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A neural signature of serial dependence has been found, which mirrors the attractive bias of visual information seen in behavioral experiments.

While animal models of Alzheimer's disease (AD) have shown altered gamma oscillations (∼40 Hz) in local neural circuits, the low signal-to-noise ratio of gamma in the resting human brain precludes its quantification via conventional spectral estimates. Phase-amplitude coupling (PAC) indicating the dynamic integration between the gamma amplitude and the phase of low-frequency (4-12 Hz) oscillations is a useful alternative to capture local gamma activity. In addition, PAC is also an index of neuronal excitability as the phase of low-frequency oscillations that modulate gamma amplitude, effectively regulates the excitability of local neuronal firing. In this study, we sought to examine the local neuronal activity and excitability using gamma PAC, within brain regions vulnerable to early AD pathophysiology-entorhinal cortex and parahippocampus, in a clinical population of patients with AD and age-matched controls. Our clinical cohorts consisted of a well-characterized cohort of AD patients (n = 50; age, 60 ± 8 years) with positive AD biomarkers, and age-matched, cognitively unimpaired controls (n = 35; age, 63 ± 5.8 years). We identified the presence or the absence of epileptiform activity in AD patients (AD patients with epileptiform activity, AD-EPI+, n = 20; AD patients without epileptiform activity, AD-EPI-, n = 30) using long-term electroencephalography (LTM-EEG) and 1-hour long magnetoencephalography (MEG) with simultaneous EEG. Using the source reconstructed MEG data, we computed gamma PAC as the coupling between amplitude of the gamma frequency (30-40 Hz) with phase of the theta (4-8 Hz) and alpha (8-12 Hz) frequency oscillations, within entorhinal and parahippocampal cortices. We found that patients with AD have reduced gamma PAC in the left parahippocampal cortex, compared to age-matched controls. Furthermore, AD-EPI+ patients showed greater reductions in gamma PAC than AD-EPI- in bilateral parahippocampal cortices. In contrast, entorhinal cortices did not show gamma PAC abnormalities in patients with AD. Our findings demonstrate the spatial patterns of altered gamma oscillations indicating possible region-specific manifestations of network hyperexcitability within medial temporal lobe regions vulnerable to AD pathophysiology. Greater deficits in AD-EPI+ suggests that reduced gamma PAC is a sensitive index of network hyperexcitability in AD patients. Collectively, the current results emphasize the importance of investigating the role of neural circuit hyperexcitability in early AD pathophysiology and explore its potential as a modifiable contributor to AD pathobiology.

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

Tau rhythms are largely defined by sound responsive alpha band (~8-13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of taus elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects data were split into 60 parallel processing pipelines entailing use of five levels of high-pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low-pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi-model AMICA [mAMICA]). Tau-related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8-13 Hz alpha band. These tau ICs showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high-pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high-pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high-pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms.

Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

BackgroundAlzheimer's disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM).MethodsSGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years).ResultsPatients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition.ConclusionsThese results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Magnetoencephalography/electroencephalography (M/EEG) can provide insights into migraine pathophysiology and help develop clinically valuable biomarkers. To integrate and summarize the existing evidence on changes in brain function in migraine, we performed a systematic review and meta-analysis (PROSPERO CRD42021272622) of resting-state M/EEG findings in migraine. We included 27 studies after searching MEDLINE, Web of Science Core Collection, and EMBASE. Risk of bias was assessed using a modified Newcastle–Ottawa Scale. Semi-quantitative analysis was conducted by vote counting, and meta-analyses of M/EEG differences between people with migraine and healthy participants were performed using random-effects models. In people with migraine during the interictal phase, meta-analysis revealed higher power of brain activity at theta frequencies (3–8 Hz) than in healthy participants. Furthermore, we found evidence for lower alpha and beta connectivity in people with migraine in the interictal phase. No associations between M/EEG features and disease severity were observed. Moreover, some evidence for higher delta and beta power in the premonitory compared to the interictal phase was found. Strongest risk of bias of included studies arose from a lack of controlling for comorbidities and non-automatized or non-blinded M/EEG assessments. These findings can guide future M/EEG studies on migraine pathophysiology and brain-based biomarkers, which should consider comorbidities and aim for standardized, collaborative approaches. [ABSTRACT FROM AUTHOR]

Background: Atypical peak alpha frequency (PAF) has been reported in children with autism spectrum disorder (ASD); however, the relationships between PAF, age, and autistic traits remain unclear. This study was conducted to investigate and compare the resting-state PAF of young children with ASD and their typically developing (TD) peers using magnetoencephalography (MEG). Methods: Nineteen children with ASD and 24 TD children, aged 5-7 years, underwent MEG under resting-state conditions. The PAFs in ten brain regions were calculated, and the associations between these findings, age, and autistic traits, measured using the Social Responsiveness Scale (SRS), were examined. Results: There were no significant differences in PAF between the children with ASD and the TD children. However, a unique positive association between age and PAF in the cingulate region was observed in the ASD group, suggesting the potential importance of the cingulate regions as a neurophysiological mechanism underlying distinct developmental trajectory of ASD. Furthermore, a higher PAF in the right temporal region was associated with higher SRS scores in TD children, highlighting the potential role of alpha oscillations in social information processing. Conclusions: This study emphasizes the importance of regional specificity and developmental factors when investigating neurophysiological markers of ASD. The distinct age-related PAF patterns in the cingulate regions of children with ASD and the association between right temporal PAF and autistic traits in TD children provide novel insights into the neurobiological underpinnings of ASD. These findings pave the way for future research on the functional implications of these neurophysiological patterns and their potential as biomarkers of ASD across the lifespan. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Magnetoencephalography (MEG) is a non-invasive imaging technique for directly measuring the external magnetic field generated from synchronously activated pyramidal neurons in the brain. The optically pumped magnetometer (OPM) is known for its less expensive, non-cryogenic, movable and user-friendly custom-design provides the potential for a change in functional neuroimaging based on MEG. Methods: An array of OPMs covering the opposite sides of a subject's head is placed inside a magnetically shielded room (MSR) and responses evoked from the auditory cortices are measured. Results: High signal-to-noise ratio auditory evoked response fields (AEFs) were detected by a wearable OPM-MEG system in a MSR, for which a flexible helmet was specially designed to minimize the sensor-to-head distance, along with a set of bi-planar coils developed for background field and gradient nulling. Neuronal current sources activated in AEF experiments were localized and the auditory cortices showed the highest activities. Performance of the hybrid optically pumped magnetometer-magnetoencephalography/electroencephalography (OPM-MEG/EEG) system was also assessed. Conclusions: The multi-channel OPM-MEG system performs well in a custom built MSR equipped with bi-planar coils and detects human AEFs with a flexible helmet. Moreover, the similarities and differences of auditory evoked potentials (AEPs) and AEFs are discussed, while the operation of OPM-MEG sensors in conjunction with EEG electrodes provides an encouraging combination for the exploration of hybrid OPM-MEG/EEG systems. [ABSTRACT FROM AUTHOR]

