Search Results (131)

Speech perception relies on distributed neuronal populations, yet traditional decoding often utilizes static strategies that overlook inherent temporal dependencies and dynamic regulation. Therefore, we introduce the concept of system identification into multivariate decoding. By modeling brain response characteristics through time-lagged regression between speech stimuli and neural responses, we propose a temporal response function-based representational similarity analysis method (TRF-RSA). This method models the dynamic time-lag mapping from continuous stimulus features to neural responses, effectively separating stimulus-driven coherent activity from high-dimensional noise. More importantly, it elevates the analytical perspective from static comparisons of raw signals to dynamic trajectories in weight space. We conducted an auditory experiment and incorporated high spatiotemporal resolution optically pumped magnetometer magnetoencephalography magnetoencephalography (OPM-MEG) with electroencephalography (EEG). The results showed that TRF-RSA significantly enhanced the pattern similarity between speech sounds and the ability to discriminate between pattern differences. Furthermore, it revealed stronger similarities elicited by biological vocalizations, indicating a preference in the brain for these species-specific sounds. Source localization results not only confirmed the classical speech perception network but also revealed activation in limbic and deep brain regions. By modeling the relationship between stimulus features and neural responses, TRF-RSA dynamically quantified the spatiotemporal patterns of stimulus-driven neural activity, improving the sensitivity of representational pattern decoding during the encoding process. These findings suggest that this method is a sensitive neuroimaging tool that not only advances our understanding of the spatiotemporal dynamics of speech processing but also provides a new reference for population dynamics research. Full article

Background/Objectives: COVID-19, caused by the SARS-CoV-2 virus, can lead to widespread neurological and cognitive complications, even in the absence of significant structural brain abnormalities. Understanding the evolving health concerns in the context of viral infections is critical to service member readiness, fitness, and mission completion. The potential neuroprotective effects of SARS-CoV-2 vaccination remain underexplored. Methods: Using a cross-sectional, non-human primate model (female cynomolgus macaques), we employed magnetoencephalography (MEG) to assess resting-state brain activity following vaccination with escalating doses of a novel psoralen-inactivated SARS-CoV-2 vaccine (PsIV) or a combination of PsIV and a DNA vaccine (prime boost), and subsequent challenge with the Delta variant (SARS-CoV-2 B.1.617.2). MEG scans were acquired 41 days after inoculation. Source series were constructed for 42 regions of interest for each subject, and band power was computed. Results: Band power demonstrated substantial preservation of neural activity across multiple brain regions in vaccinated subjects compared to unvaccinated controls following viral challenge. Significantly lower power was observed across the brain at all bandwidths in the unvaccinated group relative to the prime boost group. As PsIV concentration increased, spectral power increased, with the prime boost group having the greatest power. Conclusions: This approach not only underscores the role of vaccination in mitigating neuropathology but also highlights the capability of MEG to detect subtle yet significant changes in brain function that may be overlooked by other imaging modalities. These findings advance our understanding of vaccine-induced neuroprotection and establish MEG as a powerful tool for monitoring brain function in the context of viral infections. Full article

This study aimed to characterize cortical dysfunction and frequency-specific network reorganization following optic nerve injury using spin-exchange relaxation-free magnetoencephalography (SERF-MEG), and to assess the potential of MEG-derived multiscale features as sensitive functional biomarkers for clinical evaluation. In this prospective case–control study, SERF-MEG recordings were acquired during a pattern-reversal visual stimulation paradigm. Time-domain evoked components (M100/M135), global electrophysiological indices, energy-based metrics, and alpha- and beta-band phase-based functional connectivity were extracted. Network topology was quantified using graph-theoretical measures, including global and local efficiency, clustering coefficient, and assortativity. Group-level differences between patients and healthy controls were statistically analyzed. Patients showed significantly reduced M100/M135 amplitudes, prolonged M100 latency, and a lower early-component energy ratio. Functional connectivity was significantly decreased in the alpha and beta bands, accompanied by reduced global and local efficiency, mean strength, and clustering coefficient. Seed-based analyses revealed reduced connectivity predominantly in occipito-parietal and occipito-temporal pathways. SERF-MEG provides sensitive identification of cortical- and network-level functional impairments following optic nerve damage. MEG has significant clinical potential for disease diagnosis and therapy monitoring, providing a novel objective assessment tool for neuro-ophthalmological disorders. Full article

