Spatiotemporal whole-brain activity and functional connectivity of melodies recognition.

Music is a non-verbal human language, built on logical, hierarchical structures, that offers excellent opportunities to explore how the brain processes complex spatiotemporal auditory sequences. Using the high temporal resolution of magnetoencephalography, we investigated the unfolding brain dynamics of 70 participants during the recognition of previously memorized musical sequences compared to novel sequences matched in terms of entropy and information content. Measures of both whole-brain activity and functional connectivity revealed a widespread brain network underlying the recognition of the memorized auditory sequences, which comprised primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus, and hippocampus. Furthermore, while the auditory cortex responded mainly to the first tones of the sequences, the activity of higher-order brain areas such as the cingulate gyrus, frontal operculum, hippocampus, and orbitofrontal cortex largely increased over time during the recognition of the memorized versus novel musical sequences. In conclusion, using a wide range of analytical techniques spanning from decoding to functional connectivity and building on previous works, our study provided new insights into the spatiotemporal whole-brain mechanisms for conscious recognition of auditory sequences.

Examining cognition and brain networks using magnetoencephalography in paediatric autoimmune encephalitis and acute disseminated encephalomyelitis: a preliminary study.

Paediatric autoimmune encephalitis, including acute disseminated encephalomyelitis, are inflammatory brain diseases presenting with cognitive deficits, psychiatric symptoms, seizures, MRI and EEG abnormalities. Despite improvements in disease recognition and early immunotherapy, long-term outcomes in paediatric autoimmune encephalitis remain poor. Our aim was to understand functional connectivity changes that could be associated with negative developmental outcomes across different types of paediatric autoimmune encephalitis using magnetoencephalography. Participants were children diagnosed with paediatric autoimmune encephalitis at least 18 months before testing and typically developing children. All completed magnetoencephalography recording at rest, T1 MRI scans and neuropsychology testing. Brain connectivity (specifically in delta and theta) was estimated with amplitude envelope correlation, and network efficiency was measured using graph measures (global efficiency, local efficiency and modularity). Twelve children with paediatric autoimmune encephalitis (11.2 ± 3.5 years, interquartile range 9 years; 5M:7F) and 12 typically developing controls (10.6 ± 3.2 years, interquartile range 7 years; 8M:4F) participated. Children with paediatric autoimmune encephalitis did not differ from controls in working memory (t(21) = 1.449; P = 0.162; d = 0.605) but had significantly lower processing speed (t(21) = 2.463; P = 0.023; Cohen's d = 1.028). Groups did not differ in theta network topology measures. The paediatric autoimmune encephalitis group had a significantly lower delta local efficiency across all thresholds tested (d = -1.60 at network threshold 14%). Theta modularity was associated with lower working memory (ß = -0.781; t(8) = -2.588, P = 0.032); this effect did not survive correction for multiple comparisons (P(corr) = 0.224). Magnetoencephalography was able to capture specific network alterations in paediatric autoimmune encephalitis patients. This preliminary study demonstrates that magnetoencephalography is an appropriate tool for assessing children with paediatric autoimmune encephalitis and could be associated with cognitive outcomes.

Chronic cannabis use alters the spontaneous and oscillatory gamma dynamics serving cognitive control.

Regular cannabis use is associated with cortex-wide changes in spontaneous and oscillatory activity, although the functional significance of such changes remains unclear. We hypothesized that regular cannabis use would suppress spontaneous gamma activity in regions serving cognitive control and scale with task performance. Participants (34 cannabis users, 33 nonusers) underwent an interview regarding their substance use history and completed the Eriksen flanker task during magnetoencephalography (MEG). MEG data were imaged in the time-frequency domain and virtual sensors were extracted from the peak voxels of the grand-averaged oscillatory interference maps to quantify spontaneous gamma activity during the pre-stimulus baseline period. We then assessed group-level differences in spontaneous and oscillatory gamma activity, and their relationship with task performance and cannabis use metrics. Both groups exhibited a significant behavioral flanker interference effect, with slower responses during incongruent relative to congruent trials. Mixed-model ANOVAs indicated significant gamma-frequency neural interference effects in the left frontal eye fields (FEF) and left temporoparietal junction (TPJ). Further, a group-by-condition interaction was detected in the left FEF, with nonusers exhibiting stronger gamma oscillations during incongruent relative to congruent trials and cannabis users showing no difference. In addition, spontaneous gamma activity was sharply suppressed in cannabis users relative to nonusers in the left FEF and TPJ. Finally, spontaneous gamma activity in the left FEF and TPJ was associated with task performance across all participants, and greater cannabis use was associated with weaker spontaneous gamma activity in the left TPJ of the cannabis users. Regular cannabis use was associated with weaker spontaneous gamma in the TPJ and FEF. Further, the degree of use may be proportionally related to the degree of suppression in spontaneous activity in the left TPJ.

