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Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices.A comprehensive analysis of metal artifact characteristics from time, frequency, and time–frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5–8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process.The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG.

Summary: Background: Research in healthy young adults shows that characteristic patterns of brain activity define individual “brain-fingerprints” that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson’s disease (PD). Methods: We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s. Findings: The arrhythmic spectral components of cortical activity in patients with Parkinson’s disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson’s brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson’s symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson’s brain-fingerprint aligns with that of neurotransmitter systems affected by the disease’s pathophysiology. Interpretation: The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson’s disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson’s disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. Funding: Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer’s Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).

The goal of this study was to identify the specific domains of language that may be affected by deficits in rapid auditory processing in individuals with ASD. Auditory evoked fields were collected from 63 children diagnosed with ASD in order to evaluate processing of puretone sounds presented in rapid succession. Measures of language and its components were assessed via standardized clinical tools to quantify expressive and receptive language, vocabulary, articulation, and phonological processing abilities. Rapid processing was significantly and bilaterally associated with phonological awareness, vocabulary, and articulation. Phonological processing was found to mediate the relationship between rapid processing and language. M100 response latency was not significantly associated with any language measures. Results suggest that rapid processing deficits may impact the basic components of language such as phonological processing, and the downstream effect of this impact may in turn impact overall language development.

Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Childrens Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex (extreme multifocal and/or polymorphic activity), they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7 ± 2 mm (mean ± SD over all 10 patients); and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data-driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision-making for patients with intractable epilepsy.

Flexibility of behavior and the ability to rapidly switch actions is critical for adaptive living in humans. It is well established that the right-inferior frontal gyrus (R-IFG) is recruited during outright action-stopping, relating to increased beta (12-30 Hz) power. It has also been posited that inhibiting incorrect response tendencies and switching is central to motor flexibility. However, it is not known if the commonly reported R-IFG beta signature of response inhibition in action-stopping is also recruited during response conflict, which would suggest overlapping networks for stopping and switching. In the current study, we analyzed high precision magnetoencephalography (hpMEG) data recorded with multiple within subject recording sessions (trials n > 10,000) from 8 subjects during different levels of response conflict. We hypothesized that a R-IFG-triggered network for response inhibition is domain general and therefore also involved in mediating response conflict. We tested whether R-IFG showed increased beta power dependent on the level of response conflict. Using event-related spectral perturbations and linear mixed modeling, we found that R-IFG beta power increased for response conflict trials. The R-IFG beta increase was specific to trials with strong response conflict, and increased R-IFG beta power related to less error. This supports a more generalized role for R-IFG beta, beyond simple stopping behavior towards response switching.

IntroductionThe cognitive and psychotic symptoms in patients with schizophrenia (SZ) are thought to result from disrupted brain network connectivity.MethodsWe capitalize on the high spatiotemporal resolution of magnetoencephalography imaging (MEG) to record spontaneous neuronal activity in resting state networks in 21 SZ compared with 21 healthy controls (HC).ResultsWe found that SZ showed significant global disrupted functional connectivity in delta-theta (2-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz) frequencies, compared to HC. Disrupted global connectivity in alpha frequencies with bilateral frontal cortices was associated with more severe clinical psychopathology (i.e., positive psychotic symptoms). Specifically, aberrant connectivity in beta frequencies between the left primary auditory cortex and cerebellum, was linked to greater hallucination severity in SZ. Disrupted connectivity in delta-theta frequencies between the medial frontal and left inferior frontal cortex was associated with impaired cognition.DiscussionThe multivariate techniques employed in the present study highlight the importance of applying our source reconstruction techniques which leverage the high spatial localization abilities of MEG for estimating neural source activity using beamforming methods such as SAM (synthetic aperture morphometry) to reconstruct the source of brain activity, together with functional connectivity assessments, assayed with imaginary coherence metrics, to delineate how neurophysiological dysconnectivity in specific oscillatory frequencies between distinct regions underlie the cognitive and psychotic symptoms in SZ. The present findings employ powerful techniques in spatial and time-frequency domains to provide potential neural biomarkers underlying neuronal network dysconnectivity in SZ that will inform the development of innovations in future neuromodulation treatment development.

We explore the stability and dynamic properties of a hierarchical, linearized, and analytic spectral graph model for neural oscillations that integrates the structural wiring of the brain. Previously, we have shown that this model can accurately capture the frequency spectra and the spatial patterns of the alpha and beta frequency bands obtained from magnetoencephalography recordings without regionally varying parameters. Here, we show that this macroscopic model based on long-range excitatory connections exhibits dynamic oscillations with a frequency in the alpha band even without any oscillations implemented at the mesoscopic level. We show that depending on the parameters, the model can exhibit combinations of damped oscillations, limit cycles, or unstable oscillations. We determined bounds on model parameters that ensure stability of the oscillations simulated by the model. Finally, we estimated time-varying model parameters to capture the temporal fluctuations in magnetoencephalography activity. We show that a dynamic spectral graph modeling framework with a parsimonious set of biophysically interpretable model parameters can thereby be employed to capture oscillatory fluctuations observed in electrophysiological data in various brain states and diseases.

Phantom limb pain is attributed to abnormal sensorimotor cortical representations. Various feedback treatments have been applied to induce the reorganization of the sensorimotor cortical representations to reduce pain. We developed a training protocol using a brain-computer interface (BCI) to induce plastic changes in the sensorimotor cortical representation of phantom hand movements and demonstrated that BCI training effectively reduces phantom limb pain. By comparing the induced cortical representation and pain, the mechanisms worsening the pain have been attributed to the residual phantom hand representation. Based on our data obtained using neurofeedback training without explicit phantom hand movements and hand-like visual feedback, we suggest a direct relationship between cortical representation and pain. In this review, we summarize the results of our BCI training protocol and discuss the relationship between cortical representation and phantom limb pain. We propose a treatment for phantom limb pain based on real-time neuroimaging to induce appropriate cortical reorganization by monitoring cortical activities.

Abstract Background Alzheimer’s disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM). Methods SGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years). Results Patients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition. Conclusions These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

The burgeoning field of neurodegenerative disease detection and management necessitates the development of robust and comprehensive diagnostic approaches. Existing methodologies often fall short in effectively capturing the complex interplay of brain signals and genetic markers, which are crucial in the early detection and progression tracking of such diseases. This paper introduces a novel multimodal framework that leverages advanced signal processing and machine learning techniques to address these limitations, providing a more accurate and holistic understanding of neurodegenerative diseases. Our proposed model integrates multiple modalities: EEG signal analysis using Time-Frequency Analysis and Wavelet Transform, functional Magnetic Resonance Imaging (fMRI) analyzed through Independent Component Analysis (ICA) and Correlation Analysis, Magnetoencephalography (MEG) employing Beamforming and Source Localization Techniques, and Genomic Data analysis using Graph Neural Network for Genetic Pattern Recognition process. This integration is realized through the fusion of modalities using Gated Recurrent Units (GRU) and the classification into disease classes via an efficient 1D Convolutional Neural Network (CNN). The reasons for selecting these methods are twofold: they address the non-stationary characteristics of EEG signals and exploit spatial information of brain activity, while also identifying functional networks and genetic patterns associated with neurodegeneration conditions. The clinical impact of this work is profound. Tested on the BioGPS and BrainLat datasets, our framework demonstrated a 10.4% increase in precision, 8.5% increase in accuracy, 8.3% increase in recall, 9.4% increase in the Area Under the Curve (AUC), 7.5% increase in specificity, and a 2.9% reduction in delay compared to existing methods.

