2,945 research outputs found
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
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
Parallel MRI is a fast imaging technique that enables the acquisition of
highly resolved images in space or/and in time. The performance of parallel
imaging strongly depends on the reconstruction algorithm, which can proceed
either in the original k-space (GRAPPA, SMASH) or in the image domain
(SENSE-like methods). To improve the performance of the widely used SENSE
algorithm, 2D- or slice-specific regularization in the wavelet domain has been
deeply investigated. In this paper, we extend this approach using 3D-wavelet
representations in order to handle all slices together and address
reconstruction artifacts which propagate across adjacent slices. The gain
induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE:
3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal
acquisition is considered. Another important extension accounts for temporal
correlations that exist between successive scans in functional MRI (fMRI). In
addition to the case of 2D+t acquisition schemes addressed by some other
methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition
schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and
4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that
all regularization parameters are estimated in the maximum likelihood sense on
a reference scan. The gain induced by such extensions is illustrated on both
anatomical and functional image reconstruction, and also measured in terms of
statistical sensitivity for the 4D-UWR-SENSE approach during a fast
event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE
reconstruction at the subject and group levels (15 subjects) for different
contrasts of interest (eg, motor or computation tasks) and using different
parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353
PEAR: PEriodic And fixed Rank separation for fast fMRI
In functional MRI (fMRI), faster acquisition via undersampling of data can
improve the spatial-temporal resolution trade-off and increase statistical
robustness through increased degrees-of-freedom. High quality reconstruction of
fMRI data from undersampled measurements requires proper modeling of the data.
We present an fMRI reconstruction approach based on modeling the fMRI signal as
a sum of periodic and fixed rank components, for improved reconstruction from
undersampled measurements. We decompose the fMRI signal into a component which
a has fixed rank and a component consisting of a sum of periodic signals which
is sparse in the temporal Fourier domain. Data reconstruction is performed by
solving a constrained problem that enforces a fixed, moderate rank on one of
the components, and a limited number of temporal frequencies on the other. Our
approach is coined PEAR - PEriodic And fixed Rank separation for fast fMRI.
Experimental results include purely synthetic simulation, a simulation with
real timecourses and retrospective undersampling of a real fMRI dataset.
Evaluation was performed both quantitatively and visually versus ground truth,
comparing PEAR to two additional recent methods for fMRI reconstruction from
undersampled measurements. Results demonstrate PEAR's improvement in estimating
the timecourses and activation maps versus the methods compared against at
acceleration ratios of R=8,16 (for simulated data) and R=6.66,10 (for real
data). PEAR results in reconstruction with higher fidelity than when using a
fixed-rank based model or a conventional Low-rank+Sparse algorithm. We have
shown that splitting the functional information between the components leads to
better modeling of fMRI, over state-of-the-art methods
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
Dynamic functional connectivity, as measured by the time-varying covariance
of neurological signals, is believed to play an important role in many aspects
of cognition. While many methods have been proposed, reliably establishing the
presence and characteristics of brain connectivity is challenging due to the
high dimensionality and noisiness of neuroimaging data. We present a latent
factor Gaussian process model which addresses these challenges by learning a
parsimonious representation of connectivity dynamics. The proposed model
naturally allows for inference and visualization of time-varying connectivity.
As an illustration of the scientific utility of the model, application to a
data set of rat local field potential activity recorded during a complex
non-spatial memory task provides evidence of stimuli differentiation
Meta-analysis of functional neuroimaging data using Bayesian nonparametric binary regression
In this work we perform a meta-analysis of neuroimaging data, consisting of
locations of peak activations identified in 162 separate studies on emotion.
Neuroimaging meta-analyses are typically performed using kernel-based methods.
However, these methods require the width of the kernel to be set a priori and
to be constant across the brain. To address these issues, we propose a fully
Bayesian nonparametric binary regression method to perform neuroimaging
meta-analyses. In our method, each location (or voxel) has a probability of
being a peak activation, and the corresponding probability function is based on
a spatially adaptive Gaussian Markov random field (GMRF). We also include
parameters in the model to robustify the procedure against miscoding of the
voxel response. Posterior inference is implemented using efficient MCMC
algorithms extended from those introduced in Holmes and Held [Bayesian Anal. 1
(2006) 145--168]. Our method allows the probability function to be locally
adaptive with respect to the covariates, that is, to be smooth in one region of
the covariate space and wiggly or even discontinuous in another. Posterior
miscoding probabilities for each of the identified voxels can also be obtained,
identifying voxels that may have been falsely classified as being activated.
Simulation studies and application to the emotion neuroimaging data indicate
that our method is superior to standard kernel-based methods.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS523 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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