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Previous studies have demonstrated that auditory cortex activity can be influenced by cross-sensory visual inputs. Intracortical laminar recordings in nonhuman primates have suggested a feedforward (FF) type profile for auditory evoked but feedback (FB) type for visual evoked activity in the auditory cortex. To test whether cross-sensory visual evoked activity in the auditory cortex is associated with FB inputs also in humans, we analyzedmagnetoencephalography (MEG) responses fromeight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimatedMEG source waveforms for auditory cortex regions of interest, auditory evoked response showed peaks at 37 and 90 ms and visual evoked response at 125 ms. The inputs to the auditory cortex were modeled through FF- and FB-type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which links cellular- and circuit- level mechanisms toMEG signals. HNN modeling suggested that the experimentally observed auditory response could be explained by an FF input followed by an FB input, whereas the cross-sensory visual response could be adequately explained by just an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas. [ABSTRACT FROM AUTHOR]

Introduction: Musical roundness perception relies on consonance/dissonance within a rule-based harmonic context, but also on individual characteristics of the listener. The present work tackles these aspects in a combined psychoacoustic and neurophysiological study, taking into account participant's musical aptitude. Methods: Our paradigm employed cadence-like four-chord progressions, based on Western music theory. Chord progressions comprised naturalistic and artificial sounds; moreover, their single chords varied regarding consonance/dissonance and harmonic function. Thirty participants listened to the chord progressions while their cortical activity was measured with magnetoencephalography; afterwards, they rated the individual chord progressions with respect to their perceived roundness. Results: Roundness ratings differed according to the degree of dissonance in the dominant chord at the progression's third position; this effect was pronounced in listeners with high musical aptitude. Interestingly, a corresponding pattern occurred in the neuromagnetic N1m response to the fourth chord (i.e., at the progression's resolution), again with somewhat stronger differentiation among musical listeners. The N1m magnitude seemed to increase during chord progressions that were considered particularly round, with the maximum difference after the final chord; here, however, the musical aptitude effect just missed significance. Discussion: The roundness of chord progressions is reflected in participant's psychoacoustic ratings and in their transient cortical activity, with stronger differentiation among listeners with high musical aptitude. The concept of roundness might help to reframe consonance/dissonance to a more holistic, gestalt-like understanding that covers chord relations in Western music. [ABSTRACT FROM AUTHOR]

The unconscious integration of vocal and facial cues during speech perception facilitates face‐to‐face communication. Recent studies have provided substantial behavioural evidence concerning impairments in audiovisual (AV) speech perception in schizophrenia. However, the specific neurophysiological mechanism underlying these deficits remains unknown. Here, we investigated activities and connectivities centered on the auditory cortex during AV speech perception in schizophrenia. Using magnetoencephalography, we recorded and analysed event‐related fields in response to auditory (A: voice), visual (V: face) and AV (voice–face) stimuli in 23 schizophrenia patients (13 males) and 22 healthy controls (13 males). The functional connectivity associated with the subadditive response to AV stimulus (i.e., [AV] < [A] + [V]) was also compared between the two groups. Within the healthy control group, [AV] activity was smaller than the sum of [A] and [V] at latencies of approximately 100 ms in the posterior ramus of the lateral sulcus in only the left hemisphere, demonstrating a subadditive N1m effect. Conversely, the schizophrenia group did not show such a subadditive response. Furthermore, weaker functional connectivity from the posterior ramus of the lateral sulcus of the left hemisphere to the fusiform gyrus of the right hemisphere was observed in schizophrenia. Notably, this weakened connectivity was associated with the severity of negative symptoms. These results demonstrate abnormalities in connectivity between speech‐ and face‐related cortical areas in schizophrenia. This aberrant subadditive response and connectivity deficits for integrating speech and facial information may be the neural basis of social communication dysfunctions in schizophrenia. [ABSTRACT FROM AUTHOR]

Neural fingerprinting is the identification of individuals in a cohort based on neuroimaging recordings of brain activity. In magneto- and electroencephalography (M/EEG), it is common practice to use second-order statistical measures, such as correlation or connectivity matrices, when neural fingerprinting is performed. These measures or features typically require coupling between signal channels and often ignore the individual temporal dynamics. In this study, we show that, following recent advances in multivariate time series classification, such as the development of the RandOm Convolutional KErnel Transformation (ROCKET) classifier, it is possible to perform classification directly on short time segments from MEG resting-state recordings with remarkably high classification accuracies. In a cohort of 124 subjects, it was possible to assign windows of time series of 1 s in duration to the correct subject with above 99% accuracy. The achieved accuracies are vastly superior to those of previous methods while simultaneously requiring considerably shorter time segments. [ABSTRACT FROM AUTHOR]

Networks are a useful mathematical tool for capturing the complexity of the world. In a previous behavioral study, we showed that human adults were sensitive to the high-level network structure underlying auditory sequences, even when presented with incomplete information. Their performance was best explained by a mathematical model compatible with associative learning principles, based on the integration of the transition probabilities between adjacent and nonadjacent elements with a memory decay. In the present study, we explored the neural correlates of this hypothesis via magnetoencephalography (MEG). Participants (N = 23, 16 females) passively listened to sequences of tones organized in a sparse community network structure comprising two communities. An early difference (~150 ms) was observed in the brain responses to tone transitions with similar transition probability but occurring either within or between communities. This result implies a rapid and automatic encoding of the sequence structure. Using time-resolved decoding, we estimated the duration and overlap of the representation of each tone. The decoding performance exhibited exponential decay, resulting in a significant overlap between the representations of successive tones. Based on this extended decay profile, we estimated a long-horizon associative learning novelty index for each transition and found a correlation of this measure with the MEG signal. Overall, our study sheds light on the neural mechanisms underlying human sensitivity to network structures and highlights the potential role of Hebbian-like mechanisms in supporting learning at various temporal scales. [ABSTRACT FROM AUTHOR]

As technology develops, operators move into more remote regions, deeper offshore depths, and into regions that yield more challenges. The field of flow assurance, the coupling of multiphase flow and production chemistry, was born out of necessity as it has grown in concern to all operators. Mono Ethylene Glycol (MEG) which is commonly used to prevent hydrate formation, may assist in wax deposition and forming emulsion when combined with waxy crude. Nevertheless, once developed, it can tackle these issues as well. In this study the potential of using MEG is evaluated to release the excess pressure drop in pipeline caused by accumulation of highly viscous phase. Field trials over a gas field with condensate gas ratio around 30 that yields wax in the Mediterranean have been used for this study. MEG can be used as a heat transfer fluid that can be heated to temperature higher than water and keep its energy for longer time that helps in dissolving the deposited wax in the pipeline. The results from these trials demonstrate that cleaning the line of accumulated solids can be achieved by optimizing the volume of MEG injected with the shear force from water hammering effect while restarting the well. [ABSTRACT FROM AUTHOR]