Abstract Background Epileptic seizures are an established comorbidity of Alzheimer’s disease (AD). Subclinical epileptiform activity (SEA) as detected by 24-h electroencephalography (EEG) or magneto-encephalography (MEG) has been reported in temporal regions of clinically diagnosed AD patients. Although epileptic activity in AD probably arises in the mesial temporal lobe, electrical activity within this region might not propagate to EEG scalp electrodes and could remain undetected by standard EEG. However, SEA might lead to faster cognitive decline in AD. Aims 1. To estimate the prevalence of SEA and interictal epileptic discharges (IEDs) in a well-defined cohort of participants belonging to the AD continuum, including preclinical AD subjects, as compared with cognitively healthy controls. 2. To evaluate whether long-term-EEG (LTM-EEG), high-density-EEG (hd-EEG) or MEG is superior to detect SEA in AD. 3. To characterise AD patients with SEA based on clinical, neuropsychological and neuroimaging parameters. Methods Subjects (n = 49) belonging to the AD continuum were diagnosed according to the 2011 NIA-AA research criteria, with a high likelihood of underlying AD pathophysiology. Healthy volunteers (n = 24) scored normal on neuropsychological testing and were amyloid negative. None of the participants experienced a seizure before. Subjects underwent LTM-EEG and/or 50-min MEG and/or 50-min hd-EEG to detect IEDs. Results We found an increased prevalence of SEA in AD subjects (31%) as compared to controls (8%) (p = 0.041; Fisher’s exact test), with increasing prevalence over the disease course (50% in dementia, 27% in MCI and 25% in preclinical AD). Although MEG (25%) did not withhold a higher prevalence of SEA in AD as compared to LTM-EEG (19%) and hd-EEG (19%), MEG was significantly superior to detect spikes per 50 min (p = 0.002; Kruskall–Wallis test). AD patients with SEA scored worse on the RBANS visuospatial and attention subset (p = 0.009 and p = 0.05, respectively; Mann–Whitney U test) and had higher left frontal, (left) temporal and (left and right) entorhinal cortex volumes than those without. Conclusion We confirmed that SEA is increased in the AD continuum as compared to controls, with increasing prevalence with AD disease stage. In AD patients, SEA is associated with more severe visuospatial and attention deficits and with increased left frontal, (left) temporal and entorhinal cortex volumes. Trial registration Clinicaltrials.gov, NCT04131491. 12/02/2020.

Background: Grating orientation discrimination (GOD) is commonly used to assess somatosensory spatial processing. It allows discrimination between parallel and orthogonal orientations of tactile stimuli applied to the fingertip. Despite its widespread application, the underlying mechanisms of GOD, particularly the role of cortico-cortical interactions and local brain activity in this process, remain elusive. Therefore, we aimed to investigate how a specific cortico-cortical network and inhibitory circuits within the primary somatosensory cortex (S1) and secondary somatosensory cortex (S2) contribute to GOD. Methods: In total, 51 healthy young adults were included in our study. We recorded resting-state magnetoencephalography (MEG) and somatosensory-evoked magnetic field (SEF) in participants with open eyes. We converted the data into a source space based on individual structural magnetic resonance imaging. Next, we estimated S1- and S2-seed resting-state functional connectivity (rs-FC) at the alpha and beta bands through resting-state MEG using the amplitude envelope correlation method across the entire brain (i.e., S1/S2-seeds × 15,000 vertices × two frequencies). We assessed the inhibitory response in the S1 and S2 from SEFs using a paired-pulse paradigm. We automatically measured the GOD task in parallel and orthogonal orientations to the index finger, applying various groove widths with a custom-made device. Results: We observed a specific association between the GOD threshold (all P < 0.048) and the alpha rs-FC in the S1–superior parietal lobule and S1–adjacent to the parieto-occipital sulcus (i.e., lower rs-FC values corresponded to higher performance). In contrast, no association was observed between the local responses and the threshold. Discussion: The results of this study underpin the significance of specific cortico-cortical networks in recognizing variations in tactile stimuli.

Neurophysiological signals, comprised of both periodic (e.g., oscillatory) and aperiodic (e.g., non-oscillatory) activity, undergo complex developmental changes between childhood and adulthood. With much of the existing literature primarily focused on the periodic features of brain function, our understanding of aperiodic signals is still in its infancy. Here, we are the first to examine age-related changes in periodic (peak frequency and power) and aperiodic (slope and offset) activity using optically pumped magnetometers (OPMs), a new, wearable magnetoencephalography (MEG) technology that is particularly well-suited for studying development. We examined age-related changes in these spectral features in a sample (N=65) of toddlers (1–3 years), children (4–5 years), young adults (20–26 years), and adults (27–38 years). Consistent with the extant literature, we found significant age-related decreases in the aperiodic slope and offset, and changes in peak frequency and power that were frequency-specific; we are the first to show that the effect sizes of these changes also varied across brain regions. This work not only adds to the growing body of work highlighting the advantages of using OPMs, especially for studying development, but also contributes novel information regarding the variation of neurophysiological changes with age across the brain.

BACKGROUND AND HYPOTHESIS: Prior research has shown that patients with schizophrenia (SZ) show disruption in brain network connectivity that is thought to underlie their cognitive and psychotic symptoms. However, most studies examining functional network disruption in schizophrenia have focused on the temporally correlated coupling of the strength of network connections. Here, we move beyond correlative metrics to assay causal computations of connectivity changes in directed neural information flow, assayed from a neural source to a target in SZ. STUDY DESIGN: This study describes a whole-brain magnetoencephalography-imaging approach to examine causal computations of connectivity changes in directed neural information flow between brain regions during resting states, quantified by phase-transfer entropy (PTE) metrics, assayed from a neural source to an endpoint, in 21 SZ compared with 21 healthy controls (HC), and associations with cognitive and clinical psychotic symptoms in SZ. STUDY RESULTS: We found that SZ showed significant disruption in information flow in alpha (8-12 Hz) and beta (12-30 Hz) frequencies, compared to HC. Reduced information flow in alpha frequencies from the precuneus to the medio-ventral occipital cortex was associated with more severe clinical psychopathology (ie, positive psychotic symptoms), while reduced information flow between insula and middle temporal gyrus was associated with worsening cognitive symptoms. CONCLUSIONS: The present findings highlight the importance of delineating dysfunction in neural information flow in specific oscillatory frequencies between distinct regions that underlie the cognitive and psychotic symptoms in SZ, and provide potential neural biomarkers that could lead to innovations in future neuromodulation treatment development.

