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A non-invasive functional-brain-imaging system based on optically-pumped-magnetometers (OPM) is presented. The OPM-based magnetoencephalography (MEG) system features 20 OPM channels conforming to the subject's scalp. We have conducted two MEG experiments on three subjects: assessment of somatosensory evoked magnetic field (SEF) and auditory evoked magnetic field (AEF) using our OPM-based MEG system and a commercial MEG system based on superconducting quantum interference devices (SQUIDs). We cross validated the robustness of our system by calculating the distance between the location of the equivalent current dipole (ECD) yielded by our OPM-based MEG system and the ECD location calculated by the commercial SQUID-based MEG system. We achieved sub-centimeter accuracy for both SEF and AEF responses in all three subjects. Due to the proximity (12 mm) of the OPM channels to the scalp, it is anticipated that future OPM-based MEG systems will offer enhanced spatial resolution as they will capture finer spatial features compared to traditional MEG systems employing SQUIDs.

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance. •Replicability in dynamic functional connectivity state measures was investigated.•Twenty-eight samples each with two hundred and fifty rest-fMRI datasets were studied.•State profiles were modelled using two (clustering and fuzzy meta-state) approaches.•Both approaches showed high consistency for a range of model orders.•Surrogate testing confirmed state summary measures to be statistically significant.

Assessing brain activity during rest has become a widely used approach in developmental neuroscience. Extant literature has measured resting brain activity both during eyes-open and eyes-closed conditions, but the difference between these conditions has not yet been well characterized. Studies, limited to fMRI and EEG, have suggested that eyes-open versus -closed conditions may differentially impact neural activity, especially in visual cortices. Spontaneous cortical activity was recorded using MEG from 108 typically developing youth (9-15 years-old; 55 female) during separate sessions of eyes-open and eyes-closed rest. MEG source images were computed, and the strength of spontaneous neural activity was estimated in the canonical delta, theta, alpha, beta, and gamma bands, respectively. Power spectral density maps for eyes-open were subtracted from eyes-closed rest, and then submitted to vertex-wise regression models to identify spatially specific differences between conditions and as a function of age and sex. Relative alpha power was weaker in the eyes-open compared to -closed condition, but otherwise eyes-open was stronger in all frequency bands, with differences concentrated in the occipital cortex. Relative theta power became stronger in the eyes-open compared to the eyes-closed condition with increasing age in frontal cortex. No differences were observed between males and females. The differences in relative power from eyes-closed to -open conditions are consistent with changes observed in task-based visual sensory responses. Age differences occurred in relatively late developing frontal regions, consistent with canonical attention regions, suggesting that these differences could be reflective of developmental changes in attention processes during puberty. Taken together, resting-state paradigms using eyes-open versus -closed produce distinct results and, in fact, can help pinpoint sensory related brain activity.

Modeling dynamic interactions among network components is crucial to uncovering the evolution mechanisms of complex networks. Recently, spatio-temporal graph learning methods have achieved noteworthy results in characterizing the dynamic changes of inter-node relations (INRs). However, challenges remain: The spatial neighborhood of an INR is underexploited, and the spatio-temporal dependencies in INRs’ dynamic changes are overlooked, ignoring the influence of historical states and local information. In addition, the model’s explainability has been understudied. To address these issues, we propose an explainable spatio-temporal graph evolution learning (ESTGEL) model to model the dynamic evolution of INRs. Specifically, an edge attention module is proposed to utilize the spatial neighborhood of an INR at multi-level, i.e., a hierarchy of nested subgraphs derived from decomposing the initial node-relation graph. Subsequently, a dynamic relation learning module is proposed to capture the spatio-temporal dependencies of INRs. The INRs are then used as adjacent information to improve the node representation, resulting in comprehensive delineation of dynamic evolution of the network. Finally, the approach is validated with real data on brain development study. Experimental results on dynamic brain networks analysis reveal that brain functional networks transition from dispersed to more convergent and modular structures throughout development. Significant changes are observed in the dynamic functional connectivity (dFC) associated with functions including emotional control, decision-making, and language processing. •Dynamic evolution of brain networks with spatio-temporal dependencies are revealed.•Multi-level neighborhood information of functional connection is explored.•Brain developmental differences with dynamic functional connectivity are revealed.

