EEG source imaging with spatio‐temporal tomographic nonnegative independent component analysis

Networks are ubiquitous throughout biology, spanning the entire range from molecules to food webs and global environmental systems. Yet, despite substantial efforts by the scientific community, the inference of these networks from data still presents a problem that is unsolved in general. One frequent strategy of addressing the structure of networks is the assumption that the interactions among molecular or organismal populations are static and correlative. While often successful, these static methods are no panacea. They usually ignore the asymmetry of relationships between two species and inferences become more challenging if the network nodes represent dynamically changing quantities. Overcoming these challenges, two very different network inference approaches have been proposed in the literature: Lotka-Volterra (LV) models and Multivariate Autoregressive (MAR) models. These models are computational frameworks with different mathematical structures which, nevertheless, have both been proposed for the same purpose of inferring the interactions within coexisting population networks from observed time-series data. Here, we assess these dynamic network inference methods for the first time in a side-by-side comparison, using both synthetically generated and ecological datasets. Multivariate Autoregressive and Lotka-Volterra models are mathematically equivalent at the steady state, but the results of our comparison suggest that Lotka-Volterra models are generally superior in capturing the dynamics of networks with non-linear dynamics, whereas Multivariate Autoregressive models are better suited for analyses of networks of populations with process noise and close-to linear behavior. To the best of our knowledge, this is the first study comparing LV and MAR approaches. Both frameworks are valuable tools that address slightly different aspects of dynamic networks.

INTRODUCTION Localization-related epilepsy frequently involves mesial temporal lobe structures (MTLS), but sometimes presurgical workup can be confusing since shortage of definite structural lesional evidence or inconsistency among multimodal tools. Occasionally invasive workup is applied. This study evaluates the application of multimodal workup in surgical strategy making of localization-related epilepsy involving MTLS. METHODS Our study includes patients with significant or subtle MTLS abnormality (or magnetic resonance imaging (MRI) negative) in 3.0T MRI and then underwent resective surgeries encompassing MTLS. In addition to conventional workup tools, accurate EEG source imaging (ESI) technique based on individual head models is available. Patients accepted either 1-stage resective surgeries or staged surgeries (SEEG implantation + stage-2 resective surgeries). Contributions of structural MRI, fluorodeoxyglucose-positron emission tomography (FDG-PET), accurate ESI, and ictal EEG to surgical strategies were evaluated by criteria defining epileptogenic zone (resective scope in cases with good outcome). RESULTS Thirty-five patients who achieved Engel grade I + II outcome after resective surgeries were included. All together, 68.6% cases showed ESI sources totally falling into resective scope. 42.9% ictal EEG estimates, 57.1% PET focal hypometabolistic regions, 74.3% MRI structural lesions fitted the resective scope/side. In total, 75% 1-stage cases showed ESI sources being confined in MTLS region, while PET showed focal hypometabolism in 66.7% and MRI indicated single MTLS lesions (including subtle ones) in 79.2% cases. In total, 54.5% ESI sources and 63.6% MRI abnormality (single, including subtle ones) in staged cases showed complete concordance with SEEG findings. In subtle lesional/MRI negative cases, ESI generated sources confined within MTLS in 77.8% cases, while PET estimates are focally localizing in 44.4% cases and 55.6% showed subtle MRI lesions which were firstly diagnosed “negative.” CONCLUSION Multimodal noninvasive workup are contributable to help decide strategy of 1-stage resective surgeries and SEEG plans in localization-related epilepsy. High-quality structural MRI, accurate ESI technique based on scalp EEG as well as FDG-PET are key to presurgical planning of epilepsy involving MTLS.

The curtain of technical limitations impeding rat multichannel non-invasive electroencephalography (EEG) has risen. Given the importance of this preclinical model, development and validation of EEG source imaging (ESI) is essential. We investigate the validity of well-known human ESI methodologies in rats which individual tissue geometries have been approximated by those extracted from an MRI template, leading also to imprecision in electrode localizations. With the half and fifth sensitivity volumes we determine both the theoretical minimum electrode separation for non-redundant scalp EEG measurements and the electrode sensitivity resolution, which vary over the scalp because of the head geometry. According to our results, electrodes should be at least ~3 to 3.5mm apart for an optimal configuration. The sensitivity resolution is generally worse for electrodes at the boundaries of the scalp measured region, though, by analogy with human montages, concentrates the sensitivity enough to localize sources. Cramér-Rao lower bounds of source localization errors indicate it is theoretically possible to achieve ESI accuracy at the level of anatomical structures, such as the stimulus-specific somatosensory areas, using the template. More validation for this approximation is provided through the comparison between the template and the individual lead field matrices, for several rats. Finally, using well-accepted inverse methods, we demonstrate that somatosensory ESI is not only expected but also allows exploring unknown phenomena related to global sensory integration. Inheriting the advantages and pitfalls of human ESI, rat ESI will boost the understanding of brain pathophysiological mechanisms and the evaluation of ESI methodologies, new pharmacological treatments and ESI-based biomarkers.