Brain processes associated with emotion perception from biological motion have been largely investigated using point-light displays that are devoid of pictorial information and not representative of everyday life. In this study, we investigated the brain signals evoked when perceiving emotions arising from body movements of virtual pedestrians walking in a community environment. Magnetoencephalography was used to record brain activation in 21 healthy young adults discriminating the emotional gaits (neutral, angry, happy) of virtual male/female pedestrians. Event-related responses in the posterior superior temporal sulcus (pSTS), fusiform body area (FBA), extrastriate body area (EBA), amygdala (AMG), and lateral occipital cortex (Occ) were examined. Brain signals were characterized by an early positive peak (P1;∼200ms) and a late positive potential component (LPP) comprising of an early (400-600ms), middle (600-1000ms) and late phase (1000-1500ms). Generalized estimating equations revealed that P1 amplitude was unaffected by emotion and gender of pedestrians. LPP amplitude showed a significant emotion X phase interaction in all regions of interest, revealing i) an emotion-dependent modulation starting in pSTS and Occ, followed by AMG, FBA and EBA, and ii) generally enhanced responses for angry vs. other gait stimuli in the middle LPP phase. LPP also showed a gender X phase interaction in pSTS and Occ, as gender affected the time course of the response to emotional gait. Present findings show that brain activation within areas associated with biological motion, form, and emotion processing is modulated by emotional gait stimuli rendered by virtual simulations representative of everyday life. [ABSTRACT FROM AUTHOR]

With the recent advent of smart wearable sensors for monitoring brain activities in real-time, the scopes for using Electroencephalograms (EEGs) and Magnetoencephalography (MEG) in mobile and dynamic environments have become more relevant. However, their application in dynamic and open environments, typical of mobile wearable use, poses challenges. Presently, there is limited clinical data on using EEG/MEG as wearables. To advance these technologies at a time when large-scale clinical trials are not feasible, many researchers have turned to realistic phantom heads to further explore EEG and MEG capabilities. However, to achieve translational results, such phantom heads should have matching geometric features and electrical properties. Here, we have designed and fabricated multilayer chopped carbon fiber–PDMS reinforced composites to represent phantom head tissues. Two types of phantom layers are fabricated, namely seven-layer and four-layer systems with a goal to achieve matching electrical conductivities in each layer. Desired electrical conductivities are obtained by varying the weight fraction of the carbon fibers in PDMS. Then, the prototype system was calibrated and tested with a 32-electrode EEG cap. The test results demonstrated that the phantom effectively generates a variety of scalp potential patterns, achieved through a finite number of internal dipole generators within the phantom sample. This innovative design holds potential as a valuable test platform for assessing wearable EEG technology as well as developing an EEG analysis process. [ABSTRACT FROM AUTHOR]

We construct hypergraphs to analyze functional brain connectivity, leveraging event-related coherence in magnetoencephalography (MEG) data during the visual perception of a flickering image. Principal network characteristics are computed for the delta, theta, alpha, beta, and gamma frequency ranges. Employing a coherence measure, a statistical estimate of correlation between signal pairs across frequencies, we generate an edge time series, depicting how an edge evolves over time. This forms the basis for constructing an edge-to-edge functional connectivity network. We emphasize hyperedges as connected components in an absolute-valued functional connectivity network. Our coherence-based hypergraph construction specifically addresses functional connectivity among four brain lobes in both hemispheres: frontal, parietal, temporal, and occipital. This approach enables a nuanced exploration of individual differences within diverse frequency bands, providing insights into the dynamic nature of brain connectivity during visual perception tasks. The results furnish compelling evidence supporting the hypothesis of cortico–cortical interactions occurring across varying scales. The derived hypergraph illustrates robust activation patterns in specific brain regions, indicative of their engagement across diverse cognitive contexts and different frequency bands. Our findings suggest potential integration or multifunctionality within the examined lobes, contributing valuable perspectives to our understanding of brain dynamics during visual perception. [ABSTRACT FROM AUTHOR]

Verb generation is among the most frequently used tasks in presurgical mapping. Because this task involves many processes, the overall brain effects are not specific. While it is necessary to identify the whole network involving noun comprehension or semantic retrieval and lexical selection to produce the verb, isolation of those components is also crucial. Here, we present data from four patients undergoing presurgical brain mapping. The study implied a reanalysis of magnetoencephalography data with a recategorization of the used items. It aimed to extract the task component that relies on the inferior frontal gyrus (IFG). The task could be applied with higher specificity when targeting frontal areas. For that, we based item classification on the selection demands imposed by the noun. It is a robust finding that the IFG carries out this selection and that a quantitative index can be calculated for each noun, which depends on the selection effort (Proceedings of the National Academy of Sciences of the United States of America, 1997; 94(26):14792–14797, Proceedings of the National Academy of Sciences of the United States of America, 1998; 95(26):15855–15860). Data showed focality and specificity, with a correlation between this derived index and source activations in the inferior frontal gyrus for all patients. Strikingly, we detected when the right‐hemisphere homologue area was involved in the selection process in two patients showing reorganization or language right lateralization. The present data are a step towards a dissection of broad specific tasks frequently used in presurgical protocols. [ABSTRACT FROM AUTHOR]

Multipole expansions have been used extensively in the Magnetoencephalography (MEG) literature for mitigating environmental interference and modelling brain signal. However, their application to Optically Pumped Magnetometer (OPM) data is challenging due to the wide variety of existing OPM sensor and array designs. We therefore explore how such multipole models can be adapted to provide stable models of brain signal and interference across OPM systems. Firstly, we demonstrate how prolate spheroidal (rather than spherical) harmonics can provide a compact representation of brain signal when sampling on the scalp surface with as few as 100 channels. We then introduce a type of orthogonal projection incorporating this basis set. The Adaptive Multipole Models (AMM), which provides robust interference rejection across systems, even in the presence of spatially structured nonlinearity errors (shielding factor is the reciprocal of the maximum fractional nonlinearity error). Furthermore, this projection is always stable, as it is an orthogonal projection, and will only ever decrease the white noise in the data. However, for array designs that are suboptimal for spatially separating brain signal and interference, this method can remove brain signal components. We contrast these properties with the more typically used multipole expansion, Signal Space Separation (SSS), which never reduces brain signal amplitude but is less robust to the effect of sensor nonlinearity errors on interference rejection and can increase noise in the data if the system is sub‐optimally designed (as it is an oblique projection). We conclude with an empirical example utilizing AMM to maximize signal to noise ratio (SNR) for the stimulus locked neuronal response to a flickering visual checkerboard in a 128‐channel OPM system and demonstrate up to 40 dB software shielding in real data. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) is a neurophysiological technique based on the detection of brain magnetic fields. Whole‐head MEG systems typically house a few hundred sensors requiring cryogenic cooling in a rigid one‐size‐fits‐all (commonly adult‐sized) helmet to keep a thermal insulation space. This leads to an increased brain‐to‐sensor distance in children, because of their smaller head circumference, and decreased signal‐to‐noise ratio. MEG allows detection and localization of interictal and ictal epileptiform discharges, and pathological high frequency oscillations, as a part of the presurgical assessment of children with refractory focal epilepsy, where electroencephalography is not contributive. MEG can also map the eloquent cortex before surgical resection. MEG also provides insights into the physiopathology of both generalized and focal epilepsy. On‐scalp recordings based on cryogenic‐free sensors have demonstrated their use in the field of childhood focal epilepsy and should become a reference technique for diagnosing epilepsy in the paediatric population. What this paper adds: Magnetoencephalography (MEG) contributes to the diagnosis and understanding of paediatric epilepsy.On‐scalp MEG recordings demonstrate some advantages over cryogenic MEG. What this paper adds: Magnetoencephalography (MEG) contributes to the diagnosis and understanding of paediatric epilepsy.On‐scalp MEG recordings demonstrate some advantages over cryogenic MEG. [ABSTRACT FROM AUTHOR]