Altered neural synchronization in response to 2 Hz amplitude-modulated tones in the auditory cortex of children with Autism Spectrum Disorder: An MEG study.

OBJECTIVE: Some studies have hypothesized that atypical neural synchronization at the delta frequency band in the auditory cortex is associated with phonological and language skills in children with Autism Spectrum Disorder (ASD), but it is still poorly understood. This study investigated this neural activity and addressed the relationships between auditory response and behavioral measures of children with ASD. METHODS: We used magnetoencephalography and individual brain models to investigate 2 Hz Auditory Steady-State Response (ASSR) in 20 primary-school-aged children with ASD and 20 age-matched typically developing (TD) controls. RESULTS: First, we found a between-group difference in the localization of the auditory response, so as the topology of 2 Hz ASSR was more superior and posterior in TD children when comparing to children with ASD. Second, the power of 2 Hz ASSR was reduced in the ASD group. Finally, we observed a significant association between the amplitude of neural response and language skills in children with ASD. CONCLUSIONS: The study provided the evidence of reduced neural response in children with ASD and its relation to language skills. SIGNIFICANCE: These findings may inform future interventions targeting auditory and language impairments in ASD population.

Macroscopic brain dynamics beyond contralateral primary motor cortex for movement prediction.

This study investigates the complex relationship between upper limb movement direction and macroscopic neural signals in the brain, which is critical for understanding brain-computer interfaces (BCI). Conventional BCI research has primarily focused on a local area, such as the contralateral primary motor cortex (M1), relying on the population-based decoding method with microelectrode arrays. In contrast, macroscopic approaches such as electroencephalography (EEG) and magnetoencephalography (MEG) utilize numerous electrodes to cover broader brain regions. This study probes the potential differences in the mechanisms of microscopic and macroscopic methods. It is important to determine which neural activities effectively predict movements. To investigate this, we analyzed MEG data from nine right-handed participants while performing arm-reaching tasks. We employed dynamic statistical parametric mapping (dSPM) to estimate source activity and built a decoding model composed of long short-term memory (LSTM) and a multilayer perceptron to predict movement trajectories. This model achieved a high correlation coefficient of 0.79 between actual and predicted trajectories. Subsequently, we identified brain regions sensitive to predicting movement direction using the integrated gradients (IG) method, which assesses the predictive contribution of each source activity. The resulting salience map demonstrated a distribution without significant differences across motor-related regions, including M1. Predictions based solely on M1 activity yielded a correlation coefficient of 0.42, nearly half as effective as predictions incorporating all source activities. This suggests that upper limb movements are influenced by various factors such as movement coordination, planning, body and target position recognition, and control, beyond simple muscle activity. All of the activities are needed in the decoding model using macroscopic signals. Our findings also revealed that contralateral and ipsilateral hemispheres contribute equally to movement prediction, implying that BCIs could potentially benefit patients with brain damage in the contralateral hemisphere by utilizing brain signals from the ipsilateral hemisphere. In conclusion, this study demonstrates that macroscopic activity from large brain regions significantly contributes to predicting upper limb movement. Non-invasive BCI systems would require a comprehensive collection of neural signals from multiple brain regions.

Source localization comparison and combination of OPM-MEG and fMRI to detect sensorimotor cortex responses.