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document. [ABSTRACT FROM AUTHOR]

Functional connectivity in electroencephalography (EEG) and magnetoencephalography (MEG) data is commonly assessed by using measures that are insensitive to instantaneously interacting sources and as such would not give rise to false positive interactions caused by instantaneous mixing of true source signals (first-order mixing). Recent studies, however, have drawn attention to the fact that such measures are still susceptible to instantaneous mixing from lagged sources (i.e. second-order mixing) and that this can lead to a large number of false positive interactions. In this study we relate first- and second-order mixing effects on the cross-spectra of reconstructed source activity to the properties of the resolution operators that are used for the reconstruction. We derive two identities that relate first- and second-order mixing effects to the transformation properties of measurement and source configurations and exploit them to establish several basic properties of signal mixing. First, we provide a characterization of the configurations that are maximally and minimally sensitive to second-order mixing. It turns out that second-order mixing effects are maximal when the measurement locations are far apart and the sources coincide with the measurement locations. Second, we provide a description of second-order mixing effects in the vicinity of the measurement locations in terms of the local geometry of the point-spread functions of the resolution operator. Third, we derive a version of Lagrange's identity for cross-talk functions that establishes the existence of a trade-off between the magnitude of first- and second-order mixing effects. It also shows that, whereas the magnitude of first-order mixing is determined by the inner product of cross-talk functions, the magnitude of second-order mixing is determined by a generalized cross-product of cross-talk functions (the wedge product) which leads to an intuitive geometric understanding of the trade-off. All results are derived within the general framework of random neural fields on cortical manifolds. [ABSTRACT FROM AUTHOR]

The primate including the human hippocampus implicated in episodic memory and navigation represents a spatial view, very different from the place representations in rodents. To understand this system in humans, and the computations performed, the pathway for this spatial view information to reach the hippocampus was analysed in humans. Whole-brain effective connectivity was measured with magnetoencephalography between 30 visual cortical regions and 150 other cortical regions using the HCP-MMP1 atlas in 21 participants while performing a 0-back scene memory task. In a ventromedial visual stream, V1–V4 connect to the ProStriate region where the retrosplenial scene area is located. The ProStriate region has connectivity to ventromedial visual regions VMV1–3 and VVC. These ventromedial regions connect to the medial parahippocampal region PHA1–3, which, with the VMV regions, include the parahippocampal scene area. The medial parahippocampal regions have effective connectivity to the entorhinal cortex, perirhinal cortex, and hippocampus. In contrast, when viewing faces, the effective connectivity was more through a ventrolateral visual cortical stream via the fusiform face cortex to the inferior temporal visual cortex regions TE2p and TE2a. A ventromedial visual cortical 'Where' stream to the hippocampus for spatial scenes was supported by diffusion topography in 171 HCP participants at 7 T. A ventromedial cortical 'Where' visual pathway in humans for spatial scenes is revealed with MEG. It is fundamental to episodic memory and navigation. [ABSTRACT FROM AUTHOR]

Introduction: Electroconvulsive therapy (ECT) remains a critical intervention for treatment-resistant depression (MDD), yet its neurobiological underpinnings are not fully understood. This pilot study aims to investigate changes in loudness dependence of auditory evoked potentials (LDAEP), a proposed biomarker of serotonergic activity, in patients undergoing ECT. Methods: High-resolution magnetoencephalography (MEG) was utilized to measure LDAEP in nine depressed patients receiving right unilateral ECT. We hypothesized that ECT would reduce the LDAEP slope, reflecting enhanced serotonergic neurotransmission. Depression severity and cognitive performance were assessed using the 24-item Hamilton Depression Rating Scale (HDRS24) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), respectively. Results: Contrary to our hypothesis, findings indicated a significant increase in LDAEP post-ECT (t8 = 3.17, p = .013). The increase in LDAEP was not associated with changes in depression severity or cognitive performance. Discussion: The observed increase in LDAEP suggests a more complex interaction between ECT and neurobiological systems, rather than a direct reflection of serotonergic neurotransmission. Potential mechanisms for this increase include ECT’s impact on serotonergic, dopaminergic, glutamatergic, and GABAergic receptor activity, neuroplasticity involving brain-derived neurotrophic factor (BDNF), and inflammatory modulators such as TNF-α. Our results highlight the multifaceted effects of ECT on brain function, necessitating further research to elucidate these interactions. [ABSTRACT FROM AUTHOR]

Evidence accumulates that the cerebellum's role in the brain is not restricted to motor functions. Rather, cerebellar activity seems to be crucial for a variety of tasks that rely on precise event timing and prediction. Due to its complex structure and importance in communication, human speech requires a particularly precise and predictive coordination of neural processes to be successfully comprehended. Recent studies proposed that the cerebellum is indeed a major contributor to speech processing, but how this contribution is achieved mechanistically remains poorly understood. The current study aimed to reveal a mechanism underlying cortico-cerebellar coordination and demonstrate its speech-specificity. In a reanalysis of magnetoencephalography data, we found that activity in the cerebellum aligned to rhythmic sequences of noise-vocoded speech, irrespective of its intelligibility. We then tested whether these "entrained" responses persist, and how they interact with other brain regions, when a rhythmic stimulus stopped and temporal predictions had to be updated. We found that only intelligible speech produced sustained rhythmic responses in the cerebellum. During this "entrainment echo," but not during rhythmic speech itself, cerebellar activity was coupled with that in the left inferior frontal gyrus, and specifically at rates corresponding to the preceding stimulus rhythm. This finding represents evidence for specific cerebellum-driven temporal predictions in speech processing and their relay to cortical regions. [ABSTRACT FROM AUTHOR]

