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Despite the growing interest in the non-human animal emotionality, we currently know little about the human brain processing of non-conspecific emotional expressions. Here, we characterized the millisecond-scale temporal dynamics of human brain responses to conspecific human and non-conspecific canine emotional facial expressions. Our results revealed generally similar cortical responses to human and dog facial expressions in the occipital cortex during the first 500 ms, temporal cortex at 100-500 ms and parietal cortex at 150-350 ms from the stimulus onset. Responses to dog faces were pronounced at the latencies in temporal cortices corresponding to the time windows of early posterior negativity (EPN) and late posterior positivity (LPP), suggesting attentional engagement to emotionally salient stimuli. We also utilized support vector machine -based classifiers to discriminate between the brain responses to different images. The subject trait-level empathy correlated with the accuracy of classifying the brain responses of aggressive from happy dog faces, and happy from neutral human faces. This result likely reflects the attentional enhancement provoked by the subjective ecological salience of the stimuli., (© The Author(s) 2024. Published by Oxford University Press.)

Introduction: The Minimum Information About BIobank Data Sharing (MIABIS) is a biobank-specific terminology enabling the sharing of biobank-related data for different purposes across a wide range of database implementations. After 4 years in use and with the first version of the individual-level MIABIS component Sample , Sample donor, and Event , it was necessary to revise the terminology, especially to include biobanks that work more in the data domain than with samples. Materials & Methods: Nine use-cases representing different types of biobanks, studies, and networks participated in the development work. They represent types of data, specific sample types, or levels of organization that were not included earlier in MIABIS. To support our revision of the Biobank entity, we conducted a survey of European biobanks to chart the services they provide. An important stakeholder group for biobanks include researchers as the main users of biobanks. To be able to render MIABIS more researcher-friendly, we collected different sample/data requests to analyze the terminology adjustment needs in detail. During the update process, the Core terminology was iteratively reviewed by a large group of experts until a consensus was reached. Results: With this update, MIABIS was adjusted to encompass data-driven biobanks and to include data collections, while also describing the services and capabilities biobanks offer to their users, besides the retrospective samples. The terminology was also extended to accommodate sample and data collections of nonhuman origin. Additionally, a set of organizational attributes was compiled to describe networks. Discussion: The usability of MIABIS Core v3 was increased by extending it to cover more topics of the biobanking domain. Additionally, the focus was on a more general terminology and harmonization of attributes with the individual-level entities Sample, Sample donor, and Event to keep the overall terminology minimal. With this work, the internal semantics of the MIABIS terminology was improved.

Introduction: There has been a growing interest in studying brain activity under naturalistic conditions. However, the relationship between individual differences in ongoing brain activity and psychological characteristics is not well understood. We investigated this connection, focusing on the association between oscillatory activity in the brain and individually characteristic dispositional traits. Given the variability of unconstrained resting states among individuals, we devised a paradigm that could harmonize the state of mind across all participants., Methods: We constructed task contrasts that included focused attention (FA), self-centered future planning, and rumination on anxious thoughts triggered by visual imagery. Magnetoencephalography was recorded from 28 participants under these 3 conditions for a duration of 16 min. The oscillatory power in the alpha and beta bands was converted into spatial contrast maps, representing the difference in brain oscillation power between the two conditions. We performed permutation cluster tests on these spatial contrast maps. Additionally, we applied penalized canonical correlation analysis (CCA) to study the relationship between brain oscillation patterns and behavioral traits., Results: The data revealed that the FA condition, as compared to the other conditions, was associated with higher alpha and beta power in the temporal areas of the left hemisphere and lower alpha and beta power in the parietal areas of the right hemisphere. Interestingly, the penalized CCA indicated that behavioral inhibition was positively correlated, whereas anxiety was negatively correlated, with a pattern of high oscillatory power in the bilateral precuneus and low power in the bilateral temporal regions. This unique association was found in the anxious-thoughts condition when contrasted with the focused-attention condition., Conclusion: Our findings suggest individual temperament traits significantly affect brain engagement in naturalistic conditions. This research underscores the importance of considering individual traits in neuroscience and offers an effective method for analyzing brain activity and psychological differences., (© 2024 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