Paroxysmal patterns of slow cortical activity have been detected in EEG recordings from individuals with age-related neuropathology and have been shown to be correlated with cognitive dysfunction and blood-brain barrier disruption in these participants. The prevalence of these events in healthy participants, however, has not been studied. In this work, we inspect MEG recordings from 623 healthy participants from the Cam-CAN dataset for the presence of paroxysmal slow wave events (PSWEs). PSWEs were detected in approximately 20% of healthy participants in the dataset, and participants with PSWEs tended to be older and have lower cognitive performance than those without PSWEs. In addition, event features changed linearly with age and cognitive performance, resulting in longer and slower events in older adults, and more widespread events in those with low cognitive performance. These findings provide the first evidence of PSWEs in a subset of purportedly healthy adults. Going forward, these events may have utility as a diagnostic biomarker for atypical brain activity in older adults. • Paroxysmal slow cortical events are detectable in 20% of healthy human participants • Participants with events tend to be older and have reduced cognitive performance • Events become longer, slower, and more prevalent with age [ABSTRACT FROM AUTHOR]

Introduction: To investigate cortical network changes using Magnetoencephalography (MEG) signals in Parkinson's disease (PD) patients undergoing Magnetic Resonance-guided Focused Ultrasound (MRgFUS) thalamotomy. Methods: We evaluated the MEG signals in 16 PD patients with drug-refractory tremor before and after 12-month from MRgFUS unilateral lesion of the ventralis intermediate nucleus (Vim) of the thalamus contralateral to the most affected body side. We recorded patients 24 h before (T0) and 24 h after MRgFUS (T1). We analyzed signal epochs recorded at rest and during the isometric extension of the hand contralateral to thalamotomy. We evaluated cortico-muscular coherence (CMC), the out-strength index from non-primary motor areas to the pre-central area and connectivity indexes, using generalized partial directed coherence. Statistical analysis was performed using RMANOVA and post hoc t-tests. Results: Most changes found at T1 compared to T0 occurred in the beta band and included: (1) a re-adjustment of CMC distribution; (2) a reduced outstrength from non-primary motor areas toward the precentral area; (3) strongly reduced clustering coefficient values. These differences mainly occurred during motor activation and with few statistically significant changes at rest. Correlation analysis showed significant relationships between changes of out-strength and clustering coefficient in non-primary motor areas and the changes in clinical scores. Discussion: One day after MRgFUS thalamotomy, PD patients showed a topographically reordered CMC and decreased cortico-cortical flow, together with a reduced local connection between different nodes. These findings suggest that the reordered cortico-muscular and cortical-networks in the beta band may represent an early physiological readjustment related to MRgFUS Vim lesion. [ABSTRACT FROM AUTHOR]

This paper emphasizes on amalgamation of MIMO antenna with DRA structures; explicitly useful to wireless applications. The feeding mechanism is accomplished by the symmetrically positioned tapered Y-shape lines. The design also encompasses a CSRR geometry along with two metallic reflectors. The incorporation of CSRR introduces novel investigation into traditional designs, resulting in improved isolation performance of the MIMO design. In order to demonstrate the effectiveness of the mentioned antenna, we performed a simulation using ANSYS-HFSS. Subsequently, a physical model was developed and tested. It confirms four different resonating points as 2.56, 4.08, 5.76, and 9.12 GHz with isolations of 36, 47.3, 33.9, and 33 dB respectively. In addition, a few other important parameters relevant to MIMO have been considered and presented in the form of ECC, MEG, DG, CCL, and TARC which claim the dominancy of the stated antenna under the MIMO diversity scenario. In summary, we have presented a model that demonstrates a strong correlation between the simulated and measured outcomes. [ABSTRACT FROM AUTHOR]

Source localization from M/EEG data is a fundamental step in many analysis pipelines, including those aiming at clinical applications such as the pre-surgical evaluation in epilepsy. Among the many available source localization algorithms, SESAME (SEquential SemiAnalytic Montecarlo Estimator) is a Bayesian method that distinguishes itself for several good reasons: it is highly accurate in localizing focal sources with comparably little sensitivity to input parameters; it allows the quantification of the uncertainty of the reconstructed source(s); it accepts user-defined a priori high-and low-probability search regions in input; it can localize the generators of neural oscillations in the frequency domain. Both a Python and a MATLAB implementation of SESAME are available as open-source packages under the name of SESAMEEG and are well integrated with the main software packages used by the M/EEG community; moreover, the algorithm is part of the commercial software BESA Research (from version 7.0 onwards). While SESAMEEG is arguably simpler to use than other source modeling methods, it has amuch richer output that deserves to be described thoroughly. In this article, after a gentlemathematical introduction to the algorithm, we provide a complete description of the available output and show several use cases on experimental M/EEG data. [ABSTRACT FROM AUTHOR]

Capacity limitations in visual tasks can be observed when the number of task-related objects increases. An influential idea is that such capacity limitations are determined by competition at the neural level: two objects that are encoded by shared neural populations interfere more in behavior (e.g., visual search) than two objects encoded by separate neural populations. However, the neural representational similarity of objects varies across brain regions and across time, raising the questions of where and when competition determines task performance. Furthermore, it is unclear whether the association between neural representational similarity and task performance is common or unique across tasks. Here, we used neural representational similarity derived from fMRI, MEG, and a deep neural network (DNN) to predict performance on two visual search tasks involving the same objects and requiring the same responses but differing in instructions: cued visual search and oddball visual search. Separate groups of human participants (both sexes) viewed the individual objects in neuroimaging experiments to establish the neural representational similarity between those objects. Results showed that performance on both search tasks could be predicted by neural representational similarity throughout the visual system (fMRI), from 80 ms after onset (MEG), and in all DNN layers. Stepwise regression analysis, however, revealed taskspecific associations, with unique variability in oddball search performance predicted by early/posterior neural similarity and unique variability in cued search task performance predicted by late/anterior neural similarity. These results reveal that capacity limitations in superficially similar visual search tasks may reflect competition at different stages of visual processing. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) microstate analysis entails fnding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test–retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test–retest reliability is higher than between-participant test–retest reliability for diferent indices of state-transition dynamics, diferent networks, and diferent data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between diferent individuals and is a promising tool for performing fnger printing analysis of individuals. [ABSTRACT FROM AUTHOR]