Although the aetio‐pathogenesis of inflammatory bowel diseases (IBD) is not entirely clear, the interaction between genetic and adverse environmental factors may induce an intestinal dysbiosis, resulting in chronic inflammation having effects on the large‐scale brain network. Here, we hypothesized inflammation‐related changes in brain topology of IBD patients, regardless of the clinical form [ulcerative colitis (UC) or Crohn's disease (CD)]. To test this hypothesis, we analysed source‐reconstructed magnetoencephalography (MEG) signals in 25 IBD patients (15 males, 10 females; mean age ± SD, 42.28 ± 13.15; mean education ± SD, 14.36 ± 3.58) and 28 healthy controls (HC) (16 males, 12 females; mean age ± SD, 45.18 ± 12.26; mean education ± SD, 16.25 ± 2.59), evaluating the brain topology. The betweenness centrality (BC) of the left hippocampus was higher in patients as compared with controls, in the gamma frequency band. It indicates how much a brain region is involved in the flow of information through the brain network. Furthermore, the comparison among UC, CD and HC showed statistically significant differences between UC and HC and between CD and HC, but not between the two clinical forms. Our results demonstrated that these topological changes were not dependent on the specific clinical form, but due to the inflammatory process itself. Broader future studies involving panels of inflammatory factors and metabolomic analyses on biological samples could help to monitor the brain involvement in IBD and to clarify the clinical impact. [ABSTRACT FROM AUTHOR]

Verbal working memory (vWM) is an essential limited‐capacity cognitive system that spans the fronto‐parietal network and utilizes the subprocesses of encoding, maintenance, and retrieval. With the recent widespread use of noninvasive brain stimulation techniques, multiple recent studies have examined whether such stimulation may enhance cognitive abilities such as vWM, but the findings to date remain unclear in terms of both behavior and critical brain regions. In the current study, we applied high‐definition direct current stimulation to the left and right parietal cortices of 39 healthy adults in three separate sessions (left anodal, right anodal, and sham). Following stimulation, participants completed a vWM task during high‐density magnetoencephalography (MEG). Significant neural responses at the sensor‐level were imaged using a beamformer and whole‐brain ANOVAs were used to identify the specific neuromodulatory effects of the stimulation conditions on neural responses serving distinct phases of vWM. We found that right stimulation had a faciliatory effect relative to left stimulation and sham on theta oscillations during encoding in the right inferior frontal, while the opposite pattern was observed for left supramarginal regions. Stimulation also had a faciliatory effect on theta in occipital regions and alpha in temporal regions regardless of the laterality of stimulation. In summary, our data suggest that parietal HD‐tDCS both facilitates and interferes with neural responses underlying both the encoding and maintenance phases of vWM. Future studies are warranted to determine whether specific tDCS parameters can be tuned to accentuate the facilitation responses and attenuate the interfering aspects. [ABSTRACT FROM AUTHOR]

During aging the inter‐individual variability in both the neural and behavioral functions is likely to be emphasized. Decreased competence particularly in working memory and general executive control compromises many aspects of the quality of life also within the nonclinical population. We aimed, first, to clarify the brain basis of visual working memory and inhibition during multi‐stage natural‐like task performance, and second, to identify associations between variation in task‐related neural activity and relevant cognitive skills, namely inhibition and general working memory capacity. We recorded, using magnetoencephalography (MEG), the neural modulations associated with encoding, maintenance, and retrieval, as well as interference suppression during a visual working memory task in older adults. We quantified the neural correlates of these cognitive processes through two complementary approaches: evoked responses and oscillatory activity. Neural activity during memory retrieval and interference suppression were correlated with behavioral measures of task switching and general executive functions. Our results show that general inhibitory control induced frontocentral neural modulation across a broad range of frequencies whereas domain‐specific inhibition was limited to right posterior areas. Our findings also suggest that modulations particularly in phase‐locked evoked neural activity can be reliably associated with explicit measures of cognitive skills, with better inhibitory control linked with an early neural effect of distractor inhibition during retrieval. In general, we show that exploiting the inherent inter‐individual variability in neural measures and behavioral markers of cognition in aging populations can help establish reliable links between specific brain functions and their behavioral manifestations. [ABSTRACT FROM AUTHOR]

Aberrant information processing in the basal ganglia and connected cortical areas are key to many neurological movement disorders such as Parkinson's disease. Investigating the electrophysiology of this system is difficult in humans because non-invasive methods, such as electroencephalography or magnetoencephalography, have limited sensitivity to deep brain areas. Recordings from electrodes implanted for therapeutic deep brain stimulation, in contrast, provide clear deep brain signals but are not suited for studying cortical activity. Therefore, we combine magnetoencephalography and local field potential recordings from deep brain stimulation electrodes in individuals with Parkinson's disease. Here, we make these data available, inviting a broader scientific community to explore the dynamics of neural activity in the subthalamic nucleus and its functional connectivity to cortex. The dataset encompasses resting-state recordings, plus two motor tasks: static forearm extension and self-paced repetitive fist clenching. Most patients were recorded both in the medicated and the unmedicated state. Along with the raw data, we provide metadata on channels, events and scripts for pre-processing to help interested researchers get started. [ABSTRACT FROM AUTHOR]

Magnetically shielded rooms (MSRs) provide a near-zero field environment for magnetoencephalography (MEG) research. Due to the high cost of high-permeability materials and the weak shielding capability against low-frequency magnetic disturbance, it is necessary to further design active compensation coils combined with a closed-loop control system to achieve dynamic nulling of environmental magnetic disturbance. To enhance the performance of the dynamic nulling system, this paper proposes a novel controller design method based on Padé approximation and generalized active disturbance rejection control (GADRC). First, a precise closed-loop model of the dynamic nulling system is established. On this basis, the delay element of the optically pumped magnetometer (OPM) is approximated using the Padé approximation method, and the controller is designed within the GADRC framework. The system's stability and disturbance suppression capability are analyzed using frequency-domain methods. To validate the effectiveness of the proposed method, simulations and experiments are conducted, achieving a shielding factor greater than 40 dB at 0.1 Hz. After filtering out power frequency interference, the peak-to-peak field fluctuation is reduced from 320.3 pT to 1.8 pT. [ABSTRACT FROM AUTHOR]

