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Background: functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool to study cortical activity during movement and gait that requires further validation. This study aimed to assess (1) whether fNIRS can detect the difficult-to-measure leg area of the primary motor cortex (M1) and distinguish it from the hand area; and (2) whether fNIRS can differentiate between automatic (i.e., not requiring one's attention) and non-automatic movement processes. Special attention was attributed to systemic artifacts (i.e., changes in blood pressure, heart rate, breathing) which were assessed and corrected by short channels, i.e., fNIRS channels which are mainly sensitive to superficial scalp hemodynamics., Methods: Twenty-three seated, healthy participants tapped four fingers on a keyboard or tapped the right foot on four squares on the floor in a specific order given by a 12-digit sequence (e.g., 434141243212). Two different sequences were executed: a beforehand learned (i.e., automatic) version and a newly learned (i.e., non-automatic) version. A 36-channel fNIRS device including 12 short channels covered multiple motor-related cortical areas including M1. The fNIRS data were analyzed with a general linear model (GLM). Correlation between the expected functional hemodynamic responses (i.e. task regressor) and the short channels (i.e. nuisance regressors), necessitated performing a separate short channel regression instead of integrating them in the GLM., Results: Consistent with the M1 somatotopy, we found significant HbO increases of very large effect size in the lateral M1 channels during finger tapping (Cohen's d = 1.35, p<0.001) and significant HbO increases of moderate effect size in the medial M1 channels during foot tapping (Cohen's d = 0.8, p<0.05). The cortical activity differences between automatic and non-automatic tasks were not significantly different. Importantly, leg movements produced large systemic fluctuations, which were adequately removed by the use of all available short channels., Discussion: Our results indicate that fNIRS is sensitive to leg activity in M1, though the sensitivity is lower than for finger activity and requires rigorous correction for systemic fluctuations. We furthermore highlight that systemic artifacts may result in an unreliable GLM analysis when short channels show signals that are similar to the expected hemodynamic responses., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023. Published by Elsevier Inc.)

Source reconstruction of magnetoencephalography (MEG) has been used to assess brain reorganization after brain damage, such as stroke. Lesions result in parts of the brain having an electrical conductivity that differs from the normal values. The effect this has on the forward solutions (i.e., the propagation of electric currents and magnetic fields generated by cortical activity) is well predictable. However, their influence on source localization results is not well characterized and understood. This is specifically a concern for patient studies with asymmetric (i.e., within one hemisphere) lesions focusing on asymmetric and lateralized brain activity, such as language. In particular, it is good practice to consider the level of geometrical detail that is necessary to compute and interpret reliable source reconstruction results. To understand the effect of lesions on source estimates and propose recommendations to researchers working with clinical data, in this study we consider the trade off between improved accuracy and the additional effort to compute more realistic head models, with the aim to answer the question whether the additional effort is worth it. We simulated and analyzed the effects of a stroke lesion (i.e., an asymmetrically distributed CSF-filled cavity) in the head model with three different sizes and locations when performing MEG source reconstruction using a finite element method (FEM). We compared the effect of the lesion with a homogeneous head model that neglects the lesion. We computed displacement and attenuation/amplification maps to quantify the localization errors and signal magnitude modulation. We conclude that brain lesions leading to asymmetrically distributed CSF-filled cavities should be modeled when performing MEG source reconstruction, especially when investigating deep sources or post-stroke hemispheric lateralization of functions. The strongest effects are not only visible in perilesional areas, but can extend up to 20 mm from the lesion. Bigger lesions lead to stronger effects impacting larger areas, independently from the lesion location. Lastly, we conclude that more priority should be given to usability and accessibility of the required computational tools, to allow researchers with less technical expertise to use the improved methods that are available but currently not widely adopted yet., (Copyright © 2022. Published by Elsevier Inc.)

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research., (Copyright © 2022. Published by Elsevier Inc.)