Hopelessness is a key risk factor for suicide. This analysis explored whether hopelessness indicates a recent suicide crisis state and is linked with magnetoencephalography (MEG) oscillatory power and effective connectivity differences. Change in hopelessness ratings and effective connectivity post-ketamine were also evaluated in a subsample of high-risk individuals to evaluate correlates of dynamic changes over time. Participants (66F;44 M;1 transgender) included individuals with suicide crisis in the last two weeks (High Risk (HR), n = 14), those with past suicide attempt but no recent suicide ideation (SI) (Low Risk (LR), n = 37), clinical controls (CC, n = 33), and healthy volunteers at minimal risk (MinR, n = 27). MEG oscillatory power and clinical hopelessness ratings (via the Beck Hopelessness Scale (BHS)) were evaluated across groups. Dynamic casual modeling (DCM) evaluated connectivity within and between the anterior insula (AI) and anterior cingulate cortex (ACC). A subsample of HR individuals who received ketamine (n = 10) were evaluated at Day 1 post-infusion. The HR group reported the highest levels of hopelessness, even when adjusting for SI. MEG results linked hopelessness with reduced activity across frequency bands in salience network regions, with no group or group-by-interaction effects. Using DCM, the HR group had reduced intrinsic drive from granular Layer IV stellate cells to superficial pyramidal cells in the ACC and AI. In the pilot HR study, reduced hopelessness was linked with increased drive for this same connection post-ketamine. Hopelessness is a possible proxy for suicide risk. Electrophysiological targets for hopelessness include widespread reductions in salience network activity, particularly in the ACC and AI. [ABSTRACT FROM AUTHOR]

The electrophysiological basis of resting‐state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this article, we compare the two main existing data‐driven analysis strategies for extracting RSNs from MEG data and introduce a third approach. The first approach uses phase–amplitude coupling to determine the RSN. The second approach extracts RSN through an independent component analysis of the Hilbert envelope in different frequency bands, while the third new approach uses a singular value decomposition instead. To evaluate these approaches, we compare the MEG‐RSN to the functional magnetic resonance imaging (fMRI)‐RSN from the same subjects. Overall, it was possible to extract RSN with MEG using all three techniques, which matched the group‐specific fMRI‐RSN. Interestingly the new approach based on SVD yielded significantly higher correspondence to five out of seven fMRI‐RSN than the two existing approaches. Importantly, with this approach, all networks—except for the visual network—had the highest correspondence to the fMRI networks within one frequency band. Thereby we provide further insights into the electrophysiological underpinnings of the fMRI‐RSNs. This knowledge will be important for the analysis of the electrophysiological connectome. [ABSTRACT FROM AUTHOR]

A large body of research establishes the efficacy of musical intervention in many aspects of physical, cognitive, communication, social, and emotional rehabilitation. However, the underlying neural mechanisms for musical therapy remain elusive. This study aimed to investigate the potential neural correlates of musical therapy, focusing on the changes in the topology of emotion brain network. To this end, a Bayesian statistical approach and a cross-over experimental design were employed together with two resting-state magnetoencephalography (MEG) as controls. MEG recordings of 30 healthy subjects were acquired while listening to five auditory stimuli in random order. Two resting-state MEG recordings of each subject were obtained, one prior to the first stimulus (pre) and one after the final stimulus (post). Time series at the level of brain regions were estimated using depth-weighted minimum norm estimation (wMNE) source reconstruction method and the functional connectivity between these regions were computed. The resultant connectivity matrices were used to derive two topological network measures: transitivity and global efficiency which are important in gauging the functional segregation and integration of brain network respectively. The differences in these measures between pre- and post-stimuli resting MEG were set as the equivalence regions. We found that the network measures under all auditory stimuli were equivalent to the resting state network measures in all frequency bands, indicating that the topology of the functional brain network associated with emotional regulation in healthy subjects remains unchanged following these auditory stimuli. This suggests that changes in the emotion network topology may not be the underlying neural mechanism of musical therapy. Nonetheless, further studies are required to explore the neural mechanisms of musical interventions especially in the populations with neuropsychiatric disorders.

Humans can read and comprehend text rapidly, implying that readers might process multiple words per fixation. However, the extent to which parafoveal words are previewed and integrated into the evolving sentence context remains disputed. We investigated parafoveal processing during natural reading by recording brain activity and eye movements using MEG and an eye tracker while participants silently read one-line sentences. The sentences contained an unpredictable target word that was either congruent or incongruent with the sentence context. To measure parafoveal processing, we flickered the target words at 60 Hz and measured the resulting brain responses (i.e. Rapid Invisible Frequency Tagging, RIFT) during fixations on the pre-target words. Our results revealed a significantly weaker tagging response for target words that were incongruent with the previous context compared to congruent ones, even within 100ms of fixating the word immediately preceding the target. This reduction in the RIFT response was also found to be predictive of individual reading speed. We conclude that semantic information is not only extracted from the parafovea but can also be integrated with the previous context before the word is fixated. This early and extensive parafoveal processing supports the rapid word processing required for natural reading. Our study suggests that theoretical frameworks of natural reading should incorporate the concept of deep parafoveal processing.

Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.

Structural connectivity provides the backbone for communication between neural populations. Since axonal transmission occurs on a millisecond time scale, measures of M/EEG functional connectivity sensitive to phase synchronization, such as coherence, are expected to reflect structural connectivity. We develop a model of MEG functional connectivity whose edges are constrained by the structural connectome. The edge strengths are defined by partial coherence, a measure of conditional dependence. We build a new method—the adaptive graphical lasso (AGL)—to fit the partial coherence to perform inference on the hypothesis that the structural connectome is reflected in MEG functional connectivity. In simulations, we demonstrate that the structural connectivity’s influence on the partial coherence can be inferred using the AGL. Further, we show that fitting the partial coherence is superior to alternative methods at recovering the structural connectome, even after the source localization estimates required to map MEG from sensors to the cortex. Finally, we show how partial coherence can be used to explore how distinct parts of the structural connectome contribute to MEG functional connectivity in different frequency bands. Partial coherence offers better estimates of the strength of direct functional connections and consequently a potentially better estimate of network structure.

Abstract Objective With research progress on Rolandic epilepsy (RE), its “benign” nature has been phased out. Clinicians are exhibiting an increasing tendency toward a more assertive treatment approach for RE. Nonetheless, in clinical practice, delayed treatment remains common because of the “self‐limiting” nature of RE. Therefore, this study aimed to identify an imaging marker to aid treatment decisions and select a more appropriate time for initiating therapy for RE. Methods We followed up with children newly diagnosed with RE, classified them into medicated and non‐medicated groups according to the follow‐up results, and compared them with matched healthy controls. Before beginning follow‐up visits, interictal magnetic data were collected using magnetoencephalography in treatment‐naïve recently diagnosed patients. The spectral power of the whole brain during initial diagnosis was determined using minimum normative estimation combined with the Welch technique. Results A difference was observed in the magnetic source intensity within the left caudal anterior cingulate and precentral and postcentral gyri in the delta band between the medicated and non‐medicated groups. The results revealed good discriminatory ability within the receiver operator characteristic curve. In the medicated group, there was a specific change in the frontotemporal magnetic source intensity, which shifted from high to low frequencies, compared with the healthy control group. Significance The intensity of the precentral gyrus magnetic source within the delta band showed good specificity. Considering the rigor of initial treatment, the intensity of the precentral gyrus magnetic source can provide some help as an imaging marker for initial RE treatment, particularly for the timing of treatment initiation.