Exploring the impact of 3D movie watching on the brain source activities and energy consumption by ESI and fNIRS

Multiplicative update algorithms are important tools for information retrieval, image processing, and pattern recognition. However, when the graph regularization is added to the cost function, different classes of sample data may be mapped to the same subspace, which leads to the increase of data clustering error rate. In this paper, an improved nonnegative matrix factorization (NMF) cost function is introduced. Based on the cost function, a class of novel graph regularized NMF algorithms is developed, which results in a class of extended multiplicative update algorithms with manifold structure regularization. Analysis shows that in the learning, the proposed algorithms can efficiently minimize the rank of the data representation matrix. Theoretical results presented in this paper are confirmed by simulations. For different initializations and data sets, variation curves of cost functions and decomposition data are presented to show the convergence features of the proposed update rules. Basis images, reconstructed images, and clustering results are utilized to present the efficiency of the new algorithms. Last, the clustering accuracies of different algorithms are also investigated, which shows that the proposed algorithms can achieve state-of-the-art performance in applications of image clustering.

The Gaussian and Gaussian-sigmoidal primaries are introduced as initialization functions in the nonnegative matrix factorization (NNMF) method to achieve more consistent error statistics. The proposed NNMF method with multiplicative update algorithm is employed for reconstruction of spectral data of Munsell and Colorchecker DC datasets from the corresponding CIEXYZ tristimulus values and the resultant achievements are compared with those obtained from PCA and the classical NNMF techniques. Both colorimetric and spectrophotometric errors between the original and the reconstructed spectra are used for the evaluation of reconstruction methods. While run to run variability of the classical NNMF method with multiplicative update algorithm is significantly remarkable, the stability of the suggested technique is fairly high, similar to the PCA method. The ensuing findings show that the embedding of Gaussian initializing vectors in NNMF method provide the all positive bases that enhance both the accuracy of Gaussian method and the reliability of NNMF technique.

This project assessed the use of multivariate auto-regressive (MAR) models to create forecasts of continuous vital signs in hospitalized patients. A total of 20 hours continuous (1/60Hz) heart rate and respiration rate from eight postoperative patients, where used to fit a centered MAR model for forecasting in windows of 15 minutes. The model was fitted using Markov Chain Monte Carlo sampling, and the model was evaluated on data from five additional patients. The results demonstrate an average RMSE in the forecast window of 11.4 (SD: 7.30) beats per minute for heart rate and 3.3 (SD:1.3) breaths per minute for respiration rate. These results indicate potential for forecasting vital signs in a clinical setting.

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying lengths (T = 25, 50, 75, 100, 125) using Statistical Analysis System (SAS) version 9.2. Autoregressive components for the 5 series vectors included coefficients of .80, .70, .65, .50 and .40. Error variance components included values of .20, .20, .10, .15, and .15, with cross-lagged coefficients of .10, .10, .15, .10, and .10. A Monte Carlo study revealed that in comparison to frequentist methods, the Bayesian approach provided increased sensitivity for hypothesis testing and detecting Type I error.

This study developed a driving mental fatigue estimation system based on electroencephalogram (EEG) when he drives a car in a virtual reality (VR)-based dynamic simulator. To classify driver's mental fatigue status, the features of multichannel electroencephalographic (EEG) signals of frontal, central and occipital are extracted by multivariate autoregressive (MVAR) model. Then kernel principal component analysis (KPCA) and support vector machines (SVM) are combined to identify three-levels driving mental fatigue. The results show that KPCA is an good feature extractor which can effectively reduce the dimensionality of the feature vectors. The KPCA-SVM shows good performance with higher classification accuracy (81.64%) across 10 subjects. This method could be an potential approach of classification of driving mental fatigue.

Brain computer interface (BCI) is a technology recording brain signals and translate into machine language to drive device movement. To improve the convenience and practicability of BCI systems, it is necessary to reduce the number of channels of EEG without reducing classification accuracy. In this paper, a channel selection method was proposed. Combine continuous wavelet transform (CWT) with EEG source imaging (ESI), we can find out the source distribution of two kinds of motor imagery (MI) tasks on the cortex. Then the different value of source distribution on the cortex was projected to scalp. The value on the scalp was treated as a criterion for channel selection. While ESI was applied for locating the active areas, we can not only find out the prior channels clearly, but also figure out the expected areas briefly. The satisfying results show that the combination of CWT and ESI is a potential method for dealing with MI tasks.