Source analysis of magnetoencephalography (MEG) data requires the computation of the magnetic fields induced by current sources in the brain. This so‐called MEG forward problem includes an accurate estimation of the volume conduction effects in the human head. Here, we introduce the Cut finite element method (CutFEM) for the MEG forward problem. CutFEM's meshing process imposes fewer restrictions on tissue anatomy than tetrahedral meshes while being able to mesh curved geometries contrary to hexahedral meshing. To evaluate the new approach, we compare CutFEM with a boundary element method (BEM) that distinguishes three tissue compartments and a 6‐compartment hexahedral FEM in an n = 19 group study of somatosensory evoked fields (SEF). The neural generators of the 20 ms post‐stimulus SEF components (M20) are reconstructed using both an unregularized and a regularized inversion approach. Changing the forward model resulted in reconstruction differences of about 1 centimeter in location and considerable differences in orientation. The tested 6‐compartment FEM approaches significantly increase the goodness of fit to the measured data compared with the 3‐compartment BEM. They also demonstrate higher quasi‐radial contributions for sources below the gyral crowns. Furthermore, CutFEM improves source separability compared with both other approaches. We conclude that head models with 6 compartments rather than 3 and the new CutFEM approach are valuable additions to MEG source reconstruction, in particular for sources that are predominantly radial. [ABSTRACT FROM AUTHOR]

Background: Cannabis is the most widely used psychoactive drug in the United States. While multiple studies have associated acute cannabis consumption with alterations in cognitive function (e.g., visual and spatial attention), far less is known regarding the effects of chronic consumption on the neural dynamics supporting these cognitive functions. Methods: We used magnetoencephalography (MEG) and an established visuospatial processing task to elicit multi-spectral neuronal responses in 44 regular cannabis users and 53 demographically matched non-user controls. To examine the effects of chronic cannabis use on the oscillatory dynamics underlying visuospatial processing, neural responses were imaged using a time-frequency resolved beamformer and compared across groups. Results: Neuronal oscillations serving visuospatial processing were identified in the theta (4–8 Hz), alpha (8–14 Hz), and gamma range (56–76 Hz), and these were imaged and examined for group differences. Our key results indicated that users exhibited weaker theta oscillations in occipital and cerebellar regions and weaker gamma responses in the left temporal cortices compared to non-users. Lastly, alpha oscillations did not differ, but alpha connectivity among higher-order attention areas was weaker in cannabis users relative to non-users and correlated with performance. Conclusions: Overall, these results suggest that chronic cannabis users have alterations in the oscillatory dynamics and neural connectivity serving visuospatial attention. Such alterations were observed across multiple cortical areas critical for higher-order processing and may reflect compensatory activity and/or the initial emergence of aberrant dynamics. Future work is needed to fully understand the implications of altered multispectral oscillations and neural connectivity in cannabis users. [ABSTRACT FROM AUTHOR]

Time–frequency parameterization for oscillations in specific frequency bands reflects the dynamic changes in the brain. It is related to cognitive behavior and diseases and has received significant attention in neuroscience. However, many studies do not consider the impact of the aperiodic noise and neural activity, including their time-varying fluctuations. Some studies are limited by the low resolution of the time–frequency spectrum and parameter-solved operation. Therefore, this paper proposes super-resolution time–frequency periodic parameterization of (transient) oscillation (STPPTO). STPPTO obtains a super-resolution time–frequency spectrum with Superlet transform. Then, the time–frequency representation of oscillations is obtained by removing the aperiodic component fitted in a time-resolved way. Finally, the definition of transient events is used to parameterize oscillations. The performance of this method is validated on simulated data and its reliability is demonstrated on magnetoencephalography. We show how it can be used to explore and analyze oscillatory activity under rhythmic stimulation. [ABSTRACT FROM AUTHOR]

• Bilateral somatosensory evoked spikes (SESs) (previously reported only in children) were detected in spontaneous MEG recordings of 10/30 adult subjects. • The first peak of SESs was considered to be identical to the somatosensory evoked field (SEF) M2 in latency, equivalent current dipole (ECD) position, and ECD orientation. • M2 amplitude of SEFs was significantly greater in subjects with SESs. Somatosensory evoked spikes (SESs) have been reported only in children aged under 14 years and are considered as an age-dependent phenomenon. However, we detected SESs in adult patients with epilepsy using magnetoencephalography (MEG). The present study investigated whether MEG can detect SESs in normal adults. Spontaneous MEG was recorded during measurement of somatosensory evoked fields (SEFs) for bilateral electrical median nerve stimuli in 30 healthy adults. Bilateral SESs were observed in 10 adults but none in the other 20 subjects. SESs consisted of one or two peaks, and the first peak latency corresponded to that of the second peak (M2) of SEFs. The first SES peak was identical to the M2 in isofield map pattern, as well as location and orientation of the equivalent current dipole (ECD). M2 ECD strength in the 10 subjects with SESs was larger (p <0.0001) than in 20 without SESs. All-or-nothing detection of bilateral SESs by MEG in normal adults must depend on the signal-to-noise issue of symmetrical SEFs and background brain activity. Our results further confirm the higher sensitivity of MEG compared to scalp EEG for the detection of focal cortical sources tangential to the scalp such as SESs. [ABSTRACT FROM AUTHOR]

The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research efforts. Simulating biophysically-detailed neuron models is a computationally expensive but effective method for studying local neural circuits. Recent innovations have shown that artificial neural networks (ANNs) can accurately predict the behavior of these detailed models in terms of spikes, electrical potentials, and optical readouts. While these methods have the potential to accelerate large network simulations by several orders of magnitude compared to conventional differential equation based modelling, they currently only predict voltage outputs for the soma or a select few neuron compartments. Our novel approach, based on enhanced state-of-the-art architectures for multitask learning (MTL), allows for the simultaneous prediction of membrane potentials in each compartment of a neuron model, at a speed of up to two orders of magnitude faster than classical simulation methods. By predicting all membrane potentials together, our approach not only allows for comparison of model output with a wider range of experimental recordings (patch-electrode, voltage-sensitive dye imaging), it also provides the first stepping stone towards predicting local field potentials (LFPs), electroencephalogram (EEG) signals, and magnetoencephalography (MEG) signals from ANN-based simulations. While LFP and EEG are an important downstream application, the main focus of this paper lies in predicting dendritic voltages within each compartment to capture the entire electrophysiology of a biophysically-detailed neuron model. It further presents a challenging benchmark for MTL architectures due to the large amount of data involved, the presence of correlations between neighbouring compartments, and the non-Gaussian distribution of membrane potentials. Author summary: Our research focuses on cutting-edge techniques in computational neuroscience. We specifically make use of simulations of biophysically detailed neuron models. Traditionally these methods are computationally intensive, but recent advancements using artificial neural networks (ANNs) have shown promise in predicting neural behavior with remarkable accuracy. However, existing ANNs fall short in providing comprehensive predictions across all compartments of a neuron model and only provide information on the activity of a limited number of locations along the extent of a neuron. In our study, we introduce a novel approach leveraging state-of-the-art multitask learning architectures. This approach allows us to simultaneously predict membrane potentials in every compartment of a neuron model. By distilling the underlying electrophysiology into an ANN, we significantly outpace conventional simulation methods. By accurately capturing voltage outputs across the neuron's structure, our method invites comparisons with experimental data and paves the way for predicting complex aggregate signals such as local field potentials and EEG signals. Our findings not only advance our understanding of neural dynamics but also present a significant benchmark for future research in computational neuroscience. [ABSTRACT FROM AUTHOR]