While numerous studies have examined the developmental trajectory of task-based neural oscillations during childhood and adolescence, far less is known about the evolution of spontaneous cortical activity during this time period. Likewise, many studies have shown robust sex differences in task-based oscillations during this developmental period, but whether such sex differences extend to spontaneous activity is not understood. Herein, we examined spontaneous cortical activity in 111 typically-developing youth (ages 9–15 years; 55 male). Participants completed a resting state magnetoencephalographic (MEG) recording and a structural MRI. MEG data were source imaged and the power within five canonical frequency bands (delta, theta, alpha, beta, gamma) was computed. The resulting power spectral density maps were analyzed via vertex-wise ANCOVAs to identify spatially-specific effects of age, sex, and their interaction. We found robust increases in power with age in all frequencies except delta, which decreased over time, with findings largely confined to frontal cortices. Sex effects were distributed across frontal and temporal regions; females tended to have greater delta and beta power, whereas males had greater alpha. Importantly, there was a significant age-by-sex interaction in theta power, such that males exhibited decreasing power with age while females showed increasing power with age in the bilateral superior temporal cortices. These data suggest that the strength of spontaneous activity undergoes robust change during the transition from childhood to adolescence (i.e., puberty onset), with intriguing sex differences in some cortical areas. Future developmental studies should probe task-related oscillations and spontaneous activity in parallel.

•Combine the group data with limited samples can improve the quality of directed acyclic graph estimation.•Lower global and local efficiency of the directed network found in schizophrenia patients.•Directed network induced features can better differentiate schizophrenia patients from healthy controls. Functional connectivity (FC) between brain region has been widely studied and linked with cognition and behavior of an individual. FC is usually defined as the correlation or partial correlation of fMRI blood oxygen level-dependent (BOLD) signals between two brain regions. Although FC has been effective to understand brain organization, it cannot reveal the direction of interactions. Many directed acyclic graph (DAG) based methods have been applied to study the directed interactions but their performance was limited by the small sample size while high dimensionality of the available data. By enforcing group regularization and utilizing samples from both case and control groups, we propose a joint DAG model to estimate the directed FC. We first demonstrate that the proposed model is efficient and accurate through a series of simulation studies. We then apply it to the case-control study of schizophrenia (SZ) with data collected from the MIND Clinical Imaging Consortium (MCIC). We have successfully identified decreased functional integration, disrupted hub structures and characteristic edges (CtEs) in SZ patients. Those findings have been confirmed by previous studies with some identified to be potential markers for SZ patients. A comparison of the results between the directed FC and undirected FC showed substantial differences in the selected features. In addition, we used the identified features based on directed FC for the classification of SZ patients and achieved better accuracy than using undirected FC or raw features, demonstrating the advantage of using directed FC for brain network analysis.

•Neural oscillatory activity serving motor control develops throughout adolescence.•Pubertal hormones like testosterone are a useful proxy for development.•Spontaneous beta power during the baseline predicted beta ERD strength in youth.•Testosterone predicted spontaneous baseline power and beta ERD strength.•Pubertal hormones uniquely influence the development of motor cortical oscillations. Puberty is a period of substantial hormonal fluctuations, and pubertal hormones can modulate structural and functional changes in the developing brain. Many previous studies have characterized the neural oscillatory responses serving movement, which include a beta event-related desynchronization (ERD) preceding movement onset, gamma and theta responses coinciding with movement execution, and a post-movement beta-rebound (PMBR) response following movement offset. While a few studies have investigated the developmental trajectories of these neural oscillations serving motor control, the impact of pubertal hormone levels on the maturation of these dynamics has not yet been examined. Since the timing and tempo of puberty varies greatly between individuals, pubertal hormones may uniquely impact the maturation of motor cortical oscillations distinct from other developmental metrics, such as age. In the current study we quantified these oscillations using magnetoencephalography (MEG) and utilized chronological age and measures of endogenous testosterone as indices of development during the transition from childhood to adolescence in 69 youths. Mediation analyses revealed complex maturation patterns for the beta ERD, in which testosterone predicted both spontaneous baseline and ERD power through direct and indirect effects. Age, but not pubertal hormones, predicted motor-related theta, and no relationships between oscillatory responses and developmental metrics were found for gamma or PMBR responses. These findings provide novel insight into how pubertal hormones affect motor-related oscillations, and highlight the continued development of motor cortical dynamics throughout the pubertal period.

