evaluate the above expr ..."
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VB for RFX inference In previous work we have proposed a VB algorithm for RFX inference over models. This algorithm (see equation 9 in [2]) makes use of the relation q(rm) log rmdrm = ψ(αm) − ψ ( ∑ αj) (1) where q(r) = Dir(α) and ψ() is the digamma function [1]. We can also evaluate the above expression using a sample based approximation where r (i) m q(rm) log rmdrm ≈ 1
Behavioral/Systems/Cognitive Striatal Prediction Error Modulates Cortical Coupling
"... Both perceptual inference and motor responses are shaped by learned probabilities. For example, stimulusinduced responses in sensory cortices and preparatory activity in premotor cortex reflect how (un)expected a stimulus is. This is in accordance with predictive coding accounts of brain function, ..."
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Cited by 25 (4 self)
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Both perceptual inference and motor responses are shaped by learned probabilities. For example, stimulusinduced responses in sensory cortices and preparatory activity in premotor cortex reflect how (un)expected a stimulus is. This is in accordance with predictive coding accounts of brain function, which posit a fundamental role of prediction errors for learning and adaptive behavior. We used functional magnetic resonance imaging and recent advances in computational modeling to investigate how (failures of) learned predictions about visual stimuli influence subsequent motor responses. Healthy volunteers discriminated visual stimuli that were differentially predicted by auditory cues. Critically, the predictive strengths of cues varied over time, requiring subjects to continuously update estimates of stimulus probabilities. This online inference, modeled using a hierarchical Bayesian learner, was reflected behaviorally: speed and accuracy of motor responses increased significantly with predictability of the stimuli. We used nonlinear dynamic causal modeling to demonstrate that striatal prediction errors are used to tune functional coupling in cortical networks during learning. Specifically, the degree of striatal trialbytrial prediction error activity controls the efficacy of visuomotor connectionsandthustheinfluenceofsurprisingstimulionpremotoractivity.Thisfindingsubstantiallyadvancesourunderstandingofstriatalfunction and provides direct empirical evidence for formal learning theories that posit a central role for prediction errordependent plasticity.
Comparing families of dynamic causal models
 PLoS Comput. Biol
, 2010
"... Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of ..."
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Cited by 24 (6 self)
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Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This ‘‘best model’ ’ approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data.
Functional neuroimaging of autobiographical memory
 Trends in Cognitive Neurosciences
, 2007
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Assessing the relevance of fMRIbased prior in the EEG inverse problem: a Bayesian model comparison approach
 IEEE Transactions on Signal Processing
, 2005
"... Abstract—Characterizing the cortical activity from electro and magnetoencephalography (EEG/MEG) data requires solving an illposed inverse problem that does not admit a unique solution. As a consequence, the use of functional neuroimaging, for instance, functional Magnetic Resonance Imaging (fMRI) ..."
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Cited by 11 (3 self)
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Abstract—Characterizing the cortical activity from electro and magnetoencephalography (EEG/MEG) data requires solving an illposed inverse problem that does not admit a unique solution. As a consequence, the use of functional neuroimaging, for instance, functional Magnetic Resonance Imaging (fMRI), constitutes an appealing way of constraining the solution. However, the match between bioelectric and metabolic activities is desirable but not assured. Therefore, the introduction of spatial priors derived from other functional modalities in the EEG/MEG inverse problem should be considered with caution. In this paper, we propose a Bayesian characterization of the relevance of fMRIderived prior information regarding the EEG/MEG data. This is done by quantifying the adequacy of this prior to the data, compared with that obtained using an noninformative prior instead. This quantitative comparison, using the socalled Bayes factor, allows us to decide whether the informative prior should (or not) be included in the inverse solution. We validate our approach using extensive simulations, where fMRIderived priors are built as perturbed versions of the simulated EEG sources. Moreover, we show how this inference framework can be generalized to optimize the way we should incorporate the informative prior.
Hemodynamic Models: Investigation and Application to Brain Imaging Analysis
, 2010
"... I would like to thank the many people who helped be during my PhD work, through discussions, collaborations, encouragements... Approximately in the order I met them: Olivier Faugeras, who accompanied me throughout this thesis I am particularly grateful to his energy and enthusiasm that were very he ..."
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helpful to lead all these projects and collaborations, and encourage me at difficult times, Renaud Keriven, Miklos Santha and Jacques Stern for their help in the first choices at the time of my Master, JeanBaptiste Poline, Line Garnero, Sylvain Baillet, AnneLise Paradis who helped me to learn something
Mention: NEUROSCIENCES Par
, 2012
"... Mes premiers remerciements iront bien sûr, à mon directeur de thèse, Pascal Giraux, qui a été un guide précieux pour mes premiers pas dans la recherche. Merci de m’avoir fait confiance. Ces quelques années passées à vos côtés ont été riches d'enseignements et d'échanges... Merci pour l’ent ..."
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admiration, tant sur le plan professionnel que personnel. Je suis très reconnaissante à mes deux directeurs de thèse de m’avoir aidé à mener à bien ce projet. Je remercie vivement Messieurs Jean Paysant et Christian Xerri, pour avoir accepté d’être
Recognizing Sequences of Sequences
, 2009
"... The brain’s decoding of fast sensory streams is currently impossible to emulate, even approximately, with artificial agents. For example, robust speech recognition is relatively easy for humans but exceptionally difficult for artificial speech recognition systems. In this paper, we propose that reco ..."
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Cited by 10 (4 self)
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The brain’s decoding of fast sensory streams is currently impossible to emulate, even approximately, with artificial agents. For example, robust speech recognition is relatively easy for humans but exceptionally difficult for artificial speech recognition systems. In this paper, we propose that recognition can be simplified with an internal model of how sensory input is generated, when formulated in a Bayesian framework. We show that a plausible candidate for an internal or generative model is a hierarchy of ‘stable heteroclinic channels’. This model describes continuous dynamics in the environment as a hierarchy of sequences, where slower sequences cause faster sequences. Under this model, online recognition corresponds to the dynamic decoding of causal sequences, giving a representation of the environment with predictive power on several timescales. We illustrate the ensuing decoding or recognition scheme using synthetic sequences of syllables, where syllables are sequences of phonemes and phonemes are sequences of soundwave modulations. By presenting anomalous stimuli, we find that the resulting recognition dynamics disclose inference at multiple time scales and are reminiscent of neuronal dynamics seen in the real brain.
Reinforcement Learning or Active Inference?
, 2009
"... This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a freeenergy formulation of perception. In this formulation, agents adjust their internal states and sam ..."
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Cited by 9 (2 self)
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This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a freeenergy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their freeenergy. Such agents learn causal structure in the environment and sample it in an adaptive and selfsupervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountaincar problem, using active perception or inference under the freeenergy principle. The ensuing proofofconcept may be important because the freeenergy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.
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