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eConnectome: A MATLAB toolbox for mapping and imaging of brain functional connectivity
Open accessAbstract
We have developed a MATLAB-based toolbox, eConnectome (electrophysiological connectome), for mapping and imaging functional connectivity at both the scalp and cortical levels from the electroencephalogram (EEG), as well as from the electrocorticogram (ECoG). Graphical user interfaces were designed for interactive and intuitive use of the toolbox. Major functions of eConnectome include EEG/ECoG preprocessing, scalp spatial mapping, cortical source estimation, connectivity analysis, and visualization. Granger causality measures such as directed transfer function and adaptive directed transfer function were implemented to estimate the directional interactions of brain functional networks, over the scalp and cortical sensor spaces. Cortical current density inverse imaging was implemented using a generic realistic geometry brain-head model from scalp EEGs. Granger causality could be further estimated over the cortical source domain from the inversely reconstructed cortical source signals as derived from the scalp EEG. Users may implement other connectivity estimators in the framework of eConnectome for various applications. The toolbox package is open-source and freely available at http://econnectome.umn.edu under the GNU general public license for noncommercial and academic uses.
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“The eConnectome approach (104, 105) has stirred interest in studying pathological networks such as epilepsy, where determining the source that drives seizures is of particular importance (106).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [104]
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Cites
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MethodStructural Equation Modeling: Strengths, Limitations, and Misconceptions
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MethodComparison of different cortical connectivity estimators for high‐resolution EEG recordings
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MethodSelf-citationAutoregressive Model Fitting for Control
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“, and Λ (p) are the N×N matrices of model coefficients and the model order p can be determined using the Final Prediction Error (FPE) criterion (Akaike, 1971) and Schwarz Bayesian Criterion (SBC) (Schwarz, 1978).”
MethodAdaptive AR modeling of nonstationary time series by means of Kalman filtering
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MethodMotor imagery classification by means of source analysis for brain–computer interface applications
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MethodSelf-citationIctal source analysis: Localization and imaging of causal interactions in humans
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Self-citationMultimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG
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Self-citationDynamic functional connectivity
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Neocortical seizure foci localization by means of a directed transfer function method
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Self-citationFranaszczuk, P. J., Bergey, G. K., & Kamiński, M. (1994) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(94)90163-5 · Cited by 134 · cited 1×
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Eliasmith, C. (2002) · Cited by 134
Astolfi, L., Cincotti, F., Mattia, D., Babiloni, C., Carducci, F., Basilisco, A., Rossini, P. M., Salinari, S., Ding, L., Ni, Y., et al. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.10.012 · Cited by 127
Self-citationYuan, H., Doud, A., Gururajan, A., & He, B. (2008) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2008.2003384 · Cited by 120
Self-citationDetecting nonlinearity and phase synchronization with surrogate data
Paluš, M., & Hoyer, D. (1998) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.731319 · Cited by 118 · cited 1×
“Instead, a nonparametric method based on surrogate data is used to assess the significance of the estimated connectivity measures (Theiler et al., 1992; Palus and Hoyer, 1998; Ding et al., 2007).”
MethodfMRI–EEG integrated cortical source imaging by use of time-variant spatial constraints
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Self-citationAstolfi, L., Cincotti, F., Mattia, D., Salinari, S., Babiloni, C., Basilisco, A., Rossini, P. M., Ding, L., Ni, Y., He, B., et al. (2004) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2004.10.006 · Cited by 101 · cited 1×
“This challenge was addressed by an innovative approach of combining electrophysiological source imaging with the Granger causality analysis, which has demonstrated promising applications for noninvasively delineating the brain network connectivity under normal (Astolfi et al., 2004; Babiloni et al., 2005) and patholog…”
MethodSelf-citationIdentification of epileptogenic foci from causal analysis of ECoG interictal spike activity
Wilke, C. T., van Drongelen, W., Kohrman, M. H., & He, B. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.04.024 · Cited by 82 · cited 1×
“As the causality measure is obtainable at each time instant, the ADTF allows the observation of rapidly changing influences between the cortical areas and is suitable for analysis of short duration signals such as interictal activities (Wilke et al., 2009), and event-related potentials.”