The increasing requirements for adoption of FAIR data management and sharing original research data from neuroimaging studies can be at odds with protecting the anonymity of the research participants due to the person-identifiable anatomical features in the data. We propose a solution to this dilemma for anatomical MRIs used in MEG source analysis. In MEG analysis, the channel-level data is reconstructed to the source-level using models derived from anatomical MRIs. Sharing data, therefore, requires sharing the anatomical MRI to replicate the analysis. The suggested solution is to replace the individual anatomical MRIs with individualised warped templates that can be used to carry out the MEG source analysis and that provide sufficient geometrical similarity to the original participants' MRIs. First, we demonstrate how the individualised template warping can be implemented with one of the leading open-source neuroimaging analysis toolboxes. Second, we compare results from four different MEG source reconstruction methods performed with an individualised warped template to those using the participant's original MRI. While the source reconstruction results are not numerically identical, there is a high similarity between the results for single dipole fits, dynamic imaging of coherent sources beamforming, and atlas-based virtual channel beamforming. There is a moderate similarity between minimum-norm estimates, as anticipated due to this method being anatomically constrained and dependent on the exact morphological features of the cortical sheet. We also compared the morphological features of the warped template to those of the original MRI. These showed a high similarity in grey matter volume and surface area, but a low similarity in the average cortical thickness and the mean folding index within cortical parcels. Taken together, this demonstrates that the results obtained by MEG source reconstruction can be preserved with the warped templates, whereas the anatomical and morphological fingerprint is sufficiently altered to protect the anonymity of research participants. In cases where participants consent to sharing anatomical MRI data, it remains preferable to share the original defaced data with an appropriate data use agreement. In cases where participants did not consent to share their MRIs, the individualised warped MRI template offers a good compromise in sharing data for reuse while retaining anonymity for research participants., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Beamforming is a popular method for functional source reconstruction using magnetoencephalography (MEG) and electroencephalography (EEG) data. Beamformers, which were first proposed for MEG more than two decades ago, have since been applied in hundreds of studies, demonstrating that they are a versatile and robust tool for neuroscience. However, certain characteristics of beamformers remain somewhat elusive and there currently does not exist a unified documentation of the mathematical underpinnings and computational subtleties of beamformers as implemented in the most widely used academic open source software packages for MEG analysis (Brainstorm, FieldTrip, MNE, and SPM). Here, we provide such documentation that aims at providing the mathematical background of beamforming and unifying the terminology. Beamformer implementations are compared across toolboxes and pitfalls of beamforming analyses are discussed. Specifically, we provide details on handling rank deficient covariance matrices, prewhitening, the rank reduction of forward fields, and on the combination of heterogeneous sensor types, such as magnetometers and gradiometers. The overall aim of this paper is to contribute to contemporary efforts towards higher levels of computational transparency in functional neuroimaging., (Copyright © 2021. Published by Elsevier Inc.)

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflicts of interest in relation to this manuscript., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Learning to associate written letters with speech sounds is crucial for the initial phase of acquiring reading skills. However, little is known about the cortical reorganization for supporting letter-speech sound learning, particularly the brain dynamics during the learning of grapheme-phoneme associations. In the present study, we trained 30 Finnish participants (mean age: 24.33 years, SD: 3.50 years) to associate novel foreign letters with familiar Finnish speech sounds on two consecutive days (first day ​~ ​50 ​min; second day ​~ ​25 ​min), while neural activity was measured using magnetoencephalography (MEG). Two sets of audiovisual stimuli were used for the training in which the grapheme-phoneme association in one set (Learnable) could be learned based on the different learning cues provided, but not in the other set (Control). The learning progress was tracked at a trial-by-trial basis and used to segment different learning stages for the MEG source analysis. The learning-related changes were examined by comparing the brain responses to Learnable and Control uni/multi-sensory stimuli, as well as the brain responses to learning cues at different learning stages over the two days. We found dynamic changes in brain responses related to multi-sensory processing when grapheme-phoneme associations were learned. Further, changes were observed in the brain responses to the novel letters during the learning process. We also found that some of these learning effects were observed only after memory consolidation the following day. Overall, the learning process modulated the activity in a large network of brain regions, including the superior temporal cortex and the dorsal (parietal) pathway. Most interestingly, middle- and inferior-temporal regions were engaged during multi-sensory memory encoding after the cross-modal relationship was extracted from the learning cues. Our findings highlight the brain dynamics and plasticity related to the learning of letter-speech sound associations and provide a more refined model of grapheme-phoneme learning in reading acquisition., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes. When applied carefully to MEG data with a typical SNR (3-15 ​dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Source modelling in magnetoencephalography (MEG) requires precise co-registration of the sensor array and the anatomical structure of the measured individual's head. In conventional MEG, the positions and orientations of the sensors relative to each other are fixed and known beforehand, requiring only localization of the head relative to the sensor array. Since the sensors in on-scalp MEG are positioned on the scalp, locations of the individual sensors depend on the subject's head shape and size. The positions and orientations of on-scalp sensors must therefore be measured at every recording. This can be achieved by inverting conventional head localization, localizing the sensors relative to the head - rather than the other way around. In this study we present a practical method for localizing sensors using magnetic dipole-like coils attached to the subject's head. We implement and evaluate the method in a set of on-scalp MEG recordings using a 7-channel on-scalp MEG system based on high critical temperature superconducting quantum interference devices (high-T c SQUIDs). The method allows individually localizing the sensor positions, orientations, and responsivities with high accuracy using only a short averaging time (≤ 2 ​mm, < 3° and < 3%, respectively, with 1-s averaging), enabling continuous sensor localization. Calibrating and jointly localizing the sensor array can further improve the accuracy of position and orientation (< 1 ​mm and < 1°, respectively, with 1-s coil recordings). We demonstrate source localization of on-scalp recorded somatosensory evoked activity based on co-registration with our method. Equivalent current dipole fits of the evoked responses corresponded well (within 4.2 ​mm) with those based on a commercial, whole-head MEG system., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the preprocessing method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field., (Copyright © 2018 Elsevier Inc. All rights reserved.)

