8,095 research outputs found
SMART: A statistical framework for optimal design matrix generation with application to fMRI
The general linear model (GLM) is a well established tool for analyzing
functional magnetic resonance imaging (fMRI) data. Most fMRI analyses via GLM
proceed in a massively univariate fashion where the same design matrix is used
for analyzing data from each voxel. A major limitation of this approach is the
locally varying nature of signals of interest as well as associated confounds.
This local variability results in a potentially large bias and uncontrolled
increase in variance for the contrast of interest. The main contributions of
this paper are two fold (1) We develop a statistical framework called SMART
that enables estimation of an optimal design matrix while explicitly
controlling the bias variance decomposition over a set of potential design
matrices and (2) We develop and validate a numerical algorithm for computing
optimal design matrices for general fMRI data sets. The implications of this
framework include the ability to match optimally the magnitude of underlying
signals to their true magnitudes while also matching the "null" signals to zero
size thereby optimizing both the sensitivity and specificity of signal
detection. By enabling the capture of multiple profiles of interest using a
single contrast (as opposed to an F-test) in a way that optimizes for both bias
and variance enables the passing of first level parameter estimates and their
variances to the higher level for group analysis which is not possible using
F-tests. We demonstrate the application of this approach to in vivo
pharmacological fMRI data capturing the acute response to a drug infusion, to
task-evoked, block design fMRI and to the estimation of a haemodynamic response
function (HRF) response in event-related fMRI. Our framework is quite general
and has potentially wide applicability to a variety of disciplines.Comment: 68 pages, 34 figure
Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery
We propose a calibrated multivariate regression method named CMR for fitting
high dimensional multivariate regression models. Compared with existing
methods, CMR calibrates regularization for each regression task with respect to
its noise level so that it simultaneously attains improved finite-sample
performance and tuning insensitiveness. Theoretically, we provide sufficient
conditions under which CMR achieves the optimal rate of convergence in
parameter estimation. Computationally, we propose an efficient smoothed
proximal gradient algorithm with a worst-case numerical rate of convergence
\cO(1/\epsilon), where is a pre-specified accuracy of the
objective function value. We conduct thorough numerical simulations to
illustrate that CMR consistently outperforms other high dimensional
multivariate regression methods. We also apply CMR to solve a brain activity
prediction problem and find that it is as competitive as a handcrafted model
created by human experts. The R package \texttt{camel} implementing the
proposed method is available on the Comprehensive R Archive Network
\url{http://cran.r-project.org/web/packages/camel/}.Comment: Journal of Machine Learning Research, 201
A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI
Voxel functional magnetic resonance imaging (fMRI) time courses are complex-valued signals giving rise to magnitude and phase data. Nevertheless, most studies use only the magnitude signals and thus discard half of the data that could potentially contain important information. Methods that make use of complex-valued fMRI (CV-fMRI) data have been shown to lead to superior power in detecting active voxels when compared to magnitude-only methods, particularly for small signal-to-noise ratios (SNRs). We present a new Bayesian variable selection approach for detecting brain activation at the voxel level from CV-fMRI data. We develop models with complex-valued spike-and-slab priors on the activation parameters that are able to combine the magnitude and phase information. We present a complex-valued EM variable selection algorithm that leads to fast detection at the voxel level in CV-fMRI slices and also consider full posterior inference via Markov chain Monte Carlo (MCMC). Model performance is illustrated through extensive simulation studies, including the analysis of physically based simulated CV-fMRI slices. Finally, we use the complex-valued Bayesian approach to detect active voxels in human CV-fMRI from a healthy individual who performed unilateral finger tapping in a designed experiment. The proposed approach leads to improved detection of activation in the expected motor-related brain regions and produces fewer false positive results than other methods for CV-fMRI. Supplementary materials for this article are available online
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Inverse transformed encoding models - A solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding
Techniques of multivariate pattern analysis (MVPA) can be used to decode the discrete experimental condition or a continuous modulator variable from measured brain activity during a particular trial. In functional magnetic resonance imaging (fMRI), trial-wise response amplitudes are sometimes estimated from the measured signal using a general linear model (GLM) with one onset regressor for each trial. When using rapid event-related designs with trials closely spaced in time, those estimates are highly variable and serially correlated due to the temporally extended shape of the hemodynamic response function (HRF). Here, we describe inverse transformed encoding modelling (ITEM), a principled approach of accounting for those serial correlations and decoding from the resulting estimates, at low computational cost and with no loss in statistical power. We use simulated data to show that ITEM outperforms the current standard approach in terms of decoding accuracy and analyze empirical data to demonstrate that ITEM is capable of visual reconstruction from fMRI signals
fMRI activation detection with EEG priors
The purpose of brain mapping techniques is to advance the understanding of the relationship between structure and function in the human brain in so-called activation studies. In this work, an advanced statistical model for combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings is developed to fuse complementary information about the location of neuronal activity. More precisely, a new Bayesian method is proposed for enhancing fMRI activation detection by the use of EEG-based spatial prior information in stimulus based experimental paradigms. I.e., we model and analyse stimulus influence by a spatial Bayesian variable selection scheme, and extend existing high-dimensional regression methods by incorporating prior information on binary selection indicators via a latent probit regression with either a spatially-varying or constant EEG effect. Spatially-varying effects are regularized by intrinsic Markov random field priors. Inference is based on a full Bayesian Markov Chain Monte Carlo (MCMC) approach. Whether the proposed algorithm is able to increase the sensitivity of mere fMRI models is examined in both a real-world application and a simulation study. We observed, that carefully selected EEG--prior information additionally increases sensitivity in activation regions that have been distorted by a low signal-to-noise ratio
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