Magnetoencephalography Research Articles

Analysis and suppression of cross-axis modulation coupling errors in optically pumped magnetometers

We propose a method based on sensitive-axis high-frequency modulation (SHFM) method combined with PI closed-loop feedback to suppress the cross-axis modulation coupling error (CMCE) of the optically pumped magnetometer (OPM). Cross-axis modulation is a prevalent approach for three-axis measurement of OPM, but this scheme will introduce CMCE. These errors have the potential to pose undetected hazards in applications of multi-axis magnetic field measurement such as magnetoencephalography (MEG). The SHFM method suppressed the CMCE from 18.50% to 2.10% on average compared to the traditional method. Based on the static situation we also introduce PI closed-loop feedback, which suppressed the CMCE from 22.07% in open-loop mode to 2.10% in closed-loop mode. This approach is both straightforward and efficacious, offering seamless integration into an arrayed OPM-based system. Furthermore, SHFM has the potential to mitigate the challenges associated with MEG localization accuracy and other measurement errors introduced by CMCE, and further unleash the potential of applications of OPM–MEG system.

Maternal Nutrition during Pregnancy and Offspring Brain Development: Insights from Neuroimaging.

Maternal nutrition during pregnancy is known to be important for offspring growth and health and has also been increasingly recognized for shaping offspring brain development. On the other hand, recent advancements in brain imaging technology have provided unprecedented insights into fetal, neonatal, and pediatric brain morphometry and function. This review synthesizes the current literature regarding the impact of maternal nutrition on offspring brain development, with a specific focus on findings from neuroimaging studies. The diverse effects of maternal nutrients intake or status during pregnancy on neurodevelopmental outcomes in children are discussed. Neuroimaging evidence showed associations between maternal nutrition such as food categories, macronutrients, and micronutrients including vitamins and minerals during pregnancy and child brain imaging features measured using imaging techniques such as ultrasound, magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG). This review demonstrates the capability of neuroimaging in characterizing how maternal nutrition during pregnancy impacts structure and function of the developing brain that may further influence long-term neuropsychological, cognitive, and behavioral outcomes in children. It aims to inspire future research utilizing neuroimaging to deepen our understanding of the critical impacts of maternal nutrition during pregnancy on offspring brain development.

Magnetoencephalography Profile of Patients with Drug-Resistant Focal Epilepsy and Normal MRI.

Magnetoencephalography (MEG) could be a valuable tool in the presurgical evaluation of drug-resistant epilepsy (DRE), especially when the initial evaluation is inconclusive. In this retrospective study, we describe the profile of MEG in patients with DRE and normal magnetic resonance imaging (MRI). We included patients with focal epilepsy and normal MRI who underwent presurgical evaluation for DRE. MEG profiles of these patients, including the frequency of spikes, density of clusters, number of clusters, and concordance with video electroencephalography (VEEG), were analyzed. Of the 73 patients included, magnetic source imaging (MSI) provided localizing information in 51 (69.9%) patients. Among patients with localizing MEG findings, localizing information on VEEG too was noted in 42 (57.5% of the whole cohort). Thirty-one (42.5%) patients had concordant findings with region-specific localization, six (8.2%) patients had partial concordance, and five (6.8%) subjects showed discordant findings. There was a moderate agreement for the presumed epileptogenic zone in comparing findings derived from MEG and VEEG (kappa value of 0.451, P < 0.001). The agreement was lower when MEG localized to the frontal lobe (kappa value of 0.379, P = 0.001) than the temporal lobe (kappa value 0.442, P = 0.002). MEG can provide localizing information in most patients with a normal MRI. A moderate degree of agreement between localization by MEG and VEEG was noted. These findings highlight the usefulness of MSI in the presurgical evaluation of MRI-negative DRE.

Potential Biomarkers of Dysmenorrhea Relief: A MEG Study of Hinoki Aromatherapy and Working Memory.

