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Proceedings of the Institution of... Jun 2022The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state... (Review)
Review
The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.
Topics: Brain; Electrophysiological Phenomena; Magnetoencephalography; Nerve Net
PubMed: 35465768
DOI: 10.1177/09544119221092503 -
Journal of Clinical Neurophysiology :... May 2017This study used magnetoencephalography (MEG) dipole localization and coherence measurement to evaluate the magnetic fields associated with periodic discharges. The...
PURPOSE
This study used magnetoencephalography (MEG) dipole localization and coherence measurement to evaluate the magnetic fields associated with periodic discharges. The primary goal of the study was to evaluate whether MEG could consistently localize quasiperiodic discharges that were observed on the EEG portion of the recording. The secondary objective was to evaluate whether coherence measurements would correlate with topographic maxima of epileptiform activity.
METHODS
A total of 13 inpatients, whose electrographic records demonstrated lateralized periodic discharges (LPDs), were recruited from Henry Ford Hospital neurology and intensive care units. Nine patients were found clinically to be in status epilepticus before the EEG determination of LPDs. Spontaneous cortical brain activity was recorded with 148-channel MEG for 10 minutes. Data were sampled at 508 Hz and DC-100 Hz and filtered from 1 Hz to 40 Hz. Interictal events were imaged with single equivalent current dipole localization. Magnetoencephalography coherence source imaging analysis was performed and compared with the cortical topography of LPD patterns and with the focal lesions seen on the MRI (9 patients) or computed tomography (5 patients) imaging modalities.
RESULTS
The morphology of periodic waveforms was similar between EEG and MEG portions of the study. In patients with substrate positivity on imaging studies, coherence analysis revealed a tendency for LPDs to arise from the interface between the lesion and the surrounding, uncompromised cortex rather than from the lesion itself. In nonlesional patients with recent status epilepticus, the localization of maximal coherence was in the temporal lobes.
CONCLUSIONS
This study demonstrated that MEG is able to detect and localize LPDs arising from damaged and adjacent cortex. The MEG coherence source imaging measurements also suggest the presence of epileptogenic networks perilesionally in cases with focal lesions on imaging. In patients without acute anatomic abnormality, the MEG coherence identified the epileptogenic networks in temporal lobe structures. Magnetoencephalography coherence source imaging may provide physicians with markers for differentiating between LPDs arising from acute injury currents versus LPDs arising from prolonged status epilepticus.
Topics: Aged; Aged, 80 and over; Brain Diseases; Electroencephalography; Electrophysiological Phenomena; Female; Humans; Magnetoencephalography; Male; Middle Aged; Status Epilepticus
PubMed: 27832046
DOI: 10.1097/WNP.0000000000000356 -
Neurobiology of Aging Oct 2022Aging is associated with cognitive changes, with strong variations across individuals. One way to characterize this individual variability is to use techniques such as...
Aging is associated with cognitive changes, with strong variations across individuals. One way to characterize this individual variability is to use techniques such as magnetoencephalography (MEG) to measure the dynamics of neural synchronization between brain regions, and the variability of this connectivity over time. Indeed, few studies have focused on fluctuations in the dynamics of brain networks over time and their evolution with age. We therefore characterize aging effects on MEG phase synchrony in healthy young and older adults from the Cam-CAN database. Age-related changes were observed, with an increase in the variability of brain synchronization, as well as a reversal of the direction of information transfer in the default mode network (DMN), in the delta frequency band. These changes in functional connectivity were associated with cognitive decline. Results suggest that advancing age is accompanied by a functional disorganization of dynamic networks, with a loss of communication stability and a decrease in the information transmitted.
Topics: Aged; Aging; Brain; Brain Mapping; Cognition; Humans; Magnetic Resonance Imaging; Magnetoencephalography; Nerve Net; Neural Pathways
PubMed: 35914474
DOI: 10.1016/j.neurobiolaging.2022.07.001 -
Clinical Neurophysiology : Official... Aug 2015Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network topology.... (Review)
Review
Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network topology. Moreover, the number of different analysis approaches is expanding along with the rising interest in this research area. The comparison between studies can therefore be challenging and discussion is needed to underscore methodological opportunities and pitfalls in functional connectivity and network studies. In this overview we discuss methodological considerations throughout the analysis pipeline of recording and analyzing resting state EEG and MEG data, with a focus on functional connectivity and network analysis. We summarize current common practices with their advantages and disadvantages; provide practical tips, and suggestions for future research. Finally, we discuss how methodological choices in resting state research can affect the construction of functional networks. When taking advantage of current best practices and avoid the most obvious pitfalls, functional connectivity and network studies can be improved and enable a more accurate interpretation and comparison between studies.
