Identifying good practices for detecting inter-regional linear functional connectivity from EEG

Pellegrini, Franziska; Delorme, Arnaud; Nikulin, Vadim V.; Haufe, Stefan

doi:10.1016/j.neuroimage.2023.120218

Cited by 17 publications

(26 citation statements)

References 105 publications

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“…Still, by including more than one component per ROI in the analysis, one might ensure that a genuine interaction between ROIs is captured. This observation goes in line with the recommendation to consider 3-4 SVD components per ROI from (Pellegrini et al, 2023). Averaging with sign flip, in general, led to similar PS values as 1SVD but seemed to capture the remaining effects of volume conduction less, as reflected by lower coherence and higher ImCoh (the effects were not significant after correcting for multiple comparisons).…”

Section: Discussionsupporting

confidence: 87%

“…At the same time, LCMV led to a decrease in ImCoh and LagCoh compared to eLORETA. Previous studies (Mahjoory et al, 2017; Pellegrini et al, 2023) also observed the impact of the inverse method on the estimated PS values. While the reasons behind this decrease in PS are not clear, it is important to note that the selection of the inverse method also played a role in the split multiverse analysis.…”

Section: Discussionmentioning

confidence: 74%

“…Overall, the combination of LCMV and several SVD components (the pipeline that was also recommended in a recent study by Pellegrini et al (2023)) seems to provide higher SNR and capture interactions that are specific to the frequency band of interest even for nearby ROIs within the same hemisphere. However, the effects of different processing steps might still depend on the location (within- or across-hemisphere in our case) and the size of the interacting ROIs (Mahjoory et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…While multiple approaches exist for inverse modeling and extraction of ROI time series, there is no consensus on the most appropriate pipeline in the community. Previous studies have shown that the choice of methods for inverse modeling and extraction of ROI time series affects the estimated PS values in real and simulated data (Mahjoory et al, 2017; Pellegrini et al, 2023). Therefore, multiple pipelines should be considered simultaneously to arrive at a valid conclusion about genuine neuronal connectivity based on M/EEG data.…”

Section: Introductionmentioning

confidence: 99%

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Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data

Kapralov,

Idaji,

Stephani

et al. 2023

Preprint

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Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user’s brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are not clear yet. The user’s BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used for the estimation of PS in source space, potentially hindering the reproducibility of the results. To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We combined 24 pipelines for source space analysis and three PS measures in a multiverse analysis to investigate how robust the observed effects were to the selection of the pipeline. Our results show that SNR had a between- and within-subject effect on BCI performance for the majority of the pipelines. In contrast, the effect of phase synchronization on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR. Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user’s BCI performance.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data

Kapralov,

Idaji,

Stephani

et al. 2023

Preprint

Self Cite

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“…The spatial coverage of such computations, however, is inherently limited to brain regions that are accessible with the respective recording technique. For noninvasive M/EEG, the analysis of connectivity is limited to superficial cortical brain areas, and can be biased by noise and volume conduction 12 . Invasive recordings in both human and animal studies provide more robust estimates of oscillatory connectivity but are limited to a selected number of channels from defined brain regions 13 .…”

Section: Mri Connectomics Reveal Shared Cortico-subcortical Beta Networkmentioning

confidence: 99%

Cortical beta oscillations map to shared brain networks modulated by dopamine

Chikermane,

Weerdmeester,

Rajamani

et al. 2024

Preprint

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Brain rhythms can facilitate neural communication for the maintenance of brain function. Beta rhythms (13-35 Hz) have been proposed to serve multiple domains of human ability, including motor control, cognition, memory and emotion, but the overarching organisational principles remain unknown. To uncover the circuit architecture of beta oscillations, we leverage normative brain data, analysing over 30 hours of invasive brain signals from 1772 cortical areas in epilepsy patients, to demonstrate that beta is the most distributed cortical brain rhythm. Next, we identify a shared brain network from beta dominant areas with deeper brain structures, like the basal ganglia, by mapping parametrised oscillatory peaks to whole-brain functional and structural MRI connectomes. Finally, we show that these networks share significant overlap with dopamine uptake as indicated by positron emission tomography. Our study suggests that beta oscillations emerge in cortico-subcortical brain networks that are modulated by dopamine. It provides the foundation for a unifying circuit-based conceptualisation of the functional role of beta activity beyond the motor domain and may inspire an extended investigation of beta activity as a feedback signal for closed-loop neurotherapies for dopaminergic disorders.

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Cortical networks of parkinsonian gait: a metabolic and functional connectivity study

Pellegrini,

Pozzi,

Palmisano

et al. 2024

Ann Clin Transl Neurol

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ObjectiveLocomotion is an automated voluntary movement sustained by coordinated neural synchronization across a distributed brain network. The cerebral cortex is central for adapting the locomotion pattern to the environment and alterations of cortical network dynamics can lead to gait impairments. Gait problems are a common symptom with a still unclear pathophysiology and represent an unmet therapeutical need in Parkinson's disease. Little is known about the cortical network dynamics of locomotor control in these patients.MethodsWe studied the cortical basis of parkinsonian gait by combining metabolic brain imaging with high‐density EEG recordings and kinematic measurements performed at rest and during unperturbed overground walking.ResultsWe found significant changes in functional connectivity between frontal, sensorimotor, and visuomotor cortical areas during walking as compared to resting. Specifically, hypokinetic gait was associated with poor information flow from the supplementary motor area (SMA) to precuneus and from calcarine to lingual gyrus, as well as high information flow from calcarine to cuneus.InterpretationOur findings support a role for visuomotor integration processes in PD‐related hypokinetic gait and suggest that reinforcing visual information may act as a compensatory strategy to allow SMA‐mediated feedforward locomotor control in PD.

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Identifying good practices for detecting inter-regional linear functional connectivity from EEG

Cited by 17 publications

References 105 publications

Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data

Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data

Cortical beta oscillations map to shared brain networks modulated by dopamine

Cortical networks of parkinsonian gait: a metabolic and functional connectivity study

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