2,002 research outputs found
A Bayesian Heteroscedastic GLM with Application to fMRI Data with Motion Spikes
We propose a voxel-wise general linear model with autoregressive noise and
heteroscedastic noise innovations (GLMH) for analyzing functional magnetic
resonance imaging (fMRI) data. The model is analyzed from a Bayesian
perspective and has the benefit of automatically down-weighting time points
close to motion spikes in a data-driven manner. We develop a highly efficient
Markov Chain Monte Carlo (MCMC) algorithm that allows for Bayesian variable
selection among the regressors to model both the mean (i.e., the design matrix)
and variance. This makes it possible to include a broad range of explanatory
variables in both the mean and variance (e.g., time trends, activation stimuli,
head motion parameters and their temporal derivatives), and to compute the
posterior probability of inclusion from the MCMC output. Variable selection is
also applied to the lags in the autoregressive noise process, making it
possible to infer the lag order from the data simultaneously with all other
model parameters. We use both simulated data and real fMRI data from OpenfMRI
to illustrate the importance of proper modeling of heteroscedasticity in fMRI
data analysis. Our results show that the GLMH tends to detect more brain
activity, compared to its homoscedastic counterpart, by allowing the variance
to change over time depending on the degree of head motion
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
Assessing uncertainty in dynamic functional connectivity
Functional connectivity (FC) - the study of the statistical association between time series from anatomically distinct regions (Friston, 1994, 2011) - has become one of the primary areas of research in the field surrounding resting state functional magnetic resonance imaging (rs-fMRI). Although for many years researchers have implicitly assumed that FC was stationary across time in rs-fMRI, it has recently become increasingly clear that this is not the case and the ability to assess dynamic changes in FC is critical for better understanding of the inner workings of the human brain (Hutchison et al., 2013; Chang and Glover, 2010). Currently, the most common strategy for estimating these dynamic changes is to use the sliding-window technique. However, its greatest shortcoming is the inherent variation present in the estimate, even for null data, which is easily confused with true time-varying changes in connectivity (Lindquist et al., 2014). This can have serious consequences as even spurious fluctuations caused by noise can easily be confused with an important signal. For these reasons, assessment of uncertainty in the sliding-window correlation estimates is of critical importance. Here we propose a new approach that combines the multivariate linear process bootstrap (MLPB) method and a sliding-window technique to assess the uncertainty in a dynamic FC estimate by providing its confidence bands. Both numerical results and an application to rs-fMRI study are presented, showing the efficacy of the proposed method
Improved fMRI-based Pain Prediction using Bayesian Group-wise Functional Registration
In recent years, neuroimaging has undergone a paradigm shift, moving away
from the traditional brain mapping approach toward developing integrated,
multivariate brain models that can predict categories of mental events.
However, large interindividual differences in brain anatomy and functional
localization after standard anatomical alignment remain a major limitation in
performing this analysis, as it leads to feature misalignment across subjects
in subsequent predictive models
Enhanced hyperalignment via spatial prior information
Functional alignment between subjects is an important assumption of
functional magnetic resonance imaging (fMRI) group-level analysis. However, it
is often violated in practice, even after alignment to a standard anatomical
template. Hyperalignment, based on sequential Procrustes orthogonal
transformations, has been proposed as a method of aligning shared functional
information into a common high-dimensional space and thereby improving
inter-subject analysis. Though successful, current hyperalignment algorithms
have a number of shortcomings, including difficulties interpreting the
transformations, a lack of uniqueness of the procedure, and difficulties
performing whole-brain analysis. To resolve these issues, we propose the
ProMises (Procrustes von Mises-Fisher) model. We reformulate functional
alignment as a statistical model and impose a prior distribution on the
orthogonal parameters (the von Mises-Fisher distribution). This allows for the
embedding of anatomical information into the estimation procedure by penalizing
the contribution of spatially distant voxels when creating the shared
functional high-dimensional space. Importantly, the transformations, aligned
images, and related results are all unique. In addition, the proposed method
allows for efficient whole-brain functional alignment. In simulations and
application to data from four fMRI studies we find that ProMises improves
inter-subject classification in terms of between-subject accuracy and
interpretability compared to standard hyperalignment algorithms.Comment: 28 pages, 9 figure
Enhanced hyperalignment via spatial prior information
Functional alignment between subjects is an important assumption of functional magnetic resonance imaging (fMRI) group-level analysis. However, it is often violated in practice, even after alignment to a standard anatomical template. Hyperalignment, based on sequential Procrustes orthogonal transformations, has been proposed as a method of aligning shared functional information into a common high-dimensional space and thereby improving inter-subject analysis. Though successful, current hyperalignment algorithms have a number of shortcomings, including difficulties interpreting the transformations, a lack of uniqueness of the procedure, and difficulties performing whole-brain analysis. To resolve these issues, we propose the ProMises (Procrustes von Mises–Fisher) model. We reformulate functional alignment as a statistical model and impose a prior distribution on the orthogonal parameters (the von Mises–Fisher distribution). This allows for the embedding of anatomical information into the estimation procedure by penalizing the contribution of spatially distant voxels when creating the shared functional high-dimensional space. Importantly, the transformations, aligned images, and related results are all unique. In addition, the proposed method allows for efficient whole-brain functional alignment. In simulations and application to data from four fMRI studies we find that ProMises improves inter-subject classification in terms of between-subject accuracy and interpretability compared to standard hyperalignment algorithms
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