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.

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.

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.

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.

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.

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.

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.

Abnormal phase entrainment of low- and high-gamma-band auditory steady-state responses in schizophrenia

Introduction

Gamma-band oscillatory deficits have attracted considerable attention as promising biomarkers of schizophrenia (SZ). Notably, a reduced auditory steady-state response (ASSR) in the low gamma band (40 Hz) is widely recognized as a robust finding among SZ patients. However, a comprehensive investigation into the potential utility of the high-gamma-band ASSR in detecting altered neural oscillations in SZ has not yet been conducted.

Methods

The present study aimed to assess the ASSR using magnetoencephalography (MEG) data obtained during steady-state stimuli at frequencies of 20, 30, 40, and 80 Hz from 23 SZ patients and 21 healthy controls (HCs). To evaluate the ASSR, we examined the evoked power and phase-locking factor (PLF) in the time-frequency domain for both the primary and secondary auditory cortices. Furthermore, we calculated the phase-locking angle (PLA) to examine oscillatory phase lead or delay in SZ patients. Taking advantage of the high spatial resolution of MEG, we also focused on the hemispheric laterality of low- and high-gamma-band ASSR deficits in SZ.

Results

We found abnormal phase delay in the 40 Hz ASSR within the bilateral auditory cortex of SZ patients. Regarding the 80 Hz ASSR, our investigation identified an aberrant phase lead in the left secondary auditory cortex in SZ, accompanied by reduced evoked power in both auditory cortices.

Discussion

Given that abnormal phase lead on 80 Hz ASSR exhibited the highest discriminative power between HC and SZ, we propose that the examination of PLA in the 80 Hz ASSR holds significant promise as a robust candidate for identifying neurophysiological endophenotypes associated with SZ. Furthermore, the left-hemisphere phase lead observed in the deficits of 80 Hz PLA aligns with numerous prior studies, which have consistently proposed that SZ is characterized by left-lateralized brain dysfunctions.

Correlation of motor-auditory cross-modal and auditory unimodal N1 and mismatch responses of schizophrenic patients and normal subjects: an MEG study

Introduction

It has been suggested that the positive symptoms of schizophrenic patients (hallucinations, delusions, and passivity experience) are caused by dysfunction of their internal and external sensory prediction errors. This is often discussed as related to dysfunction of the forward model that executes self-monitoring. Several reports have suggested that dysfunction of the forward model in schizophrenia causes misattributions of self-generated thoughts and actions to external sources. There is some evidence that the forward model can be measured using the electroencephalography (EEG) and magnetoencephalography (MEG) components such as N1 (m) and mismatch negativity (MMN) (m). The objective in this MEG study is to investigate differences in the N1m and MMNm-like activity generated in motor-auditory cross-modal tasks in normal control (NC) subjects and schizophrenic (SC) patients, and compared that activity with N1m and MMNm in the auditory unimodal task.

Methods

The N1m and MMNm/MMNm-like activity were recorded in 15 SC patients and 12 matched NC subjects. The N1m-attenuation effects and peak amplitude of MMNm/MMNm-like activity of the NC and SC groups were compared. Additionally, correlations between MEG measures (N1m suppression rate, MMNm, and MMNm-like activity) and clinical variables (Positive and Negative Syndrome Scale (PANSS) scores and antipsychotic drug (APD) dosages) in SC patients were investigated.

Results

It was found that (i) there was no significant difference in N1m-attenuation for the NC and SC groups, and that (ii) MMNm in the unimodal task in the SC group was significantly smaller than that in the NC group. Further, the MMNm-like activity in the cross-modal task was smaller than that of the MMNm in the unimodal task in the NC group, but there was no significant difference in the SC group. The PANSS positive symptoms and general psychopathology score were moderately negatively correlated with the amplitudes of the MMNm-like activity, and the APD dosage was moderately negatively correlated with the N1m suppression rate. However, none of these correlations reached statistical significance.

Discussion

The findings suggest that schizophrenic patients perform altered predictive processes differently from healthy subjects in latencies reflecting MMNm, depending on whether they are under forward model generation or not. This may support the hypothesis that schizophrenic patients tend to misattribute their inner experience to external agents, thus leading to the characteristic schizophrenia symptoms.