Background. The left and right temporoparietal junctions (TPJs) are two brain areas involved in several brain networks, largely studied for their diverse roles, from attentional orientation to theory of mind and, recently, predictive processing. In predictive processing, one crucial concept is prior precision, that is, the reliability of the predictions of incoming stimuli. This has been linked with modulations of alpha power as measured with electrophysiological techniques, but TPJs have seldom been studied in this framework. Methods. The present article investigates, using magnetoencephalography, whether spontaneous oscillations in pre-stimulus alpha power in the left and right TPJs can modulate brain responses during a linguistic task that requires predictive processing in literal and non-literal sentences. Results. Overall, results show that pre-stimulus alpha power in the rTPJ was associated with post-stimulus responses only in the left superior temporal gyrus, while lTPJ pre-stimulus alpha power was associated with post-stimulus activity in Broca’s area, left middle temporal gyrus, and left superior temporal gyrus. Conclusions. We conclude that both the right and left TPJs have a role in linguistic prediction, involving a network of core language regions, with differences across brain areas and linguistic conditions that can be parsimoniously explained in the context of predictive processing.

Background/Objectives: This study explored the potential of Hinoki aromatherapy to induce biomarkers of dysmenorrhea relief through working memory. Structural magnetic resonance imaging and magnetoencephalography (MEG) were used to examine their effects on neurophysiological responses to a visual working memory (VWM) test. Behavioral performance was measured to understand its effects on the overall working memory. Methods: Twenty-four healthy participants completed the VWM task during nonmenstruation and menstruation. Behavioral (accuracy and reaction time) and neurophysiological (event-related fields, source estimation, and permutation t-test on source data) measures were assessed without and with Hinoki aromatherapy. Results: A significant difference in the ratio of accuracy to reaction time was found without and with aromatherapy in participants with dysmenorrhea, suggesting that aromatherapy may improve working memory performance in this population. MEG analysis revealed high temporal resolution of evoked latency and intensity during the VWM task. Source localization of the activation aimed to identify brain areas involved in dysmenorrhea. Aromatherapy reduced signals in these areas, which may also contribute to reducing dysmenorrhea-related visual signals. Conclusions: Based on these findings, Hinoki aromatherapy may be a promising treatment option for alleviating dysmenorrhea and improving related symptoms by reducing activity in brain pain processing regions. These regions include the left entorhinal cortex, inferior temporal gyrus, primary visual cortex, retrosplenial cortex, and presubiculum. Furthermore, decreased activity in these areas with aromatherapy suggests that they could be used as biomarkers of dysmenorrhea relief.

Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.

AbstractPurposeMaterials and methodsResultsConslusions\nHIGHLIGHTSWe investigated the connectivity of neural signals from movement-related cortical areas to the primary motor area (M1) in the hemisphere contralateral to the movement side during the period of movement-related magnetic fields before movement.Participants were 13 healthy adults, and nerual signals were recorded using magnetoencephalography. Spontaneous extension of the right wrist was performed at the participant’s own pace and following a visual cue in internal (IC) and external (EC) cue tasks. The connectivity of neural signals to M1 from each movement-related motor area was assessed by Granger causality analysis (GCA). The GCA was performed on the neural activity elicited in a frequency band between 7.8 and 46.9 Hz during the pre-movement periods, which occurred durng the readiness field (RF) and the negative slope prime (NSp). F-values, as connectivity values obtained by GCA, were compared between the EC and IC cue tasks.For NSp periods, the connectivity of neural signals from the left superior frontal area (SF-L) to M1 was dominant in the IC task, whereas that from the left superior parietal area (SP-L) to M1 was dominant in the EC task. The F value in the GCA from SP-L to M1 was greater in the EC task during RF than in the IC task during equivalent periods.In the present study, there were differences in the connectivity of neural signals to M1 between IC and EC tasks. The present results suggested that the pattern of pre-movement neural activity that resulted in a movement was not uniform but differed between movement tasks just before the movement.Movement-related cortical magnetic fields were assessed with Granger causality analysisConnectivity of neural signals to M1 was different between internal and external cue tasks.Connectivity of neural signals from the frontal area was dominant to M1 in the internal cue task.Connectivity of neural signals to M1 from the parietal area was observed in the external cue task. [ABSTRACT FROM AUTHOR]

Language comprehension involves multiple hierarchical processing stages across time, space, and levels of representation. When processing a word, the sensory input is transformed into increasingly abstract representations that need to be integrated with the linguistic context. Thus, language comprehension involves both input‐driven as well as context‐dependent processes. While neuroimaging research has traditionally focused on mapping individual brain regions to the distinct underlying processes, recent studies indicate that whole‐brain distributed patterns of cortical activation might be highly relevant for cognitive functions, including language. One such pattern, based on resting‐state connectivity, is the 'principal cortical gradient', which dissociates sensory from heteromodal brain regions. The present study investigated the extent to which this gradient provides an organizational principle underlying language function, using a multimodal neuroimaging dataset of functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) recordings from 102 participants during sentence reading. We found that the brain response to individual representations of a word (word length, orthographic distance, and word frequency), which reflect visual; orthographic; and lexical properties, gradually increases towards the sensory end of the gradient. Although these properties showed opposite effect directions in fMRI and MEG, their association with the sensory end of the gradient was consistent across both neuroimaging modalities. In contrast, MEG revealed that properties reflecting a word's relation to its linguistic context (semantic similarity and position within the sentence) involve the heteromodal end of the gradient to a stronger extent. This dissociation between individual word and contextual properties was stable across earlier and later time windows during word presentation, indicating interactive processing of word representations and linguistic context at opposing ends of the principal gradient. To conclude, our findings indicate that the principal gradient underlies the organization of a range of linguistic representations while supporting a gradual distinction between context‐independent and context‐dependent representations. Furthermore, the gradient reveals convergent patterns across neuroimaging modalities (similar location along the gradient) in the presence of divergent responses (opposite effect directions). [ABSTRACT FROM AUTHOR]