Most parts of speech are voiced, exhibiting a degree of periodicity with a fundamental frequency and many higher harmonics. Some neural populations respond to this temporal fine structure, in particular at the fundamental frequency. This frequency-following response to speech consists of both subcortical and cortical contributions and can be measured through EEG as well as through magnetoencephalography (MEG), although both differ in the aspects of neural activity that they capture: EEG is sensitive to both radial and tangential sources as well as to deep sources, whereas MEG is more restrained to the measurement of tangential and superficial neural activity. EEG responses to continuous speech have shown an early subcortical contribution, at a latency of around 9 msec, in agreement with MEG measurements in response to short speech tokens, whereas MEG responses to continuous speech have not yet revealed such an early component. Here, we analyze MEG responses to long segments of continuous speech. We find an early subcortical response at latencies of 4–11 msec, followed by later right-lateralized cortical activities at delays of 20–58 msec as well as potential subcortical activities. Our results show that the early subcortical component of the FFR to continuous speech can be measured from MEG in populations of participants and that its latency agrees with that measured with EEG. They furthermore show that the early subcortical component is temporally well separated from later cortical contributions, enabling an independent assessment of both components toward further aspects of speech processing. [ABSTRACT FROM AUTHOR]

Vagus nerve stimulation (VNS) is an important treatment option for drug-refractory epilepsy (DRE), with well-established efficacy and safety in clinical practice for more than 20 years. However, it is very difficult to find the optimal electrophysiological indicators for the effectiveness of VNS on DRE because the mechanism of action is unknown. In this review, we provide an update of the potential applications of VNS outcomes in patients with drug-resistant epilepsy. Electroencephalographic (EEG) activity, event-related potentials, EEG synchronization levels, magnetoencephalographic, laryngeal muscle evoked potentials, and heart rate variability are potential biomarkers for VNS outcomes in people with DRE. [ABSTRACT FROM AUTHOR]

Even in the modern era of combination antiretroviral therapy, aberrations in motor control remain a predominant symptom contributing to age-related functional dependencies (e.g., neurocognitive impairment) in people with HIV (PWH). While recent evidence implicates aberrant mitochondrial redox environments in the modulation of neural oscillatory activity serving motor control in PWH, the contribution of important clinical and demographic factors on this bioenergetic-neural-behavioral pathway is unknown. Herein, we evaluate the predictive capacity of clinical metrics pertinent to HIV (e.g., CD4 nadir, time with viremia) and age on mitochondrial redox-regulated sensorimotor brain-behavior dynamics in 69 virally-suppressed PWH. We used state-of-the-art systems biology and neuroscience approaches, including Seahorse analyzer of mitochondrial energetics, EPR spectroscopy of intracellular oxidant levels, antioxidant activity assays pertinent to superoxide and hydrogen peroxide (H 2 O 2) redox environments, and magnetoencephalographic (MEG) imaging to quantify sensorimotor oscillatory dynamics. Our results demonstrate differential effects of redox systems on the neural dynamics serving motor function in PWH. In addition, measures of immune stability and duration of compromise due to HIV had dissociable effects on this pathway, above and beyond the effects of age alone. Moreover, peripheral measures of antioxidant activity (i.e., superoxide dismutase) fully mediated the relationship between immune stability and current behavioral performance, indicative of persistent oxidative environments serving motor control in the presence of virologic suppression. Taken together, our data suggest that disease-related factors, in particular, are stronger predictors of current redox, neural and behavioral profiles serving motor function, which may serve as effective targets for alleviating HIV-specific alterations in cognitive-motor function in the future. [Display omitted] • Motor dysfunction leads to functional dependencies in people with HIV (PWH). • Mitochondrial redox environments are known to modulate sensorimotor decline in PWH. • HIV clinical factors may impact this bioenergetic-neural-behavioral pathway. • Duration of high viral load affected motor performance in PWH through this pathway. • Measures of immune stability (CD4 nadir) predicted redox-regulated motor control. [ABSTRACT FROM AUTHOR]

It has been suggested that aberrant excitation/inhibition (E/I) balance and dysfunctional structure and function of relevant brain networks may underlie the symptoms of autism spectrum disorder (ASD). However, the nomological network linking these constructs to quantifiable measures and mechanistically relating these constructs to behavioral symptoms of ASD is lacking. Herein we describe a within-subject, controlled, proof-of-mechanism study investigating the pathophysiology of auditory/language processing in adolescents with ASD. We utilize neurophysiological and neuroimaging techniques including magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG) metrics of language network structure and function. Additionally, we apply a single, individually targeted session of continuous theta burst stimulation (cTBS) as an experimental probe of the impact of perturbation of the system on these neurophysiological and neuroimaging outcomes. MRS, fMRI, and MEG measures are evaluated at baseline and immediately prior to and following cTBS over the posterior superior temporal cortex (pSTC), a region involved in auditory and language processing deficits in ASD. Also, behavioral measures of ASD and language processing and DWI measures of auditory/language network structures are obtained at baseline to characterize the relationship between the neuroimaging and neurophysiological measures and baseline symptom presentation. We hypothesize that local gamma-aminobutyric acid (GABA) and glutamate concentrations (measured with MRS), and structural and functional activity and network connectivity (measured with DWI and fMRI), will significantly predict MEG indices of auditory/language processing and behavioral deficits in ASD. Furthermore, a single session of cTBS over left pSTC is hypothesized to lead to significant, acute changes in local glutamate and GABA concentration, functional activity and network connectivity, and MEG indices of auditory/language processing. We have completed the pilot phase of the study (n=20 Healthy Volunteer adults) and have begun enrollment for the main phase with adolescents with ASD (n=86; age 14-17). If successful, this study will establish a nomological network linking local E/I balance measures to functional and structural connectivity within relevant brain networks, ultimately connecting them to ASD symptoms. Furthermore, this study will inform future therapeutic trials using cTBS to treat the symptoms of ASD. [ABSTRACT FROM AUTHOR]