Background/Objectives: This study explored the potential of Hinoki aromatherapy to induce biomarkers of dysmenorrhea relief through working memory. Structural magnetic resonance imaging and magnetoencephalography (MEG) were used to examine their effects on neurophysiological responses to a visual working memory (VWM) test. Behavioral performance was measured to understand its effects on the overall working memory. Methods: Twenty-four healthy participants completed the VWM task during nonmenstruation and menstruation. Behavioral (accuracy and reaction time) and neurophysiological (event-related fields, source estimation, and permutation t-test on source data) measures were assessed without and with Hinoki aromatherapy. Results: A significant difference in the ratio of accuracy to reaction time was found without and with aromatherapy in participants with dysmenorrhea, suggesting that aromatherapy may improve working memory performance in this population. MEG analysis revealed high temporal resolution of evoked latency and intensity during the VWM task. Source localization of the activation aimed to identify brain areas involved in dysmenorrhea. Aromatherapy reduced signals in these areas, which may also contribute to reducing dysmenorrhea-related visual signals. Conclusions: Based on these findings, Hinoki aromatherapy may be a promising treatment option for alleviating dysmenorrhea and improving related symptoms by reducing activity in brain pain processing regions. These regions include the left entorhinal cortex, inferior temporal gyrus, primary visual cortex, retrosplenial cortex, and presubiculum. Furthermore, decreased activity in these areas with aromatherapy suggests that they could be used as biomarkers of dysmenorrhea relief.

A novel, robust, and portable platform for magnetoencephalography using optically-pumped magnetometers.

Magnetoencephalography (MEG) measures brain function via assessment of magnetic fields generated by neural currents. Conventional MEG uses superconducting sensors, which place significant limitations on performance, practicality, and deployment; however, the field has been revolutionised in recent years by the introduction of optically-pumped magnetometers (OPMs). OPMs enable measurement of the MEG signal without cryogenics, and consequently the conception of "OPM-MEG" systems which ostensibly allow increased sensitivity and resolution, lifespan compliance, free subject movement, and lower cost. However, OPM-MEG is in its infancy with existing limitations on both sensor and system design. Here, we report a new OPM-MEG design with miniaturised and integrated electronic control, a high level of portability, and improved sensor dynamic range. We show that this system produces equivalent measures compared with an established OPM-MEG instrument; specifically, when measuring task-induced beta-band, gamma-band, and evoked neuro-electrical responses, source localisations from the two systems were comparable and temporal correlation of measured brain responses was >0.7 at the individual level and >0.9 for groups. Using an electromagnetic phantom, we demonstrate improved dynamic range by running the system in background fields up to 8 nT. We show that the system is effective in gathering data during free movement (including a sitting-to-standing paradigm) and that it is compatible with simultaneous electroencephalography (EEG). Finally, we demonstrate portability by moving the system between two laboratories. Overall, our new system is shown to be a significant step forward for OPM-MEG and offers an attractive platform for next generation functional medical imaging.

Online functional connectivity analysis of large all-to-all networks in MNE Scan

Abstract The analysis of electroencephalography (EEG)/magnetoencephalography (MEG) functional connectivity has become an important tool in neuroscience. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, that is, after the conclusion of the experiment. However, online computation of functional connectivity has the potential to enable unique experimental paradigms. For example, changes of functional connectivity due to learning processes could be tracked in real time and the experiment be adjusted based on these observations. Furthermore, the connectivity estimates can be used for neurofeedback applications or the instantaneous inspection of measurement results. In this study, we present the implementation and evaluation of online sensor and source space functional connectivity estimation in the open-source software MNE Scan. Online capable implementations of several functional connectivity metrics were established in the Connectivity library within MNE-CPP and made available as a plugin in MNE Scan. Online capability was achieved by enforcing multithreading and high efficiency for all computations, so that repeated computations were avoided wherever possible, which allows for a major speed-up in the case of overlapping intervals. We present comprehensive performance evaluations of these implementations proving the online capability for the computation of large all-to-all functional connectivity networks. As a proof of principle, we demonstrate the feasibility of online functional connectivity estimation in the evaluation of somatosensory evoked brain activity

Wireless optically pumped magnetometer MEG

The current magnetoencephalography (MEG) systems, which rely on cables for control and signal transmission, do not fully realize the potential of wearable optically pumped magnetometers (OPM). This study presents a significant advancement in wireless OPM-MEG by reducing magnetization in the electronics and developing a tailored wireless communication protocol. Our protocol effectively eliminates electromagnetic interference, particularly in the critical frequency bands of MEG signals, and accurately synchronizes the acquisition and stimulation channels with the host computer's clock. We have successfully achieved single-channel wireless OPM-MEG measurement and demonstrated its reliability by replicating three well-established experiments: The alpha rhythm, auditory evoked field, and steady-state visual evoked field in the human brain. Our prototype wireless OPM-MEG system not only streamlines the measurement process but also represents a major step forward in the development of wearable OPM-MEG applications in both neuroscience and clinical research.