New biomarkers are urgently needed for many brain disorders; for example, the diagnosis of mild traumatic brain injury (mTBI) is challenging as the clinical symptoms are diverse and nonspecific. EEG and MEG studies have demonstrated several population-level indicators of mTBI that could serve as objective markers of brain injury. However, deriving clinically useful biomarkers for mTBI and other brain disorders from EEG/MEG signals is hampered by the large inter-individual variability even across healthy people. Here, we used a multivariate machine-learning approach to detect mTBI from resting-state MEG measurements. To address the heterogeneity of the condition, we employed a normative modeling approach and modeled MEG signal features of individual mTBI patients as deviations with respect to the normal variation. To this end, a normative dataset comprising 621 healthy participants was used to determine the variation in power spectra across the cortex. In addition, we constructed normative datasets based on age-matched subsets of the full normative data. To discriminate patients from healthy control subjects, we trained support-vector-machine classifiers on the quantitative deviation maps for 25 mTBI patients and 20 controls not included in the normative dataset. The best performing classifier made use of the full normative data across the entire age and frequency ranges. This classifier was able to distinguish patients from controls with an accuracy of 79%. Inspection of the trained model revealed that low-frequency activity in the theta frequency band (4-8 Hz) is a significant indicator of mTBI, consistent with earlier studies. The results demonstrate the feasibility of using normative modeling of MEG data combined with machine learning to advance diagnosis of mTBI and identify patients that would benefit from treatment and rehabilitation. The current approach could be applied to a wide range of brain disorders, thus providing a basis for deriving MEG/EEG-based biomarkers., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Itälinna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Selective auditory attention enables filtering of relevant acoustic information from irrelevant. Specific auditory responses, measurable by magneto- and electroencephalography (MEG/EEG), are known to be modulated by attention to the evoking stimuli. However, such attention effects have typically been studied in unnatural conditions (e.g. during dichotic listening of pure tones) and have been demonstrated mostly in averaged auditory evoked responses. To test how reliably we can detect the attention target from unaveraged brain responses, we recorded MEG data from 15 healthy subjects that were presented with two human speakers uttering continuously the words "Yes" and "No" in an interleaved manner. The subjects were asked to attend to one speaker. To investigate which temporal and spatial aspects of the responses carry the most information about the target of auditory attention, we performed spatially and temporally resolved classification of the unaveraged MEG responses using a support vector machine. Sensor-level decoding of the responses to attended vs. unattended words resulted in a mean accuracy of [Formula: see text] (N = 14) for both stimulus words. The discriminating information was mostly available 200-400 ms after the stimulus onset. Spatially-resolved source-level decoding indicated that the most informative sources were in the auditory cortices, in both the left and right hemisphere. Our result corroborates attention modulation of auditory evoked responses and shows that such modulations are detectable in unaveraged MEG responses at high accuracy, which could be exploited e.g. in an intuitive brain-computer interface., (© 2023. The Author(s).)