Closed‐loop auditory stimulation (CLAS) is a brain modulation technique in which sounds are timed to enhance or disrupt endogenous neurophysiological events. CLAS of slow oscillation up‐states in sleep is becoming a popular tool to study and enhance sleep's functions, as it increases slow oscillations, evokes sleep spindles and enhances memory consolidation of certain tasks. However, few studies have examined the specific neurophysiological mechanisms involved in CLAS, in part because of practical limitations to available tools. To evaluate evidence for possible models of how sound stimulation during brain up‐states alters brain activity, we simultaneously recorded electro‐ and magnetoencephalography in human participants who received auditory stimulation across sleep stages. We conducted a series of analyses that test different models of pathways through which CLAS of slow oscillations may affect widespread neural activity that have been suggested in literature, using spatial information, timing and phase relationships in the source‐localized magnetoencephalography data. The results suggest that auditory information reaches ventral frontal lobe areas via non‐lemniscal pathways. From there, a slow oscillation is created and propagated. We demonstrate that while the state of excitability of tissue in auditory cortex and frontal ventral regions shows some synchrony with the electroencephalography (EEG)‐recorded up‐states that are commonly used for CLAS, it is the state of ventral frontal regions that is most critical for slow oscillation generation. Our findings advance models of how CLAS leads to enhancement of slow oscillations, sleep spindles and associated cognitive benefits and offer insight into how the effectiveness of brain stimulation techniques can be improved. [ABSTRACT FROM AUTHOR]

Fluid intelligence (Gf) involves logical reasoning and novel problem‐solving abilities. Often, abstract reasoning tasks like Raven's progressive matrices are used to assess Gf. Prior work has shown an age‐related decline in fluid intelligence capabilities, and although many studies have sought to identify the underlying mechanisms, our understanding of the critical brain regions and dynamics remains largely incomplete. In this study, we utilized magnetoencephalography (MEG) to investigate 78 individuals, ages 20–65 years, as they completed an abstract reasoning task. MEG data was co‐registered with structural MRI data, transformed into the time–frequency domain, and the resulting neural oscillations were imaged using a beamformer. We found worsening behavioral performance with age, including prolonged reaction times and reduced accuracy. MEG analyses indicated robust oscillations in the theta, alpha/beta, and gamma range during the task. Whole brain correlation analyses with age revealed relationships in the theta and alpha/beta frequency bands, such that theta oscillations became stronger with increasing age in a right prefrontal region and alpha/beta oscillations became stronger with increasing age in parietal and right motor cortices. Follow‐up connectivity analyses revealed increasing parieto‐frontal connectivity with increasing age in the alpha/beta frequency range. Importantly, our findings are consistent with the parieto‐frontal integration theory of intelligence (P‐FIT). These results further suggest that as people age, there may be alterations in neural responses that are spectrally specific, such that older people exhibit stronger alpha/beta oscillations across the parieto‐frontal network during abstract reasoning tasks. [ABSTRACT FROM AUTHOR]

Background: The investigation of mindfulness meditation practice, classically divided into focused attention meditation (FAM), and open monitoring meditation (OMM) styles, has seen a long tradition of theoretical, affective, neurophysiological and clinical studies. In particular, the high temporal resolution of magnetoencephalography (MEG) or electroencephalography (EEG) has been exploited to fill the gap between the personal experience of meditation practice and its neural correlates. Mounting evidence, in fact, shows that human brain activity is highly dynamic, transiting between different brain states (microstates). In this study, we aimed at exploring MEG microstates at source-level during FAM, OMM and in the resting state, as well as the complexity and criticality of dynamic transitions between microstates. Methods: Ten right-handed Theravada Buddhist monks with a meditative expertise of minimum 2,265 h participated in the experiment. MEG data were acquired during a randomized block design task (6 min FAM, 6 min OMM, with each meditative block preceded and followed by 3 min resting state). Source reconstruction was performed using eLORETA on individual cortical space, and then parcellated according to the Human Connect Project atlas. Microstate analysis was then applied to parcel level signals in order to derive microstate topographies and indices. In addition, from microstate sequences, the Hurst exponent and the Lempel-Ziv complexity (LZC) were computed. Results: Our results show that the coverage and occurrence of specific microstates are modulated either by being in a meditative state or by performing a specific meditation style. Hurst exponent values in both meditation conditions are reduced with respect to the value observed during rest, LZC shows significant differences between OMM, FAM, and REST, with a progressive increase from REST to FAM to OMM. Discussion: Importantly, we report changes in brain criticality indices during meditation and between meditation styles, in line with a state-like effect of meditation on cognitive performance. In line with previous reports, we suggest that the change in cognitive state experienced in meditation is paralleled by a shift with respect to critical points in brain dynamics. [ABSTRACT FROM AUTHOR]

Background: Dementia and mild cognitive impairment are characterised by symptoms of cognitive decline, which are typically assessed using neuropsychological assessments (NPAs), such as the Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB). Magnetoencephalography (MEG) is a novel clinical assessment technique that measures brain activities (summarised as oscillatory parameters), which are associated with symptoms of cognitive impairment. However, the relevance of MEG and regional cerebral blood flow (rCBF) data obtained using single-photon emission computed tomography (SPECT) has not been examined using clinical datasets. Therefore, this study aimed to investigate the relationships among MEG oscillatory parameters, clinically validated biomarkers computed from rCBF, and NPAs using outpatient data retrieved from hospital records. Methods: Clinical data from 64 individuals with mixed pathological backgrounds were retrieved and analysed. MEG oscillatory parameters, including relative power (RP) from delta to high gamma bands, mean frequency, individual alpha frequency, and Shannon's spectral entropy, were computed for each cortical region. For SPECT data, three pathological parameters--'severity', 'extent', and 'ratio'--were computed using an easy z-score imaging system (eZIS). As for NPAs, the MMSE and FAB scores were retrieved. Results: MEG oscillatory parameters were correlated with eZIS parameters. The eZIS parameters associated with Alzheimer's disease pathology were reflected in theta power augmentation and slower shift of the alpha peak. Moreover, MEG oscillatory parameters were found to reflect NPAs. Global slowing and loss of diversity in neural oscillatory components correlated with MMSE and FAB scores, whereas the associations between eZIS parameters and NPAs were sparse. Conclusion: MEG oscillatory parameters correlated with both SPECT (i.e. eZIS) parameters and NPAs, supporting the clinical validity of MEG oscillatory parameters as pathological and symptomatic indicators. The findings indicate that various components of MEG oscillatory characteristics can provide valuable pathological and symptomatic information, making MEG data a rich resource for clinical examinations of patients with cognitive impairments. SPECT (i.e. eZIS) parameters showed no correlations with NPAs. The results contributed to a better understanding of the characteristics of electrophysiological and pathological examinations for patients with cognitive impairments, which will help to facilitate their co-use in clinical application, thereby improving patient care. [ABSTRACT FROM AUTHOR]