The emergence of biomagnetism imaging has led to the development of ultrasensitive and compact spin-exchange relaxation-free (SERF) atomic magnetometers that promise high-resolution magnetocardiography (MCG) and magnetoencephalography (MEG). However, conventional optical components are not compatible with nanofabrication processes that enable the integration of atomic magnetometers on chips, especially for elliptically polarized laser-pumped SERF magnetometers with bulky optical systems. In this study, an elliptical-polarization pumping beam (at 795 nm) is achieved through a single-piece metasurface, which results in an SERF magnetometer with a high sensitivity reaching 10.61 fT/Hz1/2 by utilizing a 87Rb vapor cell with a 3 mm inner diameter. To achieve the optimum theoretical polarization, our design combines a computer-assisted optimization algorithm with an emerging metasurface design process. The metasurface is fabricated with 550 nm thick silicon-rich silicon nitride on a 2 × 2 cm2 SiO2 substrate and features a 22.17° ellipticity angle (a deviation from the target polarization of less than 2%) and more than 80% transmittance. This study provides a feasible approach for on-chip polarization control of future all-integrated atomic magnetometers, which will further pave the way for high-resolution biomagnetism imaging and portable atomic sensing applications. [ABSTRACT FROM AUTHOR]

This study assesses the reliability of resting‐state dynamic causal modelling (DCM) of magnetoencephalography (MEG) under conductance‐based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting‐state MEG data from two sessions, acquired 2 weeks apart, from a cohort with high between‐subject variance arising from Alzheimer's disease. Our focus is not on the effect of disease, but on the reliability of the methods (as within‐subject between‐session agreement), which is crucial for future studies of disease progression and drug intervention. To assess the reliability of first‐level DCMs, we compare model evidence associated with the covariance among subject‐specific free energies (i.e., the 'quality' of the models) with versus without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the differences between the inferred DCM parameter probability distributions at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within‐subject, within‐session, and between‐epochs; (ii) within‐subject between‐session; and (iii) within‐site between‐subjects, accommodating the conditional dependency among parameter estimates. We show that for data acquired close in time, and under similar circumstances, more than 95% of inferred DCM parameters are unlikely to differ, speaking to mutual predictability over sessions. Using PEB, we show a reciprocal relationship between a conventional definition of 'reliability' and the conditional dependency among inferred model parameters. Our analyses confirm the reliability and reproducibility of the conductance‐based DCMs for resting‐state neurophysiological data. In this respect, the implicit generative modelling is suitable for interventional and longitudinal studies of neurological and psychiatric disorders. [ABSTRACT FROM AUTHOR]

Visual entrainment is a powerful and widely used research tool to study visual information processing in the brain. While many entrainment studies have focused on frequencies around 14–16 Hz, there is renewed interest in understanding visual entrainment at higher frequencies (e.g., gamma‐band entrainment). Notably, recent groundbreaking studies have demonstrated that gamma‐band visual entrainment at 40 Hz may have therapeutic effects in the context of Alzheimer's disease (AD) by stimulating specific neural ensembles, which utilize GABAergic signaling. Despite such promising findings, few studies have investigated the optimal parameters for gamma‐band visual entrainment. Herein, we examined whether visual stimulation at 32, 40, or 48 Hz produces optimal visual entrainment responses using high‐density magnetoencephalography (MEG). Our results indicated strong entrainment responses localizing to the primary visual cortex in each condition. Entrainment responses were stronger for 32 and 40 Hz relative to 48 Hz, indicating more robust synchronization of neural ensembles at these lower gamma‐band frequencies. In addition, 32 and 40 Hz entrainment responses showed typical patterns of habituation across trials, but this effect was absent for 48 Hz. Finally, connectivity between visual cortex and parietal and prefrontal cortices tended to be strongest for 40 relative to 32 and 48 Hz entrainment. These results suggest that neural ensembles in the visual cortex may resonate at around 32 and 40 Hz and thus entrain more readily to photic stimulation at these frequencies. Emerging AD therapies, which have focused on 40 Hz entrainment to date, may be more effective at lower relative to higher gamma frequencies, although additional work in clinical populations is needed to confirm these findings. Practitioner Points: Gamma‐band visual entrainment has emerged as a therapeutic approach for eliminating amyloid in Alzheimer's disease, but its optimal parameters are unknown.We found stronger entrainment at 32 and 40 Hz compared to 48 Hz, suggesting neural ensembles prefer to resonate around these relatively lower gamma‐band frequencies.These findings may inform the development and refinement of innovative AD therapies and the study of GABAergic visual cortical functions. [ABSTRACT FROM AUTHOR]