In this paper, we propose a method to estimate the position, orientation, and gain of a magnetic field sensor using a set of (large) electromagnetic coils. We apply the method for calibrating an array of optically pumped magnetometers (OPMs) for magnetoencephalography (MEG). We first measure the magnetic fields of the coils at multiple known positions using a well-calibrated triaxial magnetometer, and model these discreetly sampled fields using vector spherical harmonics (VSH) functions. We then localize and calibrate an OPM by minimizing the sum of squared errors between the model signals and the OPM responses to the coil fields. We show that by using homogeneous and first-order gradient fields, the OPM sensor parameters (gain, position, and orientation) can be obtained from a set of linear equations with pseudo-inverses of two matrices. The currents that should be applied to the coils for approximating these low-order field components can be determined based on the VSH models. Computationally simple initial estimates of the OPM sensor parameters follow. As a first test of the method, we placed a fluxgate magnetometer at multiple positions and estimated the RMS position, orientation, and gain errors of the method to be 1.0 mm, 0.2°, and 0.8%, respectively. Lastly, we calibrated a 48-channel OPM array. The accuracy of the OPM calibration was tested by using the OPM array to localize magnetic dipoles in a phantom, which resulted in an average dipole position error of 3.3 mm. The results demonstrate the feasibility of using electromagnetic coils to calibrate and localize OPMs for MEG.

•Motor control improves and association cortices mature in childhood and adolescence.•68 youth performed a motor sequencing task during magnetoencephalography (MEG).•Prefrontal beta oscillations weaken with age when planning complex sequences.•Planning-related beta becomes stronger in posterior cortices during development.•Gamma becomes stronger with age in occipital and weaker in temporal cortices. Primary motor areas in the brain mature relatively early in development, yet the control of complex movements improves through early adulthood. Neural oscillations in higher-order regions are refined in adolescence and contribute to executive processes important for complex motor control, but the neural dynamics among these regions and primary motor cortices remain poorly understood in youth. We recorded magnetoencephalography during a motor sequencing task in 68 healthy youth from ten to 17 years of age. Significant changes in oscillatory activity relative to baseline were identified at the sensor level and source reconstructed with a beamformer. Whole-brain maps of beta (18–24 Hz) and gamma (74–84 Hz) oscillatory activity were subjected to voxel-wise repeated-measures ANCOVAs to identify brain areas in which the developmental trajectory of oscillatory power differed by sequence complexity (simple/complex) or motor stage (planning/execution). Beta activity in bilateral prefrontal cortices was weaker with age during the planning of complex movements. Across simple and complex conditions, older youth tended to have stronger beta in posterior areas during planning. Finally, gamma activity across conditions was stronger with age in occipital and weaker in temporal cortices. These results suggest that the functional refinement of association cortices may drive improvements in motor control by enabling more efficient attentional and inhibitory control during formulation of the motor plan.

Fluid reasoning is the ability to problem solve in the absence of prior knowledge and is commonly conceptualized as “non-verbal” intelligence. Importantly, fluid reasoning abilities rapidly develop throughout childhood and adolescence. Although numerous studies have characterized the neural underpinnings of fluid reasoning in adults, there is a paucity of research detailing the developmental trajectory of this neural processing. Herein, we examine longitudinal changes in the neural oscillatory dynamics underlying fluid intelligence in a sample of typically developing youths. A total of 34 participants age 10 to 16 years-old completed an abstract reasoning task during magnetoencephalography (MEG) on two occasions set one year apart. We found robust longitudinal optimization in theta, beta, and gamma oscillatory activity across years of the study across a distributed network commonly implicated in fluid reasoning abilities. More specifically, activity tended to decrease longitudinally in additional, compensatory areas such as the right lateral prefrontal cortex and increase in areas commonly utilized in mature adult samples (e.g., left frontal and parietal cortices). Importantly, shifts in neural activity were associated with improvements in task performance from one year to the next. Overall, the data suggest a longitudinal shift in performance that is accompanied by a reconfiguration of the functional oscillatory dynamics serving fluid reasoning during this important period of development.