The phenomenologyand reported effects of meditation vary according to the technique practiced.While numerous studies have explored the cerebral mechanisms involved inmeditation, little research provides direct comparisons between the neuronalnetwork dynamics involved in different meditation techniques. Here, we exploreand compare brain signals recorded with magnetoencephalography (MEG) during (a)resting state, (b) focused-attention meditation (FAM) and (c) open-monitoringmeditation (OMM) in a group of expert meditators (12 monks).To this end, weestimated MEG source time courses using a minimum-norm solution and computed (1)spectral power in multiple frequency bands (delta, theta, alpha, beta andgamma), (2) graph theoretical measures, (3) long-range coupling using imaginarycoherence and weighed phase-lag index and (4) multifractal scaling parameters using Wavelet Leader-based Multifractal formalism. We compared all the measures in the three conditions(OMM, FAM and resting state) and tested for statistical significance using permutationtest (paired t-test) corrected by maximum statistics. We also used a machinelearning framework in order to see which features provide the highestclassification across conditions. Our findings reveal several differencesbetween FAM, OMM and the resting-state condition. Compared to OMM, FAM isassociated with an increase in power in regions involved in attention andperformance monitoring. In OMM, increases in activity were observed in regionsinvolved in memory and emotion processing. Moreover, OMM seems to have strongestand more connections, while resting state have connections that are weaker andfewer in number compared to OMM and FAM. We discuss these results in thecontext of previous cognitive neuroimaging studies of meditation and paths forfuture research are proposed.

Background. Electrocorticography (ECoG) measures the distribution of electrical potentials by means of electrodes grids implanted close to the cortical surface. A full interpretation of ECoG data requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of neural currents responsible for the recorded signals. Only in the last few years some methods have been proposed to solve this inverse problem [1]. Methods. This study [2] addresses the ECoG source modelling using a beamformer method. We computed the lead-field matrix which maps the neural currents onto the sensors space by a novel routine provided by the OpenMEEG framework [3]. The ECoG source-modeling problem requires to invert this matrix by means of a regularization method which reduces its intrinsic numerical instability: we performed an analysis of the condition number of the lead-field matrix for different configurations of the electrodes grid. Finally, we provided quantitative results for source modeling using a Linear Constraint Minimum Variance (LCMV) beamformer [4]. The validation of the effectiveness of beamforming in ECoG was performed both with synthetic data and with experimental data recorded during a rapid visual categorization task. Results. For all considered grids the condition number indicates that the ECoG inverse problem is mildly ill-conditioned. For realistic SNR we found a good performance of the LCMV algorithm for both localization and waveforms reconstruction. The flow of information reconstructed by analyzing real data seems consistent with both invasive monkey electrophysiology studies and non-invasive (MEG and fMRI) human studies. References: 1. Dumpelmann et al., (2012), Human brain mapping, 33(5), 1172-1188 2. Pascarella et al. (2016), Journal of Neuroscience Methods, 263(5), 134-144 3. Kybic et al., (2005), Medical Imaging, IEEE Transactions on, 24(1), 12-28 4. Van Veen et al., (1997), Biomedical Engineering, IEEE Transactions on, 44(9), 867-880

