Search Results (112)

A central challenge in movement neuroscience is developing methods for non-invasive spatiotemporal imaging of brain activity during natural, whole-body movement. We test the utility of a new brain imaging modality, optically pumped magnetoencephalography (OP-MEG), as an instrument to study the spatiotemporal dynamics of human walking. Specifically, we ask whether known physiological signals can be recovered during discrete steps involving large-scale, whole-body translation. Our findings show that by using OP-MEG, we can image the brain during large-scale, natural movements. We provide proof-of-principle evidence for movement-related changes in beta band activity during stepping vs. standing, which are source-localized to the sensorimotor cortex. This work supports the significant potential of the OP-MEG modality for addressing fundamental questions in human gait research relevant to both the physiological and pathological mechanisms of walking. Full article

The brain age gap (BAG), the divergence of an individual’s neurobiologically predicted brain age from their chronological age, is a key indicator of brain health. While BAG can be derived from diverse brain metrics, its interpretation often polarizes between early-life trait influences and current state-dependent factors like cognitive decline. Here, we propose an integrative framework that moves beyond single summary statistics by considering the full distribution of brain metrics across regions or time. We distinguish between a neural system’s “baseline” (typical values, e.g., mean) and its “capacity” (extreme values, e.g., maximum) within these distributions. To test this, we analyzed resting-state magnetoencephalography (MEG) from the Cam-CAN adult cohort, focusing on cross-frequency coupling (CFC) within functional MRI-defined networks. We derived network-specific CFC baseline (mean) and capacity (maximum) measures. Separate brain age prediction models were trained for each measure. The resulting BAGs (baseline-BAG and capacity-BAG) for each network were then correlated with cognitive performance on a picture priming task. Both baseline-BAG and capacity-BAG profiles showed associations with cognitive scores, with younger predicted brain age correlating with better performance. However, capacity-BAG exhibited more conclusive relationships, suggesting that metrics reflecting a neural system’s peak operational ability (capacity) may better capture an individual’s current cognitive state. These findings indicate that brain age models emphasizing neural capacity, rather than just baseline activity, could offer a more sensitive lens for understanding the state-dependent aspects of brain ageing. Full article

Background/Objectives: Despite the growing recognition of cognitive disengagement syndrome (CDS), previously termed sluggish cognitive tempo, as a distinct dimension of psychopathology, the neural correlates of CDS remain largely unknown. We investigated the neural correlates of CDS in children using whole-head magnetoencephalography (MEG). Methods: A community-based sample of children (N = 43, ages 8–12 years) was recruited and completed self-report ratings of CDS. MEG was recorded while the children completed an adapted version of the attention network test (ANT). Results: The results indicated that higher levels of self-reported CDS symptoms were associated with larger changes in the root-mean square (ΔRMS) (incongruent—congruent trials) in M2 and M3, suggesting children with higher levels of CDS symptoms might require greater mental effort to overcome distractors during incongruent trials. The source localization analysis initially revealed a negative correlation between child self-reported CDS symptoms and ΔM2 power (incongruent—congruent trials) in the medial prefrontal cortex (mPFC), suggesting insufficient power allocation in a region critical for attentional processing. However, this association was no longer significant after controlling for ADHD status. No significant correlation was found between self-reported CDS symptoms and alerting or orienting. Conclusions: These findings provide initial evidence of the disrupted attentional processing associated with CDS in children. Further replication and extension with larger samples are warranted. Full article

Optically pumped magnetometers (OPMs) are a promising magnetoencephalography (MEG) technology for the non-invasive measurement of human electrophysiological signals. Prior work developed biplanar background field-nulling coils necessary for OPM operation, but these were expensive to produce and required tedious error-prone manual winding of >1 km of copper wire. Here, we developed a precise and reproducible manufacturing process by fabricating these coils on two-layer printed circuit boards (PCBs). Building on open-source software (bfieldtools), we developed a pipeline to determine the optimal current loops of 1.5 × 1.5 m² biplanar nulling coils, connected these loops into a continuous conducting path across PCB layers, and printed them as pairs of 1.5 × 0.75 m² PCBs, which were soldered and mounted on an aluminum frame. Our coils achieved efficiencies of 1.3–7.1 nT/mA, similar to or higher than previous designs. We reduced the largest background field component from 21 to 2 nT, enabling OPMs in a lightly shielded room to record somatosensory evoked fields (SEFs) comparable to SQUID-MEG. Our coil system is cheaper than commercial alternatives and is available as an open-source package opmcoils, thus enabling more affordable background field nulling for OPM-MEG and realizing its potential as an accessible sensor technology for human neuroscience. Full article

