Electrical source imaging (ESI) aims at reconstructing the electrical brain activity from measurements of the electric field on the scalp. ESI is a key element in the analysis of EEG data, in both research and clinical settings. In the last twenty years several algorithms have been applied for solving the ill- posed EEG inverse problem. Most of these popular methods can be derived within a Bayesian statistical framework in which all variables can be modelled as random variables with associated probability density functions (pdf) and the solution of the inverse problem is the posterior pdf for the unknown primary current distribution conditioned on the measurements. The different methods mainly differ from each other by the quality and quantity of a priori information they use in order to solve the EEG inverse problem. In this study [1] we validate and compare ten different ESI methods (wMNE, dSPM, sLORETA, eLORETA, LCMV, dipole fitting, RAP-MUSIC, MxNE, gamma map and Sesame) "in vivo", by exploiting a recently published EEG dataset [2] for which the ground truth is known. We compare the different inverse methods under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of the parameters on the localization performance

The present paper represents a first methodological work for the construction of a robust and accurate algorithm for the solution of an inverse problem given by the identification of the parameters of a lumped mathematical model of fetal circulation introduced by G. Pennati et al. (1997). The underlying estimation techniques here applied are two global search meth- ods, respectively a Parameter Space Investigation (PSI) and the Ensemble Kalman Filter (EnKF), with a refinement performed with a local search method, i.e. Levenberg- Marquardt method (LM). The results here presented show the soundness of our methodology and opens the possibility to apply these techniques for the parameter identification of waveforms obtained from Doppler clinical measurements in the next future. Our final goal is to build a non-invasive simulation tool for the description of the circulation of fetuses in the context of a patient-specific model in order to help clinicians in early diagnosis of pathologies like cardiac distress or growth retardation.

Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its fiexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fiy pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html, and https://neuropycon.github.io/graph pype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.

Current theories of schizophrenia emphasize the role of altered information integration as the core dysfunction of this illness. While ample neuroimaging evidence for such accounts comes from investigations of spatial connectivity, understanding temporal disruptions is important to fully capture the essence of dysconnectivity in schizophrenia. Recent electrophysiology studies suggest that long-range temporal correlation (LRTC) in the amplitude dynamics of neural oscillations captures the integrity of transferred information in the healthy brain.Thus, in this study, 25 schizophrenia patients and 25 controls (8 females/group) were recorded during two five-minutes of resting-state magnetoencephalography (once with eyes-open and once with eyes-closed). We used source-level analyses to investigate temporal dysconnectivity in patients by characterizing LRTCs across cortical and sub-cortical brain regions. In addition to standard statistical assessments, we applied a machine learning framework using support vector machine to evaluate the discriminative power of LRTCs in identifying patients from healthy controls.We found that neural oscillations in schizophrenia patients were characterized by reduced signal memory and higher variability across time, as evidenced by cortical and subcortical attenuations of LRTCs in the alpha and beta frequency bands. Support vector machine significantly classified participants using LRTCs in key limbic and paralimbic brain areas, with decoding accuracy reaching 82%. Importantly, these brain regions belong to networks that are highly relevant to the symptomology of schizophrenia. These findings thus posit temporal dysconnectivity as a hallmark of altered information processing in schizophrenia, and help advance our understanding of this pathology.

A recently proposed iterated alternating sequential (IAS) MEG inverse solver algorithm, based on the coupling of a hierarchical Bayesian model with computationally efficient Krylov subspace linear solver, has been shown to perform well for both superficial and deep brain sources. However, a systematic study of its ability to correctly identify active brain regions is still missing. We propose novel statistical protocols to quantify the performance of MEG inverse solvers, focusing in particular on how their accuracy and precision at identifying active brain regions. We use these protocols for a systematic study of the performance of the IAS MEG inverse solver, comparing it with three standard inversion methods, wMNE, dSPM, and sLORETA. To avoid the bias of anecdotal tests towards a particular algorithm, the proposed protocols are Monte Carlo sampling based, generating an ensemble of activity patches in each brain region identified in a given atlas. The performance in correctly identifying the active areas is measured by how much, on average, the reconstructed activity is concentrated in the brain region of the simulated active patch. The analysis is based on Bayes factors, interpreting the estimated current activity as data for testing the hypothesis that the active brain region is correctly identified, versus the hypothesis of any erroneous attribution. The methodology allows the presence of a single or several simultaneous activity regions, without assuming that the number of active regions is known. The testing protocols suggest that the IAS solver performs well with both with cortical and subcortical activity estimation.