The ability to monitor the success of cognitive processing is referred to as metacognition. Studies of metacognition typically probe post-decision judgments of confidence, showing that we can report on the success of wide range of cognitive processes. Much less is known about our ability to monitor and report on the degree of top-down attention, an ability of paramount importance in tasks requiring sustained attention. However, it has been repeatedly shown that the degree and locus of top-down attention modulates alpha (8-14Hz) power in sensory cortices. In this study we investigated whether self-reported ratings of attention are reflected by sensory alpha power, independent from confidence and task difficulty. Subjects performed a stair-cased tactile discrimination task requiring sustained somatosensory attention. Each discrimination response was followed by a rating of their attention at the moment of stimulation, or their confidence in the discrimination response. MEG was used to estimate trial-by-trial alpha power preceding stimulation. Staircasing of task-difficulty successfully equalized performance between conditions. Both attention and confidence ratings reflected subsequent discrimination performance. Task difficulty specifically influenced confidence ratings. As expected, specifically attention ratings, but not confidence ratings, correlated negatively with contralateral somatosensory alpha power preceding tactile stimuli. Taken together, these results demonstrate that the degree of attention can be subjectively experienced and reported accurately, independent from task difficulty and knowledge about task performance., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

ConnectomeDB is a database for housing and disseminating data about human brain structure, function, and connectivity, along with associated behavioral and demographic data. It is the main archive and dissemination platform for data collected under the WU-Minn consortium Human Connectome Project. Additional connectome-style study data is and will be made available in the database under current and future projects, including the Connectome Coordination Facility. The database currently includes multiple modalities of magnetic resonance imaging (MRI) and magnetoencephalograpy (MEG) data along with associated behavioral data. MRI modalities include structural, task, resting state and diffusion. MEG modalities include resting state and task. Imaging data includes unprocessed, minimally preprocessed and analysis data. Imaging data and much of the behavioral data are publicly available, subject to acceptance of data use terms, while access to some sensitive behavioral data is restricted to qualified investigators under a more stringent set of terms. ConnectomeDB is the public side of the WU-Minn HCP database platform. As such, it is geared towards public distribution, with a web-based user interface designed to guide users to the optimal set of data for their needs and a robust backend mechanism based on the commercial Aspera fasp service to enable high speed downloads. HCP data is also available via direct shipment of hard drives and Amazon S3., (Copyright © 2015 Elsevier Inc. All rights reserved.)