BACKGROUND AND OBJECTIVES: The exploration of various neuroimaging techniques have become focal points within the field of neuroscience research. Magnetoencephalography based on optically pumped magnetometers (OPM-MEG) has shown significant potential to be the next generation of functional neuroimaging with the advantages of high signal intensity and flexible sensor arrangement. In this study, we constructed a 31-channel OPM-MEG system and performed a preliminary comparison of the temporal and spatial relationship between magnetic responses measured by OPM-MEG and blood-oxygen-level-dependent signals detected by functional magnetic resonance imaging (fMRI) during a grasping task. METHODS: For OPM-MEG, the ß-band (15-30 Hz) oscillatory activities can be reliably detected across multiple subjects and multiple session runs. To effectively localize the inhibitory oscillatory activities, a source power-spectrum ratio-based imaging method was proposed. This approach was compared with conventional source imaging methods, such as minimum norm-type and beamformer methods, and was applied in OPM-MEG source analysis. Subsequently, the spatial and temporal responses at the source-level between OPM-MEG and fMRI were analyzed. RESULTS: The effectiveness of the proposed method was confirmed through simulations compared to benchmark methods. Our demonstration revealed an average spatial separation of 10.57 ± 4.41 mm between the localization results of OPM-MEG and fMRI across four subjects. Furthermore, the fMRI-constrained OPM-MEG localization results indicated a more focused imaging extent. CONCLUSIONS: Taken together, the performance exhibited by OPM-MEG positions it as a potential instrument for functional surgery assessment.

Potential association between suicide risk, aggression, impulsivity, and the somatosensory system.

Aggression and impulsivity are linked to suicidal behaviors, but their relationship to the suicidal crisis remains unclear. This magnetoencephalography (MEG) study investigated the link between aggression, impulsivity, and resting-state MEG power and connectivity. Four risk groups were enrolled: high-risk (HR; n = 14), who had a recent suicidal crisis; lower-risk (LR; n = 41), who had a history of suicide attempts but no suicide attempt or ideation in the past year; clinical control (CC; n = 38), who had anxiety/mood disorders but no suicidal history; and minimal risk (MR; n = 28), who had no psychiatric/suicidal history. No difference in resting-state MEG power was observed between the groups. Individuals in the HR group with high self-reported aggression and impulsivity scores had reduced MEG power in regions responsible for sensory/emotion regulation vs. those in the HR group with low scores. The HR group also showed downregulated bidirectional glutamatergic feedback between the precuneus (PRE) and insula (INS) compared to the LR, CC, and MR groups. High self-reported impulsivity was linked to reduced PRE to INS feedback, whereas high risk-taking impulsivity was linked to upregulated INS to postcentral gyrus (PCG) and PCG to INS feedback. These preliminary findings suggest that glutamatergic-mediated sensory and emotion-regulation processes may function as potential suicide risk markers.

Neuromodulatory effects of parietal high-definition transcranial direct-current stimulation on network-level activity serving fluid intelligence.

Fluid intelligence (Gf) involves rational thinking skills and requires the integration of information from different cortical regions to resolve novel complex problems. The effects of non-invasive brain stimulation on Gf have been studied in attempts to improve Gf, but such studies are rare and the few existing have reached conflicting conclusions. The parieto-frontal integration theory of intelligence (P-FIT) postulates that the parietal and frontal lobes play a critical role in Gf. To investigate the suggested role of parietal cortices, we applied high-definition transcranial direct current stimulation (HD-tDCS) to the left and right parietal cortices of 39 healthy adults (age 19-33 years) for 20 min in three separate sessions (left active, right active and sham). After completing the stimulation session, the participants completed a logical reasoning task based on Raven's Progressive Matrices during magnetoencephalography. Significant neural responses at the sensor level across all stimulation conditions were imaged using a beamformer. Whole-brain, spectrally constrained functional connectivity was then computed to examine the network-level activity. Behaviourally, we found that participants were significantly more accurate following left compared to right parietal stimulation. Regarding neural findings, we found significant HD-tDCS montage-related effects in brain networks thought to be critical for P-FIT, including parieto-occipital, fronto-occipital, fronto-parietal and occipito-cerebellar connectivity during task performance. In conclusion, our findings showed that left parietal stimulation improved abstract reasoning abilities relative to right parietal stimulation and support both P-FIT and the neural efficiency hypothesis. KEY POINTS: Abstract reasoning is a critical component of fluid intelligence and is known to be served by multispectral oscillatory activity in the fronto-parietal cortices. Recent studies have aimed to improve abstract reasoning abilities and fluid intelligence overall through behavioural training, but the results have been mixed. High-definition transcranial direct-current stimulation (HD-tDCS) applied to the parietal cortices modulated task performance and neural oscillations during abstract reasoning. Left parietal stimulation resulted in increased accuracy and decreased functional connectivity between occipital regions and frontal, parietal, and cerebellar regions. Future studies should investigate whether HD-tDCS alters abstract reasoning abilities in those who exhibit declines in performance, such as healthy ageing populations.