Differential working memory function between phonological and visuospatial strategies: a magnetoencephalography study using a same visual task

Previous studies have reported that, in working memory, the processing of visuospatial information and phonological information have different neural bases. However, in these studies, memory items were presented via different modalities. Therefore, the modality in which the memory items were presented and the strategy for memorizing them were not rigorously distinguished. In the present study, we explored the neural basis of two working memory strategies. Nineteen right-handed young adults memorized seven sequential directions presented visually in a task in which the memory strategy was either visuospatial or phonological (visuospatial/phonological condition). Source amplitudes of theta-band (5-7 Hz) rhythm were estimated from magnetoencephalography during the maintenance period and further analyzed using cluster-based permutation tests. Behavioral results revealed that the accuracy rates showed no significant differences between conditions, while the reaction time in the phonological condition was significantly longer than that in the visuospatial condition. Theta activity in the phonological condition was significantly greater than that in the visuospatial condition, and the cluster in spatio-temporal matrix with p < 5% difference extended to right prefrontal regions in the early maintenance period and right occipito-parietal regions in the late maintenance period. The theta activity results did not indicate strategy-specific neural bases but did reveal the dynamics of executive function required for phonological processing. The functions seemed to move from attention control and inhibition control in the prefrontal region to inhibition of irrelevant information in the occipito-parietal region.

Localized estimation of electromagnetic sources underlying event-related fields using recurrent neural networks

Objective. To use a recurrent neural network (RNN) to reconstruct neural activity responsible for generating noninvasively measured electromagnetic signals.Approach. Output weights of an RNN were fixed as the lead field matrix from volumetric source space computed using the boundary element method with co-registered structural magnetic resonance images and magnetoencephalography (MEG). Initially, the network was trained to minimise mean-squared-error loss between its outputs and MEG signals, causing activations in the penultimate layer to converge towards putative neural source activations. Subsequently, L1 regularisation was applied to the final hidden layer, and the model was fine-tuned, causing it to favour more focused activations. Estimated source signals were then obtained from the outputs of the last hidden layer. We developed and validated this approach with simulations before applying it to real MEG data, comparing performance with beamformers, minimum-norm estimate, and mixed-norm estimate source reconstruction methods.Main results. The proposed RNN method had higher output signal-to-noise ratios and comparable correlation and error between estimated and simulated sources. Reconstructed MEG signals were also equal or superior to the other methods regarding their similarity to ground-truth. When applied to MEG data recorded during an auditory roving oddball experiment, source signals estimated with the RNN were generally biophysically plausible and consistent with expectations from the literature.Significance. This work builds on recent developments of RNNs for modelling event-related neural responses by incorporating biophysical constraints from the forward model, thus taking a significant step towards greater biological realism and introducing the possibility of exploring how input manipulations may influence localised neural activity.

Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings

Instantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.

Large-scale brain networks and intra-axial tumor surgery: a narrative review of functional mapping techniques, critical needs, and scientific opportunities

In recent years, a paradigm shift in neuroscience has been occurring from "localizationism," or the idea that the brain is organized into separately functioning modules, toward "connectomics," or the idea that interconnected nodes form networks as the underlying substrates of behavior and thought. Accordingly, our understanding of mechanisms of neurological function, dysfunction, and recovery has evolved to include connections, disconnections, and reconnections. Brain tumors provide a unique opportunity to probe large-scale neural networks with focal and sometimes reversible lesions, allowing neuroscientists the unique opportunity to directly test newly formed hypotheses about underlying brain structural-functional relationships and network properties. Moreover, if a more complete model of neurological dysfunction is to be defined as a "disconnectome," potential avenues for recovery might be mapped through a "reconnectome." Such insight may open the door to novel therapeutic approaches where previous attempts have failed. In this review, we briefly delve into the most clinically relevant neural networks and brain mapping techniques, and we examine how they are being applied to modern neurosurgical brain tumor practices. We then explore how brain tumors might teach us more about mechanisms of global brain dysfunction and recovery through pre- and postoperative longitudinal connectomic and behavioral analyses.