Resting-state magnetoencephalography (MEG) data show complex but structured spatiotemporal patterns. However, the neurophysiological basis of these signal patterns is not fully known and the underlying signal sources are mixed in MEG measurements. Here, we developed a method based on the nonlinear independent component analysis (ICA), a generative model trainable with unsupervised learning, to learn representations from resting-state MEG data. After being trained with a large dataset from the Cam-CAN repository, the model has learned to represent and generate patterns of spontaneous cortical activity using latent nonlinear components, which reflects principal cortical patterns with specific spectral modes. When applied to the downstream classification task of audio-visual MEG, the nonlinear ICA model achieves competitive performance with deep neural networks despite limited access to labels. We further validate the generalizability of the model across different datasets by applying it to an independent neurofeedback dataset for decoding the subject's attentional states, providing a real-time feature extraction and decoding mindfulness and thought-inducing tasks with an accuracy of around 70% at the individual level, which is much higher than obtained by linear ICA or other baseline methods. Our results demonstrate that nonlinear ICA is a valuable addition to existing tools, particularly suited for unsupervised representation learning of spontaneous MEG activity which can then be applied to specific goals or tasks when labelled data are scarce., 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 © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Accurate quantification of cortical engagement during mental imagery tasks remains a challenging brain-imaging problem with immediate relevance to developing brain-computer interfaces. We analyzed magnetoencephalography (MEG) data from 18 individuals completing cued motor imagery, mental arithmetic, and silent word generation tasks. Participants imagined movements of both hands (HANDS) and both feet (FEET), subtracted two numbers (SUB), and silently generated words (WORD). The task-related cortical engagement was inferred from beta band (17-25 Hz) power decrements estimated using a frequency-resolved beamforming method. In the hands and feet motor imagery tasks, beta power consistently decreased in premotor and motor areas. In the word and subtraction tasks, beta-power decrements showed engagements in language and arithmetic processing within the temporal, parietal, and inferior frontal regions. A support vector machine classification of beta power decrements yielded high accuracy rates of 74 and 68% for classifying motor-imagery (HANDS vs. FEET) and cognitive (WORD vs. SUB) tasks, respectively. From the motor-versus-nonmotor contrasts, excellent accuracy rates of 85 and 80% were observed for hands-versus-word and hands-versus-sub, respectively. A multivariate Gaussian-process classifier provided an accuracy rate of 60% for the four-way (HANDS-FEET-WORD-SUB) classification problem. Individual task performance was revealed by within-subject correlations of beta-decrements. Beta-power decrements are helpful metrics for mapping and decoding cortical engagement during mental processes in the absence of sensory stimuli or overt behavioral outputs. Markers derived based on beta decrements may be suitable for rehabilitation purposes, to characterize motor or cognitive impairments, or to treat patients recovering from a cerebral stroke., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

In MEG and EEG studies, the accuracy of the head digitization impacts the co-registration between functional and structural data. The co-registration is one of the major factors that affect the spatial accuracy in MEG/EEG source imaging. Precisely digitized head-surface (scalp) points do not only improve the co-registration but can also deform a template MRI. Such an individualized-template MRI can be used for conductivity modeling in MEG/EEG source imaging if the individual's structural MRI is unavailable. Electromagnetic tracking (EMT) systems (particularly Fastrak, Polhemus Inc., Colchester, VT, USA) have been the most common solution for digitization in MEG and EEG. However, they may occasionally suffer from ambient electromagnetic interference which makes it challenging to achieve (sub-)millimeter digitization accuracy. The current study-(i) evaluated the performance of the Fastrak EMT system under different conditions in MEG/EEG digitization, and (ii) explores the usability of two alternative EMT systems (Aurora, NDI, Waterloo, ON, Canada; Fastrak with a short-range transmitter) for digitization. Tracking fluctuation, digitization accuracy, and robustness of the systems were evaluated in several test cases using test frames and human head models. The performance of the two alternative systems was compared against the Fastrak system. The results showed that the Fastrak system is accurate and robust for MEG/EEG digitization if the recommended operating conditions are met. The Fastrak with the short-range transmitter shows comparatively higher digitization error if digitization is not carried out very close to the transmitter. The study also evinces that the Aurora system can be used for MEG/EEG digitization within a constrained range; however, some modifications would be required to make the system a practical and easy-to-use digitizer. Its real-time error estimation feature can potentially improve digitization accuracy., (© 2023. The Author(s).)

Sensory processing during development is important for the emerging cognitive skills underlying goal-directed behavior. Yet, it is not known how auditory processing in children is related to their cognitive functions. Here, we utilized combined magneto- and electroencephalographic (M/EEG) measurements in school-aged children (6-14y) to show that child auditory cortical activity at ∼250 ms after auditory stimulation predicts the performance in inhibition tasks. While unaffected by task demands, the amplitude of the left-hemisphere activation pattern was significantly correlated with the variability of behavioral response time. Since this activation pattern is typically not present in adults, our results suggest divergent brain mechanisms in adults and children for consistent performance in auditory-based cognitive tasks. This difference can be explained as a shift in cortical resources for cognitive control from sensorimotor associations in the auditory cortex of children to top-down regulated control processes involving (pre)frontal and cingulate areas in adults., Competing Interests: Declarations of interest None., (Copyright © 2022. Published by Elsevier Inc.)