A user-friendly visual brain-computer interface based on high-frequency steady-state visual evoked fields recorded by OPM-MEG.

Objective. Magnetoencephalography (MEG) shares a comparable time resolution with electroencephalography. However, MEG excels in spatial resolution, enabling it to capture even the subtlest and weakest brain signals for brain-computer interfaces (BCIs). Leveraging MEG's capabilities, specifically with optically pumped magnetometers (OPM-MEG), proves to be a promising avenue for advancing MEG-BCIs, owing to its exceptional sensitivity and portability. This study harnesses the power of high-frequency steady-state visual evoked fields (SSVEFs) to build an MEG-BCI system that is flickering-imperceptible, user-friendly, and highly accurate.Approach.We have constructed a nine-command BCI that operates on high-frequency SSVEF (58-62 Hz with a 0.5 Hz interval) stimulation. We achieved this by placing the light source inside and outside the magnetic shielding room, ensuring compliance with non-magnetic and visual stimulus presentation requirements. Five participants took part in offline experiments, during which we collected six-channel multi-dimensional MEG signals along both the vertical (Z-axis) and tangential (Y-axis) components. Our approach leveraged the ensemble task-related component analysis algorithm for SSVEF identification and system performance evaluation.Main Results.The offline average accuracy of our proposed system reached an impressive 92.98% when considering multi-dimensional conjoint analysis using data from both theZandYaxes. Our method achieved a theoretical average information transfer rate (ITR) of 58.36 bits min-1with a data length of 0.7 s, and the highest individual ITR reached an impressive 63.75 bits min-1.Significance.This study marks the first exploration of high-frequency SSVEF-BCI based on OPM-MEG. These results underscore the potential and feasibility of MEG in detecting subtle brain signals, offering both theoretical insights and practical value in advancing the development and application of MEG in BCI systems.

Simultaneous MEG and EEG source imaging of electrophysiological activity in response to acute transcranial photobiomodulation.

Introduction: Transcranial photobiomodulation (tPBM) is a non-invasive neuromodulation technique that improves human cognition. The effects of tPBM of the right forehead on neurophysiological activity have been previously investigated using EEG in sensor space. However, the spatial resolution of these studies is limited. Magnetoencephalography (MEG) is known to facilitate a higher spatial resolution of brain source images. This study aimed to image post-tPBM effects in brain space based on both MEG and EEG measurements across the entire human brain. Methods: MEG and EEG scans were concurrently acquired for 6 min before and after 8-min of tPBM delivered using a 1,064-nm laser on the right forehead of 25 healthy participants. Group-level changes in both the MEG and EEG power spectral density with respect to the baseline (pre-tPBM) were quantified and averaged within each frequency band in the sensor space. Constrained modeling was used to generate MEG and EEG source images of post-tPBM, followed by cluster-based permutation analysis for family wise error correction (p < 0.05). Results: The 8-min tPBM enabled significant increases in alpha (8-12 Hz) and beta (13-30 Hz) powers across multiple cortical regions, as confirmed by MEG and EEG source images. Moreover, tPBM-enhanced oscillations in the beta band were located not only near the stimulation site but also in remote cerebral regions, including the frontal, parietal, and occipital regions, particularly on the ipsilateral side. Discussion: MEG and EEG results shown in this study demonstrated that tPBM modulates neurophysiological activity locally and in distant cortical areas. The EEG topographies reported in this study were consistent with previous observations. This study is the first to present MEG and EEG evidence of the electrophysiological effects of tPBM in the brain space, supporting the potential utility of tPBM in treating neurological diseases through the modulation of brain oscillations.

From bench to bedside: Overview of magnetoencephalography in basic principle, signal processing, source localization and clinical applications.