MethodSelf-citationEstimating Granger causality after stimulus onset: A cautionary note
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“The adaptive DTF may be used to tackle such task, or more sophisticated analysis may be needed (Wang et al., 2008).”
MethodNUTMEG: a neuromagnetic source reconstruction toolbox.
Dalal, S. S., Zumer, J. M., Agrawal, V., Hild, K. E., Sekihara, K., & Nagarajan, S. S. (2004) · PubMed · Cited by 80
Approximate average head models for EEG source imaging
Valdes-Hernandez, P. A., von Ellenrieder, N., Ojeda, A., Kochen, S. S., Alemán‐Gómez, Y., Muravchik, C. H., & Valdes-Sosa, P. A. (2009) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2009.09.005 · Cited by 77 · cited 1×
“Such generic realistic head model has been suggested to provide improved accuracy in source analysis (Darvas et al., 2006; Valdés-Hernández et al., 2009).”
MethodGeneric head models for atlas‐based EEG source analysis
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MethodWang, X., Chen, Y., Bressler, S. L., & Ding, M. (2007) · International Journal of Neural Systems · doi:10.1142/s0129065707000944 · Cited by 74 · cited 1×
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Self-citationEEG-fMRI reciprocal functional neuroimaging
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MethodSelf-citationFunctional cortical source imaging from simultaneously recorded ERP and fMRI
Im, C., Liu, Z., Zhang, N., Chen, W., & He, B. (2006) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2006.03.015 · Cited by 25
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BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics
Xia, M., Wang, J., & He, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068910 · Cited by 4,106
GRETNA: a graph theoretical network analysis toolbox for imaging connectomics
Wang, J., Wang, X., Xia, M., Liao, X., Evans, A. C., & Yong He, A. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00386 · Cited by 1,439
“…visualize graph-based topological analyses of brain networks, such as the Brain Connectivity Toolbox (BCT; Rubinov and Sporns, 2010), eConnectome (He et al., 2011), CONN (Whitfield-Gabrieli and Nieto-Castanon, 2012), Graph-Analysis Toolbox (GAT; Hosseini et al., 2012) and GraphVar (Kruschwitz et…”
Real-time neuroimaging and cognitive monitoring using wearable dry EEG
Mullen, T. R., Kothe, C. A., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Jung, T., & Cauwenberghs, G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2481482 · Cited by 838
“For instance, eConnectome [23] offers routines for adaptive con-”
EEG Source Imaging: A Practical Review of the Analysis Steps
Michel, C. M., & Brunet, D. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00325 · Cited by 716
Hosseini, S. M. H., Hoeft, F., & Kesler, S. R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040709 · Cited by 365
“Brain Connectivity Toolbox [27]; eConnectome [28]; NetworkX (http://networkx.”
Background
Metadata
- Authors
- He, B., Dai, Y., Astolfi, L., Babiloni, F., Yuan, H., & Yang, L.
- Affiliations
- University of Minnesota (US)
- Fondazione Santa Lucia (IT)
- Corresponding author
- He, Bin
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2011-02
- Volume
- 195
- Issue
- 2
- Pages
- 261-269
- ISSN
- 0165-0270 (ISSN-L)
- PMC
- PMC3244474
- OpenAlex
- W2086498469
- Cited by
- 229
- References
- 46
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 13 · 2023: 8 · 2022: 14 · 2021: 17 · 2020: 17 · 2019: 18 · 2018: 22 · 2017: 20 · 2016: 19 · 2015: 23 · 2014: 17 · 2013: 11 · 2012: 9
- FWCI
- 4.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Neuroscience Methods (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Funders
- NIH/NIBIB (RO1EB006433, RO1EB007920)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- brain functional connectivity, EEG, electrocorticography, Granger causality, directed transfer function, MATLAB toolbox
- MeSH terms
- Brain, Brain Mapping, Computer Simulation, Diagnostic Imaging, Electroencephalography, Evoked Potentials, Humans, Signal Processing, Computer-Assisted, Software, Time Factors
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