Optically Pumped Magnetometers (OPMs) have emerged as a promising technology for developing flexible, wearable magnetoencephalography (OPM-MEG) systems, offering high spatiotemporal resolution without the need for cryogenic cooling. However, their application to phase–amplitude coupling (PAC)-based neural decoding remains largely unexplored. Investigating their decoding performance is essential for evaluating the capability of OPM-MEG in characterizing complex neural dynamics and discriminating motor states. In this study, OPM-MEG was utilized to record brain activity during rest, motor imagery, and motor execution tasks. A two-stage temporal optimization strategy combining time-resolved PAC localization and the Kullback–Leibler modulation index (KL-MI) was employed to extract robust PAC features from low-frequency phase and high-frequency amplitude coupling. α–γ and θ–γ PAC features were subsequently fed into a multiclass linear discriminant analysis (LDA) classifier for motor state decoding, and compared against baseline band-power feature decoding performance. Experimental results demonstrate that PAC features derived from OPM-MEG significantly outperform the corresponding baseline band-power features in decoding performance. Notably, α–γ PAC features effectively discriminate among different motor states, achieving a balanced accuracy of 85.91% in 10-fold cross-validation. This performance significantly exceeds the 50% one-vs-rest chance level and outperforms θ–γ PAC features. These findings provide initial evidence for the feasibility of OPM-MEG in PAC-based motor state decoding and a preliminary case study for characterizing motor-related neural dynamics in a wearable MEG system. Full article

Objective: Previous studies of deep brain stimulation-related (DBS) artifacts in magnetoencephalography (MEG) have largely focused on the sensor level. In contrast, far less is known about their effects at the source level, where neuroscientific interpretations are typically derived. This study aims to quantify how DBS artifacts distort source-level MEG imaging. Methods: The study used a phantom-based experimental setup to assess dipole-fitting accuracy while systematically varying the stimulation amplitude, DBS electrode configuration, and the distance between the dipole and the DBS electrode. Results: Dipole location, angle, and amplitude errors remained within modest ranges, with the largest location and angle errors occurring at 5 mA ring-electrode stimulation (6.19 mm and 8.31 deg, respectively) and the largest amplitude errors at 15 mA ring electrodes (13.05 nAm). Location and angle errors increased significantly as the dipole moved closer to the DBS electrode, while amplitude error showed no such relationship. Continuous head position indicator coil signal quality remained stable and reliable at DBS on condition, compared to DBS off. Conclusions: The stimulation itself does not significantly impair MEG dipole estimation, as fitting errors are similar with DBS on and off. The study introduces a quantitative framework to systematically assess DBS-related distortion via dipole-fitting error, which can also be extended to evaluate noise from other implanted or external devices. Full article

Optically pumped magnetometer-based magnetoencephalography (OPM-MEG), with its flexible sensor configuration and wide range of application scenarios, has become a powerful complement to conventional superconducting quantum interference device magnetoencephalography (SQUID-MEG). However, this higher flexibility also means that OPM-MEG sensor arrays are more susceptible to interference from complex and variable background magnetic noise. Previous research has shown that deploying reference sensors around the scalp array for noise cancellation is an effective strategy. Nonetheless, the selection of its key parameter, the spatial distance between the reference and scalp sensors, commonly termed the synthetic baseline, predominantly relies on empirical rules and lacks systematic theoretical optimization. To address this issue, this study thoroughly investigates the fundamental impact of the synthetic baseline on the system’s noise suppression performance. Simulation results demonstrate that the optimal baseline length is not a fixed value but varies systematically with environmental noise characteristics and the specific requirements of the source localization task. Building on this analysis, a Baseline Adaptive Reference Optimization (BARO) method is proposed. As an environment-driven strategy, the BARO method automatically determines the optimal baseline configuration by maximizing the output signal-to-noise ratio (SNR). Compared to traditional fixed-baseline configurations, the proposed BARO method significantly enhances the output SNR and effectively reduces the localization error of equivalent current dipoles within the brain across various simulated complex noise scenarios. This work provides a physically interpretable criterion for baseline optimization and offers theoretical support for environment-adaptive configuration of OPM-MEG sensor arrays. Full article