Magnetoencephalography (MEG) is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from neuronal activity along the order of milliseconds. Observations of brain dynamics have been used in cognitive neuroscience, the diagnosis of neurological diseases, and the brain-computer interface (BCI). In this study, we outline the basic principle, signal processing, and source localization of MEG, and describe its clinical applications for cognitive assessment, the diagnoses of neurological diseases and mental disorders, preoperative evaluation, and the BCI. This review not only provides an overall perspective of MEG, ranging from practical techniques to clinical applications, but also enhances the prevalent understanding of neural mechanisms. The use of MEG is expected to lead to significant breakthroughs in neuroscience.

Concordance between Wada, Transcranial Magnetic Stimulation, and Magnetoencephalography for Determining Hemispheric Dominance for Language: A Retrospective Study.

Determination of language hemispheric dominance (HD) in patients undergoing evaluation for epilepsy surgery has traditionally relied on the sodium amobarbital (Wada) test. The emergence of non-invasive methods for determining language laterality has increasingly shown to be a viable alternative. In this study, we assessed the efficacy of transcranial magnetic stimulation (TMS) and magnetoencephalography (MEG), compared to the Wada test, in determining language HD in a sample of 12 patients. TMS-induced speech errors were classified as speech arrest, semantic, or performance errors, and the HD was based on the total number of errors in each hemisphere with equal weighting of all errors (classic) and with a higher weighting of speech arrests and semantic errors (weighted). Using MEG, HD for language was based on the spatial extent of long-latency activity sources localized to receptive language regions. Based on the classic and weighted language laterality index (LI) in 12 patients, TMS was concordant with the Wada in 58.33% and 66.67% of patients, respectively. In eight patients, MEG language mapping was deemed conclusive, with a concordance rate of 75% with the Wada test. Our results indicate that TMS and MEG have moderate and strong agreement, respectively, with the Wada test, suggesting they could be used as non-invasive substitutes.

Neurotoxic effects of home radon exposure on oscillatory dynamics serving attentional orienting in children and adolescents.

Radon is a naturally occurring gas that contributes significantly to radiation in the environment and is the second leading cause of lung cancer globally. Previous studies have shown that other environmental toxins have deleterious effects on brain development, though radon has not been studied as thoroughly in this context. This study examined the impact of home radon exposure on the neural oscillatory activity serving attention reorientation in youths. Fifty-six participants (ages 6-14 years) completed a classic Posner cuing task during magnetoencephalography (MEG), and home radon levels were measured for each participant. Time-frequency spectrograms indicated stronger theta (3-7 Hz, 300-800 ms), alpha (9-13 Hz, 400-900 ms), and beta responses (14-24 Hz, 400-900 ms) during the task relative to baseline. Source reconstruction of each significant oscillatory response was performed, and validity maps were computed by subtracting the task conditions (invalidly cued - validly cued). These validity maps were examined for associations with radon exposure, age, and their interaction in a linear regression design. Children with greater radon exposure showed aberrant oscillatory activity across distributed regions critical for attentional processing and attention reorientation (e.g., dorsolateral prefrontal cortex, and anterior cingulate cortex). Generally, youths with greater radon exposure exhibited a reverse neural validity effect in almost all regions and showed greater overall power relative to peers with lesser radon exposure. We also detected an interactive effect between radon exposure and age where youths with greater radon exposure exhibited divergent developmental trajectories in neural substrates implicated in attentional processing (e.g., bilateral prefrontal cortices, superior temporal gyri, and inferior parietal lobules). These data suggest aberrant, but potentially compensatory neural processing as a function of increasing home radon exposure in areas critical for attention and higher order cognition.

Effective connectivity of working memory performance: a DCM study of MEG data.