Topics: Brain; Brain Mapping; Electroencephalography; Functional Neuroimaging; Humans; Magnetoencephalography; Nerve Net; Neurons
PubMed: 25511636
DOI: 10.1016/j.clinph.2014.11.018 -
Computer Methods and Programs in... Apr 2020Magnetoencephalography (MEG) is an advanced magnetic source imaging technology that measures the magnetic fields produced by neural activities. It has been extensively...
BACKGROUND AND OBJECTIVE
Magnetoencephalography (MEG) is an advanced magnetic source imaging technology that measures the magnetic fields produced by neural activities. It has been extensively used in scientific research and clinical diagnosis due to its high temporal and spatial resolution. Considering the special nature of MEG data, it needs to perform a series of processes and analysis to obtain valuable information. Therefore, the identification of data processing is a key point of MEG studies. At present, the software for MEG analysis such as FieldTrip has no Graphic User Interface (GUI) and users must write their own script to perform concrete analysis. It brings the difficulties to researchers like the doctors without experience in programming or newcomers to MEG. Thus, an open-sourced software-EasyMEG was developed. It has friendly interface with highly functions-integration.
METHODS
The functions of EasyMEG are developed based on MATLAB language to ensure the consistency of the user interface under different operating systems. EasyMEG is a highly integrated software that contains a set of functions for preprocessing, time-lock analysis, time-frequency analysis, source analysis, and plotting. EasyMEG provides a friendly GUI and allows users to complete analyses through a simple and clean interface.
RESULTS
This toolbox has been released as an open-source software on GitHub under the GNU General Public License: https://tonywu2018.github.io/EasyMEG/.
CONCLUSIONS
We hope to improve this toolbox by the power of community and wish to make EasyMEG a simple and powerful toolbox for further MEG studies.
Topics: Brain Mapping; Humans; Magnetoencephalography; Software; User-Computer Interface
PubMed: 31743827
DOI: 10.1016/j.cmpb.2019.105199 -
Scientific Reports Apr 2023Network neuroscience provides important insights into brain function by analyzing complex networks constructed from diffusion Magnetic Resonance Imaging (dMRI),...
Network neuroscience provides important insights into brain function by analyzing complex networks constructed from diffusion Magnetic Resonance Imaging (dMRI), functional MRI (fMRI) and Electro/Magnetoencephalography (E/MEG) data. However, in order to ensure that results are reproducible, we need a better understanding of within- and between-subject variability over long periods of time. Here, we analyze a longitudinal, 8 session, multi-modal (dMRI, and simultaneous EEG-fMRI), and multiple task imaging data set. We first confirm that across all modalities, within-subject reproducibility is higher than between-subject reproducibility. We see high variability in the reproducibility of individual connections, but observe that in EEG-derived networks, during both rest and task, alpha-band connectivity is consistently more reproducible than connectivity in other frequency bands. Structural networks show a higher reliability than functional networks across network statistics, but synchronizability and eigenvector centrality are consistently less reliable than other network measures across all modalities. Finally, we find that structural dMRI networks outperform functional networks in their ability to identify individuals using a fingerprinting analysis. Our results highlight that functional networks likely reflect state-dependent variability not present in structural networks, and that the type of analysis should depend on whether or not one wants to take into account state-dependent fluctuations in connectivity.
Topics: Humans; Reproducibility of Results; Nerve Net; Brain; Magnetoencephalography; Magnetic Resonance Imaging; Brain Mapping
PubMed: 37095180
DOI: 10.1038/s41598-023-33441-3 -
Pain Jun 2023Reliable and objective biomarkers promise to improve the assessment and treatment of chronic pain. Resting-state electroencephalography (EEG) is broadly available, easy...