The atomic magnetometer (AM), operating within the spin-exchange relaxation-free (SERF) regime, boasts numerous advantageous qualities, including ultrahigh sensitivity, exceptional spatial resolution, and minimal power consumption. Consequently, it emerges as a promising alternative to superconducting quantum interference devices in biomagnetic measurement applications. This paper details the development of a fully integrated SERF AM system comprising a compact sensor head and corresponding control electronics. Utilizing a 4 mm × 4 mm × 4 mm cubic vapor cell, we have successfully integrated the compact sensor into a 9 ${\mathrm{cm}}^3$ volume employing a single-beam scheme facilitated by a polarization-maintaining fiber. The in-house control electronics encompass essential components, such as the laser driver, coil driver, vapor-cell temperature controller, and transimpedance amplifier. As a result, the fully integrated SERF AM achieves a sensitivity of 25 $\mathrm{fT}/{\mathrm{Hz}}^{1/2}$@5∼100 Hz, accompanied by a bandwidth of 193 Hz, meeting the necessary criteria for magnetocardiography (MCG) and magnetoencephalography (MEG) measurements. Furthermore, the fully integrated SERF AM successfully records typical MCG and alpha rhythm MEG signals, showcasing immense potential for biomagnetic imaging applications. Full article

Background/Objectives: The dichotic listening test (DLT) is widely used to assess auditory attention and hemispheric language lateralization, with the right-ear advantage (REA) representing a robust behavioral phenomenon. Although the REA is often attributed to structural asymmetries in auditory pathways and left-hemisphere dominance for speech processing, the neural mechanisms by which selective attention modulates this asymmetry remain unclear. This study examined how directed auditory attention influences the REA and its neural correlates using magnetoencephalography (MEG)-based auditory steady-state responses (ASSRs). Methods: Fifteen right-handed participants performed directed-attention dichotic listening tasks during MEG recording. One participant was excluded from MEG analyses due to excessive noise contamination, resulting in 14 participants included in neural analyses. Participants attended to either the left or right ear throughout each session and reported the perceived stimulus from the attended ear. Dichotic speech stimuli were amplitude-modulated at 35 Hz and 45 Hz for frequency tagging. ASSR amplitudes were extracted from the left and right auditory cortices and analyzed in relation to behavioral accuracy using correlation analyses and analysis of covariance (ANCOVA). Results: Behavioral accuracy was significantly higher during right-ear attention than left-ear attention, indicating a residual REA. ASSR amplitudes tended to be higher during left-ear attention. Importantly, during left-ear attention, ASSR amplitude in the left auditory cortex showed a significant positive correlation with behavioral accuracy, whereas no such association was observed during right-ear attention. Conclusions: These findings indicate that the REA reflects a dynamic interaction between structural auditory asymmetry and top-down attentional control, with successful left-ear listening relying on compensatory recruitment of the left auditory cortex. Full article

Background/Objectives: The incidence of dementia and the concurrent burden on healthcare will increase with a population that continues to age. Pharmaceutical interventions for dementia carry negative side effects, ineffectively treat underlying causes, and fail to prevent disease onset. Therefore, non-pharmaceutical interventions such as music therapy should to be explored as a standalone or co-therapy for dementia. Music therapy improves cognitive symptoms of dementia; however, the neural mechanisms underpinning these improvements are not fully understood. Methods: To investigate potential neural mechanisms, six participants with dementia completed the Standardised Mini Mental State Examination, an n-back task, and magnetoencephalography (MEG) scanning before and after a music therapy program structured around improving executive functioning. Results: After music therapy, scores on an n-back task improved, and the MEG data revealed increased connectivity in neural networks and areas associated with compensation during executive functioning tasks. Connectivity results suggest there is preliminary evidence that music therapy improves cognitive symptoms of dementia by activating compensatory neural networks and areas; however, given the small sample size, these results should be interpreted with caution. Conclusions: The results of this hypotheses study present music therapy as a potentially viable short-term intervention which may operate by targeting compensatory neural networks and could be a long-term intervention that incorporates positive modifiable lifestyle factors, protecting the brain from dementia. Full article

