ArticleIEEE transactions on bio-medical engineering2016
Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.
Article in IEEE transactions on bio-medical engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- On the virtues and limitations of Granger-causal brain connectivity estimate: Critical analysis using neural mass models.Network neuroscience (Cambridge, Mass.) · 2026Article
- Distinct Alpha Connectivity Patterns During Response Inhibition in Alcohol Use Disorder.Human brain mapping · 2025Article
- Frequency-band specific directed connectivity networks reveal functional disruptions and pathogenic patterns in temporal lobe epilepsy: a MEG study.Scientific reports · 2025Article
- Altered Directed-Connectivity Network in Temporal Lobe Epilepsy: A MEG Study.Sensors (Basel, Switzerland) · 2025Article
- Cortex level connectivity between ACT-R modules during EEG-based n-back task.Cognitive neurodynamics · 2024Article
- Rejuvenating classical brain electrophysiology source localization methods with spatial graph Fourier filters for source extents estimation.Brain informatics · 2024Article
- Low-density scalp electrical source imaging of the ictal onset zone network using source coherence maps.Frontiers in neurology · 2024Article
- Electroencephalography derived connectivity informing epilepsy surgical planning: Towards clinical applications and future perspectives.NeuroImage. Clinical · 2024Review
- Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy.Brain topography · 2024Article
- Fusion Graph Representation of EEG for Emotion Recognition.Sensors (Basel, Switzerland) · 2023Article
- In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?Frontiers in neuroscience · 2023Review
- NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis.NeuroImage · 2022Article
- Imaging the extent and location of spatiotemporally distributed epileptiform sources from MEG measurements.NeuroImage. Clinical · 2022Article
- Changes of Effective Connectivity in the Alpha Band Characterize Differential Processing of Audiovisual Information in Cross-Modal Selective Attention.Neuroscience bulletin · 2020Article
- Feasibility of Eye Tracking Assisted Vestibular Rehabilitation Strategy Using Immersive Virtual Reality.Clinical and experimental otorhinolaryngology · 2019Article
- Electrophysiological Brain Connectivity: Theory and Implementation.IEEE transactions on bio-medical engineering · 2019Article
- Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy.Frontiers in computational neuroscience · 2019Article
- Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity.Frontiers in neurology · 2019Review
- Electromagnetic Brain Source Imaging by Means of a Robust Minimum Variance Beamformer.IEEE transactions on bio-medical engineering · 2018Article
- A Time-Varying Connectivity Analysis from Distributed EEG Sources: A Simulation Study.Brain topography · 2018Article
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5 authors.
Funding
Abstract
objectiveCombined source-imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a noninvasive fashion. Source-imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source-imaging algorithms to both find the network nodes [regions of interest (ROI)] and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses, and apply Granger analysis on the extracted series to study brain networks under realistic conditions.
methodsSource-imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from interictal and ictal signals recorded by EEG and/or Magnetoencephalography (MEG).
resultsLocalization errors of network nodes are less than 5 mm and normalized connectivity errors of ∼20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection.
conclusionOur study indicates that combined source-imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). SIGNIFICANCE: The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.
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