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Brain oscillations from EEG and MEG shed light on neurophysiological mechanisms of human behavior. However, to extract information on cortical processing, researchers have to rely on source localization methods that can be very broadly classified into current density estimates such as exact low-resolution brain electromagnetic tomography (eLORETA), minimum norm estimates (MNE), and beamformers such as dynamic imaging of coherent sources (DICS) and linearly constrained minimum variance (LCMV). These algorithms produce a distributed map of brain activity underlying sustained and transient responses during neuroimaging studies of behavior. On the other hand, there are very few comparative analyses that evaluates the "ground truth detection" capabilities of these methods. The current article evaluates the reliability in estimation of sources of spectral event generators in the cortex using a two-pronged approach. First, simulated EEG data with point dipoles and distributed dipoles are used to validate the accuracy and sensitivity of each one of these methods of source localization. The abilities of the techniques were tested by comparing the localization error, focal width, false positive (FP) ratios while detecting already known location of neural activity generators under varying signal-to-noise ratios (SNRs). Second, empirical EEG data during auditory steady state responses (ASSRs) in human participants were used to compare the distributed nature of source localization. All methods were successful in recovery of point sources in favorable signal to noise scenarios and could achieve high hit rates if FPs are ignored. Interestingly, focal activation map is generated by LCMV and DICS when compared to eLORETA while control of FPs is much superior in eLORETA. Subsequently drawbacks and strengths of each method are highlighted with a detailed discussion on how to choose a technique based on empirical requirements., (Copyright © 2019 Halder et al.)

Communication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that enables the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex- and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 “familiar vs. unfamiliar vs. scrambled faces” dataset. The goal is to educate both novice and experienced data analysts with the “tricks of the trade” necessary to successfully perform this type of analysis on their own data. [ABSTRACT FROM AUTHOR]

Abstract: Writing is a highly skilled and overlearned movement. In patients suffering from writer’s cramp, a focal task-induced dystonia, writing is impaired or even impossible due to involuntary muscle contractions and abnormal posture, which occur as soon as the person picks up a pen or within writing a few words. The underlying pathophysiological mechanisms of this movement disorder are not fully understood up to now. The aim of the present study was to unravel the oscillatory network underlying physiological writing in healthy subjects and dystonic writing in writer’s cramp patients. Using whole-head magnetoencephalography (MEG) and the analysis tool dynamic imaging of coherent sources (DICS) we studied oscillatory neural coupling during writing in eleven healthy subjects and eight patients suffering from writer’s cramp. Simultaneous recording of brain activity with MEG and activity of forearm and hand muscles with surface electromyography (EMG) was performed while subjects were writing for five minutes with their dominant right hand. Applying DICS sources of strongest cerebro-muscular coherence and cerebro-cerebral coherence during writing were identified, which consistently included six brain areas in both, the control subjects and the patients: contralateral and ipsilateral sensorimotor cortex, ipsilateral cerebellum, contralateral thalamus, contralateral premotor and posterior parietal cortex. Coherence between cortical sources and muscles appeared primarily in the frequency of writing movements (3–7Hz) while coherence between cerebral sources occurred primarily around 10Hz (8–13Hz). Interestingly, consistent coupling between both sensorimotor cortices was observed in patients only, whereas coupling between ipsilateral cerebellum and the contralateral posterior parietal cortex was found in control subjects only. These results are consistent with the often described bilateral pathophysiology and impaired sensorimotor integration in writer’s cramp patients. [Copyright &y& Elsevier]

Abstract: Cortical rhythmic activity is increasingly employed for characterizing human brain function. Using MEG, it is possible to localize the generators of these rhythms. Traditionally, the source locations have been estimated using sequential dipole modeling. Recently, two new methods for localizing rhythmic activity have been developed, Dynamic Imaging of Coherent Sources (DICS) and Frequency-Domain Minimum Current Estimation (MCEFD). With new analysis methods emerging, the researcher faces the problem of choosing an appropriate strategy. The aim of this study was to compare the performance and reliability of these three methods. The evaluation was performed using measured data from four healthy subjects, as well as with simulations of rhythmic activity. We found that the methods gave comparable results, and that all three approaches localized the principal sources of oscillatory activity very well. Dipole modeling is a very powerful tool once appropriate subsets of sensors have been selected. MCEFD provides simultaneous localization of sources and was found to give a good overview of the data. With DICS, it was possible to separate close-by sources that were not retrieved by the other two methods. [Copyright &y& Elsevier]

Magnetoencephalography (MEG) has, in comparison with other functional imaging modalities, unique properties which makes it the prime candidate for the noninvasive investigation of long-range oscillatory interactions in the human brain. Recent methodological developments based on spatial filtering introduced the computation of functional tomographic maps covering the entire brain and representing the distribution of coherence to a given reference signal or the distribution of power. Because of the spatially inhomogeneous sensitivity profile of the MEG sensors, the spatial resolution of the resulting functional maps is not isotropic across the brain. Here, we introduce a convenient analytic expression for the computation of the spatial resolution at any given point in the brain. We derive the dependence of the resolution on the signal-to-noise ratio and on the changes of the leadfields. The resolution map can be displayed on anatomical MRI in the same way as the functional maps. In addition, we establish a procedure for computing a confidence volume of local maxima which is based on a bootstrap method. The confidence volume is a measure for the uncertainty of the localization. It is important for assigning local maxima of activation to specific anatomical structures and may be used to test for differences in localization between different experimental conditions. [Copyright &y& Elsevier]