Testosterone levels sharply rise during the transition from childhood to adolescence and these changes are known to be associated with changes in human brain structure. During this same developmental window, there are also robust changes in the neural oscillatory dynamics serving verbal working memory processing. Surprisingly, whereas many studies have investigated the effects of chronological age on the neural oscillations supporting verbal working memory, none have probed the impact of endogenous testosterone levels during this developmental period. Using a sample of 89 youth aged 6–14 years‐old, we collected salivary testosterone samples and recorded magnetoencephalography during a modified Sternberg verbal working memory task. Significant oscillatory responses were identified and imaged using a beamforming approach and the resulting maps were subjected to whole‐brain ANCOVAs examining the effects of testosterone and sex, controlling for age, during verbal working memory encoding and maintenance. Our primary results indicated robust testosterone‐related effects in theta (4–7 Hz) and alpha (8–14 Hz) oscillatory activity, controlling for age. During encoding, females exhibited weaker theta oscillations than males in right cerebellar cortices and stronger alpha oscillations in left temporal cortices. During maintenance, youth with greater testosterone exhibited weaker alpha oscillations in right parahippocampal and cerebellar cortices, as well as regions across the left‐lateralized language network. These results extend the existing literature on the development of verbal working memory processing by showing region and sex‐specific effects of testosterone, and are the first results to link endogenous testosterone levels to the neural oscillatory activity serving verbal working memory, above and beyond the effects of chronological age. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Magnetoencephalography (MEG) and magnetic resonance imaging (MRI) are non‐invasive imaging techniques that offer effective means for disease diagnosis. A more straightforward and optimized method is presented for designing gradient coils which are pivotal parts of the above imaging systems.A novel design method based on stream function combining an optimization algorithm is proposed to obtain highly linear gradient coil.Two‐dimensional Fourier expansion of the current field on the surface where the coil is located and the equipotential line of the expansion term superposition according to the number of turns of the coil are used to represent the coil shape. Particle swarm optimization is utilized to optimize the coil shape while linearity and field uniformity are used as parameters to evaluate the coil performance. Through this method, the main parameters such as input current distribution region, coil turns, desired magnetic field strength, expansion order and iteration times can be combined in a given solution space to optimize coil design.Simulation results show that the maximum linearity spatial deviation of the designed bi‐planar x‐gradient coil compared with that of target field method is reduced from 14% to 0.54%, and that of the bi‐planar z‐gradient coil is reduced from 8.98% to 0.52%. Similarly, that of the cylindrical x‐gradient coil is reduced from 2% to 0.1%, and that of the cylindrical z‐gradient coil is reduced from 0.87% to 0.45%. The similar results are found in the index of inhomogeneity error. Moreover, it has also been verified experimentally that the result of measured magnetic field is consist with simulated result.The proposed method provides a straightforward way that simplifies the design process and improves the linearity of designed gradient coil, which could be beneficial to realize better magnetic field in engineering applications. [ABSTRACT FROM AUTHOR]

Summary The present communication presents a novel compact microstrip line feed dual‐band four‐element MIMO (multiple input multiple output) antenna for 5G sub‐6GHz N79 and WiFi‐6E bands with a defined size of 48 × 48 × 1.6 mm3. The FR4 substrate used in the construction of the designated antenna has a thickness of 1.6 mm, a relative permittivity of 4.4, and loss tangent values of 0.02. The proposed MIMO architecture consists of four equilateral triangle‐shaped antenna elements on top and ground with inverted L‐shaped stubs on the substrate's bottom layer. The ground structure of the antenna is a connected ground and is formed by defected ground parts which are connected with inverted L‐shaped stubs. The orthogonal structure of the MIMO antenna elements, with four armed square stubs among them, enhanced the isolation between surrounding antennas. The proposed four‐port MIMO antenna is built and tested in to validate design, scattering, and radiation performance, as well as MIMO diversity features. The designed antenna has more than 15 dB isolation and operates at 4.75 GHz from 4.45 to 5.2 GHz (5G sub‐6 GHz N79 band) and 6.3 GHz from 5.95 to 7.15 GHz (WiFi‐6E band). Furthermore, the design provides omnidirectional patterns, high gain (6.5dBi) and efficiency (>95%), a low envelope correlation coefficient (<0.00001) and acceptable diversity gain (10 dB), a good total active refraction coefficient (−10 dB) and low channel capacity loss (0.03 bit/s/Hz), and a considerable mean effective gain (MEG = −3 dB) and MEG ratio (0 dB). Because the simulated and tested results match so well, the antenna is a good candidate for 5G sub‐6 GHz N79 band operations. [ABSTRACT FROM AUTHOR]

Background: An imbalance of excitatory and inhibitory synaptic transmission in multiple sclerosis (MS) may lead to cognitive impairment, such as impaired working memory. The 1/f slope of electroencephalography/magnetoencephalography (EEG/MEG) power spectra is shown to be a non-invasive proxy of excitation/inhibition balance. A flatter slope is associated with higher excitation/lower inhibition. Objectives: To assess the 1/f slope modulation induced by stimulus and its association with behavioral and cognitive measures. Methods: We analyzed MEG recordings of 38 healthy controls (HCs) and 79 people with multiple sclerosis (pwMS) while performing an n-back task including target and distractor stimuli. Target trials require an answer, while distractor trials do not. We computed the 1/f spectral slope through the fitting oscillations and one over f (FOOOF) algorithm within the time windows 1 second before and after each stimulus presentation. Results: We observed a flatter 1/f slope after distractor stimuli in pwMS compared to HCs. The 1/f slope was significantly steeper after stimulus for both HCs and pwMS and was significantly correlated with reaction times. This modulation in 1/f slope was significantly correlated with visuospatial memory assessed by the BVMT-R test. Conclusion: Our results suggest possible inhibitory mechanism deficits in pwMS during a working memory task. [ABSTRACT FROM AUTHOR]

• Performance of temporally extended signal space separation (tSSS) was investigated in terms of the choice of the origin of the sphere model. • Individual sphere model resulted in better goodness of fit and confidence volume. • Interictal epileptiform discharges (IEDs) were more influenced by this choice than somatosensory evoked fields was. Temporally extended signal space separation (tSSS) is a powerful method for artifact suppression in magnetoencephalography (MEG). Because tSSS first separates MEG signals coming from inside and outside a certain sphere, definition of the sphere origin is important. For this study, we explored the influence of origin choice on tSSS performance in spontaneous and evoked activity from epilepsy patients. Interictal epileptiform discharges (IEDs) and somatosensory evoked fields (SEFs) were processed with two tSSSs: one with the default origin of (0, 0, 40 mm) in the head coordinate, and the other with an individual origin estimated using each patient's anatomical magnetic resonance imaging (MRI). Equivalent current dipoles (ECDs) were calculated for the data. The ECD location and quality of estimation were compared across conditions. MEG data from 21 patients revealed marginal differences in ECD location, but the estimation quality inferred from goodness of fit (GOF) and confidence volume (CV) was better for the tSSS with individual origins. This choice affected IEDs more than it affected SEFs. Individual sphere model resulted in better GOF and CV. Application of tSSS using an individual origin would be more desirable when available. This parameter might influence spontaneous activity more strongly. [ABSTRACT FROM AUTHOR]

