Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies

Taberna, Gaia Amaranta; Samogin, Jessica; Marino, Marco; Mantini, Dante

doi:10.3390/brainsci11060741

Cited by 10 publications

(13 citation statements)

References 75 publications

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“…Further, the cap was placed on the participants’ head prior to each EEG resting state assessment, so that the participants were not restricted by the cap during exercise. The replacement of the cap may have resulted in systematic error in electrode position from measurement to measurement, which can affect brain network analysis outcomes and consequently test–retest reliability when these deviations are > 0.5 cm 38 . Since digitizing electrode positions requires a minimum of additional time of about 15 min 39 , this procedure was not feasible for the present study analyzing the effects of acute exercise bouts on brain outcomes, but should be considered for future longitudinal studies on brain networks.…”

Section: Discussionmentioning

confidence: 99%

EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks

Büchel

Lehmann

Sandbakk

et al. 2021

Sci Rep

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The interaction of acute exercise and the central nervous system evokes increasing interest in interdisciplinary research fields of neuroscience. Novel approaches allow to monitor large-scale brain networks from mobile electroencephalography (EEG) applying graph theory, but it is yet uncertain whether brain graphs extracted after exercise are reliable. We therefore aimed to investigate brain graph reliability extracted from resting state EEG data before and after submaximal exercise twice within one week in male participants. To obtain graph measures, we extracted global small-world-index (SWI), clustering coefficient (CC) and characteristic path length (PL) based on weighted phase leg index (wPLI) and spectral coherence (Coh) calculation. For reliability analysis, Intraclass-Correlation-Coefficient (ICC) and Coefficient of Variation (CoV) were computed for graph measures before (REST) and after POST) exercise. Overall results revealed poor to excellent measures at PRE and good to excellent ICCs at POST in the theta, alpha-1 and alpha-2, beta-1 and beta-2 frequency band. Based on bootstrap-analysis, a positive effect of exercise on reliability of wPLI based measures was observed, while exercise induced a negative effect on reliability of Coh-based graph measures. Findings indicate that brain graphs are a reliable tool to analyze brain networks in exercise contexts, which might be related to the neuroregulating effect of exercise inducing functional connections within the connectome. Relative and absolute reliability demonstrated good to excellent reliability after exercise. Chosen graph measures may not only allow analysis of acute, but also longitudinal studies in exercise-scientific contexts.

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Section: Discussionmentioning

confidence: 99%

EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks

Büchel

Lehmann

Sandbakk

et al. 2021

Sci Rep

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“…Among the limitations of our research, concerning the EEG analysis, the computed head model was based on electrode positioning templates and standard anatomy, which partially limited the accuracy of source localization. Future studies aiming to replicate or broaden these findings could be based on higher-density EEG recordings, for which the exact positioning of the EEG sensors (Taberna et al, 2019) as well as an individual head model for each subject should be available (Taberna et al, 2021). Moreover, we only concentrated on a female population; it would be interesting to assess gender differences in rs-FC by also including males that exhibit trait emotion dysregulation, a population which is largely neglected in the existing literature.…”

Section: Discussionmentioning

confidence: 99%

“…Starting from the source localized EEG data, we focused on the signals from a set of seeds representative of commonly investigated RSNs, including the DMN, DAN, VAN, LN, SMN, and VN. Their coordinates were based on previous research (Samogin et al, 2019(Samogin et al, , 2020Taberna et al, 2021). The MNI coordinates of the individual seeds that were used for FC analysis (Taberna et al, 2021) are listed in Table 2.…”

Section: Eeg Signal Processingmentioning

confidence: 99%

“…Their coordinates were based on previous research (Samogin et al, 2019(Samogin et al, , 2020Taberna et al, 2021). The MNI coordinates of the individual seeds that were used for FC analysis (Taberna et al, 2021) are listed in Table 2.…”

Section: Eeg Signal Processingmentioning

confidence: 99%

“…FIGURE 1 | Graphic representation of the seeds we used for each network(Taberna et al, 2021). For the individual seed coordinates, see also Table2.…”

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confidence: 99%

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Ventral Attention Network Correlates With High Traits of Emotion Dysregulation in Community Women — A Resting-State EEG Study

Fusina

Marino

Spironelli

et al. 2022

Front. Hum. Neurosci.