Brainstem degeneration is a prominent feature of spinocerebellar ataxia type 3 (SCA3), involving structures that execute binaural synchronization with microsecond precision. As a consequence, auditory processing may deteriorate during the course of disease. We tested whether the binaural "Huggins pitch" effect is suitable to study the temporal precision of brainstem functioning in SCA3 mutation carriers. We expected that they would have difficulties perceiving Huggins pitch at high frequencies, and that they would show attenuated neuromagnetic responses to Huggins pitch. The upper limit of Huggins pitch perception was psychoacoustically determined in 18 pre-ataxic and ataxic SCA3 mutation carriers and in 18 age-matched healthy controls. Moreover, the cortical N100 response following Huggins pitch onset was acquired by means of magnetoencephalography (MEG). MEG recordings were analyzed using dipole source modeling and comprised a monaural pitch condition and a no-pitch condition with simple binaural correlation changes. Compared with age-matched controls, ataxic but not pre-ataxic SCA3 mutation carriers had significantly lower frequency limits up to which Huggins pitch could be heard. Listeners with lower frequency limits also showed diminished MEG responses to Huggins pitch, but not in the two control conditions. Huggins pitch is a promising tool to assess brainstem functioning in ataxic SCA3 patients. Future studies should refine the psychophysiological setup to capture possible performance decrements also in pre-ataxic mutation carriers. Longitudinal observations will be needed to prove the potential of the assessment of Huggins pitch as a biomarker to track brainstem functioning during the disease course in SCA3. [ABSTRACT FROM AUTHOR]

Social influence plays a crucial role during the teen years, with adolescents supposedly exhibiting heightened sensitivity to their peers. In this study, we examine how social influence from different sources, particularly those with varying normative and informational significance, affect adolescents' opinion change. Furthermore, we investigated the underlying neural dynamics to determine whether these two behaviorally similar influences share their neural mechanisms. Twenty-three participants (14–17 years old) gave their opinions about facial stimuli and received feedback from either a peer group or an expert group, while brain responses were recorded using concurrent magnetoencephalography. In a second rating session, we found that participants' opinions changed in line with conflicting feedback, but only when the feedback was lower than their initial evaluation. On the neural level, conflict with peers evoked stronger neural responses than conflict with experts in the 230–400 ms time window and the right frontotemporal magnetometer channels. Nevertheless, there was no greater conformity toward peers. Moreover, conflict compared to no conflict decreased neural oscillations in the beta frequency range (20–26 Hz) at the right frontal and parietal channels. Taken together, our findings do not support the general assumption that adolescent behavior is excessively vulnerable to peer norms, although we found heightened neural sensitivity to peer feedback. [ABSTRACT FROM AUTHOR]

In recent years, steady‐state visual evoked potentials (SSVEPs) became an increasingly valuable tool to investigate neural dynamics of competitive attentional interactions and brain–computer interfaces. This is due to their good signal‐to‐noise ratio, allowing for single‐trial analysis, and their ongoing oscillating nature that enables to analyze temporal dynamics of facilitation and suppression. Given the popularity of SSVEPs, it is surprising that only a few studies looked at the cortical sources of these responses. This is in particular the case when searching for studies that assessed the cortical sources of attentional SSVEP amplitude modulations. To address this issue, we used a typical spatial attention task and recorded neuromagnetic fields (MEG) while presenting frequency‐tagged stimuli in the left and right visual fields, respectively. Importantly, we controlled for attentional deployment in a baseline period before the shifting cue. Subjects either attended to a central fixation cross or to two peripheral stimuli simultaneously. Results clearly showed that signal sources and attention effects were restricted to the early visual cortex: V1, V2, hMT+, precuneus, occipital‐parietal, and inferior‐temporal cortex. When subjects attended to central fixation first, shifting attention to one of the peripheral stimuli resulted in a significant activation increase for the to‐be‐attended stimulus with no activation decrease for the to‐be‐ignored stimulus in hMT+ and inferio‐temporal cortex, but significant SSVEF decreases from V1 to occipito‐parietal cortex. When attention was first deployed to both rings, shifting attention away from one ring basically resulted in a significant activation decrease in all areas for the then‐to‐be‐ignored stimulus. Although the cortical generators of steady‐state visual evoked responses as recorded by EEG or MEG (SSVEPs or SSVEFs, respectively) have been described in humans, how these generators are involved in the modulation of these evoked oscillatory responses by spatial attention is less known. Here, we describe how distinct sub‐divisions of the early human visual cortex contribute differently to the modulation of SSVEFs by spatial attention. [ABSTRACT FROM AUTHOR]

Objective: With research progress on Rolandic epilepsy (RE), its "benign" nature has been phased out. Clinicians are exhibiting an increasing tendency toward a more assertive treatment approach for RE. Nonetheless, in clinical practice, delayed treatment remains common because of the "self‐limiting" nature of RE. Therefore, this study aimed to identify an imaging marker to aid treatment decisions and select a more appropriate time for initiating therapy for RE. Methods: We followed up with children newly diagnosed with RE, classified them into medicated and non‐medicated groups according to the follow‐up results, and compared them with matched healthy controls. Before beginning follow‐up visits, interictal magnetic data were collected using magnetoencephalography in treatment‐naïve recently diagnosed patients. The spectral power of the whole brain during initial diagnosis was determined using minimum normative estimation combined with the Welch technique. Results: A difference was observed in the magnetic source intensity within the left caudal anterior cingulate and precentral and postcentral gyri in the delta band between the medicated and non‐medicated groups. The results revealed good discriminatory ability within the receiver operator characteristic curve. In the medicated group, there was a specific change in the frontotemporal magnetic source intensity, which shifted from high to low frequencies, compared with the healthy control group. Significance: The intensity of the precentral gyrus magnetic source within the delta band showed good specificity. Considering the rigor of initial treatment, the intensity of the precentral gyrus magnetic source can provide some help as an imaging marker for initial RE treatment, particularly for the timing of treatment initiation. [ABSTRACT FROM AUTHOR]

The presence of solvent impurities can significantly impact the corrosion performance of stainless steel when exposed to CO2-saturated NaCl (Sodium Chloride) solutions. Solvent impurities, originating from various sources, may introduce chemical changes that interact with the metal surface and alter its corrosion resistance. Under simulated flow circumstances, the corrosion performance of stainless steels in the presence of contaminants such Monoethyleneglycol (MEG) and oxygen in a CO2 environment was investigated. Utilizing a rotating cage, the corrosion performance of stainless steel was assessed, and mass loss measurements were used to calculate the corrosion rates. According to the experimental findings, the oxygenated MEG exhibits a very low rate of corrosion. SEM and EDX surface analysis techniques were utilized to examine the corrosion product that had developed on the metal surface. [ABSTRACT FROM AUTHOR]

