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Li M, Wang Y, Lopez-Naranjo C, Hu S, Reyes RCG, Paz-Linares D, Areces-Gonzalez A, Hamid AIA, Evans AC, Savostyanov AN, Calzada-Reyes A, Villringer A, Tobon-Quintero CA, Garcia-Agustin D, Yao D, Dong L, Aubert-Vazquez E, Reza F, Razzaq FA, Omar H, Abdullah JM, Galler JR, Ochoa-Gomez JF, Prichep LS, Galan-Garcia L, Morales-Chacon L, Valdes-Sosa MJ, Tröndle M, Zulkifly MFM, Abdul Rahman MRB, Milakhina NS, Langer N, Rudych P, Koenig T, Virues-Alba TA, Lei X, Bringas-Vega ML, Bosch-Bayard JF, Valdes-Sosa PA. Harmonized-Multinational qEEG norms (HarMNqEEG). Neuroimage 2022; 256:119190. [PMID: 35398285 DOI: 10.1016/j.neuroimage.2022.119190] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/23/2022] [Accepted: 04/05/2022] [Indexed: 12/14/2022] Open
Abstract
This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.
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Affiliation(s)
- Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Carlos Lopez-Naranjo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shiang Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei 230601, China
| | | | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba
| | - Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Río, Cuba
| | - Aini Ismafairus Abd Hamid
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Alexander N Savostyanov
- Humanitarian Institute, Novosibirsk State University, Novosibirsk 630090, Russia; Laboratory of Psychophysiology of Individual Differences, Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine, Novosibirsk 630117, Russia; Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia
| | | | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany; Center for Stroke Research, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Carlos A Tobon-Quintero
- Grupo Neuropsicología y Conducta - GRUNECO, Faculty of Medicine, Universidad de Antioquia, Colombia; Research Department, Institución Prestadora de Servicios de Salud IPS Universitaria, Colombia
| | - Daysi Garcia-Agustin
- Cuban Center for Neurocience, La Habana, Cuba; The Cuban center aging longevity and health, Havana Cuba
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, China
| | | | - Faruque Reza
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Fuleah Abdul Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hazim Omar
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Jafri Malin Abdullah
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Universiti Sains Malaysia Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Janina R Galler
- Division of Pediatric Gastroenterology and Nutrition, Massachusetts General Hospital for Children, Boston, MA, United States Massachusetts General Hospital for Children, Boston, MA, United States
| | - John F Ochoa-Gomez
- Grupo Neuropsicología y Conducta - GRUNECO, Faculty of Medicine, Universidad de Antioquia, Colombia; Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Leslie S Prichep
- Research & Development, BrainScope Company, Inc. Bethesda, MD, United States; Department of Psychiatry (Ret.), Brain Research Laboratories, NYU School of Medicine, New York, NY, United States
| | | | - Lilia Morales-Chacon
- Department of Clinical Neurophysiology, International Center for Neurological Restoration, Playa, Havana 11300, Cuba
| | | | - Marius Tröndle
- Department of Methods of Plasticity Research, Institute of Psychology, University of Zurich, Zurich, Switzerland; University Research Priority Program "Dynamic of Healthy Aging", University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - Mohd Faizal Mohd Zulkifly
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Muhammad Riddha Bin Abdul Rahman
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; School of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Kuala Nerus 21300, Malaysia
| | - Natalya S Milakhina
- Laboratory of Psychophysiology of Individual Differences, Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine, Novosibirsk 630117, Russia; Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia
| | - Nicolas Langer
- Department of Methods of Plasticity Research, Institute of Psychology, University of Zurich, Zurich, Switzerland; University Research Priority Program "Dynamic of Healthy Aging", University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - Pavel Rudych
- Laboratory of Psychophysiology of Individual Differences, Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine, Novosibirsk 630117, Russia; Department of Information Technologies Novosibirsk State University, Novosibirsk 630090, Russia; Federal Research Center for Information and Computational Technologies, Biomedical Data Processing Lab, Novosibirsk 630090, Russia
| | - Thomas Koenig
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | | | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba.
| | - Jorge F Bosch-Bayard
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada.
| | - Pedro Antonio Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba.
