The magnetoencephalogram (MEG) based on array optically pumped magnetometers (OPMs) has the potential of replacing conventional cryogenic superconducting quantum interference device. Phase synchronization is a common method for measuring brain oscillations and functional connectivity. Verifying the feasibility and fidelity of OPM-MEG in measuring phase synchronization will help its widespread application in the study of aforementioned neural mechanisms. The analysis method on source-level time series can weaken the influence of instantaneous field spread effect. In this paper, the OPM-MEG was used for measuring the evoked responses of 20Hz rhythmic and arrhythmic median nerve stimulation, and the inter-trial phase synchronization (ITPS) and inter-reginal phase synchronization (IRPS) of primary somatosensory cortex (SI) and secondary somatosensory cortex (SII) were analysed. The results find that under rhythmic condition, the evoked responses of SI and SII show continuous oscillations and the effect of resetting phase. The values of ITPS and IRPS significantly increase at the stimulation frequency of 20Hz and its harmonic of 40Hz, whereas the arrhythmic stimulation does not exhibit this phenomenon. Moreover, in the initial stage of stimulation, the ITPS and IRPS values are significantly higher at Mu rhythm in the rhythmic condition compared to arrhythmic. In conclusion, the results demonstrate the ability of OPM-MEG in measuring phase pattern and functional connectivity on source-level, and may also prove beneficial for the study on the mechanism of rhythmic stimulation therapy for rehabilitation.

Multivariate pattern analysis (MVPA) has played an extensive role in interpreting brain activity, which has been applied in studies with modalities such as functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). The advent of wearable MEG systems based on optically pumped magnetometers (OPMs), i.e., OP-MEG, has broadened the application of bio-magnetism in the realm of neuroscience. Nonetheless, it also raises challenges in temporal decoding analysis due to the unique attributes of OP-MEG itself. The efficacy of decoding performance utilizing multimodal fusion, such as MEG-EEG, also remains to be elucidated. In this regard, we investigated the impact of several factors, such as processing methods, models and modalities, on the decoding outcomes of OP-MEG. Our findings indicate that the number of averaged trials, dimensionality reduction (DR) methods, and the number of cross-validation folds significantly affect the decoding performance of OP-MEG data. Additionally, decoding results vary across modalities and fusion strategy. In contrast, decoder type, resampling frequency, and sliding window length exert marginal effects. Furthermore, we introduced mutual information (MI) to investigate how information loss due to OP-MEG data processing affect decoding accuracy. Our study offers insights for linear decoding research using OP-MEG and expand its application in the fields of cognitive neuroscience.

The brain represents information through the encoding of neural populations, where the activity patterns of these neural groups constitute the content of this information. Understanding these activity patterns and their dynamic changes is of significant importance to cognitive neuroscience and related research areas. Current studies focus more on brain regions that show differential responses to stimuli, but they lack the ability to capture information about the representational or process‐level dynamics within these regions. In this study, we recorded neural data from 10 healthy participants during auditory experiments using optically pumped magnetometer magnetoencephalography (OPM‐MEG) and electroencephalography (EEG). We constructed representational similarity matrices (RSMs) to investigate the similarity of neural response patterns during auditory decoding. The results indicate that RSA can reveal the dynamic changes in pattern similarity during different stages of auditory processing through the neural activity patterns reflected by OPM‐MEG. Comparisons with EEG results showed that both techniques captured the same processes during the early stages of auditory decoding. However, differences in sensitivity at later stages highlighted both common and distinct aspects of neural representation between the two modalities. Further analysis indicated that this process involved widespread neural network activation, including the Heschl's gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, parahippocampal gyrus, and orbitofrontal gyrus. This study demonstrates that the combination of OPM‐MEG and RSA is sufficiently sensitive to detect changes in pattern similarity during neural representation processes and to identify their anatomical origins, offering new insights and references for the future application of RSA and other multivariate pattern analysis methods in the MEG field.

Optically pumped magnetometer magnetoencephalography (OPM-MEG) represents a novel method for recording neural signals in the brain, offering the potential to measure critical neuroimaging characteristics such as effective brain networks. Effective brain networks describe the causal relationships and information flow between brain regions. In constructing effective brain networks using Granger causality, the noise in the multivariate autoregressive model (MVAR) is typically assumed to follow a Gaussian distribution. However, in experimental measurements, the statistical characteristics of noise are difficult to ascertain. In this paper, a Granger causality method based on a cost-reference particle filter (CRPF) is proposed for constructing effective brain networks under unknown noise conditions. Simulation results show that the average estimation errors of the MVAR model coefficients using the CRPF method are reduced by 53.4% and 82.4% compared to the Kalman filter (KF) and maximum correntropy filter (MCF) under Gaussian noise, respectively. The CRPF method reduces the average estimation errors by 88.1% and 85.8% compared to the MCF under alpha-stable distribution noise and the KF method under pink noise conditions, respectively. In an experiment, the CRPF method recoversthe latent characteristics of effective connectivity of benchmark somatosensory stimulation data in rats, human finger movement, and auditory oddball paradigms measured using OPM-MEG, which is in excellent agreement with known physiology. The simulation and experimental results demonstrate the effectiveness of the proposed algorithm and OPM-MEG for measuring effective brain networks.

Semantic processing is one of the core functions of human higher-level cognition. The neural mechanism of the decoding process is attached to great significance for understanding language, cognition, and clinical applications. The key issue in decoding semantic representation is how to enhance the sensitivity of task-related neural signals to effectively distinguish the spatial patterns under different stimuli. However, current studies are limited by the sensitivity to task-irrelevant noise, the trade-offs in spatial and temporal resolution inherent in neuroimaging modalities. Therefore, this study proposed a novel representation similarity analysis method based on spatial projection. By integrating optically pumped magnetometer magnetoencephalography (OPM-MEG) with multi-channel electroencephalography (EEG), we conducted a semantic congruity decoding analysis. The results showed that the optimized RSA framework significantly delineated the deflection of neural representations to different stimuli. Notably, semantic processing exhibited clear frequency-band specificity, where low-frequency bands (particularly δ and θ) accounted for a substantially larger proportion of semantic representation variance compared to higher frequencies. Further analysis revealed that semantic processing involved the refined spatiotemporal evolution of neural patterns, and semantic incongruity significantly enhanced the similarity of patterns. In addition, the source localization results not only verified the classical language network, but also found the involvement of the limbic system (such as the parahippocampal gyrus and insula), suggesting that semantic processing involved a wider range of memory and regulatory networks. This study not only expands the understanding to semantic neural representations from the multi-dimensional aspects, but also highlights the potential of spatial projection optimization combined with multivariate analysis for high-precision neural decoding.

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.