• Sentence completion is associated with three distinct patterns of cortical oscillations in magnetoencephalography (MEG). • The semantic control network displays differential interhemispheric dynamics in its temporal component during this task. • Demonstrates the potential of MEG in preoperative language mapping and understanding language alterations in neurological disorders. To investigate cortical oscillations during a sentence completion task (SC) using magnetoencephalography (MEG), focusing on the semantic control network (SCN), its leftward asymmetry, and the effects of semantic control load. Twenty right-handed adults underwent MEG while performing SC, consisting of low cloze (LC: multiple responses) and high cloze (HC: single response) stimuli. Spectrotemporal power modulations as event-related synchronizations (ERS) and desynchronizations (ERD) were analyzed: first, at the whole-brain level; second, in key SCN regions, posterior middle/inferior temporal gyri (pMTG/ITG) and inferior frontal gyri (IFG), under different semantic control loads. Three cortical response patterns emerged: early (0–200 ms) theta-band occipital ERS; intermediate (200–700 ms) semantic network alpha/beta-band ERD; late (700–3000 ms) dorsal language stream alpha/beta/gamma-band ERD. Under high semantic control load (LC), pMTG/ITG showed prolonged left-sided engagement (ERD) and right-sided inhibition (ERS). Left IFG exhibited heightened late (2500–2550 ms) beta-band ERD with increased semantic control load (LC vs. HC). SC involves distinct cortical responses and depends on the left IFG and asymmetric engagement of the pMTG/ITG for semantic control. Future use of SC in neuromagnetic preoperative language mapping and for understanding the pathophysiology of language disorders in neurological conditions. [ABSTRACT FROM AUTHOR]

• Avalanche transition matrices are used to investigate the spatio-temporal spreading of neuronal avalanches in ALS; • ALS patients present higher values of the nodal strength in both cortical and sub-cortical brain areas, as compared to controls; • The nodal strength directly correlates with disease duration, corroborating the clinical relevance of brain dynamics. To test the hypothesis that patients affected by Amyotrophic Lateral Sclerosis (ALS) show an altered spatio-temporal spreading of neuronal avalanches in the brain, and that this may related to the clinical picture. We obtained the source-reconstructed magnetoencephalography (MEG) signals from thirty-six ALS patients and forty-two healthy controls. Then, we used the construct of the avalanche transition matrix (ATM) and the corresponding network parameter nodal strength to quantify the changes in each region, since this parameter provides key information about which brain regions are mostly involved in the spreading avalanches. ALS patients presented higher values of the nodal strength in both cortical and sub-cortical brain areas. This parameter correlated directly with disease duration. In this work, we provide a deeper characterization of neuronal avalanches propagation in ALS, describing their spatio-temporal trajectories and identifying the brain regions most likely to be involved in the process. This makes it possible to recognize the brain areas that take part in the pathogenic mechanisms of ALS. Furthermore, the nodal strength of the involved regions correlates directly with disease duration. Our results corroborate the clinical relevance of aperiodic, fast large-scale brain activity as a biomarker of microscopic changes induced by neurophysiological processes. [ABSTRACT FROM AUTHOR]

• PTSD is associated with lower coupling between regional beta-band amplitude envelopes. • Findings are primarily explained by differences in coupling of the infraslow fluctuations of the amplitude envelopes. • Failure to coordinate infraslow amplitude fluctuations across large-scale cortical networks promotes network dysfunction in PTSD. Coupling between the amplitude envelopes (AEs) of regional cortical activity reflects mechanisms that coordinate the excitability of large-scale cortical networks. We used resting-state MEG recordings to investigate the association between alterations in the coupling of cortical AEs and symptoms of post-traumatic stress disorder (PTSD). Participants (n = 96) were service members with combat exposure and various levels of post-traumatic stress severity (PTSS). We assessed the correlation between PTSS and (1) coupling of broadband cortical AEs of beta band activity, (2) coupling of the low- (<0.5 Hz) and high-frequency (>0.5 Hz) components of the AEs, and (3) their time-varying patterns. PTSS was associated with widespread hypoconnectivity assessed from the broadband AE fluctuations, which correlated with subscores for the negative thoughts and feelings/emotional numbing (NTF/EN) and hyperarousal clusters of symptoms. Higher NTF/EN scores were also associated with smaller increases in resting-state functional connectivity (rsFC) with time during the recordings. The distinct patterns of rsFC in PTSD were primarily due to differences in the coupling of low-frequency (infraslow) fluctuations of the AEs of beta band activity. Our findings implicate the mechanisms underlying the regulation/coupling of infraslow oscillations in the alterations of rsFC assessed from broadband AEs and in PTSD symptomatology. Altered coordination of infraslow amplitude fluctuations across large-scale cortical networks can contribute to network dysfunction and may provide a target for treatment in PTSD. [ABSTRACT FROM AUTHOR]

[Display omitted] • Healthy human MEG resting state sensorimotor activity showed good to excellent test–retest stability across two separate sessions. • 2–3 minute recordings were sufficient to obtain stable test–retest results and automation of analysis was successful in 86% • 'Resting sensorimotor phenotype' is a stable feature of individuals' resting brain activity with potential as a clinical biomarker. Diseases affecting sensorimotor function impair physical independence. Reliable functional clinical biomarkers allowing early diagnosis or targeting treatment and rehabilitation could reduce this burden. Magnetoencephalography (MEG) non-invasively measures brain rhythms such as the somatomotor 'rolandic' rhythm which shows intermittent high-amplitude beta (14–30 Hz) 'events' that predict behavior across tasks and species and are altered by sensorimotor neurological diseases. We assessed test–retest stability, a prerequisite for biomarkers, of spontaneous sensorimotor aperiodic (1/f) signal and beta events in 50 healthy human controls across two MEG sessions using the intraclass correlation coefficient (ICC). Beta events were determined using an amplitude-thresholding approach on a narrow-band filtered amplitude envelope obtained using Morlet wavelet decomposition. Resting sensorimotor characteristics showed good to excellent test–retest stability. Aperiodic component (ICC 0.77–0.88) and beta event amplitude (ICC 0.74–0.82) were very stable, whereas beta event duration was more variable (ICC 0.55–0.7). 2–3 minute recordings were sufficient to obtain stable results. Analysis automatization was successful in 86%. Sensorimotor beta phenotype is a stable feature of an individual's resting brain activity even for short recordings easily measured in patients. Spontaneous sensorimotor beta phenotype has potential as a clinical biomarker of sensorimotor system integrity. [ABSTRACT FROM AUTHOR]