In the present study, we revisit the MEG inverse problem, regularization and depth weighting from a Bayesian hierarchical point of view: the primary unknown is the discretized current density and each dipole has a preferred direction extracted from the MRI of the subject and encoded in the prior distribution. The variance of each dipole is described by its hyperprior density: this hypermodel is used to build the Iterative Alternating Sequential (IAS) algorithm with the novel feature that the parameters are determined using an empirical Bayes approach. We test the performance of the IAS algorithm against synthetic but realistic data. We simulate the neural activity generated by cortical patches located in several cerebral regions including deep regions as Insula, posterior Cingulate, Cerebellum and Hippocampus. Then, we reconstruct the activity by the IAS method with and without the physiological prior. The tests show that the physiological prior significantly improves the localization of the activity also in the case when the neural sources are located in deep regions. We compare the performance of the IAS method against the results obtained using two of the most popolar inversion methods: wMNE and dSPM. A measure based on Bayesian factors is used to quantify the reliability of the reconstructions. Finally, the three inversion methods are applied to a set of auditory real data. The Bayesian hierarchical model provides a very natural interpretation for sensitivity weighting, and the parameters in the hyperprior provide a tool for controlling the quality of the solution in terms of focality, thus leading to a flexible algorithm that can handle both sparse and distributed sources. References 1. Calvetti D, Pitolli F, Somersalo E and Vantaggi B(2015) ArXiv:1503.06844 2. Calvetti D, Pascarella A, Pitolli F, Somersalo E and Vantaggi B(2015) Inverse Problems 31(12) 3. Lin FH et al.(2006) Neuroimage 31 160-171 4. Tadel et al.(2011) Computational intelligence and neuroscience, 2011:8

Motor learning induces changes in resting-state (RS) network properties in fronts-parietal (Albert et al, 2009) and sensorimotor (Taubert et al, 2011) networks. This study explores the putative modulations of spontaneous resting-state oscillations following a sensori-motor learning task. The task consisted in lifting a load with the right hand, which triggered the unloading of a load suspended to the left forearm (Paulignan et al., 1989). Because learning stabilizes quickly, a temporal delay was implemented, hence placing the subject in a dynamic learning state. Sixteen adults performed a resting state sessions in which they fixated a grey crosshair on a white background before and after two motor learning conditions: The subjects were instructed to lift with their right arm a load (800 g) placed on the ipsilateral haptic space. In the LEARNED condition, voluntary lifting of the object with the right arm instantaneously triggered the unloading of the load placed on the left arm. In the DYNAMIC LEARNING condition, a time delay was implemented per block between lifting and the resulting unloading. MEG signals were recorded using a 275-channel MEG CTF system. The performance was constant in the LEARNED condition, while postural stabilization increased during the DYNAMIC LEARNING condition (p<.001). Minimum-norm estimation revealed that alpha power (8-12 Hz) generators were located bilaterally within the pre-central gyri, the post-central gyri, the inferior parietal gyri and the superior parietal gyri. Most importantly, comparison of RS power pre and post learning revealed a significant increase of sensori-motor alpha power contralateral to postural side, only after the DYNAMIC LEARNING condition (p<.05). Our RS MEG connectivity and graph theoretical analyses also showed significant changes following motor learning. The RS oscillatory network modulations we observed following dynamic motor learning could be specifically related to sustained sensori-motor learning processes, distinct from novel skill acquisition.

The phenomenology and reported effects of meditation vary according to the technique practiced. While numerous studies have explored the cerebral mechanisms involved in meditation, little research provides direct comparisons between the neuronal network dynamics involved in different meditation techniques. Here, we explore and compare brain signals recorded with magnetoencephalography (MEG) during (a) focused-attention meditation (FAM), and (b) open-monitoring meditation (OMM) in a group of expert meditators (12 monks). To this end, we estimated MEG source time courses using minimum-norm and computed spectral power in multiple frequency bands (delta, theta, alpha, beta and gamma), graph theoretical measures and multifractal scaling parameters in both conditions. Preliminary findings reveal several differences between FAM and OMM. Interestingly, OMM was associated with higher theta power in the right temporal pole. We discuss these results in the context of previous cognitive neuroimaging studies of meditation and paths for future research are proposed.