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) offers the flexibility to position sensors closer to the scalp, which improves the signal-to-noise ratio compared to conventional superconducting quantum interference device (SQUID) systems. However, the spatial resolution of OPM-MEG critically depends on sensor placement, especially when the number of sensors is limited. In this study, we present a methodology for optimizing OPM-MEG sensor layouts using each subject’s anatomical information derived from individual magnetic resonance imaging (MRI). We generated realistic forward models from reconstructed head surfaces and simulated magnetic fields produced by equivalent current dipoles (ECDs). We compared multiple simulation strategies, including ECDs randomly distributed across the cortical surface and ECDs constrained to regions of interest. For each simulated magnetic field map (MFM) database, we applied the sequential selection algorithm (SSA) to identify sensor positions that maximized information capture. Unlike previous approaches relying on large measurement databases, this simulation-driven strategy eliminates the need for extensive pre-existing recordings. We benchmarked the performance of the personalized layouts using OPM-MEG datasets of auditory evoked fields (AEFs) derived from real whole-head SQUID-MEG measurements. Our results show that simulation-based SSA optimization improves the coverage of cortical regions of interest, reduces the number of sensors required for accurate source reconstruction, and yields sensor configurations that perform comparably to layouts optimized using measured data. To evaluate the quality of estimated MFMs, we applied metrics such as the correlation coefficient (CC), root-mean-square error, and relative error. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95) capture most of the information contained in full-head MFMs. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles and found that localization errors were < 5 mm. The results further indicate that SSA performance is insensitive to individualized head geometry, supporting the feasibility of using representative anatomical models and highlighting the potential of this approach for clinical OPM-MEG applications. Full article

Accurate co-registration between on-scalp Optically Pumped Magnetometer (OPM)–Magnetoencephalography (MEG) sensors and anatomical Magnetic Resonance Imaging (MRI) remains a critical bottleneck restricting the spatial fidelity of source localization. Optical Scanning Image (OSI) methods can provide high spatial accuracy but depend on surface visibility and cannot directly determine the internal sensitive point of each OPM sensor. Coil-based magnetic dipole localization, in contrast, targets the sensor’s internal sensitive volume and is robust to occlusion, yet its accuracy is affected by coil fabrication imperfections and the validity of the dipole approximation. To integrate the complementary advantages of both approaches, we propose a hybrid co-registration framework that combines Rigid Coil Structures (RCS), magnetic dipole-based sensor localization, and optical orientation constraints. A complete multi-stage co-registration pipeline is established through a unified mathematical formulation, including MRI–OSI alignment, OSI–RCS transformation, and final RCS–sensor localization. Systematic simulations are conducted to evaluate the accuracy of the magnetic dipole approximation for both cylindrical helical coils and planar single-turn coils. The results quantify how wire diameter, coil radius, and turn number influence dipole model fidelity and offer practical guidelines for coil design. Experiments using 18 coils and 11 single-axis OPMs demonstrate positional accuracy of a few millimeters, and optical orientation priors suppress dipole-only orientation ambiguity in unstable channels. To improve the stability of sensor orientation estimation, optical scanning of surface markers is incorporated as a soft constraint, yielding substantial improvements for channels that exhibit unstable results under dipole-only optimization. Overall, the proposed hybrid framework demonstrates the feasibility of combining magnetic and optical information for robust OPM–MEG co-registration. Full article

Background/Objectives: Subcortical local field potentials (LFPs) provide a valuable in vivo window into the neurophysiology of the dystonia network. These signals can be recorded through Deep Brain Stimulation (DBS) devices and combined with whole-head techniques such as magnetoencephalography (MEG) to study cortical–subcortical interactions. However, simultaneous LFP-MEG acquisition poses challenges, including interference from the DBS device and synchronization issues. We present preliminary data on the feasibility and signal quality of concurrent LFP and MEG recordings in dystonia patients. Methods: We assessed simultaneous MEG-LFP recordings in 11 patients with inherited or idiopathic dystonia who underwent bilateral DBS lead implantation in the Globus Pallidus Internus (GPi). Two synchronization strategies were tested: (1) the Tapping method, using an accelerometer placed on the DBS device, and (2) the Stimulation method, which generated detectable artifacts during sham stimulation. Results: Both methods successfully aligned MEG and LFP signals with a mean temporal delay of 91 ± 22 ms for the Tapping method and 288 ± 166 ms for the Stimulation method. Post-implantation signal-to-noise ratio analysis revealed slight degradation but no significant impact on MEG quality (gradiometers: −0.12 ± 1.85 dB; magnetometers: −0.47 ± 2.03 dB). Conclusions: Simultaneous MEG-LFP recordings in dystonic patients are feasible, yielding high-quality signals, and reliable synchronization. Temporal alignment improved with practice, suggesting a short learning curve. This method opens new opportunities to study cortical-subcortical dynamics and strengthens the potential of combining MEG-LFP approaches for investigating dystonia. Full article