The MagnetoEncephaloGraphy (MEG) has gained great interest in neurorehabilitation training due to its high temporal resolution. The challenge is to localize the active regions of the brain in a fast and accurate way. In this paper we use an inversion method based on random spatial sampling to solve the real-time MEG inverse problem. Several numerical tests on synthetic but realistic data show that the method takes just a few hundredths of a second on a laptop to produce an accurate map of the electric activity inside the brain. Moreover, it requires very little memory storage. For these reasons the random sampling method is particularly attractive in real-time MEG applications.

Magnetoencephalography (MEG) aims at reconstructing the unknown neuroelectric activity in the brain from non-invasive measurements of the magnetic field induced by neural sources. The solution of this ill-posed, ill-conditioned inverse problem is usually dealt with using regularization techniques that are often time-consuming, and computationally and memory storage demanding. In this paper we analyze how a slimmer procedure, random sampling, affects the estimation of the brain activity generated by both synthetic and real sources.

We present Visbrain, a Python open-source package that offers a comprehensive visualization suite for neuroimaging and electrophysiological brain data. Visbrain consists of two levels of abstraction: (1) objects which represent highly configurable neurooriented visual primitives (3D brain, sources connectivity, etc.) and (2) graphical user interfaces for higher level interactions. The object level offers flexible and modular tools to produce and automate the production of figures using an approach similar to that of Matplotlib with subplots. The second level visually connects these objects by controlling properties and interactions through graphical interfaces. The current release of Visbrain (version 0.4.2) contains 14 different objects and three responsive graphical user interfaces, built with PyQt: Signal, for the inspection of time-series and spectral properties, Brain for any type of visualization involving a 3D brain and Sleep for polysomnographic data visualization and sleep analysis. Each module has been developed in tight collaboration with end-users, i.e., primarily neuroscientists and domain experts, who bring their experience to make Visbrain as transparent as possible to the recording modalities (e.g., intracranial EEG, scalp-EEG, MEG, anatomical and functional MRI). Visbrain is developed on top of VisPy, a Python package providing high-performance 2D and 3D visualization by leveraging the computational power of the graphics card. Visbrain is available on Github and comes with a documentation, examples, and datasets (http://visbrain.org).