Reliable and objective biomarkers promise to improve the assessment and treatment of chronic pain. Resting-state electroencephalography (EEG) is broadly available, easy to use, and cost efficient and, therefore, appealing as a potential biomarker of chronic pain. However, results of EEG studies are heterogeneous. Therefore, we conducted a systematic review (PROSPERO CRD42021272622) of quantitative resting-state EEG and magnetoencephalography (MEG) studies in adult patients with different types of chronic pain. We excluded populations with severe psychiatric or neurologic comorbidity. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semiquantitative data synthesis was conducted using modified albatross plots. We included 76 studies after searching MEDLINE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and EMBASE. For cross-sectional studies that can serve to develop diagnostic biomarkers, we found higher theta and beta power in patients with chronic pain than in healthy participants. For longitudinal studies, which can yield monitoring and/or predictive biomarkers, we found no clear associations of pain relief with M/EEG measures. Similarly, descriptive studies that can yield diagnostic or monitoring biomarkers showed no clear correlations of pain intensity with M/EEG measures. Risk of bias was high in many studies and domains. Together, this systematic review synthesizes evidence on how resting-state M/EEG might serve as a diagnostic biomarker of chronic pain. Beyond, this review might help to guide future M/EEG studies on the development of pain biomarkers.
Topics: Adult; Humans; Magnetoencephalography; Chronic Pain; Cross-Sectional Studies; Electroencephalography; Biomarkers
PubMed: 36409624
DOI: 10.1097/j.pain.0000000000002825 -
ELife Apr 2024A core aspect of human speech comprehension is the ability to incrementally integrate consecutive words into a structured and coherent interpretation, aligning with the...
A core aspect of human speech comprehension is the ability to incrementally integrate consecutive words into a structured and coherent interpretation, aligning with the speaker's intended meaning. This rapid process is subject to multidimensional probabilistic constraints, including both linguistic knowledge and non-linguistic information within specific contexts, and it is their interpretative coherence that drives successful comprehension. To study the neural substrates of this process, we extract word-by-word measures of sentential structure from BERT, a deep language model, which effectively approximates the coherent outcomes of the dynamic interplay among various types of constraints. Using representational similarity analysis, we tested BERT parse depths and relevant corpus-based measures against the spatiotemporally resolved brain activity recorded by electro-/magnetoencephalography when participants were listening to the same sentences. Our results provide a detailed picture of the neurobiological processes involved in the incremental construction of structured interpretations. These findings show when and where coherent interpretations emerge through the evaluation and integration of multifaceted constraints in the brain, which engages bilateral brain regions extending beyond the classical fronto-temporal language system. Furthermore, this study provides empirical evidence supporting the use of artificial neural networks as computational models for revealing the neural dynamics underpinning complex cognitive processes in the brain.
Topics: Humans; Comprehension; Speech; Brain; Magnetoencephalography; Language
PubMed: 38577982
DOI: 10.7554/eLife.89311 -
NeuroImage Feb 2019Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of...
Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method.
Topics: Adult; Brain; Data Interpretation, Statistical; Electroencephalography; Humans; Magnetoencephalography; Models, Theoretical
PubMed: 30496819
DOI: 10.1016/j.neuroimage.2018.11.026 -
Cerebral Cortex (New York, N.Y. : 1991) Apr 2023We used magnetoencephalography (MEG) and event-related potentials (ERPs) to track the time-course and localization of evoked activity produced by expected, unexpected...
We used magnetoencephalography (MEG) and event-related potentials (ERPs) to track the time-course and localization of evoked activity produced by expected, unexpected plausible, and implausible words during incremental language comprehension. We suggest that the full pattern of results can be explained within a hierarchical predictive coding framework in which increased evoked activity reflects the activation of residual information that was not already represented at a given level of the fronto-temporal hierarchy ("error" activity). Between 300 and 500 ms, the three conditions produced progressively larger responses within left temporal cortex (lexico-semantic prediction error), whereas implausible inputs produced a selectively enhanced response within inferior frontal cortex (prediction error at the level of the event model). Between 600 and 1,000 ms, unexpected plausible words activated left inferior frontal and middle temporal cortices (feedback activity that produced top-down error), whereas highly implausible inputs activated left inferior frontal cortex, posterior fusiform (unsuppressed orthographic prediction error/reprocessing), and medial temporal cortex (possibly supporting new learning). Therefore, predictive coding may provide a unifying theory that links language comprehension to other domains of cognition.
Topics: Comprehension; Brain Mapping; Semantics; Magnetoencephalography; Frontal Lobe
PubMed: 36130089
DOI: 10.1093/cercor/bhac356