It has been demonstrated that alpha activity is lateralized when attention is directed to the left or right visual hemifield. We investigated whether real-time neurofeedback training of the alpha lateralization enhances participants' ability to modulate posterior alpha lateralization and causes subsequent short-term changes in visual detection performance. The experiment consisted of three phases: (i) pre-training assessment, (ii) neurofeedback phase and (iii) post-training assessment. In the pre- and post-training phases we measured the threshold to covertly detect a cued faint Gabor stimulus presented in the left or right hemifield. During magnetoencephalography (MEG) neurofeedback, two face stimuli superimposed with noise were presented bilaterally. Participants were cued to attend to one of the hemifields. The transparency of the superimposed noise and thus the visibility of the stimuli were varied according to the momentary degree of hemispheric alpha lateralization. In a double-blind procedure half of the participants were provided with sham feedback. We found that hemispheric alpha lateralization increased with the neurofeedback training; this was mainly driven by an ipsilateral alpha increase. Surprisingly, comparing pre- to post-training, detection performance decreased for a Gabor stimulus presented in the hemifield that was un-attended during neurofeedback. This effect was not observed in the sham group. Thus, neurofeedback training alters alpha lateralization, which in turn decreases performances in the untrained hemifield. Our findings suggest that alpha oscillations play a causal role for the allocation of attention. Furthermore, our neurofeedback protocol serves to reduce the detection of unattended visual information and could therefore be of potential use for training to reduce distractibility in attention deficit patients, but also highlights that neurofeedback paradigms can have negative impact on behavioral performance and should be applied with caution., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Increased beta (13-30 Hz) oscillatory synchrony in basal ganglia-cortical circuits is a physiological characteristic of Parkinson's disease (PD). While the function of the beta rhythm is unknown, there is evidence that its modulation serves a predictive role, in preparation of future actions. We investigate the relation between predictive beta modulation and entrainment of brain oscillations in a task inviting behavioral entrainment by a regular task structure. MEG was recorded during a serial choice response task, in a group of 12 PD patients and 12 control subjects. In one condition, the reaction stimuli allowed for temporal preparation only (random condition), while in a second condition (predictable condition) the reaction stimuli allowed both temporal and effector preparation. Reaction times were identical between groups, and both groups benefited equally from the known effector side in the predictable condition. Analysis of oscillatory activity, by contrast, revealed marked differences between groups. In patients, the proportion of preparatory beta power desynchronization preceding the reaction stimuli was significantly smaller than in controls, while the proportion of beta desynchronization following the events was larger. In addition to this shift from prospective to reactive modulation of beta-band oscillations, patients showed a trend to reduced motor cortical pre-stimulus delta phase synchronization, and later gamma power synchronization than controls. Delta phase synchronization was, furthermore, significantly correlated with predictive beta desynchronization, supporting the relevance of hierarchical coupling between oscillations of different frequencies for the analysis of oscillatory changes in PD. Together, these features of task-related oscillatory activity indicate that entrainment fails to engender the same predictive mode of motor activation in PD patients as in healthy controls., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Neurophysiological studies in non-human primates have provided evidence for simultaneous activation of competing responses in the (pre)motor cortex. Human evidence, however, is limited, partly because experimental approaches have often mapped competing responses to paired effectors represented in opposite hemispheres, which restricts the analysis to between-hemisphere comparisons and allows simultaneous execution. A demonstration of competition between different movement plans in the motor cortex is more compelling when simultaneous execution of the alternative responses is ruled out and they are represented in one motor cortex. Therefore, in the current MEG study we have used a unimanual Eriksen flanker paradigm with alternative responses assigned to flexion and extension of the right index finger, activating different direction-sensitive neurons within the finger representation area of the same motor cortex. Results showed that for stimuli eliciting response competition the pre-response motor cortex beta-band (17-29 Hz) power decreased stronger than for stimuli that did not induce response competition. Furthermore, response competition elicited an additional pre-response mid-frontal high-gamma band (60-90 Hz) power increase. Finally, larger gamma-band effect sizes correlated with greater behavioral response delay induced by response competition. Taken together, our results provide evidence for co-activation of competing responses in the human brain, consistent with evidence from non-human primates., (Copyright © 2013 Elsevier Inc. All rights reserved.)