Introduction : Neuroimaging studies have shown that in adults, the motor learning induced alterations of the functional connectivity assessed during Resting State Networks (RSN) is age-dependent (Mary et al., 2017). Motor learning relies on the build-up of new sensori-motor representations, which has been studied using the bar-man task in adults (Barlaam, Vaugoyeau, Fortin, Assaiante, & Schmitz, 2016; Paulignan, Dufossé, Hugon, & Massion, 1989) and in children (Schmitz et al, 2002).. The aim of this study was to investigate the modulations of functional connectivity after a motor learning task in the childs resting state network. Method : 20 children aged 7 to 12 (12 boys; age 9y 9m ; age 1y et 8m) took part in the study. The resting state tasks consisted of a 3 session closed-eyes recording of 45 seconds. The first one was used as a baseline and the next two have been set around the motor learning task to evaluate the effect of motor learning on the connectivity in the RSN. The motor learning task was a load-lifting task where the participant was asked to lift a weight using its right hand which triggered the fall of a weight attached to the supporting left arm (Paulignan et al, 1989). We continuously recorded the neuromagnetic signals using a 275 channels CTFMEG system. To quantify the functional connectivity between brain regions, coherency analyses have been conducted, using the imaginary part of the coherency, corresponding to the correlation coefficient between two signals in the alpha and beta frequency bands. Moreover, graph theory analysis has provided an overview of the network organisation after the motor task. All resultshave been analysed using data-related permutation statistic with a 0.005 significance threshold (calculated as a relation between the number of conditions (n=2) and the number of subjects (N=20); = 1/, therefore < 0.005). 2 of 3 Results : The motor learning behavioural performances were assessed using a learning curve model throughout the trials which revealed a significant global learning effect (F(7 ;19) = 50,62 ; p <0,0001). In the alpha band (8-12Hz) : Permutation analysis showed an increase of the functional connectivity in the RSN (<0.005) when contrasting before and after the sensorimotor learning in the primary motor cortices (M1) along with the inferior frontal gyrus (IFG) and the premotor cortices. In the beta band (15-29Hz) : Significant increase of the functional connectivity was also found in this frequency band when contrasting before and after the sensorimotor learning in the somatosensory cortex andin the precuneus gyrus. Interestingly, we found that functional connectivity measured in pairs of brain areas (such as the premotor cortex and the cerebellum) in the pre-learning RSN was predictive of the behavioural learning performance. Conclusion : Our study showed that, after a motor learning task, the functional connectivity measured in the RSN increases between regions involved in the build-up of sensorimotor representations (such as the supplementary motor area (SMA); the primary motor dorsal area (PMd) ; the primary motor cortex (M1) and even the somatosensory cortexs (S1 & S2)), in children. References : Barlaam, F., Vaugoyeau, M., Fortin, C., Assaiante, C., & Schmitz, C. (2016). Shift of the muscular inhibition latency during on-line acquisition of anticipatory postural adjustments. PLoS ONE, 11(5). https://doi.org/10.1371/journal.pone.0154775 Mary, A., Wens, V., Op De Beeck, M., Leproult, R., De Tiège, X., & Peigneux, P. (2017). Resting-state Functional Connectivity is an Age-dependent Predictor of Motor Learning Abilities. Cerebral Cortex, 27(10), 49234932. https://doi.org/10.1093/cercor/bhw286 Paulignan, Y., Dufossé, M., Hugon, M., & Massion, J. (1989). Acquisition of co-ordination between posture and movement in a bimanual task. Experimental Brain Research, 77(2), 337348. https://doi.org/10.1007/BF00274991

Biological systems tend to display complex behaviour with a power-law (1/f - like) distribution. In the brain, this translates into neural activity that exhibits scale-free, temporal or spatial, properties (He, 2014). Scaleinvariance has been observed across different neuroimaging modalities and conditions (Linkenkaer-Hansen, 2001; He, 2014; Ciuciu et al. 2012). Beyond previously used features, recent electrophysiology studies have shown the presence of long-range temporal correlations (LRTCs) in the amplitude dynamics of alpha and beta oscillations (Nikulin et al. 2012). Disease, such as psychosis, can alter the temporal properties of neuronal activity and, consequently, potentially affect information integration (Fernandez et al. 2013). The goals of this study were to: (a) measure and compare scale-free dynamics in schizophrenia patients (SZ) and controls using magnetoencephalography (MEG), and (b) classify subjects using machine-learning tools. Five minutes of resting-state MEG were acquired for 25 SZ patients and 25 controls during eyes open and eyes closed conditions. Detrended Fluctuation analysis (DFA) was applied to resting alpha and beta band oscillatory amplitudes to investigate LRTCs in each subject group, at both the sensor and the source levels. Permutation tests with maximum statistics correction were used to explore group differences (p < 0.01). Finally, machine-learning, using support vector machine (SVM) and a 10-fold cross-validation technique, was applied to classify controls and patients based on DFA values. Statistically significant decoding was assessed using binomial law statistics and permutation tests (p < 0.001). Results/Discussion: Significant group differences were observed between the two groups at both sensor and source levels. Specifically, sensormeasured DFA were found to be significantly attenuated in SZ patients compared to controls over the temporo-parietal areas in the alpha-band, and over central brain regions in the beta-band. Source-level analysis improved anatomical. Relative group differences in DFA were found up to 20% in both alpha and beta bands. Specifically, compared to controls, attenuated DFA values were observed in the frontal, temporal and occipital poles and the cuneus in the alpha band, and in the occipital pole, cuneus and mid frontal gyrus in the beta band. Finally, the machine-learning algorithm successfully classified the groups using the measure of DFA with up to 76% decoding accuracy. The combination of classical statistical measures and machine learning tools in our study illustrate the interest of using features of scale-free dynamics to enhance our understanding of schizophrenia and potentially find a new path for early clinical diagnosis.

