scholarly journals Microstate EEGlab toolbox: An introductory guide

2018 ◽  
Author(s):  
Andreas Trier Poulsen ◽  
Andreas Pedroni ◽  
Nicolas Langer ◽  
Lars Kai Hansen
Keyword(s):  
Graphical User Interface ◽  
Large Scale ◽  
Brain Network ◽  
Network Activity ◽  
Eeg Signal ◽  
Widespread Application ◽  
New Methods ◽  
Temporal Features ◽  
Spatio Temporal ◽  
Microstate Analysis

AbstractEEG microstate analysis offers a sparse characterisation of the spatio-temporal features of large-scale brain network activity. However, despite the concept of microstates is straight-forward and offers various quantifications of the EEG signal with a relatively clear neurophysiological interpretation, a few important aspects about the currently applied methods are not readily comprehensible. Here we aim to increase the transparency about the methods to facilitate widespread application and reproducibility of EEG microstate analysis by introducing a new EEGlab toolbox for Matlab. EEGlab and the Microstate toolbox are open source, allowing the user to keep track of all details in every analysis step. The toolbox is specifically designed to facilitate the development of new methods. While the toolbox can be controlled with a graphical user interface (GUI), making it easier for newcomers to take their first steps in exploring the possibilities of microstate analysis, the Matlab framework allows advanced users to create scripts to automatise analysis for multiple subjects to avoid tediously repeating steps for every subject. This manuscript provides an overview of the most commonly applied microstate methods as well as a tutorial consisting of a comprehensive walk-through of the analysis of a small, publicly available dataset.

scholarly journals Early alterations of large-scale brain networks temporal dynamics in young children with autism

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Aurélie Bochet ◽  
Holger Franz Sperdin ◽  
Tonia Anahi Rihs ◽  
Nada Kojovic ◽  
Martina Franchini ◽  
...  
Keyword(s):  
Large Scale ◽  
Developmental Stages ◽  
Temporal Dynamics ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Brain Network ◽  
Clustering Methods ◽  
Early Developmental Stages ◽  
Spatio Temporal ◽  
Early Developmental

AbstractAutism spectrum disorders (ASD) are associated with disruption of large-scale brain network. Recently, we found that directed functional connectivity alterations of social brain networks are a core component of atypical brain development at early developmental stages in ASD. Here, we investigated the spatio-temporal dynamics of whole-brain neuronal networks at a subsecond scale in 113 toddlers and preschoolers (66 with ASD) using an EEG microstate approach. We first determined the predominant microstates using established clustering methods. We identified five predominant microstate (labeled as microstate classes A–E) with significant differences in the temporal dynamics of microstate class B between the groups in terms of increased appearance and prolonged duration. Using Markov chains, we found differences in the dynamic syntax between several maps in toddlers and preschoolers with ASD compared to their TD peers. Finally, exploratory analysis of brain–behavioral relationships within the ASD group suggested that the temporal dynamics of some maps were related to conditions comorbid to ASD during early developmental stages.

scholarly journals Age-related changes in spatial and temporal features of resting state fMRI

2017 ◽  
Author(s):  
Shruti G. Vij ◽  
Jason S. Nomi ◽  
Dina R. Dajani ◽  
Lucina Q. Uddin
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Spectral Composition ◽  
Brain Network ◽  
Functional Networks ◽  
Functional Changes ◽  
Age Related ◽  
Temporal Features ◽  
Functional Brain Network ◽  
Age Related Changes

AbstractDevelopment and aging are associated with functional changes in the brain across the lifespan. These changes can be detected in spatial and temporal features of resting state functional MRI (rs-fMRI) data. Independent vector analysis (IVA) is a whole-brain multivariate approach that can be used to comprehensively assess these changes in spatial and temporal features. We present a multi-dimensional approach to assessing age-related changes in spatial and temporal features of statistically independent components identified by IVA in a cross-sectional lifespan sample (ages 6-85 years). We show that while large-scale brain network configurations remain consistent throughout the lifespan, changes continue to occur in both local organization and in the spectral composition of these functional networks. We show that the spatial extent of functional networks decreases with age, but with no significant change in the peak functional loci of these networks. Additionally, we show differential age-related patterns across the frequency spectrum; lower frequency correlations decrease across the lifespan whereas higher-frequency correlations increase. These changes indicate an increasing stability of networks with age. In addition to replicating results from previous studies, the current results uncover new aspects of functional brain network changes across the lifespan that are frequency band-dependent.