With the exponential increase in data dimension and complexity, conducting state-of-the-art brain network analyses using MEG and EEG is becoming an increasingly challenging and time-consuming endeavor. Here we describe NeuroPype, a free open-source Python package we developed for efficient multi-thread processing of MEG and EEG studies. The proposed package is based on NiPype and MNE-Python and benefits from standard Python packages such as NumPy and SciPy. The pipeline also incorporates several existing wrappers, such as a Freesurfer Pyhton-wrapper for multi-subject MRI segmentation. Through the efficient combination of multiple neuroimaging and MEG/EEG packages, NeuroPype provides a common and fast framework for advanced MEG/EEG analyses. The highlights of the pipeline, include data pre-processing and cleaning, sensor or source-level connectivity analyses (Imaginary and standard coherence, phase-lag index, phase-locking, etc.), and graph-theoretical metrics (including modular partitions). The pipeline design, data structure and analysis workflow is described and future additions will be discussed.

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 based on the Nipype framework , a tool developed in fMRI field, which facilitates data analyses by wrapping many commonly-used neuro-imaging software into a common python framework.

Introduction: The brain is a connected network, requiring complex-system measures to describe its organization principles [1,2]. Here, we aim at testing whether the normalized compression distance (NCD) [3] is a suitable quantifier of the functional connectivity between cortical regions. This new measure estimates the information shared by two signals comparing the compression length of one signal given the other, without requiring any representation of the single in harmonics or selecting a specific time window where to compare the two signals. We show that this new measure is a good candidate to estimate the inter-nodes connectivity since it displays features 'expected' for brain connectivity, i.e. it is maximal between homologous cortical areas, it is higher for dominant cortical areas, it depends on age. In order to do it we estimated the NCD between functionally homologous primary somatosensory areas (S1) activities, testing the above-mentioned properties. Methods: Twenty-eight healthy, right-handed volunteers participated in the study. We recorded brain magnetic activity in the left and right Rolandic regions by a 28-channel magnetoencephalographic (MEG) system. We recorded rest activity for 3 min in each hemisphere. MEG activity was also collected during the electrical stimulation of the contralateral median nerve at the wrist delivered via surface disks. Elicited electric pulses were 0.2 ms in duration and 631 ms of inter-stimulus interval. Left and right median nerves were separately stimulated, totaling about 200 artifact-free trials for each. We used the Functional Source Separation (FSS) [4,5] algorithm to identify functionally homologous areas in the two hemispheres devoted to the somatosensory hand representation (FS_S1). Therefore, we calculated NCD between the left and right FS_S1s at rest. NCD is a parameter-free, quasi-universal similarity measure, computed from the lengths of compressed data files, singly and in pairwise concatenation. In other terms, NCD defines that two objects are similar if we can significantly "compress" one given the information of the other. We compared the similarity between the left and right homologous areas in single subjects and across the whole group. In particular, we compared the similarity of the activities in the two hemispheres of the same subject, with that in the same or in the opposite hemisphere of different subjects in the group of people. Results: NCD was minimal (maximal functional connectivity) between the neuronal activities of hemispheric functionally homologous areas in the same subject, i.e the NCD between the left and right FS_S1 of the same person was smaller than across different subjects (p<10 -7 consistently). NCD was smaller within the left dominant hemisphere than within the non dominant right one (p=3o10-7), suggesting that more skilled cortical areas express more tuned neuronal activities. Finally, it became more variable in older than younger people (p=.01), indicating that it is sensitive to proprioceptive and sensorimotor skills degradation typical of aging. Conclusions: NCD displayed an excellent ability in quantifying the similarity among neuronal activities, catching the maximal similarity expected for functionally homologous cortical areas of the two hemispheres. It was also sensitive to dominant- and age-dependent properties of somatosensory representation activities. This ability to catch key features of neuronal activity's dynamics indicates NCD as a good candidate for studies of brain functional connectivity, able to overcome the limitations intrinsic to the classical Fourier or autoregressive estimates in assessing the dynamics of two-nodes functional conections.