Background. With the exponential increase in data dimension and methodological complexities, brain networks analysis with MEG and EEG has become an increasingly challenging and time-consuming endeavor. To date, performing all the data processing steps that are required for a complete MEG/EEG analysis pipeline often require the use of a multitude of software packages and in-house or custom tools (e.g. MRI segmentation, pre-processing, source reconstruction, graph theoretical analysis, statistics). This is not only cumbersome, but may also increase sources of errors and hinders replication of results. Here we describe NeuroPycon, an open-source, multi-modal brain data analysis kit which provides Python-based pipelines for advanced multi-thread processing of fMRI, MEG, and EEG data, with a focus on connectivity and graph analyses [1]. Methods. NeuroPycon is based on the NiPype framework [2] which facilitates data analyses by wrapping numerous commonly-used neuroimaging software solutions into a common python framework. NeuroPycon allows accessing and interfacing with the existing open-science neuroimaging software and signal processing toolboxes, within a unified framework relying on several freely available Python packages which are developed for efficient and fast parallel processing. The current implementation of NeuroPycon comprises three different packages: 2 of 3 - ephypype is mainly based on MNE-Python package [3] and includes pipelines for electrophysiology analyses. Current implementation features MEG/EEG data import, data pre-processing and cleaning via an automatic removal of eyes and heart-related artefacts, and sensor or source-level connectivity analyses - graphpype is based on radatools [4], a set of freely distributed applications aimed at analyses of Complex Networks. It comprises pipelines for functional connectivity studies which heavily exploit graph-theoretical metrics including among other things modular partitions - neuropycon_cli is a command line interface for the ephypype package. Notably, NeuroPycon pipelines can be used in a stand-alone mode but they can also be combined within building blocks to form a larger workflow, in which case the input of one pipeline comes from the outputs of the others. Each pipeline, based upon the nipype engine, is defined by connecting different nodes, with each node being either a user-defined function or a python-wrapped external routine (as MNE-python modules or radatools functions). Results and Discussion. NeuroPycon provides a common and fast framework to develop workflows for advanced neuroimaging data analyses. Several workflows have already been developed to analyze different datasets coming from MEG and EEG studies, such as EEG sleep data and MEG resting state measurements. Furthermore, pipelines defined in graphpype have already been used to perform graph theoretical analysis on a different fMRI datasets. Results visualisation for NeuroPycon is provided through the visbrain (http://visbrain.org/), an open-source multi-purpose python software devoted to graphical representation of neuroscientific data and built on top of VisPy [5], a high-performance visualization library leveraging GPU acceleration. NeuroPycon will shortly be available for download via github (installation via Docker) and is currently being documented (https://neuropycon.github.io/neuropycon_doc/). Future developments will include fusion of multi-modal data (ex. MEG and fMRI or iEEG and fMRI) and feature an increased compatibility with the existing Python packages of interest such as machine learning tools. References: 1. Bullmore E, Sporns O (2009), Nat Rev Neuroscience 2. Gorgolewski et al. (2011) Front. Neuroinformatics 3. Gramfort et al. (2013), Front. Neuroscience 4. http://deim.urv.cat/~sergio.gomez/radatools.php 5. Campagnola et al. (2015), Proceedings of the 14th Python in Science Conference

We present a Python open-source package called Visbrain that offers a coherent visualization suite for multi-modal brain data (intracranial and scalp EEG, MEG, structural and functional MRI). The current version of Visbrain is essentially articulated around four modules dedicated to 1) 3D visualization of functional and/or connectivity results (Brain), 2) polysomnographic data visualization and sleep analysis (Sleep, [1]), 3) data mining and basic plotting functions (Signal), 4) topographic representation (Topo). We also included functions for page layout and export of paper-ready high-quality figure. Those modules come with a modular and powerful graphical user interfaces built with PyQt. Each module has been developed in collaboration with neuroscientists and experts in the field and provides a comprehensive set of functionalities. Visbrain is developed on top of VisPy [2], a Python package providing high performance 2D and 3D visualization by leveraging the computational power of the graphic card. This package is available on Github and comes with an extensive documentation, examples and datasets (see http://visbrain.org).

