Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network

Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this paper a novel data-synthesized spatio-temporally convolutional encoder-decoder network method termed DST-CedNet is proposed for ESI. DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a convolutional encoder-decoder network (CedNet) to learn a robust mapping from the measured electroencephalography/magnetoencephalography (E/MEG) signals to the brain activity. In particular, by incorporating prior knowledge regarding dynamical brain activities, a novel data synthesis strategy is devised to generate large-scale samples for effectively training CedNet. This stands in contrast to traditional ESI methods where the prior information is often enforced via constraints primarily aimed for mathematical convenience. Extensive numerical experiments as well as analysis of a real MEG and Epilepsy EEG dataset demonstrate that DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.Comment: 15 pages, 14 figures, and journa

EEG/MEG Sparse Source Imaging and Its Application in Epilepsy

This dissertation is a summary of my Ph.D. work on the development of sparse source imaging technologies based on electroencephalography (EEG) and magneto-encephalography (MEG) and their application to noninvasively reconstruct brain activation from external surface measurements. Conventional sparse source imaging (SSI) methods using the ℓ1-norm regularization to enforce sparseness in the original source domain leads to over-focused solutions and causes bias in estimating spatially extended brain sources. I address the over-focused issue in the ℓ1-norm regularization technique framework by exploring sparseness in the transform domains. First, I apply a SSI method that uses the variation transform, i.e. V-SSI, on clinical MEG interictal recordings from partial epilepsy patients. Estimated epileptic sources by V-SSI are validated using clinical pre-surgical evaluation data and surgical outcomes. Second, I implement a novel face-based wavelet transform, which can efficiently compress brain activation signals into sparse representations on a multi-resolution cortical source model, into the SSI technology framework. The proposed wavelet-based SSI (W-SSI) demonstrates a significantly improved ability in inferring both brain source locations and extents as compared with conventional ℓ2-norm regularizations in obtaining EEG/MEG inverse solutions and other SSI technologies. Furthermore, the face-based wavelet also indicates better performance than a previously reported vertex-based wavelet in W-SSI. I evaluate the W-SSI method and conduct the comparison studies using both simulations and real data collected from partial epilepsy patients. Lastly, I further propose the concept of using multiple transforms in the SSI technology framework and investigated a new SSI method by enforcing sparseness in both variation and face-based wavelet domains, termed as VW-SSI. I conduct simulation studies, which demonstrate that VW-SSI has significantly better detection accuracies in both source locations and extents than conventional ℓ2-norm regularizations and other SSI methods, including SSI, V-SSI, and W-SSI. I further validate the VW-SSI method using clinical MEG data from both language and motor experiments collected from epilepsy patients again to localize their important functional brain areas. The results indicate that VW-SSI provides a performance advantage in detecting neural phenomena that have been extremely difficult to recognize by other EEG/MEG inverse solutions. It thus suggests that the sparse source imaging technique is promising to serve as a non-invasive tool in assisting pre-surgical planning for partial epilepsy patients

Reconstructing Resting State Networks from EEG

Resting state networks (RSNs) have been found in human brains during awake resting states. RSNs are composed of spatially distributed regions in which spontaneous activity fluctuations are temporally and dynamically correlated. In contrast to task-related brain activities, RSNs reflect intrinsic functional organizations and rhythms of the human brain when it is not engaged in any task and/or disturbed by external stimuli. To date, RSNs have been widely studied using functional magnetic resonance imaging (fMRI), which has identified various RSNs associated with different brain functions. More recently, due to the advantage of millisecond temporal resolution, both electroencephalography (EEG) and magnetoencephalography (MEG) have been used to investigate RSNs and their electrophysiological underpinnings. Despite these advantages, current RSN studies using EEG/MEG, as compared with those using fMRI, are still at their infant stage in many aspects, such as the quality of spatial pattern reconstructions and the reliability of detections. These limitations require further studies to obtain accurate reconstructions of RSNs directly from EEG/MEG data. My research aims to develop, optimize, and validate a variety of computational and analytical frameworks to reconstruct and investigate RSNs based on EEG data. In this dissertation, several studies have been conducted as outlined below. Firstly, a comparison in defining RSNs at the sensor space and at the source space was performed to evaluate the accuracy in reconstructing RSN spatial patterns. Results from both simulated and experimental data indicated that the analysis in the source space performed better in reconstructing various features of RSNs. Secondly, a new computational framework for reconstructing RSNs with human EEG data was developed. The proposed framework utilized independent component analysis (ICA) on short-time Fourier transformed inverse source maps imaged from EEG data and statistical correlation analysis to generate cortical tomography of electrophysiological RSNs. The proposed framework was validated using three sets of experimental data. The results indicated that the framework is reliable and efficient in the reconstruction of RSNs. Thirdly, an advanced inverse source imaging (ISI) method was used in the established framework discussed above to improve the spatial estimation of RSNs. The comparison between the new and conventional frameworks suggested that the ISI method significantly improved the accuracy of spatial estimations of RSNs. Fourthly, an ICA-based framework was used to assess RSN alternations under different conditions, which has been the model to identify imaging biomarkers, for example, for diseased patients as compared with healthy control. The results from both simulated and experimental data indicated that the framework could detect RSN alternations due to condition differences. My results further suggest that the framework could provide a finer resolution in detecting RSN changes as a contrast for multi-level (more than 2) condition differences, which can be used to study the difference, for example, among patients with a long history of a certain disorder, a short history, and healthy control. Overall, the findings of this dissertation study provided insights into the underlying electrophysiological basis of RSNs. More importantly, this study developed new frameworks that can be used as powerful tools for future investigations of more characteristics of RSNs, in particular for those not available in fMRI, e.g., spectral patterns