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The European Journal of Neuroscience Apr 2023Electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings during language processing can provide relevant insights on neuroplasticity in clinical... (Review)
Review
Electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings during language processing can provide relevant insights on neuroplasticity in clinical populations (including patients with aphasia). To use EEG and MEG in a longitudinal way, the outcome measures should be consistent across time in healthy individuals. Therefore, the current study provides a review on the test-retest reliability of EEG and MEG measures elicited during language paradigms in healthy adults. PubMed, Web of Science and Embase were searched for relevant articles based on specific eligibility criteria. In total, 11 articles were included in this literature review. The test-retest reliability of the P1, N1 and P2 is systematically considered to be satisfactory, whereas findings are more variable for event-related potentials/fields occurring later in time. The within subject consistency of EEG and MEG measures during language processing can be influenced by multiple variables such as the stimulus presentation mode, the offline reference choice and the required amount of cognitive resources during the task. To conclude, most of the available results are favourable regarding the longitudinal use of EEG and MEG measures elicited during language paradigms in healthy young individuals. In view to the use of these techniques in patients with aphasia, future research should focus on whether the same findings apply to different age groups.
Topics: Adult; Humans; Reproducibility of Results; Electroencephalography; Magnetoencephalography; Language; Aphasia
PubMed: 36864752
DOI: 10.1111/ejn.15948 -
Sensors (Basel, Switzerland) Nov 2022Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological...
Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological signals are linked with cortical brain oscillations. Yet, gut-brain coupling has not been investigated with electroencephalography (EEG) during cognitive brain engagement or during hunger-related gut engagement. In this study in 14 young adults (7 females, mean ± SD age 25.71 ± 8.32 years), we study gut-brain coupling using simultaneous EEG and EGG during hunger and satiety states measured in separate visits, and compare responses both while resting as well as during a cognitively demanding working memory task. We find that EGG-EEG phase-amplitude coupling (PAC) differs based on both satiety state and cognitive effort, with greater PAC modulation observed in the resting state relative to working memory. We find a significant interaction between gut satiation levels and cognitive states in the left fronto-central brain region, with larger cognitive demand based differences in the hunger state. Furthermore, strength of PAC correlated with behavioral performance during the working memory task. Altogether, these results highlight the role of gut-brain interactions in cognition and demonstrate the feasibility of these recordings using scalable sensors.
Topics: Young Adult; Female; Humans; Adolescent; Adult; Brain; Cognition; Magnetoencephalography; Rest; Electroencephalography
PubMed: 36501942
DOI: 10.3390/s22239242 -
NeuroImage Oct 2022Multimodal neuroimaging plays an important role in neuroscience research. Integrated noninvasive neuroimaging modalities, such as magnetoencephalography (MEG),...
Multimodal neuroimaging plays an important role in neuroscience research. Integrated noninvasive neuroimaging modalities, such as magnetoencephalography (MEG), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), allow neural activity and related physiological processes in the brain to be precisely and comprehensively depicted, providing an effective and advanced platform to study brain function. Noncryogenic optically pumped magnetometer (OPM) MEG has high signal power due to its on-scalp sensor layout and enables more flexible configurations than traditional commercial superconducting MEG. Here, we integrate OPM-MEG with EEG and fNIRS to develop a multimodal neuroimaging system that can simultaneously measure brain electrophysiology and hemodynamics. We conducted a series of experiments to demonstrate the feasibility and robustness of our MEG-EEG-fNIRS acquisition system. The complementary neural and physiological signals simultaneously collected by our multimodal imaging system provide opportunities for a wide range of potential applications in neurovascular coupling, wearable neuroimaging, hyperscanning and brain-computer interfaces.
Topics: Brain; Brain-Computer Interfaces; Electroencephalography; Humans; Magnetoencephalography; Neuroimaging
PubMed: 35777634
DOI: 10.1016/j.neuroimage.2022.119420 -
Human Brain Mapping Jun 2020Electrophysiological signals from the cerebellum have traditionally been viewed as inaccessible to magnetoencephalography (MEG) and electroencephalography (EEG). Here,...