In this paper, we propose a method to estimate the position, orientation, and gain of a magnetic field sensor using a set of (large) electromagnetic coils. We apply the method for calibrating an array of optically pumped magnetometers (OPMs) for magnetoencephalography (MEG). We first measure the magnetic fields of the coils at multiple known positions using a well-calibrated triaxial magnetometer, and model these discreetly sampled fields using vector spherical harmonics (VSH) functions. We then localize and calibrate an OPM by minimizing the sum of squared errors between the model signals and the OPM responses to the coil fields. We show that by using homogeneous and first-order gradient fields, the OPM sensor parameters (gain, position, and orientation) can be obtained from a set of linear equations with pseudo-inverses of two matrices. The currents that should be applied to the coils for approximating these low-order field components can be determined based on the VSH models. Computationally simple initial estimates of the OPM sensor parameters follow. As a first test of the method, we placed a fluxgate magnetometer at multiple positions and estimated the RMS position, orientation, and gain errors of the method to be 1.0 mm, 0.2°, and 0.8%, respectively. Lastly, we calibrated a 48-channel OPM array. The accuracy of the OPM calibration was tested by using the OPM array to localize magnetic dipoles in a phantom, which resulted in an average dipole position error of 3.3 mm. The results demonstrate the feasibility of using electromagnetic coils to calibrate and localize OPMs for MEG.

Study Design: A prospective interventional case series., Objectives: To explore changes in the modulation of cortical sensorimotor oscillations after long-term paired associative stimulation (PAS) in participants with spinal cord injury (SCI)., Setting: BioMag Laboratory, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland., Methods: Five patients with chronic incomplete SCI received unilateral spinal PAS to upper limb for 16-22 days. Changes in the modulation of sensorimotor oscillations in response to tactile stimulus and active and imaginary hand movements were assessed with magnetoencephalography recorded before and after the intervention., Results: PAS restored the modulation of sensorimotor oscillations in response to active hand movement in four patients, whereas the modulation following tactile stimulation remained unaltered. The observed change was larger in the hemisphere that received PAS and preceded the clinical effect of the intervention., Conclusions: Long-term spinal PAS treatment, which enhances the motor functions of SCI patients, also restores the modulation of cortical sensorimotor oscillations., (© 2022. The Author(s).)

Brain-computer interfaces (BCI) can be designed with several feedback modalities. To promote appropriate brain plasticity in therapeutic applications, the feedback should guide the user to elicit the desired brain activity and preferably be similar to the imagined action. In this study, we employed magnetoencephalography (MEG) to measure neurophysiological changes in healthy subjects performing motor imagery (MI) -based BCI training with two different feedback modalities. The MI-BCI task used in this study lasted 40-60 min and involved imagery of right- or left-hand movements. 8 subjects performed the task with visual and 14 subjects with proprioceptive feedback. We analysed power changes across the session at multiple frequencies in the range of 4-40 Hz with a generalized linear model to find those frequencies at which the power increased significantly during training. In addition, the power increase was analysed for each gradiometer, separately for alpha (8-13 Hz), beta (14-30 Hz) and gamma (30-40 Hz) bands, to find channels showing significant linear power increase over the session. These analyses were applied during three different conditions: rest, preparation, and MI. Visual feedback enhanced the amplitude of mainly high beta and gamma bands (24-40 Hz) in all conditions in occipital and left temporal channels. During proprioceptive feedback, in contrast, power increased mainly in alpha and beta bands. The alpha-band enhancement was found in multiple parietal, occipital, and temporal channels in all conditions, whereas the beta-band increase occurred during rest and preparation mainly in the parieto-occipital region and during MI in the parietal channels above hand motor regions. Our results show that BCI training with proprioceptive feedback increases the power of sensorimotor rhythms in the motor cortex, whereas visual feedback causes mainly a gamma-band increase in the visual cortex. MI-BCIs should involve proprioceptive feedback to facilitate plasticity in the motor cortex., Competing Interests: The authors have declared that no competing interests exist.