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In recent years, many studies have focused on resting-state brain activity, and especially on functional connectivity (FC), an approach that typically describes the statistical interdependence of activity in distant brain regions through specific networks. Our aim was to study the neurophysiological correlates of emotion dysregulation. Therefore, we expected that both the Default Mode Network (DMN), and the Ventral Attention Network (VAN) would have been involved. Indeed, the latter plays a role in the automatic orienting of attention towards biologically salient stimuli and includes key regions for emotion control and modulation. Starting from a community sample of 422 female students, we selected 25 women with high traits of emotion dysregulation (HD group) and 25 with low traits (LD group). They underwent a 64-channel EEG recording during a five-minute resting state with eyes open. Seed-based FC was computed on the EEG Alpha band (8–13 Hz) as a control band, and on EEG Gamma power (30–50 Hz) as the relevant measure. The power within each network and inter-network connectivity (Inter-NC) was also calculated. Analysis of the EEG Gamma band revealed, in the HD group, higher levels of Inter-NC between the VAN and all other resting-state networks as compared with the LD group, while no differences emerged in the Alpha band. Concerning correlations, Alpha power in the VAN was negatively correlated in the HD group with affective lability (ALS-18 questionnaire), both for total score (ρ = –0.52, pFDR < 0.01) and the Depression/Elation subscale) ρ = −0.45, pFDR < 0.05). Consistent with this, in the Gamma band, a positive correlation was found between VAN spectral power and the Depression/Elation subscale of ALS-18, again in the HD group only (ρ = 0.47, pFDR < 0.05). In conclusion, both resting state FC and network power in the VAN were found to be related to high emotion dysregulation, even in our non-clinical sample with high traits. Emotion dysregulation was characterized, in the EEG gamma band, by a VAN strongly connected to all other networks, a result that points, in women prone to emotion dysregulation, to a strong automatic orienting of attention towards their internal state, bodily sensations, and emotionally intense related thoughts.

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Frequency‐dependent modulation of neural oscillations across the gait cycle

Zhao

Bonassi²,

Samogin

et al. 2022

Human Brain Mapping

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Balance and walking are fundamental to support common daily activities. Relatively accurate characterizations of normal and impaired gait features were attained at the kinematic and muscular levels. Conversely, the neural processes underlying gait dynamics still need to be elucidated. To shed light on gait‐related modulations of neural activity, we collected high‐density electroencephalography (hdEEG) signals and ankle acceleration data in young healthy participants during treadmill walking. We used the ankle acceleration data to segment each gait cycle in four phases: initial double support, right leg swing, final double support, left leg swing. Then, we processed hdEEG signals to extract neural oscillations in alpha, beta, and gamma bands, and examined event‐related desynchronization/synchronization (ERD/ERS) across gait phases. Our results showed that ERD/ERS modulations for alpha, beta, and gamma bands were strongest in the primary sensorimotor cortex (M1), but were also found in premotor cortex, thalamus and cerebellum. We observed a modulation of neural oscillations across gait phases in M1 and cerebellum, and an interaction between frequency band and gait phase in premotor cortex and thalamus. Furthermore, an ERD/ERS lateralization effect was present in M1 for the alpha and beta bands, and in the cerebellum for the beta and gamma bands. Overall, our findings demonstrate that an electrophysiological source imaging approach based on hdEEG can be used to investigate dynamic neural processes of gait control. Future work on the development of mobile hdEEG‐based brain–body imaging platforms may enable overground walking investigations, with potential applications in the study of gait disorders.

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Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies

Cited by 10 publications

References 75 publications

EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks

EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks

Ventral Attention Network Correlates With High Traits of Emotion Dysregulation in Community Women — A Resting-State EEG Study

Frequency‐dependent modulation of neural oscillations across the gait cycle

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