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Bosch-Bayard J, Biscay RJ, Fernandez T, Otero GA, Ricardo-Garcell J, Aubert-Vazquez E, Evans AC, Harmony T. EEG effective connectivity during the first year of life mirrors brain synaptogenesis, myelination, and early right hemisphere predominance. Neuroimage 2022; 252:119035. [PMID: 35218932 DOI: 10.1016/j.neuroimage.2022.119035] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/25/2021] [Accepted: 02/22/2022] [Indexed: 10/19/2022] Open
Abstract
INTRODUCTION The maturation of electroencephalogram (EEG) effective connectivity in healthy infants during the first year of life is described. METHODS Participants: A cross-sectional sample of 125 healthy at-term infants, from 0 to 12 months of age, underwent EEG in a state of quiet sleep. PROCEDURES The EEG primary currents at the source were described with the sLoreta method. An unmixing algorithm was applied to reduce the leakage, and the isolated effective coherence, a direct and directed measurement of information flow, was calculated. RESULTS AND DISCUSSION Initially, the highest indices of connectivity are at the subcortical nuclei, continuing to the parietal lobe, predominantly the right hemisphere, then expanding to temporal, occipital, and finally the frontal areas, which is consistent with the myelination process. Age-related connectivity changes were mostly long-range and bilateral. Connections increased with age, mainly in the right hemisphere, while they mainly decreased in the left hemisphere. Increased connectivity from 20 to 30 Hz, mostly at the right hemisphere. These findings were consistent with right hemisphere predominance during the first three years of life. Theta and alpha connections showed the greatest changes with age. Strong connectivity was found between the parietal, temporal, and occipital regions to the frontal lobes, responsible for executive functions and consistent with behavioral development during the first year. The thalamus exchanges information bidirectionally with all cortical regions and frequency bands. CONCLUSIONS The maturation of EEG connectivity during the first year in healthy infants is very consistent with synaptogenesis, reductions in synaptogenesis, myelination, and functional and behavioral development.
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Affiliation(s)
- Jorge Bosch-Bayard
- McGill Center for Integrative Neuroscience (MCIN), Ludmer Center for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal H3A2B4, Canada
| | - Rolando J Biscay
- Centro de Investigación en Matemáticas, Guanajuato 36023, Mexico
| | - Thalia Fernandez
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico
| | - Gloria A Otero
- Facultad de Medicina, Universidad Autónoma del Estado de México, Toluca de Lerdo 50180, Mexico
| | - Josefina Ricardo-Garcell
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico
| | | | - Alan C Evans
- McGill Center for Integrative Neuroscience (MCIN), Ludmer Center for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal H3A2B4, Canada
| | - Thalia Harmony
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico.
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Valdes-Sosa PA, Galan-Garcia L, Bosch-Bayard J, Bringas-Vega ML, Aubert-Vazquez E, Rodriguez-Gil I, Das S, Madjar C, Virues-Alba T, Mohades Z, MacIntyre LC, Rogers C, Brown S, Valdes-Urrutia L, Evans AC, Valdes-Sosa MJ. The Cuban Human Brain Mapping Project, a young and middle age population-based EEG, MRI, and cognition dataset. Sci Data 2021; 8:45. [PMID: 33547313 PMCID: PMC7865011 DOI: 10.1038/s41597-021-00829-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 01/12/2021] [Indexed: 11/25/2022] Open
Abstract
The Cuban Human Brain Mapping Project (CHBMP) repository is an open multimodal neuroimaging and cognitive dataset from 282 young and middle age healthy participants (31.9 ± 9.3 years, age range 18–68 years). This dataset was acquired from 2004 to 2008 as a subset of a larger stratified random sample of 2,019 participants from La Lisa municipality in La Habana, Cuba. The exclusion criteria included the presence of disease or brain dysfunctions. Participant data that is being shared comprises i) high-density (64–120 channels) resting-state electroencephalograms (EEG), ii) magnetic resonance images (MRI), iii) psychological tests (MMSE, WAIS-III, computerized go-no go reaction time), as well as iv,) demographic information (age, gender, education, ethnicity, handedness, and weight). The EEG data contains recordings with at least 30 minutes in duration including the following conditions: eyes closed, eyes open, hyperventilation, and subsequent recovery. The MRI consists of anatomical T1 as well as diffusion-weighted (DWI) images acquired on a 1.5 Tesla system. The dataset presented here is hosted by Synapse.org and available at https://chbmp-open.loris.ca. Measurement(s) | functional brain measurement | Technology Type(s) | electroencephalography (EEG) • magnetic resonance imaging (MRI) • neuropsychological testing | Factor Type(s) | age of participants • gender of participants • handedness of participants • educational level of participants | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Location | Cuba |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13277348
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Affiliation(s)
- Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences and Technology of China, Chengdu, China. .,Cuban Neuroscience Center, La Habana, Cuba.