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that measures weak magnetic fields generated by neural electrical activity in the brain. Traditional MEG systems use superconducting quantum interference device (SQUID) sensors, which require cryogenic cooling and employ a dense array of sensors to capture magnetic field maps (MFMs) around the head. Recent advancements have introduced optically pumped magnetometers (OPMs) as a promising alternative. Unlike SQUIDs, OPMs do not require cooling and can be placed closer to regions of interest (ROIs). This study aims to optimize the layout of OPM-MEG sensors, maximizing information capture with a limited number of sensors. We applied a sequential selection algorithm (SSA), originally developed for body surface potential mapping in electrocardiography, which requires a large database of full-head MFMs. While modern OPM-MEG systems offer full-head coverage, expected future clinical use will benefit from simplified procedures, where handling a lower number of sensors is easier and more efficient. To explore this, we converted full-head SQUID-MEG measurements of auditory-evoked fields (AEFs) into OPM-MEG layouts with 80 sensor sites. System conversion was done by calculating a current distribution on the brain surface using minimum norm estimation (MNE). We evaluated the SSA’s performance under different protocols, for example, using measurements of single or combined OPM components. We assessed the quality of estimated MFMs using metrics, such as the correlation coefficient (CC), root-mean-square error, and relative error. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95, localization error < 1 mm) capture most of the information contained in full-head MFMs. Our main finding is that for event-related fields, such as AEFs, which primarily originate from focal sources, a significantly smaller number of sensors than currently used in conventional MEG systems is sufficient to extract relevant information. Full article

Background and Objectives: Currently, there are no proven solutions to remediate cognitive deficits in people with a mild traumatic brain injury (mTBI). One common issue is visual timing deficits, which may be due to processing deficits in dorsal visual pathways. Methods: This study investigates whether a new intervention (PATH) aimed at improving these visual timing deficits is more effective than conventional cognitive therapies that either remediate: (1) pattern discrimination deficits (ventral visual pathway): Orientation Discrimination (OD), or (2) working memory deficits using ReCollect task, for 10 subjects between the ages of 26–60 years old. This study tests the ability of three different cognitive therapies to improve the primary outcome: visual working memory (VWM), and secondary outcomes: processing speed, auditory working memory, and selective attention in mTBI subjects based on neuropsychological tests administered before and after 36 30-min training sessions Monday, Wednesday and Friday mornings. Results: On average, the PATH group exhibited a 35% improvement in VWM, compared to 15% for ReCollect and 5% for OD. A repeated-measures ANOVA found that improving dorsal stream function improved VWM significantly more than found after the other two interventions. The results reveal the importance of strengthening dorsal pathways more than conventional cognitive therapies to improve cognitive skills after mTBI. A biomarker, MagnetoEncephaloGraphy (MEG) brain recordings, using an N-Back task for subjects in treatment groups, verified these improvements as well. Conclusions: The data from this preliminary study are very promising for a new method improving the brain’s timing, more effective than conventional therapies, to improve cognitive deficits in mTBI patients. Full article

Magnetoencephalography (MEG) is a vital tool for understanding neural dynamics, offering a noninvasive technique for measuring subtle magnetic field variations around the scalp generated by synchronized neuronal activity. Two prominent sensor technologies exist: the well-established superconducting quantum interference device (SQUID) and the more recent optically pumped magnetometer (OPM). Although many studies have compared these technologies using human-subject data in neuroscience and clinical studies, a direct hardware-level comparison using dry phantoms remains unexplored. This study presents a framework for comparing SQUID- with OPM-MEG systems in a controlled environment using a dry phantom that emulates neuronal activity, allowing strict control over physiological artifacts. Data were obtained from SQUID and OPM systems within the same shielded room, ensuring consistent environmental noise control and shielding conditions. Positioning the OPM sensors closer to the signal source resulted in a signal amplitude approximately 3–4 times larger than that detected by the SQUID-MEG system. However, the source localization error of the OPM-MEG system was approximately three times larger than that obtained by the SQUID-MEG system. The cause of the large source localization error was discussed in terms of sensor-to-source distance, sensor count, signal–noise ratio, and the spatial coverage provided by the sensor array of the source signal. Full article