To investigate the utility of EEG source imaging to lateralize ictal patterns in frontal lobe epilepsy, which were nonlateralized by standard EEG analysis. Prospective analysis of 17 seizures in 8 patients with unilateral frontal lobe epilepsy MRI lesions and nonlateralizing ictal scalp EEG. We applied four EEG source imaging techniques (phase maps, symmetric dipoles, low-resolution brain electromagnetic tomography analysis, and classical low-resolution brain electromagnetic tomography analysis recursively applied) to the averaged seizure pattern. We tested (1) the ability of these techniques to lateralize seizure patterns, (2) the agreement of the lateralization result with MRI lesion side and subdural EEG recordings, individually for each method and for concordance of all. We found lateralizing results in 5 of 17 seizures when analyzing phase maps, 8 of 17 when analyzing dipoles, and 5 of 17 in both classical low-resolution brain electromagnetic tomography analysis recursively applied and low-resolution brain electromagnetic tomography analysis. No discordance with the MRI lesion side was seen when analyzing phase maps, whereas dipole analysis was discordant to the MRI lesion in two seizures, classical low-resolution brain electromagnetic tomography analysis recursively applied in one, and low-resolution brain electromagnetic tomography analysis in two. Agreement between all imaging methods was found in three seizures (three patients), all in line with the side of the MRI lesion. Advanced EEG review methods and source localization provide useful lateralizing information in difficult frontal lobe epilepsy seizure patterns.

Epilepsy is a relatively frequent disease in children, with considerable impact on cognitive and social life. Successful epilepsy surgery depends on unambiguous focus identification and requires a comprehensive presurgical workup, including several neuroimaging techniques [magnetic resonance imaging, positron emission tomography (PET), and single-photon emission computed tomography (SPECT)]. These may be difficult to apply in younger or developmentally delayed children or both, requiring sedation, and hence, a significant workforce. Modern electric source imaging (ESI) provides accurate epileptic source-localization information in most patients, with minimal patient discomfort or need for cooperation. The purpose of the present study was to determine the usefulness of ESI in pediatric EEG recordings performed with routine electrode arrays. Preoperative EEGs recorded from 19 to 29 scalp electrodes were reviewed, and interictal epileptiform activity was analyzed by using a linear source-imaging procedure (depth-weighted minimum norm) in combination with statistical parametric mapping. In 27 (90%) of 30 patients, the ESI correctly localized the epileptogenic region. These numbers compare favorably with the results from other imaging techniques in the same patients (PET, 82%; ictal SPECT, 70%). In extratemporal epilepsy, ESI was correct in all cases, and in temporal lobe epilepsy, in 10 of 13 cases. In two temporal lobe patients showing less-accurate ESI results, 128-electrode data could be analyzed, and in both cases, the 128-electrode ESI was correct. ESI with standard clinical EEG recordings provides excellent localizing information in pediatric patients, in particular in extratemporal lobe epilepsy. The lower yield in temporal lobe epilepsy seems to be due to undersampling of basal temporal areas with routine scalp recordings.

Electroencephalography source imaging (ESI) is a useful technique to localize the generators from a given scalp electric measurement and to investigate the temporal dynamics of the large-scale neural circuits. By introducing reasonable priors from other modalities, ESI reveals the most probable sources and communication structures at every moment in time. Here, we review the available priors from such techniques as magnetic resonance imaging (MRI), functional MRI (fMRI), and positron emission tomography (PET). The modality's specific contribution is analyzed from the perspective of source reconstruction. For spatial priors, EEG-correlated fMRI, temporally coherent networks (TCNs) and resting-state fMRI are systematically introduced in the ESI. Moreover, the fiber tracking (diffusion tensor imaging, DTI) and neuro-stimulation techniques (transcranial magnetic stimulation, TMS) are also introduced as the potential priors, which can help to draw inferences about the neuroelectric connectivity in the source space. We conclude that combining EEG source imaging with other complementary modalities is a promising approach toward the study of brain networks in cognitive and clinical neurosciences.

Automated classification of non-motor mental task in electroencephalogram based brain-computer interface using multivariate autoregressive model in the intrinsic mode function domain

The nonnegative convex polyhedral cone (NCPC) learning is discussed in this paper. By exploiting the multiplicative update nonnegative quadratic programming, a multiplicative update algorithm is developed for NCPC learning. The proposed algorithm is promising for nonnegative matrix factorization (NMF) and we verify this by numerical experiments.