The current state of empirical investigations refers to consciousness as an all-or-none phenomenon. However, a recent theoretical account opens up this perspective by proposing a partial level (between nil and full) of conscious perception. In the well-studied case of single-word reading, short-lived exposure can trigger incomplete word-form recognition wherein letters fall short of forming a whole word in one's conscious perception thereby hindering word-meaning access and report. Hence, the processing from incomplete to complete word-form recognition straightforwardly mirrors a transition from partial to full-blown consciousness. We therefore hypothesized that this putative functional bottleneck to consciousness (i.e. the perceptual boundary between partial and full conscious perception) would emerge at a major key hub region for word-form recognition during reading, namely the left occipito-temporal junction. We applied a real-time staircase procedure and titrated subjective reports at the threshold between partial (letters) and full (whole word) conscious perception. This experimental approach allowed us to collect trials with identical physical stimulation, yet reflecting distinct perceptual experience levels. Oscillatory brain activity was monitored with magnetoencephalography and revealed that the transition from partial-to-full word-form perception was accompanied by alpha-band (7-11 Hz) power suppression in the posterior left occipito-temporal cortex. This modulation of rhythmic activity extended anteriorly towards the visual word form area (VWFA), a region whose selectivity for word-forms in perception is highly debated. The current findings provide electrophysiological evidence for a functional bottleneck to consciousness thereby empirically instantiating a recently proposed partial perspective on consciousness. Moreover, the findings provide an entirely new outlook on the functioning of the VWFA as a late bottleneck to full-blown conscious word-form perception., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Magnetoencephalography (MEG) is measured above the head, which makes it sensitive to variations of the head position with respect to the sensors. Head movements blur the topography of the neuronal sources of the MEG signal, increase localization errors, and reduce statistical sensitivity. Here we describe two novel and readily applicable methods that compensate for the detrimental effects of head motion on the statistical sensitivity of MEG experiments. First, we introduce an online procedure that continuously monitors head position. Second, we describe an offline analysis method that takes into account the head position time-series. We quantify the performance of these methods in the context of three different experimental settings, involving somatosensory, visual and auditory stimuli, assessing both individual and group-level statistics. The online head localization procedure allowed for optimal repositioning of the subjects over multiple sessions, resulting in a 28% reduction of the variance in dipole position and an improvement of up to 15% in statistical sensitivity. Offline incorporation of the head position time-series into the general linear model resulted in improvements of group-level statistical sensitivity between 15% and 29%. These tools can substantially reduce the influence of head movement within and between sessions, increasing the sensitivity of many cognitive neuroscience experiments., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Magnetoencephalographic (MEG) recordings are a rich source of information about the neural dynamics underlying cognitive processes in the brain, with excellent temporal and good spatial resolution. In recent years there have been considerable advances in MEG hardware developments and methods. Sophisticated analysis techniques are now routinely applied and continuously improved, leading to fascinating insights into the intricate dynamics of neural processes. However, the rapidly increasing level of complexity of the different steps in a MEG study make it difficult for novices, and sometimes even for experts, to stay aware of possible limitations and caveats. Furthermore, the complexity of MEG data acquisition and data analysis requires special attention when describing MEG studies in publications, in order to facilitate interpretation and reproduction of the results. This manuscript aims at making recommendations for a number of important data acquisition and data analysis steps and suggests details that should be specified in manuscripts reporting MEG studies. These recommendations will hopefully serve as guidelines that help to strengthen the position of the MEG research community within the field of neuroscience, and may foster discussion in order to further enhance the quality and impact of MEG research., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Phase-synchronization is a manifestation of interaction between neuronal groups measurable from LFP, EEG or MEG signals, however, volume conduction can cause the coherence and the phase locking value to spuriously increase. It has been shown that the imaginary component of the coherency (ImC) cannot be spuriously increased by volume-conduction of independent sources. Recently, it was proposed that the phase lag index (PLI), which estimates to what extent the phase leads and lags between signals from two sensors are nonequiprobable, improves on the ImC. Compared to ImC, PLI has the advantage of being less influenced by phase delays. However, sensitivity to volume-conduction and noise, and capacity to detect changes in phase-synchronization, is hindered by the discontinuity of the PLI, as small perturbations turn phase lags into leads and vice versa. To solve this problem, we introduce a related index, namely the weighted phase lag index (WPLI). Differently from PLI, in WPLI the contribution of the observed phase leads and lags is weighted by the magnitude of the imaginary component of the cross-spectrum. We demonstrate two advantages of the WPLI over the PLI, in terms of reduced sensitivity to additional, uncorrelated noise sources and increased statistical power to detect changes in phase-synchronization. Another factor that can affect phase-synchronization indices is sample-size bias. We show that, when directly estimated, both PLI and the magnitude of the ImC have typically positively biased estimators. To solve this problem, we develop an unbiased estimator of the squared PLI, and a debiased estimator of the squared WPLI., (Copyright © 2011 Elsevier Inc. All rights reserved.)