The primary objective of this study is to evaluate and compare the classification performance of feed-forward neural networks (FFNNs) and one-dimensional convolutional neural networks (1D-CNNs) on magnetoencephalography (MEG) signals from epileptic patients. MEG signals were recorded using the NEUROMAG-122 whole-brain superconducting quantum interference device (SQUID), installed, and operated in our laboratory. The dataset comprised over 5000 MEG segments, each one with a duration of 1 s and sampled at a frequency of 256 Hz. Each segment was classified by expert neurologists as either epileptic or non-epileptic. The FFNN with five hidden layers demonstrated promising results, achieving a classification accuracy of approximately 92%. The 1D-CNN, utilizing four layers, achieved an accuracy of 90.4%, with a significantly reduced training time. Building on these findings, the study’s secondary objective was to enhance the artificial neural network (ANN) model by incorporating transfer learning–stacked generalization for FFNN in various configurations. These enhancements successfully improved the performance of the pretrained network by approximately 1%. Full article

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that manifests through motor and non-motor symptoms. Understanding the alterations in brain connectivity associated with PD remains a challenge that is crucial for enhancing diagnosis and clinical management. Methods: This study utilized Magnetoencephalography (MEG) to investigate brain connectivity in PD patients compared to healthy controls (HCs) by applying eigenvector centrality (EC) measures across different frequency bands. Results: Our findings revealed significant differences in EC between PD patients and HCs in the alpha (8–12 Hz) and beta (13–30 Hz) frequency bands. To go into further detail, in the alpha frequency band, PD patients in the frontal lobe showed higher EC values compared to HCs. Additionally, we found statistically significant correlations between EC measures and clinical impairment scores (UPDRS-III). Conclusions: The proposed results suggest that MEG-derived EC measures can reveal important alterations in brain connectivity in PD, potentially serving as biomarkers for disease severity. Full article

Background/Objectives: The main objective of this systematic review was to explore the role of magnetoencephalography (MEG) in the diagnosis, assessment, and monitoring of mild traumatic brain injury (mTBI) and post-concussion syndrome (PCS). We aimed to evaluate the potential of some MEG biomarkers in detecting subtle brain abnormalities often missed by conventional imaging techniques. Methods: A systematic review was conducted using 25 studies that administered MEG to examine mTBI and PCS patients. The quality of the studies was assessed based on selection, comparability, and outcomes. Studies were analyzed for their methodology, evaluated parameters, and the clinical implications of using MEG for mTBI diagnosis. Results: MEG detected abnormal brain oscillations, including increased delta, theta, and gamma waves and disruptions in functional connectivity, particularly in the default mode and frontoparietal networks of patients suffering from mTBI. MEG consistently revealed abnormalities in mTBI patients even when structural imaging was normal. The use of MEG in monitoring recovery showed significant reductions in abnormal slow-wave activity corresponding to clinical improvements. Machine learning algorithms applied to MEG data demonstrated high sensitivity and specificity in distinguishing mTBI patients from healthy controls and predicting clinical outcomes. Conclusions: MEG provides a valuable diagnostic and prognostic tool for mTBI and PCS by identifying subtle neurophysiological abnormalities. The high temporal resolution and the ability to assess functional brain networks make MEG a promising complement to conventional imaging. Future research should focus on integrating MEG with other neuroimaging modalities and standardizing MEG protocols for clinical use. Full article