[Display omitted] • The functional anatomy of the P3 is still unclear. • This study applied an auditory oddball paradigm in M/EEG and fMRI. • M/EEG and fMRI independently suggest a major P3 source in retro-splenial cortex. The P3 is an event-related response observed in relation to task-relevant sensory events. Despite its ubiquitous presence, the neural generators of the P3 are controversial and not well identified. We compared source analysis of combined magneto- and electroencephalography (M/EEG) data with functional magnetic resonance imaging (fMRI) and simulation studies to better understand the sources of the P3 in an auditory oddball paradigm. Our results suggest that the dominant source of the classical, postero-central P3 lies in the retro-splenial cortex of the ventral cingulate gyrus. A second P3 source in the anterior insular cortex contributes little to the postero-central maximum. Multiple other sources in the auditory, somatosensory, and anterior midcingulate cortex are active in an overlapping time window but can be functionally dissociated based on their activation time courses. The retro-splenial cortex is a dominant source of the parietal P3 maximum in EEG. These results provide a new perspective for the interpretation of the extensive research based on the P3 response. [ABSTRACT FROM AUTHOR]

• Brain activity allows for distinguishing Parkinsonian tremor from voluntary movements. • Subthalamic nucleus activity alone is not sufficient for the distinction; cortical data is required for this. • Dopaminergic medication does not affect classification performance. To distinguish Parkinsonian rest tremor and different voluntary hand movements by analyzing brain activity. We re-analyzed magnetoencephalography and local field potential recordings from the subthalamic nucleus of six patients with Parkinson's disease. Data were obtained after withdrawal from dopaminergic medication (Med Off) and after administration of levodopa (Med On). Using gradient-boosted tree learning, we classified epochs as tremor, fist-clenching, forearm extension or tremor-free rest. Subthalamic activity alone was insufficient for distinguishing the four different motor states (balanced accuracy mean: 38%, std: 7%). The combination of cortical and subthalamic features, in contrast, allowed for a much more accurate classification (balanced accuracy mean: 75%, std: 17%). Adding a single cortical area improved balanced accuracy by 17% on average, as compared to classification based on subthalamic activity alone. In most patients, the most informative cortical areas were sensorimotor cortical regions. Decoding performance was similar in Med On and Med Off. Electrophysiological recordings allow for distinguishing several motor states, provided that cortical signals are monitored in addition to subthalamic activity. By combining cortical recordings, subcortical recordings and machine learning, adaptive deep brain stimulation systems might be able to detect tremor specifically and to respond adequately to several motor states. [ABSTRACT FROM AUTHOR]

• Diplegic cerebral palsy is associated with enhanced functional connectivity in the sensorimotor networks. • In typically developed subjects frontoparietal connectivity in beta range correlates with kinaesthesia performance. • fMRI and MEG both show enhanced connectivity in cerebral palsy group, but the affected regions are different. To assess the effects to functional connectivity (FC) caused by lesions related to spastic diplegic cerebral palsy (CP) in children and adolescents using multiple imaging modalities. We used resting state magnetoencephalography (MEG) envelope signals in alpha, beta and gamma ranges and resting state functional magnetic resonance imaging (fMRI) signals to quantify FC between selected sensorimotor regions of interest (ROIs) in 11 adolescents with spastic diplegic cerebral palsy and 24 typically developing controls. Motor performance of the hands was quantified with gross motor, fine motor and kinesthesia tests. In fMRI, participants with CP showed enhanced FC within posterior parietal regions; in MEG, they showed enhanced interhemispheric FC between sensorimotor regions and posterior parietal regions both in alpha and lower beta bands. There was a correlation between the kinesthesia score and fronto-parietal connectivity in the control population. CP is associated with enhanced FC in sensorimotor network. This difference is not correlated with hand coordination performance. The effect of the lesion is likely not fully captured by temporal correlation of ROI signals. Brain lesions can show as increased temporal correlation of activity between remote brain areas. We suggest this effect is likely separate from typical physiological correlates of functional connectivity. [ABSTRACT FROM AUTHOR]

The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation. [ABSTRACT FROM AUTHOR]

In this study, we aimed to find biomarkers of autism in young children. We recorded magnetoencephalography (MEG) in thirty children (4–7 years) with autism and thirty age, gender-matched controls while they were watching cartoons. We focused on characterizing neural oscillations by amplitude (power spectral density, PSD) and phase (preferred phase angle, PPA). Machine learning based classifier showed a higher classification accuracy (88%) for PPA features than PSD features (82%). Further, by a novel fusion method combining PSD and PPA features, we achieved an average classification accuracy of 94% and 98% for feature-level and score-level fusion, respectively. These findings reveal discriminatory patterns of neural oscillations of autism in young children and provide novel insight into autism pathophysiology. [ABSTRACT FROM AUTHOR]

Laboratory studies reveal that young women with premenstrual syndrome (PMS) often exhibit decreased reward processing during the late luteal phase. However, studies based on the self-reports find opposite results (e.g., higher craving for high-sweet-fat food). These differences may lie in the difference between the stimulus used and measuring the different aspects of the reward. The present study was designed to expand previous work by using a classic monetary reward paradigm, simultaneously examining the motivational (i.e., reward anticipation, "wanting") and emotional (i.e., reward outcome, "liking") components of reward processing in women with high premenstrual symptoms (High PMS). College female students in their early twenties with High PMS (n = 20) and low premenstrual symptoms (Low PMS, n = 20) completed a monetary incentive delay task during their late luteal phase when the premenstrual symptoms typically peak. Brain activities in the reward anticipation phase and outcome phase were recorded using the magnetoencephalographic (MEG) imaging technique. No group differences were found in various behavioral measurements. For the MEG results, in the anticipation phase, when High PMS participants were presented with cues that predicted the upcoming monetary gains, they showed higher event-related magnetic fields (ERFs) than when they were presented with neutral non-reward cues. This pattern was reversed in Low PMS participants, as they showed lower reward cue-elicited ERFs than non-reward cue-elicited ones (cluster mass = 2560, cluster size = 891, p =.03, corrected for multiple comparisons), mainly in the right medial orbitofrontal and lateral orbitofrontal cortex (cluster mass = 375, cluster size = 140, p =.03, corrected for multiple comparisons). More importantly, women with High PMS had an overall significantly higher level of ERFs than women with Low PMS (cluster mass = 8039, cluster size = 2937, p =.0009, corrected for multiple comparisons) in the bilateral precentral gyrus, right postcentral gyrus, and left superior temporal gyrus (right: cluster mass = 410, cluster size = 128, p =.03; left: cluster mass = 352, cluster size = 98, p =.05; corrected for multiple comparisons). In the outcome phase, women with High PMS showed significantly lower theta power than the Low PMS ones for the expected non-reward feedback in the bilateral temporal-parietal regions (cluster mass = 47620, cluster size = 18308, p =.01, corrected for multiple comparisons). These findings reveal that the severity of PMS might alter reward anticipation. Specifically, women with High PMS displayed increased brain activities to reward-predicting cues and increased action preparation after the cues appear. [ABSTRACT FROM AUTHOR]

