Incorporating quantitative EEG analysis into the MNI Open Science neuroinformatics ecosystem
Poster No:
1424
Submission Type:
Abstract Submission
Authors:
Jorge Bosch-Bayard1,2,3, Christine Rogers1, Eduardo Aubert3, Shawn Brown4, Gregory Kiar1,5, Tristan Glatard5,1, Lidice Galán-García3, Maria Bringas Vega3,2, Trinidad Virues3, Samir Das1, Cecile Madjar1, Zia Mohades1, Leigh MacIntyre1, Alan Evans1, Pedro Valdes-Sosa3,1,2
Institutions:
1McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada, 2University of Electronic Science and Technology of China UESTC, Chengdu, China, 3Cuban Neuroscience Center, Havana, Cuba, 4Pittsburgh Super Computing Centre, Pittsburgh, PA, 5Concordia University, Montreal, Canada
First Author:
Jorge Bosch-Bayard, PhD
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University|University of Electronic Science and Technology of China UESTC|Cuban Neuroscience Center
Montreal, Canada|Chengdu, China|Havana, Cuba
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University|University of Electronic Science and Technology of China UESTC|Cuban Neuroscience Center
Montreal, Canada|Chengdu, China|Havana, Cuba
Co-Author(s):
Christine Rogers
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada
Gregory Kiar
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University|Concordia University
Montreal, Canada|Montreal, Canada
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University|Concordia University
Montreal, Canada|Montreal, Canada
Tristan Glatard
Concordia University|McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada|Montreal, Canada
Concordia University|McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada|Montreal, Canada
Maria Bringas Vega, Dr.
Cuban Neuroscience Center|University of Electronic Science and Technology of China UESTC
Havana, Cuba|Chengdu, China
Cuban Neuroscience Center|University of Electronic Science and Technology of China UESTC
Havana, Cuba|Chengdu, China
Samir Das
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada
Cecile Madjar
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada
Zia Mohades
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada
Leigh MacIntyre
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada
Alan Evans
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montreal, Canada
Pedro Valdes-Sosa
Cuban Neuroscience Center|McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University|University of Electronic Science and Technology of China UESTC
Havana, Cuba|Montreal, Canada|Chengdu, China
Cuban Neuroscience Center|McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University|University of Electronic Science and Technology of China UESTC
Havana, Cuba|Montreal, Canada|Chengdu, China
Introduction:
Revived interest in electrophysiology, driven by the maturity of EEG source imaging, has led to new informatics challenges (7). Integrating sophisticated EEG analysis with high-performance computing is pivotal to promulgating standardized methods across research and clinical settings (1).
In response, a collaboration from the Cuban Neuroscience Center (CNEURO), the University of Electronic Science and Technology of China (UESTC) and the Montreal Neurological Institute (MNI) is incorporating CNEURO's quantitative EEG methods into the MNI Open Neuroscience ecosystem, based on the LORIS and CBRAIN data- and tool-sharing platforms (3).
CNEURO's Quantitative EEG toolbox (qEEGt) was recently released via CBRAIN (9). Its VARETA source imaging method (2), age regression equations and calculation of z- spectra are published on GitHub and Zenodo. The qEEGt toolbox leverages Bayesian estimation of source localization and connectivity for improved spatial resolution, and produces age-corrected normative SPM maps of EEG log source spectra. Given the impact of SPM across neuroimaging (5), such open-access toolkits hold similar potential for electrophysiology.
In response, a collaboration from the Cuban Neuroscience Center (CNEURO), the University of Electronic Science and Technology of China (UESTC) and the Montreal Neurological Institute (MNI) is incorporating CNEURO's quantitative EEG methods into the MNI Open Neuroscience ecosystem, based on the LORIS and CBRAIN data- and tool-sharing platforms (3).
CNEURO's Quantitative EEG toolbox (qEEGt) was recently released via CBRAIN (9). Its VARETA source imaging method (2), age regression equations and calculation of z- spectra are published on GitHub and Zenodo. The qEEGt toolbox leverages Bayesian estimation of source localization and connectivity for improved spatial resolution, and produces age-corrected normative SPM maps of EEG log source spectra. Given the impact of SPM across neuroimaging (5), such open-access toolkits hold similar potential for electrophysiology.
Methods:
Quantitative EEG (6) extracts frequency-specific data via EEG frequency analysis at each scalp electrode. Developing SPM for EEG involved a 3D extension of qEEG using source imaging. Compliance to normative data was tested for each electrode and frequency band, creating a z-transform for each log spectral value for a population age. Topographic scalp maps display results as statistical deviations from norms, enabling visual data review and multivariate identification of brain pathology (7).
qEEGt was developed based on the Cuban Human Brain Mapping Project (7). An "average head model"(4) was applied and validated to calculate an approximate lead field for ESI. Validated in different health systems for several decades, it is acknowledged as the first application of SPM in electrophysiology (5).