Drug-resistant epilepsy (DRE) is often treated with surgery or neuromodulation. Specifically, responsive neurostimulation (RNS) is a widely used therapy that is programmed to detect abnormal brain activity and intervene with tailored stimulation. Despite the success of RNS, some patients require further interventions. However, having an RNS device in situ is a hindrance to the performance of neuroimaging techniques. Magnetoencephalography (MEG), a non-invasive neurophysiologic and functional imaging technique, aids epilepsy assessment and surgery planning. MEG performed post-RNS is complicated by signal distortions. This study proposes an independent component analysis (ICA)-based approach to enhance MEG signal quality, facilitating improved assessment for epilepsy patients with implanted RNS devices. Three epilepsy patients, two with RNS implants and one without, underwent MEG scans. Preprocessing included temporal signal space separation (tSSS) and an automated ICA-based approach with MNE-Python. Power spectral density (PSD) and signal-to-noise ratio (SNR) were analyzed, and MEG dipole analysis was conducted using single equivalent current dipole (SECD) modeling. The ICA-based noise removal preprocessing method substantially improved the signal-to-noise ratio (SNR) for MEG data from epilepsy patients with implanted RNS devices. Qualitative assessment confirmed enhanced signal readability and improved MEG dipole analysis. ICA-based processing markedly enhanced MEG data quality in RNS patients, emphasizing its clinical relevance. [ABSTRACT FROM AUTHOR]

Explored through EEG/MEG, auditory stimuli function as a suitable research probe to reveal various neural activities, including event-related potentials, brain oscillations and functional connectivity. Accumulating evidence in this field stems from studies investigating neuroplasticity induced by long-term auditory training, specifically cross-sectional studies comparing musicians and non-musicians as well as longitudinal studies with musicians. In contrast, studies that address the neural effects of short-term interventions whose duration lasts from minutes to hours are only beginning to be featured. Over the past decade, an increasing body of evidence has shown that short-term auditory interventions evoke rapid changes in neural activities, and oscillatory fluctuations can be observed even in the prestimulus period. In this scoping review, we divided the extracted neurophysiological studies into three groups to discuss neural activities with short-term auditory interventions: the pre-stimulus period, during stimulation, and a comparison of before and after stimulation. We show that oscillatory activities vary depending on the context of the stimuli and are greatly affected by the interplay of bottom-up and top-down modulational mechanisms, including attention. We conclude that the observed rapid changes in neural activitiesin the auditory cortex and the higher-order cognitive part of the brain are causally attributed to short-term auditory interventions. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) recordings are often contaminated by interference that can exceed the amplitude of physiological brain activity by several orders of magnitude. Furthermore, the activity of interference sources may spatially extend (known as source leakage) into the activity of brain signals of interest, resulting in source estimation inaccuracies. This problem is particularly apparent when using MEG to interrogate the effects of brain stimulation on large‐scale cortical networks. In this technical report, we develop a novel denoising approach for suppressing the leakage of interference source activity into the activity representing a brain region of interest. This approach leverages spatial and temporal domain projectors for signal arising from prespecified anatomical regions of interest. We apply this denoising approach to reconstruct simulated evoked response topographies to deep brain stimulation (DBS) in a phantom recording. We highlight the advantages of our approach compared to the benchmark—spatiotemporal signal space separation—and show that it can more accurately reveal brain stimulation‐evoked response topographies. Finally, we apply our method to MEG recordings from a single patient with Parkinson's disease, to reveal early cortical‐evoked responses to DBS of the subthalamic nucleus. [ABSTRACT FROM AUTHOR]

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g. , the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g , the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales. • A novel generative model (RHoMM) can predict the dynamics of MEG/EEG time-series. • RHoMM is based on a recurrent Hopfield neural network, connecting brain regions. • RHoMM is data-driven, uses a few hyperparameters, and can be easily scaled. • The functional architecture generated by RHoMM's reproduces the experimental data. [ABSTRACT FROM AUTHOR]

This paper reports a novel four-port multiband MIMO antenna technology radiating electromagnetic energy which is designed and fabricated on FR4 substrate for n77/n78 and n79 bands which are implemented for mobile users with compact dimensions of 0.997λ0 × 0.997λ0 (λ0 at 3.25 GHz) mm2. The miniaturization of the design utilizes a square slotted substrate with an additional four circular-rectangular dielectric for EM wave propagation. The top plane of the substrate is utilized with a printed circular patch which is also etched with five-star-fractal geometry and is connected to a microstrip feed line. The bottom plane corresponds to the partial ground which is chamfered at the edges. Also, a circular parasitic structure is placed beneath the radiating patch. The complete structure forms 4-port MIMO configuration covering bandwidth of 3.25–3.90 GHz (n77/n78: S(11max) = − 24.52 dB at 3.55 GHz) and 4.37–4.95 GHz (n79: S11(max) = − 19.79 dB at 4.51 GHz). The simulated and measured results extracted from the simulator, equivalent circuit model, and developed prototype are in good agreement. The MIMO antenna offers good radiation patterns in both principal planes with reduced cross-polarizations. The exceptional diversity performance including ECC(4 × 4) < 0.005, DG(4 × 4) ≈ 10.0 dB, TARC(4 × 4) < − 10.0 dB, CCL(4 × 4) < 0.0052 b/s/Hz and MEG(4 × 4) ≈ − 3.08 dB suggest the MIMO antenna is viable for 5G-Sub 6 GHz applications. [ABSTRACT FROM AUTHOR]

Symptomatic dermographism (SD) is a common form of urticaria, which is triggered by stroking the skin. Brain involvement in its aetiology was investigated by means of magnetoencephalography (MEG) after provocation with histamine and dermography. Wheals were induced by histamine skin prick test and dermography in twelve SD patients and fourteen controls. Itch severity was scored on a Visual Analogue Scale (VAS). Relative power and functional connectivity (FC) were measured using a 306‐channel whole‐head MEG system at baseline and 10 min after histamine and dermography, and contrasted between groups and conditions. Furthermore, wheal diameter and itch scores after these procedures were correlated with the MEG values. SD patients had higher itch scores after histamine and dermography. No significant group‐differences were observed in relative power or FC for any condition. In both groups, power decreases were mostly observed in the beta band, and power increases in the alpha bands, after provocation, with more regions involved in patients compared to controls. Increased FC was seen after histamine in patients, and after dermography in controls. In patients only, dermography and histamine wheal size correlated with the alpha2 power in the regions of interest that showed significant condition effects after these procedures. Our findings may be cautiously interpreted as aberrant itch processing, and suggest involvement of the central nervous system in the aetiology of SD. [ABSTRACT FROM AUTHOR]

