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eConnectome specifications

Information


Unique identifier OMICS_10614
Name eConnectome
Alternative name Electrophysiological Connectome
Software type Package/Module
Interface Command line interface, Graphical user interface
Restrictions to use Academic or non-commercial use
Operating system Unix/Linux, Mac OS, Windows
Programming languages MATLAB
License GNU General Public License version 2.0
Computer skills Advanced
Version 2.0
Stability Stable
Maintained Yes

Versioning


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Maintainer


  • person_outline eConnectome

Publication for Electrophysiological Connectome

eConnectome citations

 (13)
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Distinguishing Anesthetized from Awake State in Patients: A New Approach Using One Second Segments of Raw EEG

2018
Front Hum Neurosci
PMCID: 5826260
PMID: 29515381
DOI: 10.3389/fnhum.2018.00040
call_split See protocol

[…] oaded into Matlab R2015a using the Biosys toolbox as implemented in EEGlab (Delorme and Makeig, ), and the resulting data was processed using in-house scripts. Specifically, the “DTF” function in the eConnectome toolbox (He et al., ) was employed to calculate the DTF for every 1-s segment of the raw data, without any filtering or artifact removal. Filtering and artifact removal was deliberately av […]

library_books

The electrophysiological connectome is maintained in healthy elders: a power envelope correlation MEG study

2017
Sci Rep
PMCID: 5656690
PMID: 29070789
DOI: 10.1038/s41598-017-13829-8

[…] y index allows uncovering similar RSNs as fMRI,. Results showed that no functional connection (either within or between networks) appeared modulated by aging. These results therefore suggest that the electrophysiological connectome is maintained in healthy elders. This observation contrasts with the available fMRI literature demonstrating substantial changes in functional connectivity with healthy […]

library_books

The influence of corticospinal activity on TMS evoked activity and connectivity in healthy subjects: A TMS EEG study

2017
PLoS One
PMCID: 5383066
PMID: 28384197
DOI: 10.1371/journal.pone.0174879

[…] Connectivity analysis was performed using the adaptive Directed Transfer Function (aDTF) [] within the eConnectome MATLAB toolbox []. Connectivity was calculated amongst regions of interest (ROIs) with centers located at the maximum of the current density activation corresponding to each peak of the TE […]

library_books

Successful Object Encoding Induces Increased Directed Connectivity in Presymptomatic Early Onset Alzheimer’s Disease

2017
PMCID: 5147495
PMID: 27792014
DOI: 10.3233/JAD-160803

[…] proves with the amount of data [], and the cortical current density source model [] was used to solve the inverse problem through the weighted Minimum Norm Estimation (wMNE) algorithm included in the eConnectome software []. Solving the inverse problem implies that the signal captured by electrodes is represented as current density signal in 7850 voxels, where each voxel is of 4 mm3, that cover th […]

call_split

Decoding of top down cognitive processing for SSVEP controlled BMI

2016
Sci Rep
PMCID: 5093690
PMID: 27808125
DOI: 10.1038/srep36267
call_split See protocol

[…] DTF) has been developed to describe causality among an arbitrary number of signals. Granger causality analysis has shown potential for non-invasively delineating brain network connectivity. Using the eConnectome software, functional connectivity was mapped for each experimental condition. Granger causality was investigated in the frequency band from 5 Hz to 8 Hz, which includes the range of the st […]

library_books

Brain dynamics of post‐task resting state are influenced by expertise: Insights from baseball players

2016
Hum Brain Mapp
PMCID: 5113676
PMID: 27448098
DOI: 10.1002/hbm.23321

[…] ere projected onto the broadband data and subtracted out. These BCG‐free data were then re‐referenced from the 43 bipolar channels to the 34‐electrode space.We performed cortical source imaging using eConnectome [He et al., ]. eConnectome uses the cortical current density source model to solve the inverse problem in order to determine cortical source distribution. After producing a high resolution […]

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eConnectome institution(s)
Department of Biomedical Engineering, University of Minnesota, MN, USA; Center for Neuroengineering, University of Minnesota, MN, USA
eConnectome funding source(s)
eConnectome was developed with support from the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under grants RO1 EB006433 and RO1 EB007920.

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