To provide a stable and re-usable execution environment, a Boutiques JSON descriptor for qEEG was created to define the runtime parameters. This enables users to process data stored either in LORIS or on CBRAIN-connected servers. A Singularity container provides a machine-independent installation of qEEGt. In parallel, the LORIS platform has integrated support for the BIDS-EEG standard (8).
qEEGt was developed based on the Cuban Human Brain Mapping Project (7). An "average head model"(4) was applied and validated to calculate an approximate lead field for ESI. Validated in different health systems for several decades, it is acknowledged as the first application of SPM in electrophysiology (5).
To provide a stable and re-usable execution environment, a Boutiques JSON descriptor for qEEG was created to define the runtime parameters. This enables users to process data stored either in LORIS or on CBRAIN-connected servers. A Singularity container provides a machine-independent installation of qEEGt. In parallel, the LORIS platform has integrated support for the BIDS-EEG standard (8).
Results:
The CBRAIN qEEGt toolkit provides different scalp and source visualization tools for calculation of EEG scalp as well as source spectra, comparison of z-spectra to normative data, visualization, and coherence measurements, among others. CBRAIN handles the remote job execution and monitoring. Once finished, the results can be served back to data repositories such as LORIS. Interactive visualization of qEEGt results is launched directly in a CBRAIN browser. This ReactJS component shows maps for narrow- (Fig.1) and broad-band (Fig.2) models, and coherence maps per frequency, among others.
·Fig.1: Narrow band model visualization showing raw spectra at scalp per electrode, and corresponding 3D tomographic for given frequency. Also available: graphs for Z values, at sensors and sources.
·Fig.2: Broad band model visualization of raw and Z spectra showing Absolute Power (AP), Relative Power (RP) and Mean Frequency (MF) of the individual’s spectra respectively.
Conclusions:
Across source imaging methods, qEEGt uniquely calculates SPM comparing spectral parameters against validated normative data, with published regression equations for analysis at both electrode and source level. Clinical value is supported by the assessment of deviation(1) and an integrated Global Scale Factor, improving compatibility and robustness across recording systems.
EEG, while older and more accessible than neuroimaging techniques with 3D spatial resolution, has been overlooked despite its sensitivity and temporal resolution. As neurophysiological data collection grows, open toolkits on open platforms as described here will serve the research community for better understanding of spatiotemporal dynamics in the brain.
EEG, while older and more accessible than neuroimaging techniques with 3D spatial resolution, has been overlooked despite its sensitivity and temporal resolution. As neurophysiological data collection grows, open toolkits on open platforms as described here will serve the research community for better understanding of spatiotemporal dynamics in the brain.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Methods Development
Neuroinformatics and Data Sharing:
Workflows
Informatics Other 2
Novel Imaging Acquisition Methods:
EEG
Keywords:
Atlasing
Computational Neuroscience
Electroencephaolography (EEG)
Informatics
Multivariate
Source Localization
Statistical Methods
Systems
Workflows
Other - Open Science
1|2Indicates the priority used for review
My abstract is being submitted as a Software Demonstration.
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
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Provide references using author date format
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3. Das, S., et al, (2015), The MNI data-sharing and processing ecosystem. NeuroImage, 124(Pt B), 1188– 1195. https://doi.org/10.1016/j.neuroimage.2015.08.076
4. Evans, A.C., et al, (1993), 3D statistical neuroanatomical models from 305 MRI volumes. In Proceedings of IEEE- Nuclear Science Symposium and Medical Imaging Conference (pp. 1813–1817). Retrieved from http://www.citeulike.org/user/nguizard/article/7253872
5. Friston, K. J. et al, (2007), Statistical parametric mapping : the analysis of funtional brain images (1st ed.). Amsterdam ;;Boston: Elsevier/Academic Press. Retrieved from https://www.worldcat.org/title/statistical- parametric-mapping-the-analysis-of-functional-brain-images/oclc/104803228
6. John, E., et al, (1977), Neurometrics. Science, 196(4297), 1393–1410. https://doi.org/10.1126/science.867036
7. Hernandez-gonzalez, G., et al, (2011), Clinical Eeg And Neuroscience, 42(3).
8. Pernet, D.C., et al (2018), BIDS- EEG: an extension to the Brain Imaging Data Structure (BIDS) Specification for electroencephalography. PsyArxiv. https://doi.org/10.31234/OSF.IO/63A4Y
9. Sherif, T., et al, (2014), CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research. Frontiers in Neuroinformatics, 8, 54. https://doi.org/10.3389/fninf.2014.00054
10. Valdés-Hernández, P. A., et al, (2009), Approximate average head models for EEG source imaging. Journal of Neuroscience Methods, 185(1), 125–132. https://doi.org/10.1016/j.jneumeth.2009.09.005