| | | | - Jorge Bosch-Bayard
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences and Technology of China, Chengdu, China.,Cuban Neuroscience Center, La Habana, Cuba.,McGill Centre for Integrative Neurosciences MCIN. Ludmer Centre for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences and Technology of China, Chengdu, China.,Cuban Neuroscience Center, La Habana, Cuba
| | | | | | - Samir Das
- McGill Centre for Integrative Neurosciences MCIN. Ludmer Centre for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Cecile Madjar
- McGill Centre for Integrative Neurosciences MCIN. Ludmer Centre for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | | | - Zia Mohades
- McGill Centre for Integrative Neurosciences MCIN. Ludmer Centre for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Leigh C MacIntyre
- McGill Centre for Integrative Neurosciences MCIN. Ludmer Centre for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Christine Rogers
- McGill Centre for Integrative Neurosciences MCIN. Ludmer Centre for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Shawn Brown
- McGill Centre for Integrative Neurosciences MCIN. Ludmer Centre for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | | | - Alan C Evans
- McGill Centre for Integrative Neurosciences MCIN. Ludmer Centre for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Mitchell J Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences and Technology of China, Chengdu, China.,Cuban Neuroscience Center, La Habana, Cuba
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Bosch-Bayard J, Aubert-Vazquez E, Brown ST, Rogers C, Kiar G, Glatard T, Scaria L, Galan-Garcia L, Bringas-Vega ML, Virues-Alba T, Taheri A, Das S, Madjar C, Mohaddes Z, MacIntyre L, Evans AC, Valdes-Sosa PA. A Quantitative EEG Toolbox for the MNI Neuroinformatics Ecosystem: Normative SPM of EEG Source Spectra. Front Neuroinform 2020; 14:33. [PMID: 32848689 PMCID: PMC7427620 DOI: 10.3389/fninf.2020.00033] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 06/26/2020] [Indexed: 01/20/2023] Open
Abstract
The Tomographic Quantitative Electroencephalography (qEEGt) toolbox is integrated with the Montreal Neurological Institute (MNI) Neuroinformatics Ecosystem as a docker into the Canadian Brain Imaging Research Platform (CBRAIN). qEEGt produces age-corrected normative Statistical Parametric Maps of EEG log source spectra testing compliance to a normative database. This toolbox was developed at the Cuban Neuroscience Center as part of the first wave of the Cuban Human Brain Mapping Project (CHBMP) and has been validated and used in different health systems for several decades. Incorporation into the MNI ecosystem now provides CBRAIN registered users access to its full functionality and is accompanied by a public release of the source code on GitHub and Zenodo repositories. Among other features are the calculation of EEG scalp spectra, and the estimation of their source spectra using the Variable Resolution Electrical Tomography (VARETA) source imaging. Crucially, this is completed by the evaluation of z spectra by means of the built-in age regression equations obtained from the CHBMP database (ages 5-87) to provide normative Statistical Parametric Mapping of EEG log source spectra. Different scalp and source visualization tools are also provided for evaluation of individual subjects prior to further post-processing. Openly releasing this software in the CBRAIN platform will facilitate the use of standardized qEEGt methods in different research and clinical settings. An updated precis of the methods is provided in Appendix I as a reference for the toolbox. qEEGt/CBRAIN is the first installment of instruments developed by the neuroinformatic platform of the Cuba-Canada-China (CCC) project.
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Affiliation(s)
- Jorge Bosch-Bayard
- The Clinical Hospital of Chengdu Brain Sciences Institute, University of Electronic Science and Technology of China UESTC, Chengdu, China
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
- Cuban Neuroscience Centre, Havana, Cuba
| | | | - Shawn T. Brown
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Christine Rogers
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Gregory Kiar
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Tristan Glatard
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Lalet Scaria
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | | | - Maria L. Bringas-Vega
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
- Cuban Neuroscience Centre, Havana, Cuba
| | | | - Armin Taheri
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Samir Das
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Cecile Madjar
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Zia Mohaddes
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Leigh MacIntyre
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - CHBMP
- Cuban Neuroscience Centre, Havana, Cuba
| | - Alan C. Evans
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Pedro A. Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences Institute, University of Electronic Science and Technology of China UESTC, Chengdu, China
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
- Cuban Neuroscience Centre, Havana, Cuba
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