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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.

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.