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Gaël Varoquaux

  1. Markov models for fMRI correlation structure: is brain functional connectivity small world, or decomposable into networks?.

    Tue, 02/07/2012 - 15:01 — SomNambul
    Authors: Gaël Varoquaux, Jean Baptiste Poline, Bertrand Thirion, Alexandre Gramfort
    Subjects: Applications
    Abstract

    Correlations in the signal observed via functional Magnetic Resonance Imaging
    (fMRI), are expected to reveal the interactions in the underlying neural
    populations through hemodynamic response. In particular, they highlight
    distributed set of mutually correlated regions that correspond to brain
    networks related to different cognitive functions. Yet graph-theoretical
    studies of neural connections give a different picture: that of a highly
    integrated system with small-world properties: local clustering but with short
    pathways across the complete structure.

  2. Brain covariance selection: better individual functional connectivity models using population prior.

    Tue, 08/31/2010 - 15:01 — SomNambul
    Authors: Gaël Varoquaux, Jean Baptiste Poline, Bertrand Thirion, Alexandre Gramfort
    Subjects: Machine Learning
    Abstract

    Spontaneous brain activity, as observed in functional neuroimaging, has been
    shown to display reproducible structure that expresses brain architecture and
    carries markers of brain pathologies. An important view of modern neuroscience
    is that such large-scale structure of coherent activity reflects modularity
    properties of brain connectivity graphs. However, to date, there has been no
    demonstration that the limited and noisy data available in spontaneous activity
    observations could be used to learn full-brain probabilistic models that
    generalize to new data.

  3. Detection of brain functional-connectivity difference in post-stroke patients using group-level covariance modeling.

    Tue, 08/31/2010 - 15:01 — SomNambul
    Authors: Gaël Varoquaux, Bertrand Thirion, Flore Baronnet, Andreas Kleinschmidt, Pierre Fillard
    Subjects: Applications
    Abstract

    Functional brain connectivity, as revealed through distant correlations in
    the signals measured by functional Magnetic Resonance Imaging (fMRI), is a
    promising source of biomarkers of brain pathologies. However, establishing and
    using diagnostic markers requires probabilistic inter-subject comparisons.
    Principled comparison of functional-connectivity structures is still a
    challenging issue. We give a new matrix-variate probabilistic model suitable
    for inter-subject comparison of functional connectivity matrices on the
    manifold of Symmetric Positive Definite (SPD) matrices.

  4. ICA-based sparse feature recovery from fMRI datasets.

    Mon, 06/14/2010 - 15:01 — SomNambul
    Authors: Philippe Ciuciu, Gaël Varoquaux, Jean Baptiste Poline, Bertrand Thirion, Merlin Keller
    Subjects: Applications
    Abstract

    Spatial Independent Components Analysis (ICA) is increasingly used in the
    context of functional Magnetic Resonance Imaging (fMRI) to study cognition and
    brain pathologies. Salient features present in some of the extracted
    Independent Components (ICs) can be interpreted as brain networks, but the
    segmentation of the corresponding regions from ICs is still ill-controlled.
    Here we propose a new ICA-based procedure for extraction of sparse features
    from fMRI datasets. Specifically, we introduce a new thresholding procedure
    that controls the deviation from isotropy in the ICA mixing model.

  5. CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series.

    Wed, 11/25/2009 - 16:01 — SomNambul
    Authors: Gaël Varoquaux, Sepideh Sadaghiani, Jean Baptiste Poline, Bertrand Thirion
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    Spatial Independent Component Analysis (ICA) is an increasingly used
    data-driven method to analyze functional Magnetic Resonance Imaging (fMRI)
    data. To date, it has been used to extract meaningful patterns without prior
    information. However, ICA is not robust to mild data variation and remains a
    parameter-sensitive algorithm. The validity of the extracted patterns is hard
    to establish, as well as the significance of differences between patterns
    extracted from different groups of subjects.

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