People with HIV exhibit spectrally distinct patterns of rhythmic cortical activity serving cognitive flexibility

Despite effective antiretroviral therapy, cognitive impairment remains prevalent among people with HIV (PWH) and decrements in executive function are particularly prominent. One component of executive function is cognitive flexibility, which integrates a variety of executive functions to dynamically adapt one's behavior in response to changing contextual demands. Though substantial work has illuminated HIV-related aberrations in brain function, it remains unclear how the neural oscillatory dynamics serving cognitive flexibility are affected by HIV-related alterations in neural functioning. Herein, 149 participants (PWH: 74; seronegative controls: 75) between the ages of 29–76 years completed a perceptual feature matching task that probes cognitive flexibility during high-density magnetoencephalography (MEG). Neural responses were decomposed into the time-frequency domain and significant oscillatory responses in the theta (4–8 Hz), alpha (10–16 Hz), and gamma (74–98 Hz) spectral windows were imaged using a beamforming approach. Whole-brain voxel-wise comparisons were then conducted on these dynamic functional maps to identify HIV-related differences in the neural oscillatory dynamics supporting cognitive flexibility. Our findings indicated group differences in alpha oscillatory activity in the cingulo-opercular cortices, and differences in gamma activity were found in the cerebellum. Across all participants, alpha and gamma activity in these regions were associated with performance on the cognitive flexibility task. Further, PWH who had been treated with antiretroviral therapy for a longer duration and those with higher current CD4 counts had alpha responses that more closely resembled those of seronegative controls, suggesting that optimal clinical management of HIV infection is associated with preserved neural dynamics supporting cognitive flexibility.

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy.

For children with drug-resistant epilepsy (DRE), seizure freedom relies on the delineation and resection (or ablation/disconnection) of the epileptogenic zone (EZ) while preserving the eloquent brain areas. The development of a reliable and noninvasive localization method that provides clinically useful information for the localization of the EZ is, therefore, crucial to achieving successful surgical outcomes. Electric and magnetic source imaging (ESI and MSI) have been increasingly utilized in the presurgical evaluation of these patients showing promising findings in the delineation of epileptogenic as well as eloquent brain areas. Moreover, the combination of ESI and MSI into a single solution, namely electromagnetic source imaging (EMSI), performed on simultaneous high-density electroencephalography (HD-EEG) and magnetoencephalography (MEG) recordings has shown higher source localization accuracy than either modality alone. Despite these encouraging findings, such techniques are performed in only a few tertiary epilepsy centers, are rarely recorded simultaneously, and are underutilized in pediatric cohorts. This study illustrates the experimental setup for recording simultaneous MEG and HD-EEG data as well as the methodological framework for analyzing these data aiming to localize the irritative zone, the seizure onset zone, and eloquent brain areas in children with DRE. More specifically, the experimental setups are presented for (i) recording and localizing interictal and ictal epileptiform activity during sleep and (ii) recording visual-, motor-, auditory-, and somatosensory-evoked responses and mapping relevant eloquent brain areas (i.e., visual, motor, auditory, and somatosensory) during visuomotor task, as well as auditory and somatosensory stimulations. Detailed steps of the data analysis pipeline are further presented for performing EMSI as well as individual ESI and MSI using equivalent current dipole (ECD) and dynamic statistical parametric mapping (dSPM).

