Author
François Tadel
Other affiliations: Joseph Fourier University, Montreal Neurological Institute and Hospital, French Institute of Health and Medical Research ...read more
Bio: François Tadel is an academic researcher from University of Grenoble. The author has contributed to research in topics: Medicine & Stereoelectroencephalography. The author has an hindex of 10, co-authored 19 publications receiving 2778 citations. Previous affiliations of François Tadel include Joseph Fourier University & Montreal Neurological Institute and Hospital.
Papers
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TL;DR: Brainstorm as discussed by the authors is a collaborative open-source application dedicated to magnetoencephalography (MEG) and EEG data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data.
Abstract: Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).
2,637 citations
TL;DR: An auditory paradigm that evaluates cerebral responses to violations of temporal regularities that are either local in time or global across several seconds suggests that the presence of the global effect is a signature of conscious processing, although it can be absent in conscious subjects who are not aware of theglobal auditory regularities.
Abstract: Can conscious processing be inferred from neurophysiological measurements? Some models stipulate that the active maintenance of perceptual representations across time requires consciousness. Capitalizing on this assumption, we designed an auditory paradigm that evaluates cerebral responses to violations of temporal regularities that are either local in time or global across several seconds. Local violations led to an early response in auditory cortex, independent of attention or the presence of a concurrent visual task, whereas global violations led to a late and spatially distributed response that was only present when subjects were attentive and aware of the violations. We could detect the global effect in individual subjects using functional MRI and both scalp and intracerebral event-related potentials. Recordings from 8 noncommunicating patients with disorders of consciousness confirmed that only conscious individuals presented a global effect. Taken together these observations suggest that the presence of the global effect is a signature of conscious processing, although it can be absent in conscious subjects who are not aware of the global auditory regularities. This simple electrophysiological marker could thus serve as a useful clinical tool.
551 citations
TL;DR: This article describes these Brainstorm interactive and scripted features via illustration through the complete analysis of group data from 16 participants in a MEG vision study.
Abstract: Brainstorm is a free, open-source Matlab and Java application for multimodal electrophysiology data analytics and source imaging [primarily MEG, EEG and depth recordings, and integration with MRI and functional near infrared spectroscopy (fNIRS)]. We also provide a free, platform-independent executable version to users without a commercial Matlab license. Brainstorm has a rich and intuitive graphical user interface, which facilitates learning and augments productivity for a wider range of neuroscience users with little or no knowledge of scientific coding and scripting. Yet, it can also be used as a powerful scripting tool for reproducible and shareable batch processing of (large) data volumes. This article describes these Brainstorm interactive and scripted features via illustration through the complete analysis of group data from 16 participants in a MEG vision study.
114 citations
Montreal Neurological Institute and Hospital1, Technical University of Madrid2, Stanford University3, UCL Institute of Neurology4, Université Paris-Saclay5, Cognition and Brain Sciences Unit6, Karolinska Institutet7, Radboud University Nijmegen8, University of Grenoble9, French Institute of Health and Medical Research10
TL;DR: MEG-BIDS is proposed as a principled solution to store, organise, process and share the multidimensional data volumes produced by the modality, and includes well-defined metadata, to facilitate future data harmonisation and sharing efforts.
Abstract: We present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG measures brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS was a solution to organise magnetic resonance imaging (MRI) data. The nature and acquisition parameters of MRI and MEG data are strongly dissimilar. Although there is no standard data format for MEG, we propose MEG-BIDS as a principled solution to store, organise, process and share the multidimensional data volumes produced by the modality. The standard also includes well-defined metadata, to facilitate future data harmonisation and sharing efforts. This responds to unmet needs from the multimodal neuroimaging community and paves the way to further integration of other techniques in electrophysiology. MEG-BIDS builds on MRI-BIDS, extending BIDS to a multimodal data structure. We feature several data-analytics software that have adopted MEG-BIDS, and a diverse sample of open MEG-BIDS data resources available to everyone.
97 citations
Max Planck Society1, The Feinstein Institute for Medical Research2, Lawrence Berkeley National Laboratory3, University of Milan4, University of Grenoble5, New York University6, Stanford University7, Baylor College of Medicine8, University of Florida9, University of Texas at Austin10, Johns Hopkins University11, Harvard University12, University of California, Berkeley13, French Institute of Health and Medical Research14, University of Western Ontario15, Mayo Clinic16, University of Washington17, Karolinska Institutet18, Utrecht University19, University of California, Los Angeles20, University of Oregon21, Joseph Fourier University22, University of California, San Diego23, The Turing Institute24, University of Cambridge25, Forschungszentrum Jülich26
TL;DR: The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible.
Abstract: The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.
92 citations
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TL;DR: Brainstorm as discussed by the authors is a collaborative open-source application dedicated to magnetoencephalography (MEG) and EEG data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data.
Abstract: Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).
2,637 citations
TL;DR: Converging neuroimaging and neurophysiological data point to objective neural measures of conscious access: late amplification of relevant sensory activity, long-distance cortico-cortical synchronization at beta and gamma frequencies, and "ignition" of a large-scale prefronto-parietal network.
1,736 citations
TL;DR: MNE-Python as discussed by the authors is an open-source software package that provides state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions.
Abstract: Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.
1,723 citations
TL;DR: Detailed information about the MNE package is given and typical use cases are described while also warning about potential caveats in analysis.
1,447 citations
TL;DR: Evidence is shown that distinct sorts of spatial attention can have different effects on visual conscious perception, and Fronto-parietal networks important for spatial attention constitute plausible neural substrates for the interactions between exogenous spatial attention and conscious perception.
Abstract: The relationships between spatial attention and conscious perception are currently the object of intense debate. Recent evidence of double dissociations between attention and consciousness cast doubt on the time-honored concept of attention as a gateway to consciousness. Here we review evidence from behavioral, neurophysiologic, neuropsychological, and neuroimaging experiments, showing that distinct sorts of spatial attention can have different effects on visual conscious perception. While endogenous, or top-down attention, has weak influence on subsequent conscious perception of near-threshold stimuli, exogenous, or bottom-up forms of spatial attention appear instead to be a necessary, although not sufficient, step in the development of reportable visual experiences. Fronto-parietal networks important for spatial attention, with peculiar inter-hemispheric differences, constitute plausible neural substrates for the interactions between exogenous spatial attention and conscious perception.
931 citations