206 research outputs found
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
Sharing deep generative representation for perceived image reconstruction from human brain activity
Decoding human brain activities via functional magnetic resonance imaging
(fMRI) has gained increasing attention in recent years. While encouraging
results have been reported in brain states classification tasks, reconstructing
the details of human visual experience still remains difficult. Two main
challenges that hinder the development of effective models are the perplexing
fMRI measurement noise and the high dimensionality of limited data instances.
Existing methods generally suffer from one or both of these issues and yield
dissatisfactory results. In this paper, we tackle this problem by casting the
reconstruction of visual stimulus as the Bayesian inference of missing view in
a multiview latent variable model. Sharing a common latent representation, our
joint generative model of external stimulus and brain response is not only
"deep" in extracting nonlinear features from visual images, but also powerful
in capturing correlations among voxel activities of fMRI recordings. The
nonlinearity and deep structure endow our model with strong representation
ability, while the correlations of voxel activities are critical for
suppressing noise and improving prediction. We devise an efficient variational
Bayesian method to infer the latent variables and the model parameters. To
further improve the reconstruction accuracy, the latent representations of
testing instances are enforced to be close to that of their neighbours from the
training set via posterior regularization. Experiments on three fMRI recording
datasets demonstrate that our approach can more accurately reconstruct visual
stimuli
Inter-subject neural code converter for visual image representation.
Brain activity patterns differ from person to person, even for an identical stimulus. In functional brain mapping studies, it is important to align brain activity patterns between subjects for group statistical analyses. While anatomical templates are widely used for inter-subject alignment in functional magnetic resonance imaging (fMRI) studies, they are not sufficient to identify the mapping between voxel-level functional responses representing specific mental contents. Recent work has suggested that statistical learning methods could be used to transform individual brain activity patterns into a common space while preserving representational contents. Here, we propose a flexible method for functional alignment, "neural code converter, " which converts one subject's brain activity pattern into another's representing the same content. The neural code converter was designed to learn statistical relationships between fMRI activity patterns of paired subjects obtained while they saw an identical series of stimuli. It predicts the signal intensity of individual voxels of one subject from a pattern of multiple voxels of the other subject. To test this method, we used fMRI activity patterns measured while subjects observed visual images consisting of random and structured patches. We show that fMRI activity patterns for visual images not used for training the converter could be predicted from those of another subject where brain activity was recorded for the same stimuli. This confirms that visual images can be accurately reconstructed from the predicted activity patterns alone. Furthermore, we show that a classifier trained only on predicted fMRI activity patterns could accurately classify measured fMRI activity patterns. These results demonstrate that the neural code converter can translate neural codes between subjects while preserving contents related to visual images. While this method is useful for functional alignment and decoding, it may also provide a basis for brain-to-brain communication using the converted pattern for designing brain stimulation
Connectivity-informed Sparse Classifiers for fMRI Brain Decoding
International audienceIn recent years, sparse regularization has become a dominant means for handling the curse of dimensionality in functional magnetic resonance imaging (fMRI) based brain decoding problems. Enforcing sparsity alone, however, neglects the interactions between connected brain areas. Methods that additionally impose spatial smoothness would account for local but not long-range interactions. In this paper, we propose incorporating connectivity into sparse classifier learning so that both local and long-range connections can be jointly modeled. On real data, we demonstrate that integrating connectivity information inferred from diffusion tensor imaging (DTI) data provides higher classification accuracy and more interpretable classifier weight patterns than standard classifiers. Our results thus illustrate the benefits of adding neurologically-relevant priors in fMRI brain decoding
Regularized brain reading with shrinkage and smoothing
Functional neuroimaging measures how the brain responds to complex stimuli.
However, sample sizes are modest, noise is substantial, and stimuli are high
dimensional. Hence, direct estimates are inherently imprecise and call for
regularization. We compare a suite of approaches which regularize via
shrinkage: ridge regression, the elastic net (a generalization of ridge
regression and the lasso), and a hierarchical Bayesian model based on small
area estimation (SAE). We contrast regularization with spatial smoothing and
combinations of smoothing and shrinkage. All methods are tested on functional
magnetic resonance imaging (fMRI) data from multiple subjects participating in
two different experiments related to reading, for both predicting neural
response to stimuli and decoding stimuli from responses. Interestingly, when
the regularization parameters are chosen by cross-validation independently for
every voxel, low/high regularization is chosen in voxels where the
classification accuracy is high/low, indicating that the regularization
intensity is a good tool for identification of relevant voxels for the
cognitive task. Surprisingly, all the regularization methods work about equally
well, suggesting that beating basic smoothing and shrinkage will take not only
clever methods, but also careful modeling.Comment: Published at http://dx.doi.org/10.1214/15-AOAS837 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Improved brain pattern recovery through ranking approaches
International audienceInferring the functional specificity of brain regions from functional Magnetic Resonance Images (fMRI) data is a challenging statistical problem. While the General Linear Model (GLM) remains the standard approach for brain mapping, supervised learning techniques (a.k.a.} decoding) have proven to be useful to capture multivariate statistical effects distributed across voxels and brain regions. Up to now, much effort has been made to improve decoding by incorporating prior knowledge in the form of a particular regularization term. In this paper we demonstrate that further improvement can be made by accounting for non-linearities using a ranking approach rather than the commonly used least-square regression. Through simulation, we compare the recovery properties of our approach to linear models commonly used in fMRI based decoding. We demonstrate the superiority of ranking with a real fMRI dataset
- …