Visual working memory (WM) engages several nodes of a large-scale network that includes frontal, parietal, and visual regions; however, little is understood about how these regions interact to support WM behavior. In particular, it is unclear whether network dynamics during WM maintenance primarily represent feedforward or feedback connections. This question has important implications for current debates about the relative roles of frontoparietal and visual regions in WM maintenance. In the current study, we investigated the network activity supporting WM using MEG data acquired while healthy subjects performed a multi-item delayed estimation WM task. We used computational modeling of behavior to discriminate correct responses (high accuracy trials) from two different types of incorrect responses (low accuracy and swap trials), and dynamic causal modeling of MEG data to measure effective connectivity. We observed behaviorally dependent changes in effective connectivity in a brain network comprising frontoparietal and early visual areas. In comparison with high accuracy trials, frontoparietal and frontooccipital networks showed disrupted signals depending on type of behavioral error. Low accuracy trials showed disrupted feedback signals during early portions of WM maintenance and disrupted feedforward signals during later portions of maintenance delay, while swap errors showed disrupted feedback signals during the whole delay period. These results support a distributed model of WM that emphasizes the role of visual regions in WM storage and where changes in large scale network configurations can have important consequences for memory-guided behavior.

A tutorial on fitting joint models of M/EEG and behavior to understand cognition.

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.

Magnetoencephalography (MEG) Data Processing in Epilepsy Patients with Implanted Responsive Neurostimulation (RNS) Devices.

Drug-resistant epilepsy (DRE) is often treated with surgery or neuromodulation. Specifically, responsive neurostimulation (RNS) is a widely used therapy that is programmed to detect abnormal brain activity and intervene with tailored stimulation. Despite the success of RNS, some patients require further interventions. However, having an RNS device in situ is a hindrance to the performance of neuroimaging techniques. Magnetoencephalography (MEG), a non-invasive neurophysiologic and functional imaging technique, aids epilepsy assessment and surgery planning. MEG performed post-RNS is complicated by signal distortions. This study proposes an independent component analysis (ICA)-based approach to enhance MEG signal quality, facilitating improved assessment for epilepsy patients with implanted RNS devices. Three epilepsy patients, two with RNS implants and one without, underwent MEG scans. Preprocessing included temporal signal space separation (tSSS) and an automated ICA-based approach with MNE-Python. Power spectral density (PSD) and signal-to-noise ratio (SNR) were analyzed, and MEG dipole analysis was conducted using single equivalent current dipole (SECD) modeling. The ICA-based noise removal preprocessing method substantially improved the signal-to-noise ratio (SNR) for MEG data from epilepsy patients with implanted RNS devices. Qualitative assessment confirmed enhanced signal readability and improved MEG dipole analysis. ICA-based processing markedly enhanced MEG data quality in RNS patients, emphasizing its clinical relevance.

Contrasting MEG effects of anodal and cathodal high-definition TDCS on sensorimotor activity during voluntary finger movements.

Introduction: Protocols for noninvasive brain stimulation (NIBS) are generally categorized as "excitatory" or "inhibitory" based on their ability to produce short-term modulation of motor-evoked potentials (MEPs) in peripheral muscles, when applied to motor cortex. Anodal and cathodal stimulation are widely considered excitatory and inhibitory, respectively, on this basis. However, it is poorly understood whether such polarity-dependent changes apply for neural signals generated during task performance, at rest, or in response to sensory stimulation. Methods: To characterize such changes, we measured spontaneous and movement-related neural activity with magnetoencephalography (MEG) before and after high-definition transcranial direct-current stimulation (HD-TDCS) of the left motor cortex (M1), while participants performed simple finger movements with the left and right hands. Results: Anodal HD-TDCS (excitatory) decreased the movement-related cortical fields (MRCF) localized to left M1 during contralateral right finger movements while cathodal HD-TDCS (inhibitory), increased them. In contrast, oscillatory signatures of voluntary motor output were not differentially affected by the two stimulation protocols, and tended to decrease in magnitude over the course of the experiment regardless. Spontaneous resting state oscillations were not affected either. Discussion: MRCFs are thought to reflect reafferent proprioceptive input to motor cortex following movements. Thus, these results suggest that processing of incoming sensory information may be affected by TDCS in a polarity-dependent manner that is opposite that seen for MEPs-increases in cortical excitability as defined by MEPs may correspond to reduced responses to afferent input, and vice-versa.

Attention Drives Visual Processing and Audiovisual Integration During Multimodal Communication.