A single brief visual stimulus accompanied by two brief tactile stimuli is frequently perceived incorrectly as two flashes, a phenomenon called double-flash illusion (DFI). We investigated whether the DFI is accompanied by changes in rhythmic neuronal activity, using magnetoencephalography in human subjects. Twenty-two subjects received visuo-tactile stimulation and reported the number of perceived visual stimuli. We sorted trials with identical physical stimulation according to the reported subjective percept and assessed differences in spectral power in somatosensory and occipital sensors. In DFI trials, occipital sensors displayed a contralateral enhancement of gamma-band (80-140 Hz) activity in response to stimulation. In somatosensory sensors, the DFI was associated with an increase of spectral power for low frequencies (5-17.5 Hz) around stimulation and a decrease of spectral power in the 22.5-30 Hz range between 450 and 750 ms post-stimulation. In summary, several components of rhythmic activity predicted variable subjective experience for constant physical stimulation. Notably, the enhanced occipital gamma-band activity during DFI was similar in time and frequency extent to the somatosensory gamma-band response to tactile stimulation. We speculate that the DFI might therefore occur when the somatosensory gamma-response is transmitted to visual cortex. This transmission might be supported by the observed modulations in low-frequency activity., (Copyright © 2010 Elsevier Inc. All rights reserved.)

In October 2009, a group of neurologists, neurosurgeons, computational neuroscientists, and engineers congregated to present novel developments transforming human electrocorticography (ECoG) beyond its established relevance in clinical epileptology. The contents of the proceedings advanced the role of ECoG in seizure detection and prediction, neurobehavioral research, functional mapping, and brain-computer interface technology. The meeting established the foundation for future work on the methodology and application of surface brain recordings., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Groups of activated neurons typically synchronize in the gamma-frequency band (30-100 Hz), and gamma-band synchronization has been implicated in numerous cognitive functions. Those functions are ultimately expressed as behavior and therefore, functional gamma-band synchronization should be directly related to behavior. We recorded the magnetoencephalogram in human subjects and used a visual stimulus to induce occipital gamma-band activity. We found that the strength of this gamma-band activity at a given moment predicted the speed with which the subject was able to report a change in the stimulus. This predictive effect was restricted in time, frequency and space: It started only around 200 ms before the behaviorally relevant stimulus change, was present only between 50 and 80 Hz, and was significant only in bilateral middle occipital gyrus, while the peak of overall visually induced gamma-band activity was found in the calcarine sulcus. These results suggest that visually induced gamma-band activity is functionally relevant for the efficient transmission of stimulus change information to brain regions issuing the corresponding motor response., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

Insight into how brain structures interact is critical for understanding the principles of functional brain architectures and may lead to better diagnosis and therapy for neuropsychiatric disorders. We recorded, simultaneously, magnetoencephalographic (MEG) signals and subcortical local field potentials (LFP) in a Parkinson's disease (PD) patient with bilateral deep brain stimulation (DBS) electrodes in the subthalamic nucleus (STN). These recordings offer a unique opportunity to characterize interactions between the subcortical structures and the neocortex. However, high-amplitude artefacts appeared in the MEG. These artefacts originated from the percutaneous extension wire, rather than from the actual DBS electrode and were locked to the heart beat. In this work, we show that MEG beamforming is capable of suppressing these artefacts and quantify the optimal regularization required. We demonstrate how beamforming makes it possible to localize cortical regions whose activity is coherent with the STN-LFP, extract artefact-free virtual electrode time-series from regions of interest and localize cortical areas exhibiting specific task-related power changes. This furnishes results that are consistent with previously reported results using artefact-free MEG data. Our findings demonstrate that physiologically meaningful information can be extracted from heavily contaminated MEG signals and pave the way for further analysis of combined MEG-LFP recordings in DBS patients., (2009 Elsevier Inc. All rights reserved.)

