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

Bochet, Aurélie; Sperdin, Holger Franz; Rihs, Tonia A.; Kojovic, Nada; Franchini, Martina; Jan, Reem Kais; Michel, Christoph M.; Schaer, Marie

doi:10.1038/s42003-021-02494-3

Cited by 30 publications

(12 citation statements)

References 42 publications

(54 reference statements)

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“…This may be because the participants involved in the study were all healthy college students. Compared to the subjects with cognitive impairment, the difference in cognitive function of the participants may be much less than that of previous studies (Bochet et al, 2021;Li et al, 2022;Nishida et al, 2013). Meanwhile, this result is in line with the results of Kim et al (2021), who suggest that transition probability can reflect a considerable change in cognition, but is not suitable for reflecting the degree of difference in task performance.…”

Section: Feature Selection and Classificationsupporting

confidence: 78%

Evidence for modulation of EEG microstates by mental workload levels and task types

Chen,

Ke,

et al. 2023

Human Brain Mapping

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Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large‐scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi‐attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within‐task and 78% for cross‐task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.

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Section: Feature Selection and Classificationsupporting

confidence: 78%

Evidence for modulation of EEG microstates by mental workload levels and task types

Chen,

Ke,

et al. 2023

Human Brain Mapping

View full text Add to dashboard Cite

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“…This lends support to the relative stability of broad microstate topographies across age, despite observations of age-related alterations to specific microstate temporal features such as duration, coverage, and occurrence. Age-related changes in specific temporal parameters of microstates have been previously reported elsewhere (Koenig et al, 2002;Tomescu et al, 2018) and have also been shown be altered across several neurodevelopmental, psychiatric, and neurological illnesses (Andreou et al, 2014;Baldini et al, 2023;Bochet et al, 2021;da Cruz et al, 2020), thus highlighting their clinical relevance. More broadly, these findings appear to parallel previous fMRI work characterising functional brain organisation and development in children spanning a similar ages (e.g., 7-9 years; Supekar et al, 2009).…”

Section: Discussionmentioning

confidence: 56%

“…Prior to running the microstate analyses, the EEG data were bandpass filtered between 1 and 40 Hz (fourth order zero-phase Butterworth filter) and were down-sampled to 250 Hz to reduce computational burden (Bochet et al, 2021). Microstate analysis was conducted in MATLAB implementing the open-source Microstate EEGLAB toolbox (version 1.0) (Poulsen et al, 2018).…”

Section: Microstate Analysismentioning

confidence: 99%

EEG microstates in early‐to‐middle childhood show associations with age, biological sex, and alpha power

Hill,

Bailey,

Zomorrodi

et al. 2023

Human Brain Mapping

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Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large‐scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early‐to‐middle childhood (4–12 years of age). Linear regression models were used to examine if participants' age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A–E) across both eyes‐closed and eyes‐open resting‐state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f‐like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes‐open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large‐scale functional brain networks during early‐to‐middle childhood, extending understanding of neural dynamics across this important period for brain development.

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“…The data were filtered again at 2–20 Hz before calculation to remove the effect of high‐frequency noise. The highest spatial correlation was observed to exist between four and six types of microstates during numerous studies, 10 , 23 , 24 , 25 hence this was chosen as the clustering range. The specific process of microstate clustering could be divided into the following three steps.…”

Section: Methodsmentioning

confidence: 99%

Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness

Li,

Gao,

Yang

et al. 2024

CNS Neurosci Ther

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BackgroundAccurately diagnosing patients with the vegetative state (VS) and the minimally conscious state (MCS) reached a misdiagnosis of approximately 40%.MethodsA method combined microstate and dynamic functional connectivity (dFC) to study the spatiotemporal variability of the brain in disorders of consciousness (DOC) patients was proposed. Resting‐state EEG data were obtained from 16 patients with MCS and 16 patients with VS. Mutual information (MI) was used to assess the EEG connectivity in each microstate. MI‐based features with statistical differences were selected as the total feature subset (TFS), then the TFS was utilized to feature selection and fed into the classifier, obtaining the optimal feature subsets (OFS) in each microstate. Subsequently, an OFS‐based MI functional connectivity network (MIFCN) was constructed in the cortex.ResultsThe group‐average MI connectivity matrix focused on all channels revealed that all five microstates exhibited stronger information interaction in the MCS when comparing with the VS. While OFS‐based MIFCN, which only focused on a few channels, revealed greater MI flow in VS patients than in MCS patients under microstates A, B, C, and E, except for microstate D. Additionally, the average classification accuracy of OFS in the five microstates was 96.2%.ConclusionConstructing features based on microstates to distinguish between two categories of DOC patients had effectiveness.

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

Cited by 30 publications

References 42 publications

Evidence for modulation of EEG microstates by mental workload levels and task types

Evidence for modulation of EEG microstates by mental workload levels and task types

EEG microstates in early‐to‐middle childhood show associations with age, biological sex, and alpha power

Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness

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