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Magnetoencephalography (MEG) is increasingly used for presurgical planning in people with medically refractory focal epilepsy. Localization of interictal epileptiform activity, a surrogate for the seizure onset zone whose removal may prevent seizures, is challenging and depends on the use of multiple complementary techniques. Accurate and reliable localization of epileptiform activity from spontaneous MEG data has been an elusive goal. One approach toward this goal is to use a novel Bayesian inference algorithm—the Champagne algorithm with noise learning—which has shown tremendous success in source reconstruction, especially for focal brain sources. In this study, we localized sources of manually identified MEG spikes using the Champagne algorithm in a cohort of 16 patients with medically refractory epilepsy collected in two consecutive series. To evaluate the reliability of this approach, we compared the performance to equivalent current dipole (ECD) modeling, a conventional source localization technique that is commonly used in clinical practice. Results suggest that Champagne may be a robust, automated, alternative to manual parametric dipole fitting methods for localization of interictal MEG spikes, in addition to its previously described clinical and research applications.

Adaptive minimum variance based beamformers (MVB) have been successfully applied to magnetoencephalogram (MEG) and electroencephalogram (EEG) data to localize brain activities. However, the performance of these beamformers falls down in situations where correlated or interference sources exist. To overcome this problem, we propose indirect dominant mode rejection (iDMR) beamformer application in brain source localization. This method by modifying measurement covariance matrix makes MVB applicable in source localization in the presence of correlated and interference sources. Numerical results on both EEG and MEG data demonstrate that presented approach accurately reconstructs time courses of active sources and localizes those sources with high spatial resolution. In addition, the results of real AEF data show the good performance of iDMR in empirical situations. Hence, iDMR can be reliably used for brain source localization especially when there are correlated and interference sources.

This article is focused on the overview of functionality of the neurons and investigation of the current research and algorithms used for brain source localization. The human brain is made up of active neurons and continuously generates electrical impulses on scalp surface. The neurons transmit the message through the dendrites called pyramidal cells. The active parts of the brain are addressed and measured by various neuroimaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG) etc. These techniques help to diagnose pathological, physiological, mental and functional abnormalities of the brain. EEG is a high temporal resolution and a low spatial resolution technique which yields the non-invasively potential difference measurements between pair of electrodes over the scalp. It is used in understanding behavior of brain which is further used to analyze various brain disorders. EEG brain source localization has remained an active area of research in neurophysiology since last couple of decades and still being investigated in terms of its processing time, resolution, localization error, free energy, integrated techniques and algorithms applied. In this paper, several approaches of forward problem, inverse problem and Bayesian framework have been explored to address the uncertainties and issues of localization of the neural activities incurring in the brain.

Localization of brain signal sources from solely electroencephalography (EEG) has been an active area of research during the last two decades. Brain source localization is probably the most challenging and difficult operation in dealing with EEG signals due to three main reasons: the human head is nonhomogeneous; and human heads are neither spherical nor have similarity to each other, and finally, the sources can be multiple, distributed, or correlated. One of the requirements for brain source localization, particularly for the forward model, is the information about the head model. The head model is the model for which the EEG forward solution is calculated. Poor spatial resolution of EEG/magnetoencepalogram motivates research into methods that can more accurately localize the sources from the recordings using these modalities. A popular strategy in brain source localization is by using the dipole source assumption. Multiple sparse priors and iterative regularization algorithms have been used to solve the inverse localization problem.

Using electroencephalography (EEG) to elucidate the spontaneous activation of brain resting-state networks (RSNs) is nontrivial as the signal of interest is of low amplitude and it is difficult to distinguish the underlying neural sources. Using the principles of electric field topographical analysis, it is possible to estimate the meta-stable states of the brain (i.e., the resting-state topographies, so-called microstates). We estimated seven resting-state topographies explaining the EEG data set with k-means clustering (N = 164, 256 electrodes). Using a method specifically designed to localize the sources of broadband EEG scalp topographies by matching sensor and source space temporal patterns, we demonstrated that we can estimate the EEG RSNs reliably by measuring the reproducibility of our findings. After subtracting their mean from the seven EEG RSNs, we identified seven state-specific networks. The mean map includes regions known to be densely anatomically and functionally connected (superior frontal, superior parietal, insula, and anterior cingulate cortices). While the mean map can be interpreted as a “router,” crosslinking multiple functional networks, the seven state-specific RSNs partly resemble and extend previous functional magnetic resonance imaging-based networks estimated as the hemodynamic correlates of four canonical EEG microstates.

Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered ‘dipolar’ ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided. •It is currently a common practice to apply dimension reduction to EEG data using PCA before performing ICA decomposition.•We tested the quality of Independent Components (ICs) after different levels of rank reduction to a principal subspace.•PCA rank reduction adversely affected dipolarity and stability of ICs accounting for brain and known non-brain processes.•PCA rank reduction also increased inter-subject variance in IC source locations (by equivalent dipole fitting) and spectra.•For EEG data at least, PCA rank reduction should be avoided or carefully tested before applying it as a preprocessing step.

•Concise overview and explanation of beamformers for M/EEG data analysis.•Practical considerations and best practices for beamforming analyses.•Unification of terminology across popular open source software packages.•Comparison of implementations and user interfaces between software packages. Beamforming is a popular method for functional source reconstruction using magnetoencephalography (MEG) and electroencephalography (EEG) data. Beamformers, which were first proposed for MEG more than two decades ago, have since been applied in hundreds of studies, demonstrating that they are a versatile and robust tool for neuroscience. However, certain characteristics of beamformers remain somewhat elusive and there currently does not exist a unified documentation of the mathematical underpinnings and computational subtleties of beamformers as implemented in the most widely used academic open source software packages for MEG analysis (Brainstorm, FieldTrip, MNE, and SPM). Here, we provide such documentation that aims at providing the mathematical background of beamforming and unifying the terminology. Beamformer implementations are compared across toolboxes and pitfalls of beamforming analyses are discussed. Specifically, we provide details on handling rank deficient covariance matrices, prewhitening, the rank reduction of forward fields, and on the combination of heterogeneous sensor types, such as magnetometers and gradiometers. The overall aim of this paper is to contribute to contemporary efforts towards higher levels of computational transparency in functional neuroimaging.

As the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is a need for stability across a wider range of standard estimation routines. Using resting state EEG recordings of N=65 participants acquired within two studies, we present the first comprehensive assessment of the consistency of EEG source localization and functional/effective connectivity metrics across two anatomical templates (ICBM152 and Colin27), three electrical models (BEM, FEM and spherical harmonics expansions), three inverse methods (WMNE, eLORETA and LCMV), and three software implementations (Brainstorm, Fieldtrip and our own toolbox). Source localizations were found to be more stable across reconstruction pipelines than subsequent estimations of functional connectivity, while effective connectivity estimates where the least consistent. All results were relatively unaffected by the choice of the electrical head model, while the choice of the inverse method and source imaging package induced a considerable variability. In particular, a relatively strong difference was found between LCMV beamformer solutions on one hand and eLORETA/WMNE distributed inverse solutions on the other hand. We also observed a gradual decrease of consistency when results are compared between studies, within individual participants, and between individual participants. In order to provide reliable findings in the face of the observed variability, additional simulations involving interacting brain sources are required. Meanwhile, we encourage verification of the obtained results using more than one source imaging procedure. •EEG source imaging results depends on forward and inverse modeling parameters.•We quantify the consistency of localization and connectivity metrics across pipelines.•Considerable variability in connectivity is seen across inverse methods and toolboxes.•Beamformer solutions induce different connectivity patterns than linear inverses.•Future studies may employ multiple imaging pipelines to demonstrate reliability.

Gene therapy is a promising approach for hereditary deafness. We recently showed that unilateral AAV1-hOTOF gene therapy with dual adeno-associated virus (AAV) serotype 1 carrying human OTOF transgene is safe and associated with functional improvements in patients with autosomal recessive deafness 9 (DFNB9). The protocol was subsequently amended and approved to allow bilateral gene therapy administration. Here we report an interim analysis of the single-arm trial investigating the safety and efficacy of binaural therapy in five pediatric patients with DFNB9. The primary endpoint was dose-limiting toxicity at 6 weeks, and the secondary endpoint included safety (adverse events) and efficacy (auditory function and speech perception). No dose-limiting toxicity or serious adverse event occurred. A total of 36 adverse events occurred. The most common adverse events were increased lymphocyte counts (6 out of 36) and increased cholesterol levels (6 out of 36). All patients had bilateral hearing restoration. The average auditory brainstem response threshold in the right (left) ear was >95 dB (>95 dB) in all patients at baseline, and the average auditory brainstem response threshold in the right (left) ear was restored to 58 dB (58 dB) in patient 1, 75 dB (85 dB) in patient 2, 55 dB (50 dB) in patient 3 at 26 weeks, and 75 dB (78 dB) in patient 4 and 63 dB (63 dB) in patient 5 at 13 weeks. The speech perception and the capability of sound source localization were restored in all five patients. These results provide preliminary insights on the safety and efficacy of binaural AAV gene therapy for hereditary deafness. The trial is ongoing with longer follow-up to confirm the safety and efficacy findings. Chinese Clinical Trial Registry registration: ChiCTR2200063181 . An interim analysis of a single-arm trial in 5 children with hereditary deafness shows that binaural AAV gene therapy is safe and leads to hearing improvement up to 13–26 weeks of follow-up.