Although a number of high-risk breast cancer genes have been identified, including BRCA1 and BRCA2, the risk profile of many high-risk families cannot be explained using known breast cancer genes. Previously, we have shown strong indications of new breast cancer risk loci at chromosomes 2p, 6q, and 14q in a family of six generations including 10 breast cancer cases. In this study, we identified and traced four new family branches descending from siblings of the parents in the top generation of the studied family. One distantly related branch included four breast cancer cases, two of whom were diagnosed at age < 45 years. DNA samples from the cases were typed at selected polymorphic markers from all three chromosome loci, to test identical origin of the haplotypes. All four cases were shown to segregate a common 6q haplotype with a region identical to the previously identified 6q haplotype. The data strongly support a new breast cancer locus at 6q, and narrow it down to a 17 MB interval at 6q15-q21. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: We aimed to explore the value of magnetoencephalography in the presurgical evaluation of patients with posterior cortex epilepsy. Methods: A total of 39 patients with posterior cortex epilepsy (PCE) and intact magnetoencephalography (MEG) images were reviewed from August 2019 to July 2022. MEG dipole clusters were classified into single clusters, multiple clusters, and scatter dipoles based on tightness criteria. The association of the surgical outcome with MEG dipole classifications was evaluated using Fisher's exact tests. Results: Among the 39 cases, there were 24 cases of single clusters (61.5%), nine cases of multiple clusters (23.1%), and six cases of scattered dipoles (15.4%). Patients with single dipole clusters were more likely to become seizure‐free. Among single dipole cluster cases (n = 24), complete MEG dipole resection yielded a more favorable surgical outcome than incomplete resection (83.3% vs. 16.7%, p = 0.007). Patients with concordant MRI and MEG findings achieved a significantly more favorable surgical outcome than discordant patients (66.7% vs. 33.3%, p = 0.044), especially in single dipole cluster patients (87.5% vs. 25.0%, p = 0.005). Significance: MEG can provide additional valuable information regarding surgical candidate selection, epileptogenic zone localization, electrode implantation schedule, and final surgical planning in patients with posterior cortex epilepsy. [ABSTRACT FROM AUTHOR]

• EEG and MEG are useful tools for diagnosing types of epilepsy. • Lack of spikes in MEG/EEG data analysis often yields little valuable information. • Generalised epilepsy has a distinctive resting-state brain activity pattern. • The pattern is detectable regardless of the appearance of spikes. • Resting-state brain activity contains information about the subtype of epilepsy. Epilepsy is a prevalent neurological disorder characterised by repetitive seizures. It is categorised into three types: generalised epilepsy (GE), focal epilepsy (FE), and combined generalised and focal epilepsy. Correctly subtyping the epilepsy is important to select appropriate treatments. The types are mainly determined (i.e., diagnosed) by their semiologies supported by clinical examinations, such as electroencephalography and magnetoencephalography (MEG). Although these examinations are traditionally based on visual inspections of interictal epileptic discharges (IEDs), which are not always visible, alternative analyses have been anticipated. We examined if resting-state brain activities can distinguish patients with GE, which would help us to diagnose the type of epilepsy. The 5 min resting-state brain activities acquired using MEG were obtained retrospectively from 15 patients with GE. The cortical source of the activities was estimated at each frequency band from delta to high-frequency oscillation (HFO). These estimated activities were compared with reference datasets from 133 healthy individuals and control data from 29 patients with FE. Patients with GE showed larger theta in the occipital, alpha in the left temporal, HFO in the rostral deep regions, and smaller HFO in the caudal ventral regions. Their area under the curves of the receiver operating characteristic curves was around 0.8–0.9. The distinctive pattern was not found for data from FE. Patients with GE show distinctive resting-state brain activity, which could be a potential biomarker and used complementarily to classical analysis based on the visual inspection of IEDs. [ABSTRACT FROM AUTHOR]

Purpose of the Review: Magnetoencephalography (MEG) is a functional neuroimaging technique that records neurophysiology data with millisecond temporal resolution and localizes it with subcentimeter accuracy. Its capability to provide high resolution in both of these domains makes it a powerful tool both in basic neuroscience as well as clinical applications. In neurology, it has proven useful in its ability to record and localize epileptiform activity. Epilepsy workup typically begins with scalp electroencephalography (EEG), but in many situations, EEG-based localization of the epileptogenic zone is inadequate. The complementary sensitivity of MEG can be crucial in such cases, and MEG has been adopted at many centers as an important resource in building a surgical hypothesis. In this paper, we review recent work evaluating the extent of MEG influence of presurgical evaluations, novel analyses of MEG data employed in surgical workup, and new MEG instrumentation that will likely affect the field of clinical MEG. Recent Findings: MEG consistently contributes to presurgical evaluation and these contributions often change the plan for epilepsy surgery. Extensive work has been done to develop new analytic methods for localizing the source of epileptiform activity with MEG. Systems using optically pumped magnetometry (OPM) have been successfully deployed to record and localize epileptiform activity. Summary: MEG remains an important noninvasive tool for epilepsy presurgical evaluation. Continued improvements in analytic methodology will likely increase the diagnostic yield of the test. Novel instrumentation with OPM may contribute to this as well, and may increase accessibility of MEG by decreasing cost. [ABSTRACT FROM AUTHOR]

In this study, we investigated how empathic neural responses unfold over time in different empathy networks when viewing same-race and other-race individuals in dynamic painful conditions. We recorded magnetoencephalography signals from Chinese adults when viewing video clips showing a dynamic painful (or non-painful) stimulation to Asian and White models' faces to trigger painful (or neutral) expressions. We found that perceived dynamic pain in Asian models modulated neural activities in the visual cortex at 100 ms–200 ms, in the orbitofrontal and subgenual anterior cingulate cortices at 150 ms–200 ms, in the anterior cingulate cortex around 250 ms–350 ms, and in the temporoparietal junction and middle temporal gyrus around 600 ms after video onset. Perceived dynamic pain in White models modulated activities in the visual, anterior cingulate, and primary sensory cortices after 500 ms. Our findings unraveled earlier dynamic activities in multiple neural circuits in response to same-race (vs other-race) individuals in dynamic painful situations. [ABSTRACT FROM AUTHOR]

Objective: With research progress on Rolandic epilepsy (RE), its "benign" nature has been phased out. Clinicians are exhibiting an increasing tendency toward a more assertive treatment approach for RE. Nonetheless, in clinical practice, delayed treatment remains common because of the "self‐limiting" nature of RE. Therefore, this study aimed to identify an imaging marker to aid treatment decisions and select a more appropriate time for initiating therapy for RE. Methods: We followed up with children newly diagnosed with RE, classified them into medicated and non‐medicated groups according to the follow‐up results, and compared them with matched healthy controls. Before beginning follow‐up visits, interictal magnetic data were collected using magnetoencephalography in treatment‐naïve recently diagnosed patients. The spectral power of the whole brain during initial diagnosis was determined using minimum normative estimation combined with the Welch technique. Results: A difference was observed in the magnetic source intensity within the left caudal anterior cingulate and precentral and postcentral gyri in the delta band between the medicated and non‐medicated groups. The results revealed good discriminatory ability within the receiver operator characteristic curve. In the medicated group, there was a specific change in the frontotemporal magnetic source intensity, which shifted from high to low frequencies, compared with the healthy control group. Significance: The intensity of the precentral gyrus magnetic source within the delta band showed good specificity. Considering the rigor of initial treatment, the intensity of the precentral gyrus magnetic source can provide some help as an imaging marker for initial RE treatment, particularly for the timing of treatment initiation. [ABSTRACT FROM AUTHOR]