Reliable measurement of auditory-driven gamma synchrony with a single EEG electrode: A simultaneous EEG-MEG study

ObjectiveAuditory-driven gamma synchrony (GS) is linked to the function of a specific cortical circuit based on a parvalbumin+ and pyramidal neuron loop. This circuit is impaired in neuropsychiatric conditions (i.e. schizophrenia, Alzheimer's disease, stroke etc.) and its relevance in clinical practice is increasingly being recognized. Auditory stimulation at a typical gamma frequency of 40 Hz can be applied as a ‘stress test’ of excitation/inhibition (E/I) of the entire cerebral cortex, to drive GS and record it with magnetoencephalography (MEG) or high-density electroencephalography (EEG). However, these two techniques are costly and not widely available. Therefore, we assessed whether a single EEG electrode is sufficient to provide an accurate estimate of the auditory-driven GS level of the entire cortical surface while expecting the highest correspondence in the auditory and somatosensory cortices. MethodsWe measured simultaneous EEG-MEG in 29 healthy subjects, utilizing 3 EEG electrodes (C4, F4, O2) and a full MEG setup. Recordings were performed during binaural exposure to auditory gamma stimulation and during silence. We compared GS measurement of each of the three EEG electrodes separately against full MEG mapping. Time-resolved phase locking value (PLVt) was computed between EEG signals and cortex reconstructed MEG signals. ResultsDuring auditory stimulation, but not at rest, EEG captures a significant amount of GS, especially from both auditory cortices and motor-premotor regions. This was especially true for frontal (C4) and central electrodes (F4). Discussion and ConclusionsWhile hd-EEG and MEG are necessary for accurate spatial mapping of GS at rest and during auditory stimulation, a single EEG channel is sufficient to detect the global level of GS. These results have great translational potential for mapping GS in standard clinical settings.

Population receptive field models capture the event-related magnetoencephalography response with millisecond resolution

Abstract Much of the visual system is organized into visual field maps. In humans, this organization can be studied non-invasively by estimating the receptive fields of populations of neurons (population receptive fields; pRFs) with functional magnetic resonance imaging (fMRI). However, fMRI cannot capture the temporal dynamics of visual processing that operate on a millisecond scale. Magnetoencephalography (MEG) does provide this temporal resolution but generally lacks the required spatial resolution. Here, we introduce a forward modeling approach that combines fMRI and MEG, enabling us to estimate pRFs with millisecond resolution. Using fMRI, we estimated the participant’s pRFs using conventional pRF-modeling. We then combined the pRF models with a forward model that transforms the cortical responses to the MEG sensors. This enabled us to predict event-related field responses measured with MEG while the participants viewed brief (100 ms) contrast-defined bar and circle shapes. We computed the goodness of fit between the predicted and measured MEG responses across time using cross-validated variance explained. We found that the fMRI-estimated pRFs explained up to 91% of the variance in individual MEG sensor’s responses. The variance explained varied over time and peaked between 75 ms to 250 ms after stimulus onset. Perturbing the pRF positions decreased the explained variance, suggesting that the pRFs were driving the MEG responses. In conclusion, pRF models can predict event-related MEG responses, enabling routine investigation of the spatiotemporal dynamics of human pRFs with millisecond resolution.

Superconducting Self-Shielded and Zero-Boil-Off Magnetoencephalogram Systems: A Dry Phantom Evaluation.

Magnetoencephalography (MEG) systems are advanced neuroimaging tools used to measure the magnetic fields produced by neuronal activity in the human brain. However, they require significant amounts of liquid helium to keep the superconducting quantum interference device (SQUID) sensors in a stable superconducting state. Additionally, MEG systems must be installed in a magnetically shielded room to minimize interference from external magnetic fields. We have developed an advanced MEG system that incorporates a superconducting magnetic shield and a zero-boil-off system. This system overcomes the typical limitations of traditional MEG systems, such as the frequent need for liquid helium refills and the spatial constraints imposed by magnetically shielded rooms. To validate the system, we conducted an evaluation using signal source estimation. This involved a phantom with 50 current sources of known location and magnitude under active zero-boil-off conditions. Our evaluations focused on the precision of the magnetic field distribution and the quantification of estimation errors. We achieved a consistent magnetic field distribution that matched the source current, maintaining an estimation error margin within 3.5 mm, regardless of the frequency of the signal source current. These findings affirm the practicality and efficacy of the system.

Neurophysiological evidence of sensory prediction errors driving speech sensorimotor adaptation.