Source localization using recursively applied and projected MUSIC with flexible extent estimation

Magneto- and electroencephalography (M/EEG) are widespread techniques to measure neural activity in-vivo at a high temporal resolution but low spatial resolution. Locating the neural sources underlying the M/EEG poses an inverse problem, which is ill-posed. We developed a new method based on Recursive Application of Multiple Signal Classification (MUSIC). Our proposed method is able to recover not only the locations but, in contrast to other inverse solutions, also the extent of active brain regions flexibly (FLEX-MUSIC). This is achieved by allowing it to search not only for single dipoles but also dipole clusters of increasing extent to find the best fit during each recursion. FLEX-MUSIC achieved the highest accuracy for both single dipole and extended sources compared to all other methods tested. Remarkably, FLEX-MUSIC was capable to accurately estimate the level of sparsity in the source space (r = 0.82), whereas all other approaches tested failed to do so (r ≤ 0.18). The average computation time of FLEX-MUSIC was considerably lower compared to a popular Bayesian approach and comparable to that of another recursive MUSIC approach and eLORETA. FLEX-MUSIC produces only few errors and was capable to reliably estimate the extent of sources. The accuracy and low computation time of FLEX-MUSIC renders it an improved technique to solve M/EEG inverse problems both in neuroscience research and potentially in pre-surgery diagnostic in epilepsy.

Distinctive modes of cortical communications in tactile temporal order judgment

Temporal order judgment of two successive tactile stimuli delivered to our hands is often inverted when we cross our hands. The present study aimed to identify time-frequency profiles of the interactions across the cortical network associated with the crossed-hand tactile temporal order judgment task using magnetoencephalography. We found that the interactions across the cortical network were channeled to a low-frequency band (5-10 Hz) when the hands were uncrossed. However, the interactions became activated in a higher band (12-18 Hz) when the hands were crossed. The participants with fewer inverted judgments relied mainly on the higher band, whereas those with more frequent inverted judgments (reversers) utilized both. Moreover, reversers showed greater cortical interactions in the higher band when their judgment was correct compared to when it was inverted. Overall, the results show that the cortical network communicates in two distinctive frequency modes during the crossed-hand tactile temporal order judgment task. A default mode of communications in the low-frequency band encourages inverted judgments, and correct judgment is robustly achieved by recruiting the high-frequency mode.

The effects of white matter hyperintensities on MEG power spectra in population with mild cognitive impairment

Cerebrovascular disease is responsible for up to 20% of cases of dementia worldwide, but also it is a major comorbid contributor to the progression of other neurodegenerative diseases, like Alzheimer's disease. White matter hyperintensities (WMH) are the most prevalent imaging marker in cerebrovascular disease. The presence and progression of WMH in the brain have been associated with general cognitive impairment and the risk to develop all types of dementia. The aim of this piece of work is the assessment of brain functional differences in an MCI population based on the WMH volume. One-hundred and twenty-nine individuals with mild cognitive impairment (MCI) underwent a neuropsychological evaluation, MRI assessment (T1 and Flair), and MEG recordings (5 min of eyes closed resting state). Those participants were further classified into vascular MCI (vMCI; n = 61, mean age 75 ± 4 years, 35 females) or non-vascular MCI (nvMCI; n = 56, mean age 72 ± 5 years, 36 females) according to their WMH total volume, assessed with an automatic detection toolbox, LST (SPM12). We used a completely data-driven approach to evaluate the differences in the power spectra between the groups. Interestingly, three clusters emerged: One cluster with widespread larger theta power and two clusters located in both temporal regions with smaller beta power for vMCI compared to nvMCI. Those power signatures were also associated with cognitive performance and hippocampal volume. Early identification and classification of dementia pathogenesis is a crucially important goal for the search for more effective management approaches. These findings could help to understand and try to palliate the contribution of WMH to particular symptoms in mixed dementia progress.

Spatiotemporal profile of altered neural reactivity to food images in obesity: Reward system is altered automatically and predicts efficacy of weight loss intervention

Introduction

Obesity presents a significant public health problem. Brain plays a central role in etiology and maintenance of obesity. Prior neuroimaging studies have found that individuals with obesity exhibit altered neural responses to images of food within the brain reward system and related brain networks. However, little is known about the dynamics of these neural responses or their relationship to later weight change. In particular, it is unknown if in obesity, the altered reward response to food images emerges early and automatically, or later, in the controlled stage of processing. It also remains unclear if the pretreatment reward system reactivity to food images is predictive of subsequent weight loss intervention outcome.

