4,697 research outputs found
The relationship between elasticity and structure of macromolecular networks - Outline of a new approach
Statistical mechanics, chemical analysis, and elasticity studies for macromolecular networks like rubbe
Mapping cognitive ontologies to and from the brain
Imaging neuroscience links brain activation maps to behavior and cognition
via correlational studies. Due to the nature of the individual experiments,
based on eliciting neural response from a small number of stimuli, this link is
incomplete, and unidirectional from the causal point of view. To come to
conclusions on the function implied by the activation of brain regions, it is
necessary to combine a wide exploration of the various brain functions and some
inversion of the statistical inference. Here we introduce a methodology for
accumulating knowledge towards a bidirectional link between observed brain
activity and the corresponding function. We rely on a large corpus of imaging
studies and a predictive engine. Technically, the challenges are to find
commonality between the studies without denaturing the richness of the corpus.
The key elements that we contribute are labeling the tasks performed with a
cognitive ontology, and modeling the long tail of rare paradigms in the corpus.
To our knowledge, our approach is the first demonstration of predicting the
cognitive content of completely new brain images. To that end, we propose a
method that predicts the experimental paradigms across different studies.Comment: NIPS (Neural Information Processing Systems), United States (2013
Compressed Online Dictionary Learning for Fast fMRI Decomposition
We present a method for fast resting-state fMRI spatial decomposi-tions of
very large datasets, based on the reduction of the temporal dimension before
applying dictionary learning on concatenated individual records from groups of
subjects. Introducing a measure of correspondence between spatial
decompositions of rest fMRI, we demonstrates that time-reduced dictionary
learning produces result as reliable as non-reduced decompositions. We also
show that this reduction significantly improves computational scalability
Social-sparsity brain decoders: faster spatial sparsity
Spatially-sparse predictors are good models for brain decoding: they give
accurate predictions and their weight maps are interpretable as they focus on a
small number of regions. However, the state of the art, based on total
variation or graph-net, is computationally costly. Here we introduce sparsity
in the local neighborhood of each voxel with social-sparsity, a structured
shrinkage operator. We find that, on brain imaging classification problems,
social-sparsity performs almost as well as total-variation models and better
than graph-net, for a fraction of the computational cost. It also very clearly
outlines predictive regions. We give details of the model and the algorithm.Comment: in Pattern Recognition in NeuroImaging, Jun 2016, Trento, Italy. 201
On spatial selectivity and prediction across conditions with fMRI
Researchers in functional neuroimaging mostly use activation coordinates to
formulate their hypotheses. Instead, we propose to use the full statistical
images to define regions of interest (ROIs). This paper presents two machine
learning approaches, transfer learning and selection transfer, that are
compared upon their ability to identify the common patterns between brain
activation maps related to two functional tasks. We provide some preliminary
quantification of these similarities, and show that selection transfer makes it
possible to set a spatial scale yielding ROIs that are more specific to the
context of interest than with transfer learning. In particular, selection
transfer outlines well known regions such as the Visual Word Form Area when
discriminating between different visual tasks.Comment: PRNI 2012 : 2nd International Workshop on Pattern Recognition in
NeuroImaging, London : United Kingdom (2012
FAASTA: A fast solver for total-variation regularization of ill-conditioned problems with application to brain imaging
The total variation (TV) penalty, as many other analysis-sparsity problems,
does not lead to separable factors or a proximal operatorwith a closed-form
expression, such as soft thresholding for the penalty. As a result,
in a variational formulation of an inverse problem or statisticallearning
estimation, it leads to challenging non-smooth optimization problemsthat are
often solved with elaborate single-step first-order methods. When thedata-fit
term arises from empirical measurements, as in brain imaging, it isoften very
ill-conditioned and without simple structure. In this situation, in proximal
splitting methods, the computation cost of thegradient step can easily dominate
each iteration. Thus it is beneficialto minimize the number of gradient
steps.We present fAASTA, a variant of FISTA, that relies on an internal solver
forthe TV proximal operator, and refines its tolerance to balance
computationalcost of the gradient and the proximal steps. We give benchmarks
andillustrations on "brain decoding": recovering brain maps from
noisymeasurements to predict observed behavior. The algorithm as well as
theempirical study of convergence speed are valuable for any non-exact
proximaloperator, in particular analysis-sparsity problems
Burnout in the Workplace: A Review of the Data and Policy Responses in the EU
This report looks at the extent of burnout experienced by workers in the EU, based on national research. As a starting point, the report sets out to consider whether burnout is viewed as a medical or occupational disease. It then examines the work determinants associated with burnout and looks at the effects of burnout, including psychosocial and physical work factors, work intensity and work organisation. It also reviews national strategies and policies regarding this issue, the involvement of the social partners in the current debate, as well as preventive actions currently in place
HRF estimation improves sensitivity of fMRI encoding and decoding models
Extracting activation patterns from functional Magnetic Resonance Images
(fMRI) datasets remains challenging in rapid-event designs due to the inherent
delay of blood oxygen level-dependent (BOLD) signal. The general linear model
(GLM) allows to estimate the activation from a design matrix and a fixed
hemodynamic response function (HRF). However, the HRF is known to vary
substantially between subjects and brain regions. In this paper, we propose a
model for jointly estimating the hemodynamic response function (HRF) and the
activation patterns via a low-rank representation of task effects.This model is
based on the linearity assumption behind the GLM and can be computed using
standard gradient-based solvers. We use the activation patterns computed by our
model as input data for encoding and decoding studies and report performance
improvement in both settings.Comment: 3nd International Workshop on Pattern Recognition in NeuroImaging
(2013
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