PET and fMRI experiments have previously shown that several brain regions in the frontal and parietal lobe are involved in working memory maintenance. MEG and EEG experiments have shown parametric increases with load for oscillatory activity in posterior alpha and frontal theta power. In the current study we investigated whether the areas found with fMRI can be associated with these alpha and theta effects by measuring simultaneous EEG and fMRI during a modified Sternberg task This allowed us to correlate EEG at the single trial level with the fMRI BOLD signal by forming a regressor based on single trial alpha and theta power estimates. We observed a right posterior, parametric alpha power increase, which was functionally related to decreases in BOLD in the primary visual cortex and in the posterior part of the right middle temporal gyrus. We relate this finding to the inhibition of neuronal activity that may interfere with WM maintenance. An observed parametric increase in frontal theta power was correlated to a decrease in BOLD in regions that together form the default mode network. We did not observe correlations between oscillatory EEG phenomena and BOLD in the traditional WM areas. In conclusion, the study shows that simultaneous EEG-fMRI recordings can be successfully used to identify the emergence of functional networks in the brain during the execution of a cognitive task.

An important step in perceptual processing is the integration of information from different sensory modalities into a coherent percept. It has been suggested that such crossmodal binding might be achieved by transient synchronization of neurons from different modalities in the gamma-frequency range (>30 Hz). Here we employed a crossmodal priming paradigm, modulating the semantic congruency between visual-auditory natural object stimulus pairs, during the recording of the high density electroencephalogram (EEG). Subjects performed a semantic categorization task. Analysis of the behavioral data showed a crossmodal priming effect (facilitated auditory object recognition) in response to semantically congruent stimuli. Differences in event-related potentials (ERP) were found between 250 and 350 ms, which were localized to left middle temporal gyrus (BA 21) using a distributed linear source model. Early gamma-band activity (40-50 Hz) was increased between 120 ms and 180 ms following auditory stimulus onset for semantically congruent stimulus pairs. Source reconstruction for this gamma-band response revealed a maximal increase in left middle temporal gyrus (BA 21), an area known to be related to the processing of both complex auditory stimuli and multisensory processing. The data support the hypothesis that oscillatory activity in the gamma-band reflects crossmodal semantic-matching processes in multisensory convergence sites.

Rhythmic synchronization likely subserves interactions among neuronal groups. One of the best studied rhythmic synchronization phenomena in the human nervous system is the beta-band (15-30 Hz) synchronization in the motor system. In this study, we imaged structures across the human brain that are synchronized to the motor system's beta rhythm. We recorded whole-head magnetoencephalograms (MEG) and electromyograms (EMG) of left/right extensor carpi radialis muscle during left/right wrist extension. We analyzed coherence, on the one hand between the EMG and neuronal sources in the brain, and on the other hand between different brain sources, using a spatial filtering approach. Cortico-muscular coherence analysis revealed a spatial maximum of coherence to the muscle in motor cortex contralateral to the muscle in accordance with earlier findings. Moreover, by applying a two-dipole source model, we unveiled significantly coherent clusters of voxels in the ipsilateral cerebellar hemisphere and ipsilateral cerebral motor regions. The spatial pattern of coherence to the right and left arm EMG was roughly mirror reversed across the midline, in agreement with known physiology. Subsequently, we analyzed the brain-wide pattern of beta-band coherence to the motor cortex contralateral to the contracting muscle. This analysis did not reveal any convincing pattern. Because the prior cortico-muscular analysis had demonstrated the expected pattern in our data, this negative finding demonstrates a current limitation of the applied method for cortico-cortical coherence analysis. We conclude that during an isometric muscle contraction, several distributed brain regions form a brain-wide beta-band network for motor control.