Methods

In this study, we presented high-calorie and low-calorie food, and nonfood images to individuals with obesity, who were then prescribed lifestyle changes, and matched normal-weight controls, and examined neural reactivity using magnetoencephalography (MEG). We performed whole-brain analysis to explore and characterize large-scale dynamics of brain systems affected in obesity, and tested two specific hypotheses: (1) in obese individuals, the altered reward system reactivity to food images occurs early and automatically, and (2) pretreatment reward system reactivity predicts the outcome of lifestyle weight loss intervention, with reduced activity associated with successful weight loss.

Results

We identified a distributed set of brain regions and their precise temporal dynamics that showed altered response patterns in obesity. Specifically, we found reduced neural reactivity to food images in brain networks of reward and cognitive control, and elevated reactivity in regions of attentional control and visual processing. The hypoactivity in reward system emerged early, in the automatic stage of processing (< 150 ms post-stimulus). Reduced reward and attention responsivity, and elevated neural cognitive control were predictive of weight loss after six months in treatment.

Discussion

In summary, we have identified, for the first time with high temporal resolution, the large-scale dynamics of brain reactivity to food images in obese versus normal-weight individuals, and have confirmed both our hypotheses. These findings have important implications for our understanding of neurocognition and eating behavior in obesity, and can facilitate development of novel integrated treatment strategies, including tailored cognitive-behavioral and pharmacological therapies.

Analysis of task-related MEG functional brain networks using dynamic mode decomposition

Objective.Functional connectivity networks explain the different brain states during the diverse motor, cognitive, and sensory functions. Extracting connectivity network configurations and their temporal evolution is crucial for understanding brain function during diverse behavioral tasks.Approach.In this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography data during motor and memory tasks.Main results.The framework generates dominant connectivity brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the proposed methodology with both the PCA-based and DMD-based approaches extracts similar dominant connectivity networks and their corresponding temporal dynamics.Significance.We believe that the proposed methodology with both the PCA and the DMD approaches has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks.

A Review of Effects of Spinal Cord Stimulation on Spectral Features in Resting-State Electroencephalography

BACKGROUND

Spinal cord stimulation (SCS) is an effective therapy for patients with refractory chronic pain syndromes. Although studies have shown that SCS has both spinal and supraspinal effects, the current understanding of cortical effects is still limited. Neuroimaging techniques, such as magnetoencephalography (MEG) and electroencephalography (EEG), combined here as M/EEG, can reveal modulations in ongoing resting-state cortical activity. We aim to provide an overview of available literature on resting-state M/EEG in patients with chronic pain who have been treated with SCS.

MATERIALS AND METHODS

We searched multiple online data bases for studies on SCS, chronic pain, and resting-state M/EEG. Primary outcome measures were changes in spectral features, combined with brain regions in which these changes occurred.

RESULTS

We included eight studies reporting various SCS paradigms (tonic, burst, high-dose, and high-frequency stimulation) and revealing heterogeneity in outcome parameters. We summarized changes in cortical activity in various frequency bands: theta (4-7 Hz), alpha (7-12 Hz), beta (13-30 Hz), and gamma (30-44 Hz). In multiple studies, the somatosensory cortex showed modulation of cortical activity under tonic, burst, and high-frequency stimulation. Changes in connectivity were found in the dorsal anterior cingulate cortex, dorsolateral prefrontal cortex, and parahippocampus.

CONCLUSIONS

The large heterogeneity observed in outcome measures is probably caused by the large variety in study designs, stimulation paradigms, and spectral features studied. Paresthesia-free paradigms have been compared with tonic stimulation in multiple studies. These studies suggest modulation of medial, lateral, and descending pathways for paresthesia-free stimulation, whereas tonic stimulation predominantly modulates lateral and descending pathways. Moreover, multiple studies have reported an increased alpha peak frequency, increased alpha power, and/or decreased theta power when SCS was compared with baseline, indicating modulation of thalamocortical pathways. Further studies with well-defined groups of responders and nonresponders to SCS are recommended to independently study the cortical effects of pain relief and SCS.