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Philippe Ciuciu

Researcher at French Institute for Research in Computer Science and Automation

Publications -  162
Citations -  3713

Philippe Ciuciu is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Compressed sensing & Iterative reconstruction. The author has an hindex of 29, co-authored 151 publications receiving 3418 citations. Previous affiliations of Philippe Ciuciu include Université Paris-Saclay & French Institute of Health and Medical Research.

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Letter Binding and Invariant Recognition of Masked Words Behavioral and Neuroimaging Evidence

TL;DR: The results indicate that an invariant binding of letters into words is achieved unconsciously through a series of increasingly invariant stages in the left occipito-temporal pathway.
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Dealing with the shortcomings of spatial normalization: multi-subject parcellation of fMRI datasets.

TL;DR: A novel technique for intra‐subject parcellation based on spectral clustering that delineates homogeneous and connected regions and a hierarchical method to derive group parcels that are spatially coherent across subjects and functionally homogeneous are introduced.
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Scale-Free and Multifractal Time Dynamics of fMRI Signals during Rest and Task.

TL;DR: Results indicate that most fMRI signals appear multifractal at rest except in non-cortical regions, and task-related modulation of multifractality appears only significant in functional networks and thus can be considered as the key property disentangling functional networks from artifacts.
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Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment

TL;DR: This paper develops a threefold extension to previous works on the estimation of the blood oxygen level-dependent response to a stimulus, as measured in functional magnetic resonance imaging (fMRI) data, considering asynchronous event-related paradigms account for different trial types and integrate several fMRI sessions into the estimation.
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Robust Bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information.

TL;DR: This work extends and develops a previously proposed method that makes use of basic yet relevant temporal information about the underlying physiological process of the brain BOLD response in order to infer the HRF in a Bayesian framework and shows great improvement in terms of estimation error, variance, and bias.