During communication in real-life settings, our brain often needs to integrate auditory and visual information and at the same time actively focus on the relevant sources of information, while ignoring interference from irrelevant events. The interaction between integration and attention processes remains poorly understood. Here, we use rapid invisible frequency tagging and magnetoencephalography to investigate how attention affects auditory and visual information processing and integration, during multimodal communication. We presented human participants (male and female) with videos of an actress uttering action verbs (auditory; tagged at 58 Hz) accompanied by two movie clips of hand gestures on both sides of fixation (attended stimulus tagged at 65 Hz; unattended stimulus tagged at 63 Hz). Integration difficulty was manipulated by a lower-order auditory factor (clear/degraded speech) and a higher-order visual semantic factor (matching/mismatching gesture). We observed an enhanced neural response to the attended visual information during degraded speech compared to clear speech. For the unattended information, the neural response to mismatching gestures was enhanced compared to matching gestures. Furthermore, signal power at the intermodulation frequencies of the frequency tags, indexing nonlinear signal interactions, was enhanced in the left frontotemporal and frontal regions. Focusing on the left inferior frontal gyrus, this enhancement was specific for the attended information, for those trials that benefitted from integration with a matching gesture. Together, our results suggest that attention modulates audiovisual processing and interaction, depending on the congruence and quality of the sensory input.

Comparing the efficacy of awake and sedated MEG to TMS in mapping hand sensorimotor cortex in a clinical cohort.

Non-invasive methods such as Transcranial Magnetic Stimulation (TMS) and magnetoencephalography (MEG) aid in the pre-surgical evaluation of patients with epilepsy or brain tumor to identify sensorimotor cortices. MEG requires sedation in children or patients with developmental delay. However, TMS can be applied to awake patients of all ages with any cognitive abilities. In this study, we compared the efficacy of TMS with MEG (in awake and sedated states) in identifying the hand sensorimotor areas in patients with epilepsy or brain tumors. We identified 153 patients who underwent awake- (n = 98) or sedated-MEG (n = 55), along with awake TMS for hand sensorimotor mapping as part of their pre-surgical evaluation. TMS involved stimulating the precentral gyrus and recording electromyography responses, while MEG identified the somatosensory cortex during median nerve stimulation. Awake-MEG had a success rate of 92.35 % and TMS had 99.49 % (p-value = 0.5517). However, in the sedated-MEG cohort, TMS success rate of 95.61 % was significantly higher compared to MEG's 58.77 % (p-value = 0.0001). Factors affecting mapping success were analyzed. Logistic regression across the entire cohort identified patient sedation as the lone significant predictor, contrary to age, lesion, metal, and number of antiseizure medications (ASMs). A subsequent analysis replaced sedation with anesthetic drug dosage, revealing no significant predictors impacting somatosensory mapping success under sedation. This study yields insights into the utility of TMS and MEG in mapping hand sensorimotor cortices and underscores the importance of considering factors that influence eloquent cortex mapping limitations during sedation.

The neurophysiology of closed-loop auditory stimulation in sleep: A magnetoencephalography study.

Closed-loop auditory stimulation (CLAS) is a brain modulation technique in which sounds are timed to enhance or disrupt endogenous neurophysiological events. CLAS of slow oscillation up-states in sleep is becoming a popular tool to study and enhance sleep's functions, as it increases slow oscillations, evokes sleep spindles and enhances memory consolidation of certain tasks. However, few studies have examined the specific neurophysiological mechanisms involved in CLAS, in part because of practical limitations to available tools. To evaluate evidence for possible models of how sound stimulation during brain up-states alters brain activity, we simultaneously recorded electro- and magnetoencephalography in human participants who received auditory stimulation across sleep stages. We conducted a series of analyses that test different models of pathways through which CLAS of slow oscillations may affect widespread neural activity that have been suggested in literature, using spatial information, timing and phase relationships in the source-localized magnetoencephalography data. The results suggest that auditory information reaches ventral frontal lobe areas via non-lemniscal pathways. From there, a slow oscillation is created and propagated. We demonstrate that while the state of excitability of tissue in auditory cortex and frontal ventral regions shows some synchrony with the electroencephalography (EEG)-recorded up-states that are commonly used for CLAS, it is the state of ventral frontal regions that is most critical for slow oscillation generation. Our findings advance models of how CLAS leads to enhancement of slow oscillations, sleep spindles and associated cognitive benefits and offer insight into how the effectiveness of brain stimulation techniques can be improved.