In this paper, we analyze spatial sampling of electro- (EEG) and magnetoencephalography (MEG), where the electric or magnetic field is typically sampled on a curved surface such as the scalp. By simulating fields originating from a representative adult-male head, we study the spatial-frequency content in EEG as well as in on- and off-scalp MEG. This analysis suggests that on-scalp MEG, off-scalp MEG and EEG can benefit from up to 280, 90 and 110 spatial samples, respectively. In addition, we suggest a new approach to obtain sensor locations that are optimal with respect to prior assumptions. The approach also allows to control, e.g., the uniformity of the sensor locations. Based on our simulations, we argue that for a low number of spatial samples, model-informed non-uniform sampling can be beneficial. For a large number of samples, uniform sampling grids yield nearly the same total information as the model-informed grids., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2021. Published by Elsevier Inc.)

Objective: Magnetoencephalography (MEG) signals typically reflect a mixture of neuromagnetic fields, subject-related artifacts, external interference and sensor noise. Even inside a magnetically shielded room, external interference can be significantly stronger than brain signals. Methods such as signal-space projection (SSP) and signal-space separation (SSS) have been developed to suppress this residual interference, but their performance might not be sufficient in cases of strong interference or when the sources of interference change over time., Methods: Here we suggest a new method, extended signal-space separation (eSSS), which combines a physical model of the magnetic fields (as in SSS) with a statistical description of the interference (as in SSP). We demonstrate the performance of this method via simulations and experimental MEG data., Results: The eSSS method clearly outperforms SSS and SSP in interference suppression regardless of the extent of a priori information available on the interference sources. We also show that the method does not cause location or amplitude bias in dipole modeling., Conclusion: Our eSSS method provides better data quality than SSP or SSS and can be readily combined with other SSS-based methods, such as spatiotemporal SSS or head movement compensation. Thus, eSSS extends and complements the interference suppression techniques currently available for MEG., Significance: Due to its ability to suppress external interference to the level of sensor noise, eSSS can facilitate single-trial data analysis, exemplified in automated analysis of epileptic data. Such an enhanced suppression is especially important in environments with large interference fields.

Dogs process faces and emotional expressions much like humans, but the time windows important for face processing in dogs are largely unknown. By combining our non-invasive electroencephalography (EEG) protocol on dogs with machine-learning algorithms, we show category-specific dog brain responses to pictures of human and dog facial expressions, objects, and phase-scrambled faces. We trained a support vector machine classifier with spatiotemporal EEG data to discriminate between responses to pairs of images. The classification accuracy was highest for humans or dogs vs. scrambled images, with most informative time intervals of 100-140 ms and 240-280 ms. We also detected a response sensitive to threatening dog faces at 30-40 ms; generally, responses differentiating emotional expressions were found at 130-170 ms, and differentiation of faces from objects occurred at 120-130 ms. The cortical sources underlying the highest-amplitude EEG signals were localized to the dog visual cortex.

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.)

Electrophysiological signals recorded intracranially show rich frequency content spanning from near-DC to hundreds of hertz. Noninvasive electromagnetic signals measured with electroencephalography (EEG) or magnetoencephalography (MEG) typically contain less signal power in high frequencies than invasive recordings. Particularly, noninvasive detection of gamma-band activity (>30 Hz) is challenging since coherently active source areas are small at such frequencies and the available imaging methods have limited spatial resolution. Compared to EEG and conventional SQUID-based MEG, on-scalp MEG should provide substantially improved spatial resolution, making it an attractive method for detecting gamma-band activity. Using an on-scalp array comprised of eight optically pumped magnetometers (OPMs) and a conventional whole-head SQUID array, we measured responses to a dynamic visual stimulus known to elicit strong gamma-band responses. OPMs had substantially higher signal power than SQUIDs, and had a slightly larger relative gamma-power increase over the baseline. With only eight OPMs, we could obtain gamma-activity source estimates comparable to those of SQUIDs at the group level. Our results show the feasibility of OPMs to measure gamma-band activity. To further facilitate the noninvasive detection of gamma-band activity, the on-scalp OPM arrays should be optimized with respect to sensor noise, the number of sensors and intersensor spacing., (© 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