Electrophysiological signals from the cerebellum have traditionally been viewed as inaccessible to magnetoencephalography (MEG) and electroencephalography (EEG). Here, we challenge this position by investigating the ability of MEG and EEG to detect cerebellar activity using a model that employs a high-resolution tessellation of the cerebellar cortex. The tessellation was constructed from repetitive high-field (9.4T) structural magnetic resonance imaging (MRI) of an ex vivo human cerebellum. A boundary-element forward model was then used to simulate the M/EEG signals resulting from neural activity in the cerebellar cortex. Despite significant signal cancelation due to the highly convoluted cerebellar cortex, we found that the cerebellar signal was on average only 30-60% weaker than the cortical signal. We also made detailed M/EEG sensitivity maps and found that MEG and EEG have highly complementary sensitivity distributions over the cerebellar cortex. Based on previous fMRI studies combined with our M/EEG sensitivity maps, we discuss experimental paradigms that are likely to offer high M/EEG sensitivity to cerebellar activity. Taken together, these results show that cerebellar activity should be clearly detectable by current M/EEG systems with an appropriate experimental setup.
Topics: Cerebellar Cortex; Computer Simulation; Electroencephalography; Humans; Magnetic Resonance Imaging; Magnetoencephalography; Models, Theoretical; Transcranial Magnetic Stimulation
PubMed: 32115870
DOI: 10.1002/hbm.24951 -
NeuroImage Aug 2023We present a hierarchical empirical Bayesian framework for testing hypotheses about neurotransmitters' concertation as empirical prior for synaptic physiology using...
We present a hierarchical empirical Bayesian framework for testing hypotheses about neurotransmitters' concertation as empirical prior for synaptic physiology using ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of individuals' neurophysiological observations. At the second level, individuals' 7T-MRS estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the group-wise evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction (BMR), parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare alternative model evidence of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the subset of synaptic connections that are influenced by individual differences in neurotransmitter levels, as measured by 7T-MRS. We demonstrate the method using resting-state MEG (i.e., task-free recording) and 7T-MRS data from healthy adults. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. Using within-subject split-sampling of the MEG dataset (i.e., validation by means of a held-out dataset), we show that model comparison for hypothesis testing can be highly reliable. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to reveal the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.
Topics: Adult; Humans; Magnetoencephalography; Bayes Theorem; Reproducibility of Results; Neurochemistry; Magnetic Resonance Spectroscopy; Models, Neurological; Magnetic Resonance Imaging
PubMed: 37244323
DOI: 10.1016/j.neuroimage.2023.120193 -
Scientific Reports Apr 2022Non-invasive human brain functional imaging with millisecond resolution can be achieved only with magnetoencephalography (MEG) and electroencephalography (EEG). MEG has...
Non-invasive human brain functional imaging with millisecond resolution can be achieved only with magnetoencephalography (MEG) and electroencephalography (EEG). MEG has better spatial resolution than EEG because signal distortion due to inhomogeneous head conductivity is negligible in MEG but serious in EEG. However, this advantage has been practically limited by the necessary setback distances between the sensors and scalp, because the Dewar vessel containing liquid helium for superconducting quantum interference devices (SQUIDs) requires a thick vacuum wall. Latest developments of high critical temperature (high-T) SQUIDs or optically pumped magnetometers have allowed closer placement of MEG sensors to the scalp. Here we introduce the use of tunnel magneto-resistive (TMR) sensors for scalp-attached MEG. Improvement of TMR sensitivity with magnetic flux concentrators enabled scalp-tangential MEG at 2.6 mm above the scalp, to target the largest signal component produced by the neural current below. In a healthy subject, our single-channel TMR-MEG system clearly demonstrated the N20m, the initial cortical component of the somatosensory evoked response after median nerve stimulation. Multisite measurement confirmed a spatially and temporally steep peak of N20m, immediately above the source at a latency around 20 ms, indicating a new approach to non-invasive functional brain imaging with millimeter and millisecond resolutions.