BACKGROUND: Chronic hypertension is known to be a major contributor to cognitive decline, with executive function and working memory being among the domains most commonly affected. Despite the growing literature on such dysfunction in patients with hypertension, the underlying neural processes are poorly understood. METHODS: In this cross-sectional study, we examine these neural processes by having participants with controlled hypertension, uncontrolled hypertension, and healthy controls perform a verbal working memory task during magnetoencephalography. Neural oscillations associated with the encoding and maintenance components of the working memory task were imaged and statistically evaluated among the 3 groups. RESULTS: Differences related to hypertension emerged during the encoding phase, where the hypertension groups exhibited weaker α-β oscillatory responses compared with controls in the left parietal cortices, whereas such oscillatory activity differed between the 2 hypertension groups in the right prefrontal regions. Importantly, these neural responses in the prefrontal and parietal cortices during encoding were also significantly associated with behavioral performance across all participants. CONCLUSIONS: Overall, our data suggest that hypertension is associated with neurophysiological abnormalities during working memory encoding, whereas the neural processes serving maintenance seem to be preserved. The right hemispheric neural responses likely reflected compensatory processing, which patients with controlled hypertension may use to achieve verbal working memory function at the level of controls, as opposed to the uncontrolled hypertension group where diminished resources may have limited such additional recruitment. [ABSTRACT FROM AUTHOR]

The wireless and portable equipment demand for boosting the data rate, spectral efficiency, coverage, SNR, and capacity which is fulfilled by MIMO antenna technology. In this research 4 elements hexagonal shaped antenna using MIMO techniques is designed for 2.1 GHz mobile and wireless applications. The antenna has four ports with identical radiators with hexagon shape. The special designed manifold arms of the proposed antenna design provide low correlation among the radiators. The inter-port isolation greater than 15 dB is achieved. The other MIMO antenna parameters like ECC is < 0.08 and the diversity gain is 10 dB in considered in 2:1 VSWR band. The proposed design ensures lower than 0.5 W/kg specific absorption rate (SAR), 3.0 dB mean effective gain (MEG) and 0.4 bits/s/Hz channel capacity loss (CCL). The peak gain of the antenna is 0.45 dBi at resonance. The E-field shows a maximum magnitude/major lobe in the direction of 190 °. The H-Field shows the major radiation in the direction of 187 °. The CST-MWS antenna designing tool is used to simulate and optimize the proposed design and it fabricated on FR-4 substrate. The research focuses on various parameters associated with MIMO antenna viz SAR, S-parameters, envelop correlation coefficient (ECC), radiation patterns, CCL, Diversity Gain surface current distribution, and MEG. [ABSTRACT FROM AUTHOR]

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which cannot be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

Overlapping clinical presentations in primary progressive aphasia (PPA) variants present challenges for diagnosis and understanding pathophysiology, particularly in the early stages of the disease when behavioral (speech) symptoms are not clearly evident. Divergent atrophy patterns (temporoparietal degeneration in logopenic variant lvPPA, frontal degeneration in nonfluent variant nfvPPA) can partially account for differential speech production errors in the two groups in the later stages of the disease. While the existing dogma states that neurodegeneration is the root cause of compromised behavior and cortical activity in PPA, the extent to which neurophysiological signatures of speech dysfunction manifest independent of their divergent atrophy patterns remain unknown. We test the hypothesis that nonword deficits in lvPPA and nfvPPA arise from distinct patterns of neural oscillations that are unrelated to atrophy. We use a novel structure-function imaging approach integrating magnetoencephalographic imaging of neural oscillations during a non-word repetition task with voxel-based morphometry-derived measures of gray matter volume to isolate neural oscillation abnormalities independent of atrophy. We find reduced beta band neural activity in left temporal regions associated with the late stages of auditory encoding unique to patients with lvPPA and reduced high-gamma neural activity over left frontal regions associated with the early stages of motor preparation in patients with nfvPPA. Neither of these patterns of reduced cortical oscillations was explained by cortical atrophy in our statistical model. These findings highlight the importance of structure-function imaging in revealing neurophysiological sequelae in early stages of dementia when neither structural atrophy nor behavioral deficits are clinically distinct.

Abstract Magnetoencephalography/electroencephalography (M/EEG) can provide insights into migraine pathophysiology and help develop clinically valuable biomarkers. To integrate and summarize the existing evidence on changes in brain function in migraine, we performed a systematic review and meta-analysis (PROSPERO CRD42021272622) of resting-state M/EEG findings in migraine. We included 27 studies after searching MEDLINE, Web of Science Core Collection, and EMBASE. Risk of bias was assessed using a modified Newcastle–Ottawa Scale. Semi-quantitative analysis was conducted by vote counting, and meta-analyses of M/EEG differences between people with migraine and healthy participants were performed using random-effects models. In people with migraine during the interictal phase, meta-analysis revealed higher power of brain activity at theta frequencies (3–8 Hz) than in healthy participants. Furthermore, we found evidence for lower alpha and beta connectivity in people with migraine in the interictal phase. No associations between M/EEG features and disease severity were observed. Moreover, some evidence for higher delta and beta power in the premonitory compared to the interictal phase was found. Strongest risk of bias of included studies arose from a lack of controlling for comorbidities and non-automatized or non-blinded M/EEG assessments. These findings can guide future M/EEG studies on migraine pathophysiology and brain-based biomarkers, which should consider comorbidities and aim for standardized, collaborative approaches.

Emilie Groulx-Boivin,1,2 Tasha Bouchet,3 Kenneth A Myers1,2,4 1Department of Neurology and Neurosurgery, Montreal Children’s Hospital, McGill University, Montreal, Quebec, Canada; 2Department of Pediatrics, Montreal Children’s Hospital, McGill University, Montreal, Quebec, Canada; 3Department of Medicine, McGill University, Montreal, Quebec, Canada; 4Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, CanadaCorrespondence: Kenneth A Myers, Montreal Children’s Hospital, 1001 Décarie Blvd, Montreal, Quebec, H4A 3J1, Canada, Tel +1 514-412-4466, Fax +1 514-412-4373, Email kenneth.myers@mcgill.caAbstract: Absence seizures are classically associated with behavioral arrest and transient deficits in consciousness, yet substantial variability exists in the severity of the impairment. Despite several decades of research on the topic, the pathophysiology of absence seizures and the mechanisms underlying behavioral impairment remain unclear. Several rationales have been proposed including widespread cortical deactivation, reduced perception of external stimuli, and transient suspension of the default mode network, among others. This review aims to summarize the current knowledge on the neural correlates of impaired consciousness in absence seizures. We review evidence from studies using animal models of absence epilepsy, electroencephalography, functional magnetic resonance imaging, magnetoencephalography, positron emission tomography, and single photon emission computed tomography.Keywords: awareness, electroencephalography, functional magnetic resonance imaging, positron emission tomography, single photon emission computed tomography, magnetoencephalography, fMRI, MEG, PET