scholarly journals EEG resting-state large-scale brain network dynamics are related to depressive symptoms

2019 ◽  
Author(s):  
Alena Damborská ◽  
Miralena I. Tomescu ◽  
Eliška Honzírková ◽  
Richard Barteček ◽  
Jana Hořínková ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Resting State ◽  
Large Scale ◽  
Rating Scale ◽  
Depressive Symptomatology ◽  
Network Dynamics ◽  
Brain Network ◽  
Temporal Characteristics ◽  
Microstate Analysis ◽  
Depressive Episodes

AbstractBackgroundThe few previous studies on resting-state EEG microstates in depressive patients suggest altered temporal characteristics of microstates compared to those of healthy subjects. We tested whether resting-state microstate temporal characteristics could capture large-scale brain network dynamic activity relevant to depressive symptomatology.MethodsTo evaluate a possible relationship between the resting-state large-scale brain network dynamics and depressive symptoms, we performed EEG microstate analysis in patients with moderate to severe depression within bipolar affective disorder, depressive episode, and periodic depressive disorder, and in healthy controls.ResultsMicrostate analysis revealed six classes of microstates (A-F) in global clustering across all subjects. There were no between-group differences in the temporal characteristics of microstates. In the patient group, higher symptomatology on the Montgomery-Åsberg Depression Rating Scale, a questionnaire validated as measuring severity of depressive episodes in patients with mood disorders, correlated with higher occurrence of microstate A (Spearman’s rank correlation, r = 0.70, p < 0.01).ConclusionOur results suggest that the observed interindividual differences in resting-state EEG microstate parameters could reflect altered large-scale brain network dynamics relevant to depressive symptomatology during depressive episodes. These findings suggest the utility of the microstate analysis approach in an objective depression assessment.

scholarly journals Brain network constraints and recurrent neural networks reproduce unique trajectories and state transitions seen over the span of minutes in resting-state fMRI

2020 ◽  
Vol 4 (2) ◽  
pp. 448-466
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Resting State ◽  
Large Scale ◽  
Brain Network ◽  
Generative Models ◽  
Network Activity ◽  
Fmri Data ◽  
State Transitions ◽  
Whole Brain

Large-scale patterns of spontaneous whole-brain activity seen in resting-state functional magnetic resonance imaging (rs-fMRI) are in part believed to arise from neural populations interacting through the structural network (Honey, Kötter, Breakspear, & Sporns, 2007 ). Generative models that simulate this network activity, called brain network models (BNM), are able to reproduce global averaged properties of empirical rs-fMRI activity such as functional connectivity (FC) but perform poorly in reproducing unique trajectories and state transitions that are observed over the span of minutes in whole-brain data (Cabral, Kringelbach, & Deco, 2017 ; Kashyap & Keilholz, 2019 ). The manuscript demonstrates that by using recurrent neural networks, it can fit the BNM in a novel way to the rs-fMRI data and predict large amounts of variance between subsequent measures of rs-fMRI data. Simulated data also contain unique repeating trajectories observed in rs-fMRI, called quasiperiodic patterns (QPP), that span 20 s and complex state transitions observed using k-means analysis on windowed FC matrices (Allen et al., 2012 ; Majeed et al., 2011 ). Our approach is able to estimate the manifold of rs-fMRI dynamics by training on generating subsequent time points, and it can simulate complex resting-state trajectories better than the traditional generative approaches.

scholarly journals Multivariate Identification of Functional Neural Networks Underpinning Humorous Movie Viewing

2021 ◽  
Vol 11 ◽  
Author(s):  
Fa-Hsuan Lin ◽  
Hsin-Ju Lee ◽  
Wen-Jui Kuo ◽  
Iiro P. Jääskeläinen
Keyword(s):  
Large Scale ◽  
Brain Structures ◽  
Brain Network ◽  
Network Activity ◽  
Joint Analysis ◽  
Behavioral Measures ◽  
Experimental Conditions ◽  
Free Viewing ◽  
Fmri Data Analysis ◽  
Temporal Models

