Directed functional connections underlying spontaneous brain activity

Coito, Ana; Michel, Christoph M.; Vulliémoz, Serge; Plomp, Gijs

doi:10.1002/hbm.24418

Cited by 50 publications

(54 citation statements)

References 99 publications

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“…ESI was used to reconstruct the source activity underlying the distribution of scalp potentials, for each time point in each patient as in our previous studies . ESI involves the computation of the forward model and the solution of the inverse problem .…”

Section: Methodsmentioning

confidence: 99%

“…The Matlab code has entered Epilog NV (Ghent, Belgium). Connectivity estimation was performed equally with in house software written in Matlab, like in our previous studies …”

Section: Methodsmentioning

confidence: 99%

“…ESI was used to reconstruct the source activity underlying the distribution of scalp potentials, for each time point in each patient as in our previous studies. 10,12,13,15 ESI involves the computation of the forward model and the solution of the inverse problem. 2 The forward model describes how electrical currents in the brain produce the measured scalp potentials and it requires the use of an appropriate head model.…”

Section: Electrical Source Imagingmentioning

confidence: 99%

“…For each of the investigated measures (ESI, summed outflow, betweenness centrality, clustering coefficient, and efficiency), we determined in how many patients the atlas-based ROI with the highest magnitude overlapped with the presumed EZ, using the results of the average ESI or summary connectivity obtained from all marked spikes in each patient, like in our previous studies. 10,12,13,15 The percentage (%) of concordant patients was computed for each of the measures. The same procedure was done separately for TLE and ETLE patients.…”

Section: Concordance Assessment For Esi and Connectivity Measuresmentioning

confidence: 99%

“…Connectivity estimation was performed equally with in house software written in Matlab, like in our previous studies. 10,12,13,15 3 | RESULTS…”

Section: Concordance Assessment For Esi and Connectivity Measuresmentioning

confidence: 99%

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Interictal epileptogenic zone localization in patients with focal epilepsy using electric source imaging and directed functional connectivity from low‐densityEEG

et al. 2019

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Summary Objective Electrical source imaging ( ESI ) is used increasingly to estimate the epileptogenic zone ( EZ ) in patients with epilepsy. Directed functional connectivity ( DFC ) coupled to ESI helps to better characterize epileptic networks, but studies on interictal activity have relied on high‐density recordings. We investigated the accuracy of ESI and DFC for localizing the EZ , based on low‐density clinical electroencephalography (EEG) . Methods We selected patients with the following: (a) focal epilepsy, (b) interictal spikes on standard EEG, (c) either a focal structural lesion concordant with the electroclinical semiology or good postoperative outcome. In 34 patients (20 temporal lobe epilepsy [TLE] , 14 extra‐TLE [ETLE ]), we marked interictal spikes and estimated the cortical activity during each spike in 82 cortical regions using a patient‐specific head model and distributed linear inverse solution. DFC between brain regions was computed using Granger‐causal modeling followed by network topologic measures. The concordance with the presumed EZ at the sublobar level was computed using the epileptogenic lesion or the resected area in postoperative seizure‐free patients. Results ESI, summed outflow, and efficiency were concordant with the presumed EZ in 76% of the patients, whereas the clustering coefficient and betweenness centrality were concordant in 70% of patients. There was no significant difference between ESI and connectivity measures. In all measures, patients with TLE had a significantly higher ( P < 0.05) concordance with the presumed EZ than patients with with ETLE. The brain volume accepted for concordance was significantly larger in TLE . Significance ESI and DFC derived from low‐density EEG can reliably estimate the EZ from interictal spikes. Connectivity measures were not superior to ESI for EZ localization during interictal spikes, but the current validation of the localization of connectivity measure is promising for other applications.

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Section: Methodsmentioning

confidence: 99%

“…The Matlab code has entered Epilog NV (Ghent, Belgium). Connectivity estimation was performed equally with in house software written in Matlab, like in our previous studies …”

Section: Methodsmentioning

confidence: 99%

Section: Electrical Source Imagingmentioning

confidence: 99%

Section: Concordance Assessment For Esi and Connectivity Measuresmentioning

confidence: 99%

“…Connectivity estimation was performed equally with in house software written in Matlab, like in our previous studies. 10,12,13,15 3 | RESULTS…”

Section: Concordance Assessment For Esi and Connectivity Measuresmentioning

confidence: 99%

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Interictal epileptogenic zone localization in patients with focal epilepsy using electric source imaging and directed functional connectivity from low‐densityEEG

et al. 2019

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Prediction of dispositional dialectical thinking from resting‐state electroencephalography

et al. 2021

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This study aims to explore the possibility of predicting the dispositional level of dialectical thinking using resting-state electroencephalography signals. Thirty-four participants completed a self-reported measure of dialectical thinking, and their resting-state electroencephalography was recorded. After wave filtration and eye movement removal, time-frequency electroencephalography signals were converted into four frequency domains: delta (1-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), and beta (13-30 Hz). Functional principal component analysis with B-spline approximation was then applied for feature reduction. Five machine learning methods (support vector regression, least absolute shrinkage and selection operator, K-nearest neighbors, random forest, and gradient boosting decision tree) were applied to the reduced features for prediction. The model ensemble technique was used to create the best performing final model. The results showed that the alpha wave of the electroencephalography signal in the early period (12-15 s) contributed most to the prediction of dialectical thinking. With data-driven electrode selection (FC1, FCz, Fz, FC3, Cz, AFz), the prediction model achieved an average coefficient of determination of 0.45 on 200 random test sets. Furthermore, a significant positive correlation was found between the alpha value of standardized low-resolution electromagnetic tomography activity in the right dorsal anterior cingulate cortex and dialectical self-scale score. The prefrontal and midline alpha oscillations of resting electroencephalography are good predictors of the dispositional level of dialectical thinking, possibly reflecting these brain structures' involvement in dialectical thinking.

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Directed functional connections underlying spontaneous brain activity

Coito

Michel

Vulliémoz

et al. 2018

Human Brain Mapping

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Neuroimaging studies have shown that spontaneous brain activity is characterized as changing networks of coherent activity across multiple brain areas. However, the directionality of functional interactions between the most active regions in our brain at rest remains poorly understood. Here, we examined, at the whole‐brain scale, the main drivers and directionality of interactions that underlie spontaneous human brain activity by applying directed functional connectivity analysis to electroencephalography (EEG) source signals. We found that the main drivers of electrophysiological activity were the posterior cingulate cortex (PCC), the medial temporal lobes (MTL), and the anterior cingulate cortex (ACC). Among those regions, the PCC was the strongest driver and had both the highest integration and segregation importance, followed by the MTL regions. The driving role of the PCC and MTL resulted in an effective directed interaction directed from posterior toward anterior brain regions. Our results strongly suggest that the PCC and MTL structures are the main drivers of electrophysiological spontaneous activity throughout the brain and suggest that EEG‐based directed functional connectivity analysis is a promising tool to better understand the dynamics of spontaneous brain activity in healthy subjects and in various brain disorders.

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Directed functional connections underlying spontaneous brain activity

Cited by 50 publications

References 99 publications

Interictal epileptogenic zone localization in patients with focal epilepsy using electric source imaging and directed functional connectivity from low‐densityEEG

Interictal epileptogenic zone localization in patients with focal epilepsy using electric source imaging and directed functional connectivity from low‐densityEEG

Prediction of dispositional dialectical thinking from resting‐state electroencephalography

Directed functional connections underlying spontaneous brain activity

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