Individuals' phenotypes, including the brain's structure and function, are largely determined by genes and their interplay. The resting brain generates salient rhythmic patterns that can be characterized noninvasively using functional neuroimaging such as magnetoencephalography (MEG). One of these rhythms, the somatomotor (rolandic) beta rhythm, shows intermittent high amplitude "events" that predict behavior across tasks and species. Beta rhythm is altered in neurological disease. The aperiodic (1/f) signal present in electrophysiological recordings is also modulated by some neurological conditions and aging. Both sensorimotor beta and aperiodic signal could thus serve as biomarkers of sensorimotor function. Knowledge about the extent to which these brain functional measures are heritable could shed light on the mechanisms underlying their generation. We investigated the heritability and variability of human spontaneous sensorimotor beta rhythm events and aperiodic activity in 210 healthy male and female adult siblings' spontaneous MEG activity. The most heritable trait was the aperiodic 1/f signal, with a heritability of 0.87 in the right hemisphere. Time-resolved beta event amplitude parameters were also highly heritable, whereas the heritabilities for overall beta power, peak frequency, and measures of event duration remained nonsignificant. Human sensorimotor neural activity can thus be dissected into different components with variable heritability. We postulate that these differences partially reflect different underlying signalgenerating mechanisms. The 1/f signal and beta event amplitude measures may depend more on fixed, anatomical parameters, whereas beta event duration and its modulation reflect dynamic characteristics, guiding their use as potential disease biomarkers. [ABSTRACT FROM AUTHOR]

Neuroaesthetic research has focused on neural predictive processes involved in the encounter with art stimuli or the related evaluative judgements, and it has been mainly conducted unimodally. Here, with electroencephalography, magnetoencephalography and an affective priming protocol, we investigated whether and how the neural responses to non-representational aesthetic stimuli are top-down modulated by affective representational (i.e. semantically meaningful) predictions between audition and vision. Also, the neural chronometry of affect processing of these aesthetic stimuli was investigated. We hypothesized that the early affective components of crossmodal aesthetic responses are dependent on the affective and representational predictions formed in another sensory modality resulting in differentiated brain responses, and that audition and vision indicate different processing latencies for affect. The target stimuli were aesthetic visual patterns and musical chords, and they were preceded by a prime from the opposing sensory modality. We found that early auditory-cortex responses to chords were more affected by valence than the corresponding visual-cortex ones. Furthermore, the assessments of visual targets were more facilitated by affective congruency of crossmodal primes than the acoustic targets. These results indicate, first, that the brain uses early affective information for predictively guiding aesthetic responses; second, that an affective transfer of information takes place crossmodally, mainly from audition to vision, impacting the aesthetic assessment. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'. [ABSTRACT FROM AUTHOR]

The brain operates in a flexible dynamic regime, generating complex patterns of activity (i.e. neuronal avalanches). This study aimed at describing how brain dynamics change according to menstrual cycle (MC) phases. Brain activation patterns were estimated from resting-state magnetoencephalography (MEG) scans, acquired from women at early follicular (T1), peri-ovulatory (T2) and mid-luteal (T3) phases of the MC. We investigated the functional repertoire (number of brain configurations based on fast high-amplitude bursts of the brain signals) and the region-specific influence on large-scale dynamics across the MC. Finally, we assessed the relationship between sex hormones and changes in brain dynamics. A significantly larger number of visited configurations in T2 as compared to T1 was specifically observed in the beta frequency band. No relationship between changes in brain dynamics and sex hormones was evident. Finally, we showed that the left posterior cingulate gyrus and the right insula were recruited more often in the functional repertoire during T2 as compared to T1, while the right pallidum was more often part of the functional repertoires during T1 as compared to T2. In summary, we showed hormone-independent increased flexibility of the brain dynamics during the ovulatory phase. Moreover, we demonstrated that several specific brain regions play a key role in determining this change. [ABSTRACT FROM AUTHOR]

Background: Epileptic seizures are an established comorbidity of Alzheimer's disease (AD). Subclinical epileptiform activity (SEA) as detected by 24-h electroencephalography (EEG) or magneto-encephalography (MEG) has been reported in temporal regions of clinically diagnosed AD patients. Although epileptic activity in AD probably arises in the mesial temporal lobe, electrical activity within this region might not propagate to EEG scalp electrodes and could remain undetected by standard EEG. However, SEA might lead to faster cognitive decline in AD. Aims: 1. To estimate the prevalence of SEA and interictal epileptic discharges (IEDs) in a well-defined cohort of participants belonging to the AD continuum, including preclinical AD subjects, as compared with cognitively healthy controls. 2. To evaluate whether long-term-EEG (LTM-EEG), high-density-EEG (hd-EEG) or MEG is superior to detect SEA in AD. 3. To characterise AD patients with SEA based on clinical, neuropsychological and neuroimaging parameters. Methods: Subjects (n = 49) belonging to the AD continuum were diagnosed according to the 2011 NIA-AA research criteria, with a high likelihood of underlying AD pathophysiology. Healthy volunteers (n = 24) scored normal on neuropsychological testing and were amyloid negative. None of the participants experienced a seizure before. Subjects underwent LTM-EEG and/or 50-min MEG and/or 50-min hd-EEG to detect IEDs. Results: We found an increased prevalence of SEA in AD subjects (31%) as compared to controls (8%) (p = 0.041; Fisher's exact test), with increasing prevalence over the disease course (50% in dementia, 27% in MCI and 25% in preclinical AD). Although MEG (25%) did not withhold a higher prevalence of SEA in AD as compared to LTM-EEG (19%) and hd-EEG (19%), MEG was significantly superior to detect spikes per 50 min (p = 0.002; Kruskall–Wallis test). AD patients with SEA scored worse on the RBANS visuospatial and attention subset (p = 0.009 and p = 0.05, respectively; Mann–Whitney U test) and had higher left frontal, (left) temporal and (left and right) entorhinal cortex volumes than those without. Conclusion: We confirmed that SEA is increased in the AD continuum as compared to controls, with increasing prevalence with AD disease stage. In AD patients, SEA is associated with more severe visuospatial and attention deficits and with increased left frontal, (left) temporal and entorhinal cortex volumes. Trial registration: Clinicaltrials.gov, NCT04131491. 12/02/2020. [ABSTRACT FROM AUTHOR]