While univariate functional magnetic resonance imaging (fMRI) data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized the partial least squares (PLS) analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal networks activated during the first time and repeated watching of three ∼5-min comedy clips. We identified networks that were similarly activated across subjects during free viewing of the movies, including the ones associated with self-rated experienced humorousness that were composed of the frontal, parietal, and temporal areas acting in concert. In conclusion, the PLS method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify a functionally relevant brain network activity without the need for explicit temporal models.

scholarly journals Altered EEG resting-state large-scale brain network dynamics in euthymic bipolar disorder patients

2019 ◽  
Author(s):  
Alena Damborská ◽  
Camille Piguet ◽  
Jean-Michel Aubry ◽  
Alexandre G. Dayer ◽  
Christoph M. Michel ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Resting State ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Network Activity ◽  
Anxiety State ◽  
Temporal Characteristics ◽  
Functional Brain Network ◽  
And Gender

AbstractBackgroundNeuroimaging studies provided evidence for disrupted resting-state functional brain network activity in bipolar disorder (BD). Electroencephalographic (EEG) studies found altered temporal characteristics of functional EEG microstates during depressive episode within different affective disorders. Here we investigated whether euthymic patients with BD show deviant resting-state large-scale brain network dynamics as reflected by altered temporal characteristics of EEG microstates.MethodsWe used high-density EEG to explore between-group differences in duration, coverage and occurrence of the resting-state functional EEG microstates in 17 euthymic adults with BD in on-medication state and 17 age- and gender-matched healthy controls. Two types of anxiety, state and trait, were assessed separately with scores ranging from 20 to 80.ResultsMicrostate analysis revealed five microstates (A-E) in global clustering across all subjects. In patients compared to controls, we found increased occurrence and coverage of microstate A that did not significantly correlate with anxiety scores.ConclusionOur results provide neurophysiological evidence for altered large-scale brain network dynamics in BD patients and suggest the increased presence of A microstate to be an electrophysiological trait characteristic of BD.

scholarly journals Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography

2021 ◽  
Vol 17 (2) ◽  
pp. e1008689
Author(s):  
Viktor Sip ◽  
Meysam Hashemi ◽  
Anirudh N. Vattikonda ◽  
Marmaduke M. Woodman ◽  
Huifang Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Epileptic Seizures ◽  
Brain Network ◽  
Temporal Organization ◽  
Data Driven ◽  
Patient Specific ◽  
Epileptogenic Zone ◽  
Surgery Outcome ◽  
Spatio Temporal

Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.

scholarly journals Causal coupling between neural activity, metabolism, and behavior across the Drosophila brain

2020 ◽  
Author(s):  
Kevin Mann ◽  
Stephane Deny ◽  
Surya Ganguli ◽  
Thomas R. Clandinin
Keyword(s):  
Energy Metabolism ◽  
Neural Activity ◽  
Large Scale ◽  
Metabolic Flux ◽  
Fruit Fly ◽  
Brain Network ◽  
Network Activity ◽  
Tight Coupling ◽  
Behavioral States ◽  
The Brain

Coordinated activity across networks of neurons is a hallmark of both resting and active behavioral states in many species, including worms, flies, fish, mice and humans1–5. These global patterns alter energy metabolism in the brain over seconds to hours, making oxygen consumption and glucose uptake widely used proxies of neural activity6,7. However, whether changes in neural activity are causally related to changes in metabolic flux in intact circuits on the sub-second timescales associated with behavior, is unknown. Moreover, it is unclear whether transitions between rest and action are associated with spatiotemporally structured changes in neuronal energy metabolism. Here, we combine two-photon microscopy of the entire fruit fly brain with sensors that allow simultaneous measurements of neural activity and metabolic flux, across both resting and active behavioral states. We demonstrate that neural activity drives changes in metabolic flux, creating a tight coupling between these signals that can be measured across large-scale brain networks. Further, these studies reveal that the initiation of even minimal behavioral movements causes large-scale changes in the pattern of neural activity and energy metabolism, revealing unexpected structure in the functional architecture of the brain. The relationship between neural activity and energy metabolism is likely evolutionarily ancient. Thus, these studies provide a critical foundation for using metabolic proxies to capture changes in neural activity and reveal that even minimal behavioral movements are associated with changes in large-scale brain network activity.

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