High-precision sensor co-registration is a critical prerequisite for achieving high-resolution imaging in Optically Pumped Magnetometer–Magnetoencephalography (OPM-MEG) systems. The conventional magnetic dipole fitting method, essentially a multipole expansion approximation of a finite-size coil, exhibits accuracy that strongly depends on spatial geometric factors such as coil–sensor distance, dipole orientation, and the projection angle of the sensor’s sensitive axis. Moreover, the approximation error increases significantly when sensors are placed either too close to the coils or at an unfavorable angular coupling. To address this issue, we propose a sensor localization and orientation method that combines magnetic dipole-equivalent modeling with a rigid coil structure (RCS). The RCS provides stable geometric constraints and eliminates uncertainties introduced by scalp-attached coils. In addition, three objective functions (the standard Frobenius norm, a weighted Frobenius norm and the structural similarity index (SSIM)) are formulated to mitigate the imbalance caused by near-field strong signals and to improve stability under noise and error propagation. Simulation results demonstrate that both under ideal conditions and with assembly perturbations, the weighted Frobenius norm and SSIM methods consistently achieve position errors below 1 mm and orientation errors below 1°, which effectively suppress large outlier deviations and achieve better performance than the standard Frobenius norm. The results confirm the effectiveness of the proposed method in achieving both high accuracy and robustness. Beyond clarifying the primary factors influencing magnetic dipole approximation errors, this study provides a geometry-constrained and optimization-based framework, offering a feasible pathway toward the practical implementation of high-precision, multi-channel OPM-MEG systems. Full article

Concussions, classified as a type of mild traumatic brain injury (mTBI), are frequently underdiagnosed due to the subjective nature of symptoms and limitations in existing diagnostic methodologies. Current clinical evaluations, including tools such as the Sport Concussion Assessment Tool 5 (SCAT5), Balance Error Scoring System (BESS), and Vestibular Ocular Motor Screening (VOMS), demonstrate high sensitivity and specificity but often fail to capture the full complexity of concussive injuries. Emerging diagnostic approaches, such as blood biomarkers (for example, glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), S100 calcium-binding protein B (S100B), and tau) and advanced neuroimaging techniques (for example, diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI)), show promise but remain impractical for routine clinical use due to accessibility and standardization challenges. This review examines objective markers, including neuroimaging, electrophysiological measures (for example, Electroencephalography (EEG), Magnetoencephalography (MEG)), and real-time diagnostic tools, as complementary strategies to enhance traditional clinical evaluations. Findings indicate that while clinical assessments remain central to concussion diagnosis, integrating them with advanced imaging and electrophysiological tools can provide more accurate diagnostics and recovery tracking. Biomarkers, although not yet ready for widespread use, hold significant potential for future applications. Further research is required to validate these methods and establish standardized protocols to facilitate their integration into clinical practice. Full article

Optically pumped magnetometers (OPMs) present a promising opportunity to advance magnetoencephalography (MEG), enhancing the accuracy of neuronal activity recordings due to their high spatiotemporal resolution. However, to fully realize the potential of OPM-MEG as an emerging brain functional imaging technology, it is essential to measure key indicators of neural dynamics, particularly phase–amplitude coupling (PAC). PAC is a fundamental mechanism for integrating information across different frequency bands and plays an important role in various cognitive functions and neurological disorders. Therefore, measuring PAC with OPM-MEG is a crucial step toward expanding its applications. In this study, brain signals under pitch sequence stimulation were recorded using OPM-MEG to analyze the PAC effect in the primary auditory cortex (Aud) and the inferior frontal gyrus (IFG), as well as the functional connectivity between brain regions. The findings were validated through EEG control experiments. The results indicated that the PAC effect measured by OPM-MEG was largely consistent with that measured by EEG, with OPM-MEG appearing to detect PAC more prominently under the current experimental conditions. The PAC of Aud exhibited a trend of initially increasing and then decreasing centered on the target pitch, showing hemispheric symmetry. The PAC of IFG showed variations under different pitch conditions and displayed right hemisphere lateralization. Functional connectivity analysis provided convergent evidence for the mechanisms underlying the PAC effect and suggested the reliability of the OPM-MEG system in capturing cross-frequency neural dynamics. To our knowledge, this study provides the first task-based evidence that OPM-MEG can measure PAC effects in cortical regions, offering an initial foundation for future investigations of brain dynamics using this technology. Full article