Dataset Information

Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates.

ABSTRACT: Resting-state brain activities have been extensively investigated to understand the macro-scale network architecture of the human brain using non-invasive imaging methods such as fMRI, EEG, and MEG. Previous studies revealed a mechanistic origin of resting-state networks (RSNs) using the connectome dynamics modeling approach, where the neural mass dynamics model constrained by the structural connectivity is simulated to replicate the resting-state networks measured with fMRI and/or fast synchronization transitions with EEG/MEG. However, there is still little understanding of the relationship between the slow fluctuations measured with fMRI and the fast synchronization transitions with EEG/MEG. In this study, as a first step toward evaluating experimental evidence of resting state activity at two different time scales but in a unified way, we investigate connectome dynamics models that simultaneously explain resting-state functional connectivity (rsFC) and EEG microstates. Here, we introduce empirical rsFC and microstates as evaluation criteria of simulated neuronal dynamics obtained by the Larter-Breakspear model in one cortical region connected with those in other cortical regions based on structural connectivity. We optimized the global coupling strength and the local gain parameter (variance of the excitatory and inhibitory threshold) of the simulated neuronal dynamics by fitting both rsFC and microstate spatial patterns to those of experimental ones. As a result, we found that simulated neuronal dynamics in a narrow optimal parameter range simultaneously reproduced empirical rsFC and microstates. Two parameter groups had different inter-regional interdependence. One type of dynamics was synchronized across the whole brain region, and the other type was synchronized between brain regions with strong structural connectivity. In other words, both fast synchronization transitions and slow BOLD fluctuation changed based on structural connectivity in the two parameter groups. Empirical microstates were similar to simulated microstates in the two parameter groups. Thus, fast synchronization transitions correlated with slow BOLD fluctuation based on structural connectivity yielded characteristics of microstates. Our results demonstrate that a bottom-up approach, which extends the single neuronal dynamics model based on empirical observations into a neural mass dynamics model and integrates structural connectivity, effectively reveals both macroscopic fast, and slow resting-state network dynamics.

SUBMITTER: Endo H

PROVIDER: S-EPMC6978716 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates.

Endo Hidenori H Hiroe Nobuo N Yamashita Okito O

Frontiers in computational neuroscience 20200117

Resting-state brain activities have been extensively investigated to understand the macro-scale network architecture of the human brain using non-invasive imaging methods such as fMRI, EEG, and MEG. Previous studies revealed a mechanistic origin of resting-state networks (RSNs) using the connectome dynamics modeling approach, where the neural mass dynamics model constrained by the structural connectivity is simulated to replicate the resting-state networks measured with fMRI and/or fast synchron ...[more]

PMID: 32009922

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Project description:Electroencephalogram (EEG) microstates that represent quasi-stable, global neuronal activity are considered as the building blocks of brain dynamics. Therefore, the analysis of microstate sequences is a promising approach to understand fast brain dynamics that underlie various mental processes. Recent studies suggest that EEG microstate sequences are non-Markovian and nonstationary, highlighting the importance of the sequential flow of information between different brain states. These findings inspired us to model these sequences using Recurrent Neural Networks (RNNs) consisting of long-short-term-memory (LSTM) units to capture the complex temporal dependencies. Using an LSTM-based auto encoder framework and different encoding schemes, we modeled the microstate sequences at multiple time scales (200-2,000 ms) aiming to capture stably recurring microstate patterns within and across subjects. We show that RNNs can learn underlying microstate patterns with high accuracy and that the microstate trajectories are subject invariant at shorter time scales (≤400 ms) and reproducible across sessions. Significant drop in the reconstruction accuracy was observed for longer sequence lengths of 2,000 ms. These findings indirectly corroborate earlier studies which indicated that EEG microstate sequences exhibit long-range dependencies with finite memory content. Furthermore, we find that the latent representations learned by the RNNs are sensitive to external stimulation such as stress while the conventional univariate microstate measures (e.g., occurrence, mean duration, etc.) fail to capture such changes in brain dynamics. While RNNs cannot be configured to identify the specific discriminating patterns, they have the potential for learning the underlying temporal dynamics and are sensitive to sequence aberrations characterized by changes in metal processes. Empowered with the macroscopic understanding of the temporal dynamics that extends beyond short-term interactions, RNNs offer a reliable alternative for exploring system level brain dynamics using EEG microstate sequences.

