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Resting state networks (RSNs) are sets of brain regions exhibiting temporally coherent activity fluctuations in the absence of imposed task structure. RSNs have been extensively studied with fMRI in the infra-slow frequency range (nominally <10−1Hz). The topography of fMRI RSNs reflects stationary temporal correlation over minutes. However, neuronal communication occurs on a much faster time scale, at frequencies nominally in the range of 100–102Hz. We examined phase-shifted interactions in the delta (2–3.5Hz), theta (4–7Hz), alpha (8–12Hz) and beta (13–30Hz) frequency bands of resting-state source space MEG signals. These analyses were conducted between nodes of the dorsal attention network (DAN), one of the most robust RSNs, and between the DAN and other networks. Phase shifted interactions were mapped by the multivariate interaction measure (MIM), a measure of true interaction constructed from the maximization of imaginary coherency in the virtual channels comprised of voxel signals in source space. Non-zero-phase interactions occurred between homologous left and right hemisphere regions of the DAN in the delta and alpha frequency bands. Even stronger non-zero-phase interactions were detected between networks. Visual regions bilaterally showed phase-shifted interactions in the alpha band with regions of the DAN. Bilateral somatomotor regions interacted with DAN nodes in the beta band. These results demonstrate the existence of consistent, frequency specific phase-shifted interactions on a millisecond time scale between cortical regions within RSN as well as across RSNs. •We investigate the MEG correlates of fMRI-defined RSNs using imaginary coherence.•We demonstrate MIM, a method for mapping resting state coherent interactions.•MIM offers reduced sensitivity to field spread in source space.•We demonstrate frequency specific interactions between cortical loci.•Specific RSNs investigated here include DAN, somatomotor and visual systems.

We present a novel approach to the third order spectral analysis, commonly called bispectral analysis, of electroencephalographic (EEG) and magnetoencephalographic (MEG) data for studying cross-frequency functional brain connectivity. The main obstacle in estimating functional connectivity from EEG and MEG measurements lies in the signals being a largely unknown mixture of the activities of the underlying brain sources. This often constitutes a severe confounder and heavily affects the detection of brain source interactions. To overcome this problem, we previously developed metrics based on the properties of the imaginary part of coherency. Here, we generalize these properties from the linear to the nonlinear case. Specifically, we propose a metric based on an antisymmetric combination of cross-bispectra, which we demonstrate to be robust to mixing artifacts. Moreover, our metric provides complex-valued quantities that give the opportunity to study phase relationships between brain sources. The effectiveness of the method is first demonstrated on simulated EEG data. The proposed approach shows a reduced sensitivity to mixing artifacts when compared with a traditional bispectral metric. It also exhibits a better performance in extracting phase relationships between sources than the imaginary part of the cross-spectrum for delayed interactions. The method is then applied to real EEG data recorded during resting state. A cross-frequency interaction is observed between brain sources at 10Hz and 20Hz, i.e., for alpha and beta rhythms. This interaction is then projected from signal to source level by using a fit-based procedure. This approach highlights a 10–20Hz dominant interaction localized in an occipito-parieto-central network. •We investigate cross-frequency coupling in EEG using third order spectral analysis.•We generalize properties of the imaginary part of cross-spectra to a nonlinear case.•The antisymmetrized cross-bispectra offer reduced sensitivity to mixing artifacts.•We demonstrate nonlinear coupling between cortical loci at specific frequency pairs.

The Human Connectome Project (HCP) seeks to map the structural and functional connections between network elements in the human brain. Magnetoencephalography (MEG) provides a temporally rich source of information on brain network dynamics and represents one source of functional connectivity data to be provided by the HCP. High quality MEG data will be collected from 50 twin pairs both in the resting state and during performance of motor, working memory and language tasks. These data will be available to the general community. Additionally, using the cortical parcellation scheme common to all imaging modalities, the HCP will provide processing pipelines for calculating connection matrices as a function of time and frequency. Together with structural and functional data generated using magnetic resonance imaging methods, these data represent a unique opportunity to investigate brain network connectivity in a large cohort of normal adult human subjects. The analysis pipeline software and the dynamic connectivity matrices that it generates will all be made freely available to the research community. •Millisecond resolution dynamic connectivity will be provided to the HCP using MEG•MEG data will be collected at rest and during motor and cognitive task performance•Task paradigms are matched to tfMRI designs to provide cross-modal information•A number of connectivity metrics will be implemented at both sensor and source levels•Automated processing pipelines will be made publically available

The DARWIN observatory is a proposed next-generation experiment to search for particle dark matter and for the neutrinoless double beta decay of 136 Xe. Out of its 50 t total natural xenon inventory, 40 t will be the active target of a time projection chamber which thus contains about 3.6 t of 136 Xe. Here, we show that its projected half-life sensitivity is 2.4 × 10 27 year , using a fiducial volume of 5 t of natural xenon and 10 year of operation with a background rate of less than 0.2 events/(t  ·  year) in the energy region of interest. This sensitivity is based on a detailed Monte Carlo simulation study of the background and event topologies in the large, homogeneous target. DARWIN will be comparable in its science reach to dedicated double beta decay experiments using xenon enriched in 136 Xe.