The human sensorimotor system has a remarkable ability to quickly and efficiently learn movements from sensory experience. A prominent example is sensorimotor adaptation, learning that characterizes the sensorimotor system's response to persistent sensory errors by adjusting future movements to compensate for those errors. Despite being essential for maintaining and fine-tuning motor control, mechanisms underlying sensorimotor adaptation remain unclear. A component of sensorimotor adaptation is implicit (i.e., the learner is unaware of the learning process) which has been suggested to result from sensory prediction errors-the discrepancies between predicted sensory consequences of motor commands and actual sensory feedback. However, to date no direct neurophysiological evidence that sensory prediction errors drive adaptation has been demonstrated. Here, we examined prediction errors via magnetoencephalography (MEG) imaging of the auditory cortex (n = 34) during sensorimotor adaptation of speech to altered auditory feedback, an entirely implicit adaptation task. Specifically, we measured how speaking-induced suppression (SIS)--a neural representation of auditory prediction errors--changed over the trials of the adaptation experiment. SIS refers to the suppression of auditory cortical response to speech onset (in particular, the M100 response) to self-produced speech when compared to the response to passive listening to identical playback of that speech. SIS was reduced (reflecting larger prediction errors) during the early learning phase compared to the initial unaltered feedback phase. Furthermore, reduction in SIS positively correlated with behavioral adaptation extents, suggesting that larger prediction errors were associated with more learning. In contrast, such a reduction in SIS was not found in a control experiment in which participants heard unaltered feedback and thus did not adapt. In addition, in some participants who reached a plateau in the late learning phase, SIS increased (reflecting smaller prediction errors), demonstrating that prediction errors were minimal when there was no further adaptation. Together, these findings provide the first neurophysiological evidence for the hypothesis that prediction errors drive human sensorimotor adaptation.

A standardized framework to test event-based experiments

The replication crisis in experimental psychology and neuroscience has received much attention recently. This has led to wide acceptance of measures to improve scientific practices, such as preregistration and registered reports. Less effort has been devoted to performing and reporting the results of systematic tests of the functioning of the experimental setup itself. Yet, inaccuracies in the performance of the experimental setup may affect the results of a study, lead to replication failures, and importantly, impede the ability to integrate results across studies. Prompted by challenges we experienced when deploying studies across six laboratories collecting electroencephalography (EEG)/magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and intracranial EEG (iEEG), here we describe a framework for both testing and reporting the performance of the experimental setup. In addition, 100 researchers were surveyed to provide a snapshot of current common practices and community standards concerning testing in published experiments’ setups. Most researchers reported testing their experimental setups. Almost none, however, published the tests performed or their results. Tests were diverse, targeting different aspects of the setup. Through simulations, we clearly demonstrate how even slight inaccuracies can impact the final results. We end with a standardized, open-source, step-by-step protocol for testing (visual) event-related experiments, shared via protocols.io. The protocol aims to provide researchers with a benchmark for future replications and insights into the research quality to help improve the reproducibility of results, accelerate multicenter studies, increase robustness, and enable integration across studies.

Source imaging method based on diagonal covariance bases and its applications to OPM-MEG

Magnetoencephalography (MEG) is a noninvasive imaging technique used in neuroscience and clinical research. The source estimation of MEG involves solving a highly underdetermined inverse problem, which requires additional constraints to restrict the solution space. Traditional methods tend to obscure the extent of the sources. However, an accurate estimation of the source extent is important for studying brain activity or preoperatively estimating pathogenic regions. To improve the estimation accuracy of the extended source extent, the spatial constraint of sources is employed in the Bayesian framework. For example, the source is decomposed into a linear combination of validated spatial basis functions, which is proved to improve the source imaging accuracy. In this work, we further construct the spatial properties of the source using the diagonal covariance bases (DCB), which we summarize as the source imaging method SI-DCB. In this approach, specifically, the covariance matrix of the spatial coefficients is modeled as a weighted combination of diagonal covariance basis functions. The convex analysis is used to estimate noise and model parameters under the Bayesian framework. Extensive numerical simulations showed that SI-DCB outperformed five benchmark methods in accurately estimating the location and extent of patch sources. The effectiveness of SI-DCB was verified through somatosensory stimulation experiments performed on a 31-channel OPM-MEG system. The SI-DCB correctly identified the source area where each brain response occurred. The superior performance of SI-DCB suggests that it can provide a template approach for improving the accuracy of source extent estimations under a sparse Bayesian framework.