Project description:Electroencephalography (EEG) measures the brain's electrophysiological spatio-temporal activities with high temporal resolution. Multichannel and broadband analysis of EEG signals is referred to as EEG microstates (EEG-ms) and can characterize such dynamic neuronal activity. EEG-ms have gained much attention due to the increasing evidence of their association with mental activities and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). Spatially independent EEG-ms are quasi-stationary topographies (e.g., stable, lasting a few dozen milliseconds) typically classified into four canonical classes (microstates A through D). They can be identified by clustering EEG signals around EEG global field power (GFP) maxima points. We examined the EEG-ms properties and the dynamics of cohorts of mood and anxiety (MA) disorders subjects (n = 61) and healthy controls (HCs; n = 52). In both groups, we found four distinct classes of EEG-ms (A through D), which did not differ among cohorts. This suggests a lack of significant structural cortical abnormalities among cohorts, which would otherwise affect the EEG-ms topographies. However, both cohorts' brain network dynamics significantly varied, as reflected in EEG-ms properties. Compared to HC, the MA cohort features a lower transition probability between EEG-ms B and D and higher transition probability from A to D and from B to C, with a trend towards significance in the average duration of microstate C. Furthermore, we harnessed a recently introduced theoretical approach to analyze the temporal dependencies in EEG-ms. The results revealed that the transition matrices of MA group exhibit higher symmetrical and stationarity properties as compared to HC ones. In addition, we found an elevation in the temporal dependencies among microstates, especially in microstate B for the MA group. The determined alteration in EEG-ms temporal dependencies among the cohorts suggests that brain abnormalities in mood and anxiety disorders reflect aberrant neural dynamics and a temporal dwelling among ceratin brain states (i.e., mood and anxiety disorders subjects have a less dynamicity in switching between different brain states).

Project description:Background:The "Avolition-apathy" domain of the negative symptoms was found to include different symptoms by factor analytic studies on ratings derived by different scales. In particular, the relationship of anhedonia with this domain is controversial. Recently introduced negative symptom rating scales provide a better assessment of anhedonia, allowing the distinction of anticipatory and consummatory aspects, which might be related to different psychopathological dimensions. The study of associations with external validators, such as electrophysiological, brain imaging or cognitive indices, might shed further light on the status of anhedonia within the Avolition-apathy domain. Objectives:We used brain electrical microstates (MSs), which represent subsecond periods of quasi-stable scalp electrical field, associated with resting-state neural networks (and thus with global patterns of functional connectivity), to test whether the component symptoms of Avolition-apathy share the same correlates. Method:We analyzed multichannel resting EEGs in 142 individuals with schizophrenia (SCZ) and in 64 healthy controls (HC), recruited within the add-on EEG study of the Italian Network for Research on Psychoses. Relative time contribution, duration and occurrence of four MS classes (MS-A/-B/-C/-D) were calculated. Group differences on MS parameters (contribution and duration) and their associations with negative symptom domains (assessed using the Brief Negative Symptoms Scale) were investigated. Results:SCZ, in comparison to HC, showed increased contribution and duration of MS-C. The contribution of MS-A positively correlated with Avolition-apathy, but not with Expressive deficit. Within the Avolition-apathy domain, anticipatory anhedonia, avolition and asociality, but not consummatory anhedonia, showed the same correlations with MS-A contribution. Conclusion:Our findings support the existence of distinct electrophysiological correlates of Avolition-apathy with respect to Expressive deficit, and lend support to the hypothesis that only the anticipatory component of anhedonia shares the same pathophysiological underpinnings of the Avolition-apathy domain.

Dataset Information

Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates.

Publications

Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates.

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