Decoding brain imaging data are gaining popularity, with applications in brain‐computer interfaces and the study of neural representations. Decoding is typically subject‐specific and does not generalise well over subjects, due to high amounts of between subject variability. Techniques that overcome this will not only provide richer neuroscientific insights but also make it possible for group‐level models to outperform subject‐specific models. Here, we propose a method that uses subject embedding, analogous to word embedding in natural language processing, to learn and exploit the structure in between‐subject variability as part of a decoding model, our adaptation of the WaveNet architecture for classification. We apply this to magnetoencephalography data, where 15 subjects viewed 118 different images, with 30 examples per image; to classify images using the entire 1 s window following image presentation. We show that the combination of deep learning and subject embedding is crucial to closing the performance gap between subject‐ and group‐level decoding models. Importantly, group models outperform subject models on low‐accuracy subjects (although slightly impair high‐accuracy subjects) and can be helpful for initialising subject models. While we have not generally found group‐level models to perform better than subject‐level models, the performance of group modelling is expected to be even higher with bigger datasets. In order to provide physiological interpretation at the group level, we make use of permutation feature importance. This provides insights into the spatiotemporal and spectral information encoded in the models. All code is available on GitHub (https://github.com/ricsinaruto/MEG-group-decode). [ABSTRACT FROM AUTHOR]

The transition from childhood to adolescence is associated with an influx of sex hormones, which not only facilitates physical and behavioral changes, but also dramatic changes in neural circuitry. While previous work has shown that pubertal hormones modulate structural and functional brain development, few of these studies have focused on the impact that such hormones have on spontaneous cortical activity, and whether these effects are modulated by sex during this critical developmental window. Herein, we examined the effect of endogenous testosterone on spontaneous cortical activity in 71 typically‐developing youth (ages 10–17 years; 32 male). Participants completed a resting‐state magnetoencephalographic (MEG) recording, structural MRI, and provided a saliva sample for hormone analysis. MEG data were source‐reconstructed and the power within five canonical frequency bands (delta, theta, alpha, beta, and gamma) was computed. The resulting power spectral density maps were analyzed via vertex‐wise ANCOVAs to identify spatially specific effects of testosterone and sex by testosterone interactions, while covarying out age. We found robust sex differences in the modulatory effects of testosterone on spontaneous delta, beta, and gamma activity. These interactions were largely confined to frontal cortices and exhibited a stark switch in the directionality of the correlation from the low (delta) to high frequencies (beta/gamma). For example, in the delta band, greater testosterone related to lower relative power in prefrontal cortices in boys, while the reverse pattern was found for girls. These data suggest testosterone levels are uniquely related to the development of spontaneous cortical dynamics during adolescence, and such levels are associated with different developmental patterns in males and females within regions implicated in executive functioning. [ABSTRACT FROM AUTHOR]

Cerebellar differences have long been documented in autism spectrum disorder (ASD), yet the extent to which such differences might impact language processing in ASD remains unknown. To investigate this, we recorded brain activity with magnetoencephalography (MEG) while ASD and age‐matched typically developing (TD) children passively processed spoken meaningful English and meaningless Jabberwocky sentences. Using a novel source localization approach that allows higher resolution MEG source localization of cerebellar activity, we found that, unlike TD children, ASD children showed no difference between evoked responses to meaningful versus meaningless sentences in right cerebellar lobule VI. ASD children also had atypically weak functional connectivity in the meaningful versus meaningless speech condition between right cerebellar lobule VI and several left‐hemisphere sensorimotor and language regions in later time windows. In contrast, ASD children had atypically strong functional connectivity for in the meaningful versus meaningless speech condition between right cerebellar lobule VI and primary auditory cortical areas in an earlier time window. The atypical functional connectivity patterns in ASD correlated with ASD severity and the ability to inhibit involuntary attention. These findings align with a model where cerebro‐cerebellar speech processing mechanisms in ASD are impacted by aberrant stimulus‐driven attention, which could result from atypical temporal information and predictions of auditory sensory events by right cerebellar lobule VI. [ABSTRACT FROM AUTHOR]

Human individuality is likely underpinned by the constitution of functional brain networks that ensure consistency of each person's cognitive and behavioral profile. These functional networks should, in principle, be detectable by noninvasive neurophysiology. We use a method that enables the detection of dominant frequencies of the interaction between every pair of brain areas at every temporal segment of the recording period, the dominant coupling modes (DoCM). We apply this method to brain oscillations, measured with magnetoencephalography (MEG) at rest in two independent datasets, and show that the spatiotemporal evolution of DoCMs constitutes an individualized brain fingerprint. Based on this successful fingerprinting we suggest that DoCMs are important targets for the investigation of neural correlates of individual psychological parameters and can provide mechanistic insight into the underlying neurophysiological processes, as well as their disturbance in brain diseases. [ABSTRACT FROM AUTHOR]

• Functional connectivity changes in Alzheimer's disease are already detectable at the prodromal phase, Mild Cognitive Impairment. • An early non-invasive detection of electrophysiological biomarkers is a priority. • Alpha temporo-parietal desynchronization could be a potential early neurophysiological biomarker. Early synchrony alterations have been observed through electrophysiological techniques in Mild Cognitive Impairment (MCI), which is considered the intermediate phase between healthy aging (HC) and Alzheimer's disease (AD). However, the documented direction (hyper/hypo-synchronization), regions and frequency bands affected are inconsistent. This meta-analysis intended to elucidate existing evidence linked to potential neurophysiological biomarkers of AD. We conducted a random-effects meta-analysis that entailed the unbiased inclusion of Non-statistically Significant Unreported Effect Sizes ("MetaNSUE") of electroencephalogram (EEG) and magnetoencephalogram (MEG) studies investigating functional connectivity changes at rest along the healthy-pathological aging continuum, searched through PubMed, Scopus, Web of Science and PsycINFO databases until June 2023. Of the 3852 articles extracted, we analyzed 12 papers, and we found an alpha synchrony decrease in MCI compared to HC, specifically between temporal-parietal (d = -0.26) and frontal-parietal areas (d = -0.25). Alterations of alpha synchrony are present even at MCI stage. Synchrony measures may be promising for the detection of the first hallmarks of connectivity alterations, even at the prodromal stages of the AD, before clinical symptoms occur. [ABSTRACT FROM AUTHOR]