ElectroCOrticoGraphy (ECoG) is an invasive neuroimaging technique that measures electrical potentials produced by brain currents via an electrode grid implanted on the cortical surface. A full interpretation of ECoG data is difficult because it requires solving the inverse problem of reconstructing the spatio-temporal distribution of neural currents responsible of the recorded ECoG signals, which is ill-posed. Only in the last few years novel computational methods to solve this inverse problem have been developed. This study describes a beamformer method for ECoG source modeling. First, we extended the OpenMEEG software with a new method to estimate the lead-field matrix which maps the neural currents onto the sensors space. We further conducted 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. Finally, we localized sources via a Linear Constraint Minimum Variance (LCMV) beamformer method applied to both synthetic and real data.

Source modeling of EEG data is an important tool for both neuroscience and clinical applications, such as epilepsy. Despite their simplicity, multiple dipole models remain highly desirable to explain neural sources. However, estimating dipole models from EEG time-series remains a difficult task, mainly due to the ill-posedness of the inverse problem and to the fact that the number of dipoles is usually not known a priori. Recently, a Bayesian approach has been presented for multiple dipole estimation of MEG/EEG data [1,2]: the method estimates simultaneously the number of dipoles and the dipole parameters, by exploring a multiple dipole state space with a Monte Carlo procedure combined with a tempering schedule [3]. Here, we present the first validation of this method with experimental EEG data.

The inverse problem of MEG aims at estimating electromagnetic cerebral activity from measurements of the magnetic fields outside the head. After formulating the problem within the Bayesian framework, a hierarchical conditionally Gaussian prior model is introduced, including a physiologically inspired prior model that takes into account the preferred directions of the source currents. The hyperparameter vector consists of prior variances of the dipole moments, assumed to follow a non-conjugate gamma distribution with variable scaling and shape parameters. A point estimate of both dipole moments and their variances can be computed using an iterative alternating sequential updating algorithm, which is shown to be globally convergent. The numerical solution is based on computing an approximation of the dipole moments using a Krylov subspace iterative linear solver equipped with statistically inspired preconditioning and a suitable termination rule. The shape parameters of the model are shown to control the focality, and furthermore, using an empirical Bayes argument, it is shown that the scaling parameters can be naturally adjusted to provide a statistically well justified depth sensitivity scaling. The validity of this interpretation is verified through computed numerical examples. Also, a computed example showing the applicability of the algorithm to analyze realistic time series data is presented.

Magnetoencephalopgraphy (MEG) is a non-invasive functional imaging modality for mapping cerebral electromagnetic activity from measurements of the weak magnetic field that it generates. It is well known that the MEG inverse problem, i.e. the problem of identifying electric currents from the induced magnetic fields, is a severely underdetermined problem and, without complementary prior information, no unique solution can be found. In the literature, many regularization techniques were proposed. In particular, optimization-based methods usually explain the data by superficial sources even when the activity is deep in the brain. A way to make easier the identification of deep focal sources is the use of depth weighting. We revisit the MEG inverse problem, regularization and depth weighting from a Bayesian point of view by hierarchical models: The primary unknown is the discretized current density inside the head, and we postulate a conditionally Gaussian anatomical prior model. In this model, each current element, or dipole, has a preferred, albeit not fixed, direction that is extracted from the anatomical data of the subject. The variance of each dipole is not fixed a priori, but modeled itself as a random variable described by its hyperprior density. The hypermodel is then used to build a fast iterative algorithm with the novel feature that their parameters are determined using an empirical Bayes approach. The hypermodel provides a very natural Bayesian interpretation for sensitivity weighting, and the parameters in the hyperprior provide a tool for controlling the focality of the solution, thus leading to a flexible algorithm that can handle both sparse and distributed sources. To demonstrate the effects of different parameter selections under optimal conditions, we test the algorithm on synthetic but realistic data. The tests show that the hierarchical Bayesian models combined with linear algebraic methods provide a versatile framework to develop robust and flexible numerical methods, and are able to overcome some of the limitations of standard regularization techniques, for instance deep source localization. The proposed algorithm is computationally efficient, gives a direct control of how well the computed estimates satisfy the data and is designed to easily accommodate different types of prior information.