Objective: Neuroimaging studies provide evidence of disturbed resting-state brain networks in Schizophrenia (SZ). However, untangling the neuronal mechanisms that subserve these baseline alterations requires measurement of their electrophysiological underpinnings. This systematic review specifically investigates the contributions of resting-state Magnetoencephalography (MEG) in elucidating abnormal neural organization in SZ patients.& para;& para;Method: A systematic literature review of resting-state MEG studies in SZ was conducted. This literature is discussed in relation to findings from resting-state fMRI and EEG, as well as to task-based MEG research in SZ population. Importantly, methodological limitations are considered and recommendations to overcome current limitations are proposed.& para;& para;Results: Resting-state MEG literature in SZ points towards altered local and long-range oscillatory network dynamics in various frequency bands. Critical methodological challenges with respect to experiment design, and data collection and analysis need to be taken into consideration.& para;& para;Conclusion: Spontaneous MEG data show that local and global neural organization is altered in SZ patients. MEG is a highly promising tool to fill in knowledge gaps about the neurophysiology of SZ. However, to reach its fullest potential, basic methodological challenges need to be overcome.& para;& para;Significance: MEG-based resting-state power and connectivity findings could be great assets to clinical and translational research in psychiatry, and SZ in particular. (C) 2017 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

NeuroPycon is an open-source multi-modal brain data analysis kit which provides Python-based pipelines for advanced multi-thread processing of fMRI, MEG and EEG data, with a focus on connectivity and graph analyses [1]. NeuroPycon is based on NiPype framework [2] which facilitates data analyses by wrapping many commonly-used neuroimaging software into a common python framework. Therefore, a major strength of NeuroPycon is that it relies on (and interfaces with) several freely available Python packages developed for efficient and fast parallel processing and that it seamlessly connects with existing open-science neuroimaging and signal processing toolboxes. The flexible design allows users to configure analysis pipelines defined by connecting different nodes, where each node may be a user-defined function or a well-established tool or python-wrapped module (e.g. MNE-python for MEG analysis [3], etc.). The current implementation of NeuroPycon contains three different packages: - ephypype includes pipelines for electrophysiology analysis; current implementations allow for MEG/EEG data import, data pre-processing and cleaning by an automatic removal of eyes and heart related artefacts, sensor or source-level connectivity analyses - graphpype allows to study functional connectivity exploiting graph-theoretical metrics including also modular partitions - clipype is a command line interface for ephypype package. NeuroPycon will shortly be available for download via github (installation via Docker) and is currently being documented. Future developments include fusion of multi-modal data (ex. MEG and fMRI or iEEG and fMRI). References 1. Bullmore, Sporns (2009), Nat Rev Neurosci 2. Gorgolewski et al. (2011) Front. Neuroinform 3. Gramfort et al. (2013), Front. Neurosci

The effect of the choice of the inverse method on the cortico-cortical coupling analysis has been largely overlooked in the literature. Here, we set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, we created thousands of randomly located pairs of sources and varied their inter- and intra-source correlation strength, source size and spatial configuration. Then, we used the simulated pairs of sources to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, we reconstructed the sources using L2-Minimum-Norm Estimate (MNE), Linearly Constrained Minimum Variance (LCMV) beamforming, and Dynamic Imaging of Coherent Sources (DICS) beamforming; and calculated source level power and coherence maps. We evaluated the performance of the methods using the Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions of interacting point-like sources; but MNE provides better connectivity estimation than beamformers of interacting extended cortical patches, if each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each cortical patch is simulated with partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. Our conclusions can guide method selection and help improve data interpretation regarding MEG connectivity estimation.