The major limitation for the acquisition of high-quality magnetoencephalography (MEG) recordings is the presence of disturbances of physiological and technical origins: eye movements, cardiac signals, muscular contractions, and environmental noise are serious problems for MEG signal analysis. In the last years, multi-channel MEG systems have undergone rapid technological developments in terms of noise reduction, and many processing methods have been proposed for artifact rejection. Independent component analysis (ICA) has already shown to be an effective and generally applicable technique for concurrently removing artifacts and noise from the MEG recordings. However, no standardized automated system based on ICA has become available so far, because of the intrinsic difficulty in the reliable categorization of the source signals obtained with this technique. In this work, approximate entropy (ApEn), a measure of data regularity, is successfully used for the classification of the signals produced by ICA, allowing for an automated artifact rejection. The proposed method has been tested using MEG data sets collected during somatosensory, auditory and visual stimulation. It was demonstrated to be effective in attenuating both biological artifacts and environmental noise, in order to reconstruct clear signals that can be used for improving brain source localizations.

The selection of low-radioactive construction materials is of utmost importance for the success of low-energy rare event search experiments. Besides radioactive contaminants in the bulk, the emanation of radioactive radon atoms from material surfaces attains increasing relevance in the effort to further reduce the background of such experiments. In this work, we present the 222Rn emanation measurements performed for the XENON1T dark matter experiment. Together with the bulk impurity screening campaign, the results enabled us to select the radio-purest construction materials, targeting a 222Rn activity concentration of 10μBq/kg in 3.2t of xenon. The knowledge of the distribution of the 222Rn sources allowed us to selectively eliminate problematic components in the course of the experiment. The predictions from the emanation measurements were compared to data of the 222Rn activity concentration in XENON1T. The final 222Rn activity concentration of (4.5±0.1)μBq/kg in the target of XENON1T is the lowest ever achieved in a xenon dark matter experiment.

We studied MEG and fMRI responses to electric median and tibial nerve stimulation in five healthy volunteers. The aim was to compare the results with those of a previous study using only fMRI on the primary and secondary somatosensory cortices in which the somatotopic organization of SII was observed with fMRI. In the present work we focus on the comparison between fMRI activation and MEG equivalent current dipole (ECD) localizations in the SII area. The somatotopic organization of SII was confirmed by MEG, with the upper limb areas located more anteriorly and more inferiorly than the lower limb areas. In addition a substantial consistency of the ECD locations with the areas of fMRI activation was observed, with an average mismatch of about 1 cm. MEG ECDs and fMRI activation areas showed comparable differences in SI.

In this MEG study, we investigated cortical alpha/sigma and beta ERD/ERS induced by median nerve stimulation to extend previous evidence on different resonant and oscillatory behavior of SI and SII (NeuroImage 13 [2001] 662). Here, we tested whether simple somatosensory stimulation could induce a distinctive sequence of alpha/sigma and beta ERD/ERS over SII compared to SI. We found that for both alpha/sigma (around 10 Hz) and beta (around 20 Hz) rhythms, the latencies of ERD and ERS were larger in bilateral SII than in contralateral SI. In addition, the peak amplitude of alpha/sigma and beta ERS was smaller in bilateral SII than in contralateral SI. These results indicate a delayed and prolonged activation of SII responses, reflecting a protracted information elaboration possibly related to SII higher order role in the processing of somatosensory information. This temporal dynamics of alpha/sigma and beta rhythms may be related to a sequential activation scheme of SI and SII during the somatosensory information processes. Future studies should evaluate in SII the possible different functional significance of alpha/sigma with respect to beta rhythms during somatosensory processing.

We describe the purification of xenon from traces of the radioactive noble gas radon using a cryogenic distillation column. The distillation column was integrated into the gas purification loop of the XENON100 detector for online radon removal. This enabled us to significantly reduce the constant 222Rn background originating from radon emanation. After inserting an auxiliary 222Rn emanation source in the gas loop, we determined a radon reduction factor of R>27 (95% C.L.) for the distillation column by monitoring the 222Rn activity concentration inside the XENON100 detector.

The XENON1T experiment at the Laboratori Nazionali del Gran Sasso (LNGS) is the first WIMP dark matter detector operating with a liquid xenon target mass above the ton-scale. Out of its 3.2 t liquid xenon inventory, 2.0 t constitute the active target of the dual-phase time projection chamber. The scintillation and ionization signals from particle interactions are detected with low-background photomultipliers. This article describes the XENON1T instrument and its subsystems as well as strategies to achieve an unprecedented low background level. First results on the detector response and the performance of the subsystems are also presented.