The performance portrait method (PPM) can be characterized as a systematized digitalized version of the trial and error method—probably the most popular and very often used method of engineering work. Its digitization required the expansion of performance measures used to evaluate the step responses of dynamic systems. Based on process modeling, PPM also contributed to the classification of models describing linear and non-linear dynamic processes so that they approximate their dynamics using the smallest possible number of numerical parameters. From most bio-inspired procedures of artificial intelligence and optimization used for the design of automatic controllers, PPM is distinguished by the possibility of repeated application of once generated performance portraits (PPs). These represent information about the process obtained by evaluating the performance of setpoint and disturbance step responses for all relevant values of the determining loop parameters organized into a grid. It can be supported by the implementation of parallel calculations with optimized decomposition in the high-performance computing (HPC) cloud. The wide applicability of PPM ranges from verification of analytically calculated optimal settings achieved by various approaches to controller design, to the analysis as well as optimal and robust setting of controllers for processes where other known control design methods fail. One such situation is illustrated by an example of predictive integrating (PrI) controller design for processes with a dominant time-delayed sensor dynamics, representing a counterpart of proportional-integrating (PI) controllers, the most frequently used solutions in practice. PrI controllers can be considered as a generalization of the disturbance–response feedback—the oldest known method for the design of dead-time compensators by Reswick. In applications with dominant dead-time and loop time constants located in the feedback (sensors), as those, e.g., met in magnetoencephalography (MEG), it makes it possible to significantly improve the control performance. PPM shows that, despite the absence of effective analytical control design methods for such situations, it is possible to obtain high-quality optimal solutions for processes that require working with uncertain models specified by interval parameters, while achieving invariance to changes in uncertain parameters. Full article

Neural oscillations observed during semantic processing embody the function of brain language processing. Precise parameterization of the differences in these oscillations across various semantics from a time–frequency perspective is pivotal for elucidating the mechanisms of brain language processing. The superlet transform and cluster depth test were used to compute the time–frequency representation of oscillatory difference (ODTFR) between neural activities recorded by optically pumped magnetometer-based magnetoencephalography (OPM-MEG) during processing congruent and incongruent Chinese semantics. Subsequently, ODTFR was parameterized based on the definition of local events. Finally, this study calculated the specific time–frequency values at which oscillation differences occurred in multiple auditory-language-processing regions. It was found that these oscillatory differences appeared in most regions and were mainly concentrated in the beta band. The average peak frequency of these oscillatory differences was 15.7 Hz, and the average peak time was 457 ms. These findings offer a fresh perspective on the neural mechanisms underlying the processing of distinct Chinese semantics and provide references and insights for analyzing language-related brain activities recorded by OPM-MEG. Full article

Background/Objectives: A multimodal brain age estimation model could provide enhanced insights into brain aging. However, effectively integrating multimodal neuroimaging data to enhance the accuracy of brain age estimation remains a challenging task. Methods: In this study, we developed an innovative data fusion technique employing a low-rank tensor fusion algorithm, tailored specifically for deep learning-based frameworks aimed at brain age estimation. Specifically, we utilized structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and magnetoencephalography (MEG) to extract spatial–temporal brain features with different properties. These features were fused using the low-rank tensor algorithm and employed as predictors for estimating brain age. Results: Our prediction model achieved a desirable prediction accuracy on the independent test samples, demonstrating its robust performance. Conclusions: The results of our study suggest that the low-rank tensor fusion algorithm has the potential to effectively integrate multimodal data into deep learning frameworks for estimating brain age. Full article

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. Full article

The spontaneous oscillations within the brain are intimately linked to the hierarchical structures of the cortex, as evidenced by the cross-cortical gradient between parametrized spontaneous oscillations and cortical locations. Despite the significance of both peak frequency and peak time in characterizing these oscillations, limited research has explored the relationship between peak time and cortical locations. And no studies have demonstrated that the cross-cortical gradient can be measured by optically pumped magnetometer-based magnetoencephalography (OPM-MEG). Therefore, the cross-cortical gradient of parameterized spontaneous oscillation was analyzed for oscillations recorded by OPM-MEG using restricted maximum likelihood estimation with a linear mixed-effects model. It was validated that OPM-MEG can measure the cross-cortical gradient of spontaneous oscillations. Furthermore, results demonstrated the difference in the cross-cortical gradient between spontaneous oscillations during eye-opening and eye-closing conditions. The methods and conclusions offer potential to integrate electrophysiological and structural information of the brain, which contributes to the analysis of oscillatory fluctuations across the cortex recorded by OPM-MEG. Full article