Co-registration between structural head images and functional MEG data is needed for anatomically-informed MEG data analysis. Despite the efforts to minimize the co-registration error, conventional landmark- and surface-based strategies for co-registering head and MEG device coordinates achieve an accuracy of typically 5-10 mm. Recent advances in instrumentation and technical solutions, such as the development of hybrid ultra-low-field (ULF) MRI-MEG devices or the use of 3D-printed individualized foam head-casts, promise unprecedented co-registration accuracy, i.e., 2 mm or better. In the present study, we assess through simulations the impact of such an improved co-registration on MEG connectivity analysis. We generated synthetic MEG recordings for pairs of connected cortical sources with variable locations. We then assessed the capability to reconstruct source-level connectivity from these recordings for 0-15-mm co-registration error, three levels of head modeling detail (one-, three- and four-compartment models), two source estimation techniques (linearly constrained minimum-variance beamforming and minimum-norm estimation MNE) and five separate connectivity metrics (imaginary coherency, phase-locking value, amplitude-envelope correlation, phase-slope index and frequency-domain Granger causality). We found that beamforming can better take advantage of an accurate co-registration than MNE. Specifically, when the co-registration error was smaller than 3 mm, the relative error in connectivity estimates was down to one-third of that observed with typical co-registration errors. MNE provided stable results for a wide range of co-registration errors, while the performance of beamforming rapidly degraded as the co-registration error increased. Furthermore, we found that even moderate co-registration errors (>6 mm, on average) essentially decrease the difference of four- and three- or one-compartment models. Hence, a precise co-registration is important if one wants to take full advantage of highly accurate head models for connectivity analysis. We conclude that an improved co-registration will be beneficial for reliable connectivity analysis and effective use of highly accurate head models in future MEG connectivity studies., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects. Importantly, these models can successfully generalize to new subjects in real-time classification enabling more efficient brain-computer interfaces (BCI)., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

The spatial resolution of magnetoencephalography (MEG) can be increased from that of conventional SQUID-based systems by employing on-scalp sensor arrays of e.g. optically-pumped magnetometers (OPMs). However, OPMs reach sufficient sensitivity for neuromagnetic measurements only when operated in a very low absolute magnetic field of few nanoteslas or less, usually not reached in a typical magnetically shielded room constructed for SQUID-based MEG. Moreover, field drifts affect the calibration of OPMs. Static and dynamic suppression of interfering fields is thus necessary for good-quality neuromagnetic measurements with OPMs. Here, we describe an on-scalp MEG system that utilizes OPMs and external compensation coils that provide static and dynamic shielding against ambient fields. In a conventional two-layer magnetically shielded room, our coil system reduced the maximum remanent DC-field component within an 8-channel OPM array from 70 to less than 1 nT, enabling the sensors to operate in the sensitive spin exchange relaxation-free regime. When compensating field drifts below 4 Hz, a low-frequency shielding factor of 22 dB was achieved, which reduced the peak-to-peak drift from 1.3 to 0.4 nT and thereby the standard deviation of the sensor calibration from 1.7% to 0.5%. Without band-limiting the field that was compensated, a low-frequency shielding factor of 43 dB was achieved. We validated the system by measuring brain responses to electric stimulation of the median nerve. With dynamic shielding and digital interference suppression methods, single-trial somatosensory evoked responses could be detected. Our results advance the deployment of OPM-based on-scalp MEG in lighter magnetic shields., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Parkinson's Disease (PD) is currently the second most common neurodegenerative disease. One of the most characteristic symptoms of PD is resting tremor. Local Field Potentials (LFPs) have been widely studied to investigate deviations from the typical patterns of healthy brain activity. However, the inherent dynamics of the Sub-Thalamic Nucleus (STN) LFPs and their spatiotemporal dynamics have not been well characterized. In this work, we study the non-linear dynamical behaviour of STN-LFPs of Parkinsonian patients using ε -recurrence networks. RNs are a non-linear analysis tool that encodes the geometric information of the underlying system, which can be characterised (for example, using graph theoretical measures) to extract information on the geometric properties of the attractor. Results show that the activity of the STN becomes more non-linear during the tremor episodes and that ε -recurrence network analysis is a suitable method to distinguish the transitions between movement conditions, anticipating the onset of the tremor, with the potential for application in a demand-driven deep brain stimulation system.