Localized estimation of event-related neural source activity from simultaneous MEG-EEG with a recurrent neural network

Estimating intracranial current sources underlying the electromagnetic signals observed from extracranial sensors is a perennial challenge in non-invasive neuroimaging. Established solutions to this inverse problem treat time samples independently without considering the temporal dynamics of event-related brain processes.This paper describes current source estimation from simultaneously recorded magneto- and electro-encephalography (MEEG) using a recurrent neural network (RNN) that learns sequential relationships from neural data. The RNN was trained in two phases: (1) pre-training and (2) transfer learning with L1 regularization applied to the source estimation layer. Performance of using scaled labels derived from MEEG, magnetoencephalography (MEG), or electroencephalography (EEG) were compared, as were results from volumetric source space with free dipole orientation and surface source space with fixed dipole orientation. Exact low-resolution electromagnetic tomography (eLORETA) and mixed-norm L1/L2 (MxNE) source estimation methods were also applied to these data for comparison with the RNN method.The RNN approach outperformed other methods in terms of output signal-to-noise ratio, correlation and mean-squared error metrics evaluated against reference event-related field (ERF) and event-related potential (ERP) waveforms. Using MEEG labels with fixed-orientation surface sources produced the most consistent estimates.To estimate sources of ERF and ERP waveforms, the RNN generates temporal dynamics within its internal computational units, driven by sequential structure in neural data used as training labels. It thus provides a data-driven model of computational transformations from psychophysiological events into corresponding event-related neural signals, which is unique among MEEG source reconstruction solutions.

Abnormal inter-hemispheric effective connectivity from left to right auditory regions during Mismatch Negativity (MMN) tasks in psychosis

Anomalous Mismatch Negativity (MMN) in psychosis could be a consequence of disturbed neural oscillatory activity at sensory/perceptual stages of stimulus processing. This study investigated effective connectivity within and between the auditory regions during auditory odd-ball deviance tasks. The analyses were performed on two magnetoencephalography (MEG) datasets: one on duration MMN in a cohort with various diagnoses within the psychosis spectrum and neurotypical controls, and one on duration and pitch MMN in first-episode psychosis patients and matched neurotypical controls. We applied spectral Granger causality to MEG source-reconstructed signals to compute effective connectivity within and between the left and right auditory regions. Both experiments showed that duration-deviance detection was associated with early increases of effective connectivity in the beta band followed by increases in the alpha and theta bands, with the connectivity strength linked to the laterality of the MMN amplitude. Compared to controls, people with psychosis had overall smaller effective connectivity, particularly from left to right auditory regions, in the pathway where bilateral information converges toward lateralized processing, often rightward. Blunted MMN in psychosis might reflect a deficit in inter-hemispheric communication between auditory regions, highlighting a “dysconnection” already at preattentive stages of stimulus processing as a model system of widespread pathophysiology.

Improved Dipole Source Localization from Simultaneous MEG-EEG Data by Combining a Global Optimization Algorithm with a Local Parameter Search: A Brain Phantom Study.

Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.

Resting-state functional connectivity involved in tactile orientation processing

BackgroundGrating 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. MethodsIn 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. ResultsWe 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. DiscussionThe results of this study underpin the significance of specific cortico-cortical networks in recognizing variations in tactile stimuli.

Comparison between EEG and MEG of static and dynamic resting-state networks.

The characterisation of resting-state networks (RSNs) using neuroimaging techniques has significantly contributed to our understanding of the organisation of brain activity. Prior work has demonstrated the electrophysiological basis of RSNs and their dynamic nature, revealing transient activations of brain networks with millisecond timescales. While previous research has confirmed the comparability of RSNs identified by electroencephalography (EEG) to those identified by magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), most studies have utilised static analysis techniques, ignoring the dynamic nature of brain activity. Often, these studies use high-density EEG systems, which limit their applicability in clinical settings. Addressing these gaps, our research studies RSNs using medium-density EEG systems (61 sensors), comparing both static and dynamic brain network features to those obtained from a high-density MEG system (306 sensors). We assess the qualitative and quantitative comparability of EEG-derived RSNs to those from MEG, including their ability to capture age-related effects, and explore the reproducibility of dynamic RSNs within and across the modalities. Our findings suggest that both MEG and EEG offer comparable static and dynamic network descriptions, albeit with MEG offering some increased sensitivity and reproducibility. Such RSNs and their comparability across the two modalities remained consistent qualitatively but not quantitatively when the data were reconstructed without subject-specific structural MRI images.