760 research outputs found
Time Series Analysis of fMRI Data: Spatial Modelling and Bayesian Computation
Time series analysis of fMRI data is an important area of medical statistics
for neuroimaging data. The neuroimaging community has embraced mean-field
variational Bayes (VB) approximations, which are implemented in Statistical
Parametric Mapping (SPM) software. While computationally efficient, the quality
of VB approximations remains unclear even though they are commonly used in the
analysis of neuroimaging data. For reliable statistical inference, it is
important that these approximations be accurate and that users understand the
scenarios under which they may not be accurate.
We consider this issue for a particular model that includes spatially-varying
coefficients. To examine the accuracy of the VB approximation we derive
Hamiltonian Monte Carlo (HMC) for this model and conduct simulation studies to
compare its performance with VB. As expected we find that the computation time
required for VB is considerably less than that for HMC. In settings involving a
high or moderate signal-to-noise ratio (SNR) we find that the two approaches
produce very similar results suggesting that the VB approximation is useful in
this setting. On the other hand, when one considers a low SNR, substantial
differences are found, suggesting that the approximation may not be accurate in
such cases and we demonstrate that VB produces Bayes estimators with larger
mean squared error (MSE). A real application related to face perception is also
carried out. Overall, our work clarifies the usefulness of VB for the
spatiotemporal analysis of fMRI data, while also pointing out the limitation of
VB when the SNR is low and the utility of HMC in this case
Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM)
Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM)
Persistent Homology in Sparse Regression and its Application to Brain Morphometry
Sparse systems are usually parameterized by a tuning parameter that
determines the sparsity of the system. How to choose the right tuning parameter
is a fundamental and difficult problem in learning the sparse system. In this
paper, by treating the the tuning parameter as an additional dimension,
persistent homological structures over the parameter space is introduced and
explored. The structures are then further exploited in speeding up the
computation using the proposed soft-thresholding technique. The topological
structures are further used as multivariate features in the tensor-based
morphometry (TBM) in characterizing white matter alterations in children who
have experienced severe early life stress and maltreatment. These analyses
reveal that stress-exposed children exhibit more diffuse anatomical
organization across the whole white matter region.Comment: submitted to IEEE Transactions on Medical Imagin
Application of Spatial Bayesian Hierarchical Models to fMRI Data
Bayesian modelling has attracted great interest in cognitive science and offered a flexible and interpretable way to study cognitive processes using functional magnetic resonance imaging data. In this chapter, a spatial Bayesian hierarchical model is applied to an event-related fMRI study of cognitive control using the Simon test. We consider a sparse spatial generalized linear mixed-effects model to capture the spatial dependence among activated voxels and temporal parameters and to benefit computationally by reducing dimensionality. We demonstrate that the proposed model has the capability of identification of the brain areas related to cognitive tasks. Moreover, the reduction in the false positive rate is observed in the simulation study, and the relevant brain regions involved in processing cognitive control are clearly detected in a real-life fMRI example
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
The medical image analysis field has traditionally been focused on the
development of organ-, and disease-specific methods. Recently, the interest in
the development of more 20 comprehensive computational anatomical models has
grown, leading to the creation of multi-organ models. Multi-organ approaches,
unlike traditional organ-specific strategies, incorporate inter-organ relations
into the model, thus leading to a more accurate representation of the complex
human anatomy. Inter-organ relations are not only spatial, but also functional
and physiological. Over the years, the strategies 25 proposed to efficiently
model multi-organ structures have evolved from the simple global modeling, to
more sophisticated approaches such as sequential, hierarchical, or machine
learning-based models. In this paper, we present a review of the state of the
art on multi-organ analysis and associated computation anatomy methodology. The
manuscript follows a methodology-based classification of the different
techniques 30 available for the analysis of multi-organs and multi-anatomical
structures, from techniques using point distribution models to the most recent
deep learning-based approaches. With more than 300 papers included in this
review, we reflect on the trends and challenges of the field of computational
anatomy, the particularities of each anatomical region, and the potential of
multi-organ analysis to increase the impact of 35 medical imaging applications
on the future of healthcare.Comment: Paper under revie
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