Numerous studies have focused on changes in the activity in the hippocampus and higher association areas with consolidation and memory stabilization. Even though perceptual areas are engaged in memory recall, little is known about how memory stabilization is reflected in those areas. Using magnetoencephalography (MEG) we investigated changes in visual areas with memory stabilization. Subjects were trained on associating a face to one of eight locations. The first set of associations ('stabilized') was learned in three sessions distributed over a week. The second set ('labile') was learned in one session just prior to the MEG measurement. In the recall session only the face was presented and subjects had to indicate the correct location using a joystick. The MEG data revealed robust gamma activity during recall, which started in early visual cortex and propagated to higher visual and parietal brain areas. The occipital gamma power was higher for the labile than the stabilized condition (time=0.65-0.9 s). Also the event-related field strength was higher during recall of labile than stabilized associations (time=0.59-1.5 s). We propose that recall of the spatial associations prior to memory stabilization involves a top-down process relying on reconstructing learned representations in visual areas. This process is reflected in gamma band activity consistent with the notion that neuronal synchronization in the gamma band is required for visual representations. More direct synaptic connections are formed with memory stabilization, thus decreasing the dependence on visual areas.

Recent research has demonstrated that cortical motor areas are engaged when observing motor actions of others. However, little is known about the possible contribution of the motor system for evaluating the correctness of others' actions. To address this question we designed an MEG experiment in which subjects were executing and observing motor actions with and without errors. In the execution task subjects were asked to make speeded button presses according to instruction cues. During the observation task, they viewed pictures of an actor's hand making button presses which were correct or incorrect according to the cues. Time-frequency representations of the MEG data demonstrated a depression in oscillatory activity in the beta band activity (15-35 Hz) during execution followed by a beta rebound that was stronger for incorrect compared to correct executions. During the observation task, a similar time-course of the beta activity was identified and importantly the modulations were stronger for the observation of incorrect than correct actions. Sources accounting for the difference in beta activity between correct and incorrect actions were localized using a beamforming technique. Both for the execution and observation conditions sources were identified to the dorsal motor areas comprising both primary and pre-motor cortex. Our findings demonstrate that not only is cortical motor activity modulated by action observation, but the modulation increases when the observed action is erroneous. This suggests that the motor system is engaged in evaluating the correctness of the actions of others.

Amodal completion refers to the process in the visual system that enables us to perceive partly occluded objects as whole objects. Both the overall shape of a visual object (global aspect) and the region immediately surrounding the occluder (local aspect) are known to determine the process of completion. We investigated the influence of overall shape context in completion on human brain activity using MEG recordings. Subjects were presented with two different types of shapes that were partly occluded by a rectangle. In the so-called convergent shapes, the local and global contexts trigger the same completion, and in the so-called divergent shapes, local and global contexts trigger different completions. The occluder was removed after 1 s, revealing the whole (completed) shape where expectancies based on the local and global context could either be fulfilled or violated. We found an early (from 200 ms on) left occipital component, which was predominantly sensitive to the figural aspects of the uncovered continuation, but was also modulated by the congruency between the physical shape and the perceived shape (based on the contextual cues). In contrast, an early right occipital component and a later (400 ms) anterior temporal component were sensitive only to the congruency between the physical and the perceived shape, showing the relevance of the global context in amodal completion.

Neuronal gamma-band (30-100 Hz) synchronization subserves fundamental functions in neuronal processing. However, different experimental approaches differ widely in their success in finding gamma-band activity. We aimed at linking animal and human studies of gamma-band activity and at preparing optimized methods for an in-depth investigation of the mechanisms and functions of gamma-band activity and gamma-band coherence in humans. In the first step described here, we maximized the signal-to-noise ratio with which we can observe visually induced gamma-band activity in human magnetoencephalographic recordings. We used a stimulus and task design that evoked strong gamma-band activity in animals and combined it with multi-taper methods for spectral analysis and adaptive spatial filtering for source analysis. With this approach, we found human visual gamma-band activity very reliably across subjects and across multiple recording sessions of a given subject. While increases in gamma-band activity are typically accompanied by decreases in alpha- and beta-band activity, the gamma-band enhancement was often the spectral component with the highest signal-to-noise ratio. Furthermore, some subjects demonstrated two clearly separate visually induced gamma bands, one around 40 Hz and another between 70 and 80 Hz. Gamma-band activity was sustained for the entire stimulation period, which was up to 3 s. The sources of gamma-band activity were in the calcarine sulcus in all subjects. The results localize human visual gamma-band activity in frequency, time and space and the described methods allow its further investigation with great sensitivity.