A K–Rb– 83 Kr-based atomic comagnetometer for performing movement-related Magnetoencephalography (MEG) is theoretically studied in this paper. Parameters such as the spin-exchange rates, the spin-dephasing rates and the polarization of the nuclear spins are studied to configure the comagnetometer. The results show that the nuclear spin can generate a magnetic field of around 700 nT, at which the nuclear spin can compensate for a wide range of magnetic fields. In this paper, we also show the fabrication process for hybrid optical-pumping vapor cells, whereby alkali metals are mixed in a glove box that is then connected to the alkali vapor-cell fabrication system. [ABSTRACT FROM AUTHOR]

The alpha rhythm is often associated with relaxed wakefulness or idling and is altered by various factors. Abnormalities in the alpha rhythm have been linked to several neurological and psychiatric disorders, including Alzheimer's disease. Transcranial alternating current stimulation (tACS) has been proposed as a potential tool to restore a disrupted alpha rhythm in the brain by stimulating at the individual alpha frequency (IAF), although some research has produced contradictory results. In this study, we applied an lAF-tACS protocol over parieto-occipital areas to a sample of healthy subjects and measured its effects over the power spectra. Additionally, we used computational models to get a deeper understanding of the results observed in the experiment. Both experimental and numerical results showed an increase in alpha power of 8.02% with respect to the sham condition in a widespread set of regions in the cortex, excluding some expected parietal regions. This result could be partially explained by taking into account the orientation of the electric field with respect to the columnar structures of the cortex, showing that the gyrification in parietal regions could generate effects in opposite directions (hyper-/depolarization) at the same time in specific brain regions. Additionally, we used a network model of spiking neuronal populations to explore the effects that these opposite polarities could have on neural activity, and we found that the best predictor of alpha power was the average of the normal components of the electric field. To sum up, our study sheds light on the mechanisms underlying tACS brain activity modulation, using both empirical and computational approaches. Non-invasive brain stimulation techniques hold promise for treating brain disorders, but further research is needed to fully understand and control their effects on brain dynamics and cognition. Our findings contribute to this growing body of research and provide a foundation for future studies aimed at optimizing the use of non-invasive brain stimulation in clinical settings. [ABSTRACT FROM AUTHOR]

Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alternatively, replayed sequentially as temporally separate instances. In the current study, participants learned associations between images that were embedded in a directed graph network and retained this information over a brief 8 min consolidation period. During a subsequent cued recall session, participants retrieved the learned information while undergoing magnetoencephalographic recording. Using a trained stimulus decoder, we found evidence for clustered reactivation of learned material. Reactivation strength of individual items during clustered reactivation decreased as a function of increasing graph distance, an ordering present solely for successful retrieval but not for retrieval failure. In line with previous research, we found evidence that sequential replay was dependent on retrieval performance and was most evident in low performers. The results provide evidence for distinct performance-dependent retrieval mechanisms, with graded clustered reactivation emerging as a plausible mechanism to search within abstract cognitive maps.

IntroductionMusical roundness perception relies on consonance/dissonance within a rule-based harmonic context, but also on individual characteristics of the listener. The present work tackles these aspects in a combined psychoacoustic and neurophysiological study, taking into account participant’s musical aptitude.MethodsOur paradigm employed cadence-like four-chord progressions, based on Western music theory. Chord progressions comprised naturalistic and artificial sounds; moreover, their single chords varied regarding consonance/dissonance and harmonic function. Thirty participants listened to the chord progressions while their cortical activity was measured with magnetoencephalography; afterwards, they rated the individual chord progressions with respect to their perceived roundness.ResultsRoundness ratings differed according to the degree of dissonance in the dominant chord at the progression’s third position; this effect was pronounced in listeners with high musical aptitude. Interestingly, a corresponding pattern occurred in the neuromagnetic N1m response to the fourth chord (i.e., at the progression’s resolution), again with somewhat stronger differentiation among musical listeners. The N1m magnitude seemed to increase during chord progressions that were considered particularly round, with the maximum difference after the final chord; here, however, the musical aptitude effect just missed significance.DiscussionThe roundness of chord progressions is reflected in participant’s psychoacoustic ratings and in their transient cortical activity, with stronger differentiation among listeners with high musical aptitude. The concept of roundness might help to reframe consonance/dissonance to a more holistic, gestalt-like understanding that covers chord relations in Western music.

Throughout childhood and adolescence, the brain undergoes significant structural and functional changes that contribute to the maturation of multiple cognitive domains, including selective attention. Selective attention is crucial for healthy executive functioning and while key brain regions serving selective attention have been identified, their age-related changes in neural oscillatory dynamics and connectivity remain largely unknown. We examined the developmental sensitivity of selective attention circuitry in 91 typically developing youth aged 6 – 13 years old. Participants completed a number-based Simon task while undergoing magnetoencephalography (MEG) and the resulting data were preprocessed and transformed into the time-frequency domain. Significant oscillatory brain responses were imaged using a beamforming approach, and task-related peak voxels in the occipital, parietal, and cerebellar cortices were used as seeds for subsequent whole-brain connectivity analyses in the alpha and gamma range. Our key findings revealed developmentally sensitive connectivity profiles in multiple regions crucial for selective attention, including the temporoparietal junction (alpha) and prefrontal cortex (gamma). Overall, these findings suggest that brain regions serving selective attention are highly sensitive to developmental changes during the pubertal transition period.

IntroductionTo investigate cortical network changes using Magnetoencephalography (MEG) signals in Parkinson’s disease (PD) patients undergoing Magnetic Resonance-guided Focused Ultrasound (MRgFUS) thalamotomy.MethodsWe evaluated the MEG signals in 16 PD patients with drug-refractory tremor before and after 12-month from MRgFUS unilateral lesion of the ventralis intermediate nucleus (Vim) of the thalamus contralateral to the most affected body side. We recorded patients 24 h before (T0) and 24 h after MRgFUS (T1). We analyzed signal epochs recorded at rest and during the isometric extension of the hand contralateral to thalamotomy. We evaluated cortico-muscular coherence (CMC), the out-strength index from non-primary motor areas to the pre-central area and connectivity indexes, using generalized partial directed coherence. Statistical analysis was performed using RMANOVA and post hoct-tests.ResultsMost changes found at T1 compared to T0 occurred in the beta band and included: (1) a re-adjustment of CMC distribution; (2) a reduced out-strength from non-primary motor areas toward the precentral area; (3) strongly reduced clustering coefficient values. These differences mainly occurred during motor activation and with few statistically significant changes at rest. Correlation analysis showed significant relationships between changes of out-strength and clustering coefficient in non-primary motor areas and the changes in clinical scores.DiscussionOne day after MRgFUS thalamotomy, PD patients showed a topographically reordered CMC and decreased cortico-cortical flow, together with a reduced local connection between different nodes. These findings suggest that the reordered cortico-muscular and cortical-networks in the beta band may represent an early physiological readjustment related to MRgFUS Vim lesion.