Electrocorticography (ECoG) is a neurophysiological modality that measures the distribution of electrical potentials, associated with either spontaneous or evoked neural activity, by means of electrodes grids implanted close to the cortical surface. A full interpretation of ECoG data, however, requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of neural currents responsible for the recorded signals. Only in the last few years some methods have been proposed to solve this inverse problem [1]. This study addresses the ECoG source modelling using a beamformer method. First, we compute the lead-field matrix which maps the neural currents onto the sensors space: a novel routine for the computation of the lead-field matrix, based on the tools provided by the OpenMEEG framework, was used [2]. The ECoG source-modeling problem requires to invert this matrix by means of a regularization method which reduces its intrinsic numerical instability; thus, we perform an analysis of the condition number of the lead-field matrix which provides quantitative information on the numerical instability of the problem, independently of the kind of inversion algorithm applied. Finally, we provide quantitative results for source modeling using a Linear Constraint Minimum Variance (LCMV) beamformer. The validation of the effectiveness of beamforming in ECoG is performed both with synthetic data and with experimental data recorded during a rapid visual categorization task.

The brain is a connected network, requiring complex-system measures to describe its organization principles. The normalized compression distance (NCD) [1] is a parameter -free, quasi universal similarity measure that estimates the information shared by two signals comparing the compression length of one signal given the other. Here, we aim at testing whether this new measure is a suitable quantifier of the functional connectivity between cortical regions. In particular, we tested whether NCD between homologous hemispheric regions is smaller (higher connectivity) in the same person than across different people, if it is smaller in the dominant hemisphere and if it depends on age. We used the Functional Source Separation (FSS) [2] algorithm on magnetoencephalographic (MEG) data in order to identify functionally homologous areas in the two hemispheres devoted to the somatosensory contra-lateral hand representation (FS_S1) in 28 healthy people. Therefore, we calculated NCD between the left and right FS_S1s activities at rest. We found that NCD 1) between left and right FS_S1s of the same person was smaller than across different people (p<10-7consistently) 2) was smaller within the left dominant hemisphere than within the non dominant right one (p=3*10 7) and 3) became more variable in older than younger people (p=.01). This preliminary work shows that NCD, which measures the similarity of neuronal source activities via their compression sizes, displays an excellent ability in quantifying the similarity among neuronal activities, catching the maximal similarity expected for functionally homologous cortical areas of the two hemispheres. Thus, NCD seems a good candidate for two-nodes functional connectivity measure in resting state, able to overcome the limitations intrinsic to the classical Fourier or autoregressive estimates in assessing dynamics properties of the brain connectivity.

The wind effect on river water quality was illustrated by means of thermohaline measurements carried out in the Tiber River in May 2012. The survey was carried out using a boat, in stations located in the two Tiber branches: Fiumara Grande and Traiano Canal. Thermohaline variables (salinity and temperature) were used to describe the water-type patterns and to define the salt-wedge position. Although the river flow rate was rather high, saltwater intrusion happened. Wind data suggested that the more probable cause of salt-wedge intrusion was the wind action. Especially wind speeds higher than 4 m/s are able to dominate the sea current at surface layers, determining an increase in the sea level. Therefore, westerly winds determined a seawater inflow in the river mouths.

It is generally accepted that visual perception results from the activation of a feed-forward hierarchy of areas, leading to increasingly complex representations. Here we present evidence for a fundamental role of backward projections to the occipito-temporal region for understanding conceptual object properties. The evidence is based on two studies. In the first study, using high-density EEG, we showed that during the observation of how objects are used there is an early activation of occipital and temporal areas, subsequently reaching the pole of the temporal lobe, and a late reactivation of the visual areas. In the second study, using transcranial magnetic stimulation over the occipital lobe, we showed a clear impairment in the accuracy of recognition of how objects are used during both early activation and, most importantly, late occipital reactivation. These findings represent strong neurophysiological evidence that a top-down mechanism is fundamental for understanding conceptual object properties, and suggest that a similar mechanism might be also present for other higher-order cognitive functions.