An important process underlying meditation and its benefits involves the regulation of attention. Although the two main meditation categories - open-monitoring meditation (OMM) and focused-attention meditation (FAM) - are associated with different benefits and attentional processes, direct comparisons between the attentional neural mechanism of FAM and OMM are rare. This study uses magnetoencephalography (MEG) recordings in 12 expert meditators to compare FAM and OMM by assessing (i) source spectral power, (ii) seed-based functional connectivity of key regions in attention, (including anterior cingulate cortex, dorsolateral prefrontal cortex and the thalamus) and (iii) graph theory metrics that describe brain-wide efficiency of information processing. We reconstructed the source space using minimum norm estimate and computed spectral power and functional connectivity in multiple frequency bands (delta, theta, alpha, beta, gamma) using a custom-designed python-based MEG analysis pipeline (NeuroPycon). The results reveal unique patterns of neural processes specific to FAM or OMM. Among other things, compared to FAM, OMM appears to be characterized by enhanced small-world network properties. By contrast, FAM exhibits greater functional connectivity between the anterior cingulate cortex and frontal regions. These findings shed light onto the mechanisms that potentially mediate the different behavioral and attentional capacities associated with each of the two meditation techniques. Our results are discussed in the context of previous behavioral and fMRI studies on meditation and attention.

Despite numerous important contributions, the investigation of brain connectivity with magnetoencephalography (MEG) still faces multiple challenges. One critical aspect of source-level connectivity, largely overlooked in the literature, is the putative effect of the choice of the inverse method on the subsequent cortico-cortical coupling analysis. We set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, thousands of randomly located pairs of sources were created. Several parameters were manipulated, including inter- and intra-source correlation strength, source size and spatial configuration. The simulated pairs of sources were then used to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, the source level power and coherence maps were calculated using three methods (a) L2-Minimum-Norm Estimate (MNE), (b) Linearly Constrained Minimum Variance (LCMV) beamforming, and (c) Dynamic Imaging of Coherent Sources (DICS) beamforming. The performances of the methods were evaluated using Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions if the interacting cortical sources consist of point-like sources. On the other hand, MNE provides better connectivity estimation than beamformers, if the interacting sources are simulated as extended cortical patches, where each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each patch of active cortex is simulated with only partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. The insights revealed here can guide method selection and help improve data interpretation regarding MEG connectivity estimation.

New method: We computed the lead-field matrix by using a novel routine provided by the OpenMEEG software. We performed an analysis of the numerical stability of the ECoG inverse problem by computing the condition number of the lead-field matrix for different configurations of the electrodes grid. We applied a Linear Constraint Minimum Variance (LCMV) beamformer to both synthetic data and a set of real measurements recorded during a rapid visual categorization task. Background: Electrocorticography (ECoG) measures the distribution of the electrical potentials on the cortex produced by the neural currents. A full interpretation of ECoG data requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of the neural currents. This study addresses the ECoG source modeling developing a beamformer method.

With the exponential increase in data dimension and methodological complexities, conducting brain network analyses using MEG and EEG is becoming an increasingly challenging and time-consuming endeavor. To date, most of the MEG/EEG processing is done by combining software packages and custom tools which often hinders reproducibility of the experimental findings. Here we describe NeuroPype, which is a free open-source Python package we developed for efficient multi-thread processing of MEG and EEG studies. The proposed package is largely based on the NiPype framework and the MNE-Python software and benefits from standard Python packages such as NumPy and SciPy. It also incorporates several existing wrappers, such as a Freesurfer Python-wrapper for multi-subject MRI segmentation. The NeuroPype project includes three different packages: I Neuropype-ephy includes pipelines for electrophysiology analysis; current implementations allow for MEG/EEG data import, data pre-processing and cleaning by an automatic removal of eyes and heart related artefacts, sensor or source-level connectivity analyses II Neuropype-graph: functional connectivity exploiting graph-theoretical metrics including modular partitions III Neuropype-gui: a graphical interface wrapping the definition of parameters. NeuroPype provides a common and fast framework to develop workflows for advanced MEG/EEG analyses (but also fMRI and iEEG). Several pipelines have already been developed with NeuroPype to analyze different MEG and EEG datasets: e.g. EEG sleep data, MEG resting state measurements and MEG recordings in Autism. NeuroPype will be be made available via Github. Current developments will increase its compatibility with existing Python packages of interest such as machine learning tools.