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

Combining spatial priors and anatomical information for fMRI detection

In this paper, we analyze Markov Random Field (MRF) as a spatial regularizer in fMRI detection. The low signal-to-noise ratio (SNR) in fMRI images presents a serious challenge for detection algorithms, making regularization necessary to achieve good detection accuracy. Gaussian smoothing, traditionally employed to boost SNR, often produces over-smoothed activation maps. Recently, the use of MRF priors has been suggested as an alternative regularization approach. However, solving for an optimal configuration of the MRF is NP-hard in general. In this work, we investigate fast inference algorithms based on the Mean Field approximation in application to MRF priors for fMRI detection. Furthermore, we propose a novel way to incorporate anatomical information into the MRF-based detection framework and into the traditional smoothing methods. Intuitively speaking, the anatomical evidence increases the likelihood of activation in the gray matter and improves spatial coherency of the resulting activation maps within each tissue type. Validation using the receiver operating characteristic (ROC) analysis and the confusion matrix analysis on simulated data illustrates substantial improvement in detection accuracy using the anatomically guided MRF spatial regularizer. We further demonstrate the potential benefits of the proposed method in real fMRI signals of reduced length. The anatomically guided MRF regularizer enables significant reduction of the scan length while maintaining the quality of the resulting activation maps.National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.)/National Alliance for Medical Image Computing (U.S.) Grant U54-EB005149)National Science Foundation (U.S.) (Grant IIS 9610249)National Institutes of Health (U.S.) (National Center for Research Resources (U.S.)/Biomedical Informatics Research Network Grant U24-RR021382)National Institutes of Health (U.S.) (National Center for Research Resources (U.S.)/Neuroimaging Analysis Center (U.S.) Grant P41-RR13218)National Institutes of Health (U.S.) (National Institute of Neurological Disorders and Stroke (U.S.) Grant R01-NS051826)National Science Foundation (U.S.) (CAREER Grant 0642971)National Science Foundation (U.S.). Graduate Research FellowshipNational Center for Research Resources (U.S.) (FIRST-BIRN Grant)Neuroimaging Analysis Center (U.S.

Functional Magnetic Resonant Imaging detection with spatial regularization

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 99-102).Functional Magnetic Resonant Imaging (fMRI) is a non-invasive imaging technique used to study the brain. Neuroscientists have developed various algorithms to determine which voxels of the images are active. Most of these algorithms, operating on the signal of each voxel separately, are referred to as the voxel-by-voxel detectors. Among those voxel-by-voxel detectors, paired T-test and General Linear Model (GLM) are the most popular. The Mutual Information (MI) based detector has recently been introduced. It is interesting to compare these three detectors' modeling assumptions, as well as their performance, in order to understand their advantages and shortcomings. Due to the low signal-to-noise ratio (SNR), the voxel-by-voxel detectors usually result in fragmented activation pattern, which is not in agreement with our understanding of brain activation. The biological models of brain activation suggest that adjacent locations of the brain tend to be in the same activation state. We take advantage of these models and apply a Markov Random Field (MRF) spatial prior to the statistics provided by the voxel-by-voxel algorithms. MRF has been shown to be able to overcome the effect of over-smoothing, which is the major drawback of the conventional spatial regularization models such as the Gaussian smoothing model.(cont.) We adopt Mean Field, a variational algorithm, to estimate the MRF solution. We show that Mean Field can provide reasonable approximation compared with the exact solver in the case of binary MRFs, while reducing the computations by one to two orders of magnitude in our simulated and real data sets. In addition, unlike the exact solver, it can handle multiple-state MRFs. Inspired by atlas-based segmentation, we further refine the spatial regularization process by incorporating anatomical information, such as segmentation results from Magnetic Resonance Imaging (MRI), into the MRF prior. The extended MRF model encodes both tissue type and activation state. To our knowledge, our approach is the first spatial smoothing method that utilizes anatomical information without cortical surface extraction. To evaluate the smoothing models, we performed ROC and confusion matrix analysis on synthetic data. We also evaluate them by studying their ability to recover activation from significantly shorter time course using real data. Including anatomical information improves detection accuracy for both the Gaussian-smoothing-based detector and the MRF-based detector. The Gaussian-smoothing model provides poor results if we are interested in both positive and negative activation regions in the brain.(cont.) Furthermore, the anatomically guided MRF-based detector improves the detection quality compared with the anatomically guided Gaussian-smoothing-based detector for standard fMRI in standard SNR quality.by Wanmei Ou.S.M

Invertible Filter Banks on the 2-Sphere

Multiscale filtering methods, such as wavelets and steerable pyramids, have been widely used in processing and analysis of planar images and promise similar benefits in application to spherical images. While recent advances have extended some filtering methods to the sphere, many key challenges remain. This paper focuses on the self-invertibility property of filter banks, particularly desirable if images are modified in the wavelet domain. More specifically, we develop conditions for invertibility of spherical filter banks for both continuous and discrete convolution and illustrate how such conditions can be incorporated into the design of multiscale axis-symmetric wavelets

Multimodal functional imaging using fMRI-Informed regional EEG/MEG estimation

We propose a novel method, fMRI-informed regional estimation (FIRE), which utilizes information from fMRI in E/MEG source reconstruction. FIRE takes advantage of the spatial alignment between the neural and the vascular activities, while allowing for substantial differences in their dynamics. Furthermore, with the regional approach, FIRE can be efficiently applied to a dense grid of sources. Inspection of our optimization procedure reveals that FIRE is related to the re-weighted minimum-norm algorithms, the difference being that the weights in the proposed approach are computed from both the current estimates and fMRI data. Analysis of both simulated and human fMRI-MEG data shows that FIRE reduces the ambiguities in source localization present in the minimum-norm estimates. Comparisons with several joint fMRI-E/MEG algorithms demonstrate robustness of FIRE in the presence of sources silent to either fMRI or E/MEG measurements.National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41-RR13218)National Institutes of Health (U.S.) (NCRR P41-RR14075 )National Science Foundation (U.S.) (CAREER award 0642971)United States. Public Health Service (training grant DA022759-03