The Cuban Human Brain Mapping Project, a Young and Middle Age Population-based EEG, MRI, and Cognition Dataset

Overview

Journal Sci Data

Specialty Science

Date 2021 Feb 6

PMID 33547313

Citations 21

Authors

Pedro A Valdes-Sosa

Lidice Galan-Garcia

Jorge Bosch-Bayard

Maria L Bringas-Vega

Eduardo Aubert-Vazquez

Iris Rodriguez-Gil

Samir Das

Cecile Madjar

Trinidad Virues-Alba

Zia Mohades

Leigh C MacIntyre

Christine Rogers

Shawn Brown

Lourdes Valdes-Urrutia

Alan C Evans

Mitchell J Valdes-Sosa

Affiliations

Soon will be listed here.

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 .

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References

Iturria-Medina Y, Canales-Rodriguez E, Melie-Garcia L, Valdes-Hernandez P, Martinez-Montes E, Aleman-Gomez Y . Characterizing brain anatomical connections using diffusion weighted MRI and graph theory. Neuroimage. 2007; 36(3):645-60. DOI: 10.1016/j.neuroimage.2007.02.012. View

Babayan A, Erbey M, Kumral D, Reinelt J, Reiter A, Robbig J . A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Sci Data. 2019; 6:180308. PMC: 6371893. DOI: 10.1038/sdata.2018.308. View

Yamashita A, Yahata N, Itahashi T, Lisi G, Yamada T, Ichikawa N . Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias. PLoS Biol. 2019; 17(4):e3000042. PMC: 6472734. DOI: 10.1371/journal.pbio.3000042. View

Valdes P, Valdes M, Carballo J, Alvarez A, Diaz G, Biscay R . QEEG in a public health system. Brain Topogr. 1992; 4(4):259-66. DOI: 10.1007/BF01135563. View

Gorgolewski K, Auer T, Calhoun V, Craddock R, Das S, Duff E . The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016; 3:160044. PMC: 4978148. DOI: 10.1038/sdata.2016.44. View

Pomponio R, Erus G, Habes M, Doshi J, Srinivasan D, Mamourian E . Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. Neuroimage. 2019; 208:116450. PMC: 6980790. DOI: 10.1016/j.neuroimage.2019.116450. View

Koenig T, Prichep L, Lehmann D, Sosa P, Braeker E, Kleinlogel H . Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. Neuroimage. 2002; 16(1):41-8. DOI: 10.1006/nimg.2002.1070. View

John E, Karmel B, CORNING W, Easton P, Brown D, Ahn H . Neurometrics. Science. 1977; 196(4297):1393-410. DOI: 10.1126/science.867036. View

Das S, Zijdenbos A, Harlap J, Vins D, Evans A . LORIS: a web-based data management system for multi-center studies. Front Neuroinform. 2012; 5:37. PMC: 3262165. DOI: 10.3389/fninf.2011.00037. View

10.

Bosch-Bayard J, Valdes-Sosa P, Aubert-Vazquez E, John E, Harmony T, Riera-Diaz J . 3D statistical parametric mapping of EEG source spectra by means of variable resolution electromagnetic tomography (VARETA). Clin Electroencephalogr. 2001; 32(2):47-61. DOI: 10.1177/155005940103200203. View

11.

Sanabria-Diaz G, Melie-Garcia L, Iturria-Medina Y, Aleman-Gomez Y, Hernandez-Gonzalez G, Valdes-Urrutia L . Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks. Neuroimage. 2010; 50(4):1497-510. DOI: 10.1016/j.neuroimage.2010.01.028. View

12.

Bosch-Bayard J, Galan L, Aubert Vazquez E, Virues Alba T, Valdes-Sosa P . Resting State Healthy EEG: The First Wave of the Cuban Normative Database. Front Neurosci. 2020; 14:555119. PMC: 7736237. DOI: 10.3389/fnins.2020.555119. View

13.

. World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. Bull World Health Organ. 2001; 79(4):373-4. PMC: 2566407. View

14.

Folstein M, ROBINS L, Helzer J . The Mini-Mental State Examination. Arch Gen Psychiatry. 1983; 40(7):812. DOI: 10.1001/archpsyc.1983.01790060110016. View

15.

Park H, Kwon J, Youn T, Pae J, Kim J, Kim M . Statistical parametric mapping of LORETA using high density EEG and individual MRI: application to mismatch negativities in schizophrenia. Hum Brain Mapp. 2002; 17(3):168-78. PMC: 6872044. DOI: 10.1002/hbm.10059. View

16.

Valdes P, Bosch J, Grave R, Hernandez J, Riera J, Pascual R . Frequency domain models of the EEG. Brain Topogr. 1992; 4(4):309-19. DOI: 10.1007/BF01135568. View

17.

Alexander L, Escalera J, Ai L, Andreotti C, Febre K, Mangone A . An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data. 2017; 4:170181. PMC: 5735921. DOI: 10.1038/sdata.2017.181. View

18.

Harmony T, Alvarez A, Pascual R, Ramos A, Marosi E, Diaz de Leon A . EEG maturation on children with different economic and psychosocial characteristics. Int J Neurosci. 1988; 41(1-2):103-13. DOI: 10.3109/00207458808985747. View

19.

Iturria-Medina Y, Sotero R, Canales-Rodriguez E, Aleman-Gomez Y, Melie-Garcia L . Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory. Neuroimage. 2008; 40(3):1064-76. DOI: 10.1016/j.neuroimage.2007.10.060. View

20.

Hernandez-Gonzalez G, Bringas-Vega M, Galan-Garcia L, Bosch-Bayard J, Lorenzo-Ceballos Y, Melie-Garcia L . Multimodal quantitative neuroimaging databases and methods: the Cuban Human Brain Mapping Project. Clin EEG Neurosci. 2011; 42(3):149-59. DOI: 10.1177/155005941104200303. View