Spatio-temporal analysis in functional brain imaging

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 119-137).Localizing sources of activity from electroencephalography (EEG) and magnetoencephalography (MEG) measurements involves solving an ill-posed inverse problem, where infinitely many source distribution patterns can give rise to identical measurements. This thesis aims to improve the accuracy of source localization by incorporating spatio-temporal models into the reconstruction procedure. First, we introduce a novel method for current source estimation, which we call the l₁l₂-norm source estimator. The underlying model captures the sparseness of the active areas in space while encouraging smooth temporal dynamics. We compute the current source estimates efficiently by solving a second-order cone programming problem. By considering all time points simultaneously, we achieve accurate and stable results as confirmed by the experiments using simulated and human MEG data. Although the l₁l₂-norm estimator enables accurate source estimation, it still faces challenges when the current sources are close to each other in space. To alleviate problems caused by the limited spatial resolution of EEG/MEG measurements, we introduce a new method to incorporate information from functional magnetic resonance imaging (fMRI) into the estimation algorithm.(cont.) Whereas EEG/MEG record neural activity, fMRI reflects hemodynamic activity in the brain with high spatial resolution. We examine empirically the neurovascular coupling in simultaneously recorded MEG and diffuse optical imaging (DOI) data, which also reflects hemodynamic activity and is compatible with MEG recordings. Our results suggest that the neural activity and hemodynamic responses are aligned in space. However, the relationship between the temporal dynamics of the two types of signals is non-linear and varies from region to region. Based on these findings, we develop the fMRI-informed regional EEG/MEG source estimator (FIRE). This method is based on a generative model that encourages similar spatial patterns but allows for differences in time courses across imaging modalities. Our experiments with both Monte Carlo simulation and human fMRI-EEG/MEG data demonstrate that FIRE significantly reduces ambiguities in source localization and accurately captures the timing of activation in adjacent functional regions.by Wanmei Ou.Ph.D

Bayesian mapping of brain regions using compound Markov random field priors

Human brain mapping, i.e. the detection of functional regions and their connections, has experienced enormous progress through the use of functional magnetic resonance imaging (fMRI). The massive spatio-temporal data sets generated by this imaging technique impose challenging problems for statistical analysis. Many approaches focus on adequate modeling of the temporal component. Spatial aspects are often considered only in a separate postprocessing step, if at all, or modeling is based on Gaussian random fields. A weakness of Gaussian spatial smoothing is possible underestimation of activation peaks or blurring of sharp transitions between activated and non-activated regions. In this paper we suggest Bayesian spatio-temporal models, where spatial adaptivity is improved through inhomogeneous or compound Markov random field priors. Inference is based on an approximate MCMC technique. Performance of our approach is investigated through a simulation study, including a comparison to models based on Gaussian as well as more robust spatial priors in terms of pixelwise and global MSEs. Finally we demonstrate its use by an application to fMRI data from a visual stimulation experiment for assessing activation in visual cortical areas

Statistical Approaches for Estimation and Comparison of Brain Functional Connectivity

Drug addiction can lead to many health-related problems and social concerns. Functional connectivity obtained from functional magnetic resonance imaging (fMRI) data promotes a variety of fundamental understandings in such association. Due to its complex correlation structure and large dimensionality, the modeling and analysis of the functional connectivity from neuroimage are challenging. By proposing a spatio-temporal model for multi-subject neuroimage data, we incorporate voxel-level spatio-temporal dependencies of whole-brain measurements to improve the accuracy of statistical inference. To tackle large-scale spatio-temporal neuroimage data, we develop a computationally efficient algorithm to estimate the parameters. Our method is used to identify functional connectivity and detect the effect of cocaine use disorder (CUD) on functional connectivity between different brain regions. The functional connectivity identified by our spatio-temporal model matches existing studies on brain networks, and further indicates that CUD may alter the functional connectivity in the medial orbitofrontal cortex subregions and the supplementary motor areas. We further propose a method that jointly estimates the graphical models which share the common structure, while allowing for differences between categories in the data. By assigning different tuning parameters for the contrast of each categorical factor, our method could estimate the effects of multiple treatments or factors across brain regions accurately and achieve computational efficiency at the same time. Simulation studies suggest our method achieves better accuracy in network estimation compared with the joint graphical lasso method. We apply our method to the cocaine-use disorder data and identify functional connectivity in brain affected by cocaine use disorder and gender

A haemodynamic response function model in spatio-temporal diffuse optical tomography

Diffuse optical tomography (DOT) is a new and effective technique for functional brain imaging. It can detect local changes in both oxygenated and deoxygenated haemoglobin concentrations in tissue based on differential absorption at multiple wavelengths. Traditional methods in spatio-temporal analysis of haemoglobin concentrations in diffuse optical tomography first reconstruct the spatial distribution at different time instants independently, then look at the temporal dynamics on each pixel, without incorporating any temporal information as a prior in the image reconstruction. In this work, we present a temporal haemodynamic response function model described by a basis function expansion, in a joint spatio-temporal DOT reconstruction of haemoglobin concentration changes during simulated brain activation. In this joint framework, we simultaneously employ spatial regularization, spectral information and temporal assumptions. We also present an efficient algorithm for solving the associated large-scale systems. The expected improvements in spatial resolution and contrast-to-noise ratio are illustrated with simulations of human brain activation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48982/2/pmb5_19_014.pd