Topics: Brain; Brain Mapping; Electroencephalography; Humans; Magnetoencephalography; Scalp
PubMed: 35414691
DOI: 10.1038/s41598-022-10155-6 -
Journal of Clinical Neurophysiology :... Nov 2020Source localization for clinical magnetoencephalography recordings is challenging, and many methods have been developed to solve this inverse problem. The most... (Review)
Review
Source localization for clinical magnetoencephalography recordings is challenging, and many methods have been developed to solve this inverse problem. The most well-studied and validated tool for localization of the epileptogenic zone is the equivalent current dipole. However, it is often difficult to summarize the richness of the magnetoencephalography data with one or a few point sources. A variety of source localization algorithms have been developed to more fully explain the complexity of clinical magnetoencephalography data used to define the epileptogenic network. In this review, various clinically available source localization methods are described and their individual strengths and limitations are discussed.
Topics: Algorithms; Brain; Electrodes; Electroencephalography; Epilepsy; Humans; Magnetoencephalography
PubMed: 33165226
DOI: 10.1097/WNP.0000000000000487 -
Nature Neuroscience Feb 2017
Topics: Brain; Brain Mapping; Electroencephalography; Humans; Magnetic Resonance Imaging; Magnetoencephalography
PubMed: 28230839
DOI: 10.1038/nn.4522 -
IEEE Transactions on Bio-medical... Oct 2019This paper reviews the state-of-the-art neuromarkers development for the prognosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The first part of... (Review)
Review
This paper reviews the state-of-the-art neuromarkers development for the prognosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The first part of this paper is devoted to reviewing the recently emerged machine learning (ML) algorithms based on electroencephalography (EEG) and magnetoencephalography (MEG) modalities. In particular, the methods are categorized by different types of neuromarkers. The second part of the review is dedicated to a series of investigations that further highlight the differences between these two modalities. First, several source reconstruction methods are reviewed and their source-level performances explored, followed by an objective comparison between EEG and MEG from multiple perspectives. Finally, a number of the most recent reports on classification of MCI/AD during resting state using EEG/MEG are documented to show the up-to-date performance for this well-recognized data collecting scenario. It is noticed that the MEG modality may be particularly effective in distinguishing between subjects with MCI and healthy controls, a high classification accuracy of more than 98% was reported recently; whereas the EEG seems to be performing well in classifying AD and healthy subjects, which also reached around 98% of the accuracy. A number of influential factors have also been raised and suggested for careful considerations while evaluating the ML-based diagnosis systems in the real-world scenarios.
Topics: Alzheimer Disease; Biomarkers; Cognitive Dysfunction; Diagnosis, Differential; Electroencephalography; Humans; Machine Learning; Magnetoencephalography; Prognosis
PubMed: 30762522
DOI: 10.1109/TBME.2019.2898871 -
IEEE Transactions on Medical Imaging Oct 2022Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG...
Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, these analyses are manually performed by neurophysiologists and are time-consuming. Another problem is that spike identification from MEG waveforms largely depends on neurophysiologists' skills and experiences. These problems cause poor cost-effectiveness in clinical MEG examination. To overcome these problems, we fully automated spike identification and ECD estimation using a deep learning approach fully automated AI-based MEG interictal epileptiform discharge identification and ECD estimation (FAMED). We applied a semantic segmentation method, which is an image processing technique, to identify the appropriate times between spike onset and peak and to select appropriate sensors for ECD estimation. FAMED was trained and evaluated using clinical MEG data acquired from 375 patients. FAMED training was performed in two stages: in the first stage, a classification network was learned, and in the second stage, a segmentation network that extended the classification network was learned. The classification network had a mean AUC of 0.9868 (10-fold patient-wise cross-validation); the sensitivity and specificity were 0.7952 and 0.9971, respectively. The median distance between the ECDs estimated by the neurophysiologists and those using FAMED was 0.63 cm. Thus, the performance of FAMED is comparable to that of neurophysiologists, and it can contribute to the efficiency and consistency of MEG ECD analysis.
Topics: Deep Learning; Electroencephalography; Epilepsy; Humans; Image Processing, Computer-Assisted; Magnetoencephalography; Sensitivity and Specificity
PubMed: 35536808
DOI: 10.1109/TMI.2022.3173743