TY - JOUR

T1 - Harmonized-Multinational qEEG norms (HarMNqEEG)

AU - Li, Min

AU - Wang, Ying

AU - Lopez-Naranjo, Carlos

AU - Hu, Shiang

AU - Reyes, Ronaldo César García

AU - Paz-Linares, Deirel

AU - Areces-Gonzalez, Ariosky

AU - Hamid, Aini Ismafairus Abd

AU - Evans, Alan C.

AU - Savostyanov, Alexander N.

AU - Calzada-Reyes, Ana

AU - Villringer, Arno

AU - Tobon-Quintero, Carlos A.

AU - Garcia-Agustin, Daysi

AU - Yao, Dezhong

AU - Dong, Li

AU - Aubert-Vazquez, Eduardo

AU - Reza, Faruque

AU - Razzaq, Fuleah Abdul

AU - Omar, Hazim

AU - Abdullah, Jafri Malin

AU - Galler, Janina R.

AU - Ochoa-Gomez, John F.

AU - Prichep, Leslie S.

AU - Galan-Garcia, Lidice

AU - Morales-Chacon, Lilia

AU - Valdes-Sosa, Mitchell J.

AU - Tröndle, Marius

AU - Zulkifly, Mohd Faizal Mohd

AU - Abdul Rahman, Muhammad Riddha Bin

AU - Milakhina, Natalya S.

AU - Langer, Nicolas

AU - Rudych, Pavel

AU - Koenig, Thomas

AU - Virues-Alba, Trinidad A.

AU - Lei, Xu

AU - Bringas-Vega, Maria L.

AU - Bosch-Bayard, Jorge F.

AU - Valdes-Sosa, Pedro Antonio

N1 - Funding Information: The research was funded by grants (to PAVS) from the National Project for Neurotechnology of the Ministry of Science Technology and Environment of Cuba, the National Nature Science Foundation of China NSFC Grant No. 61871105 , CNS Program of UESTC (No. Y0301902610100201 ) and (to MLBV and PAVS) from the Nestlé Foundation (Validation of a long-life neural fingerprint of early malnutrition, 2017). The National Science Foundation of China (to SH) Grant with No. 62101003 . ACE and JBB were supported by: Brain Canada (243030 and 256327); CANARIE Inc (252749); Ludmer Funding (249926); Canada First Research Excellence Fund (CFREF)/HBHL Intl.Collab.Plat (252427); the (CFREF)/HBHL Big Brain Analytics, and Learning Laboratory (HIBALL), and Helmholtz (252428); and the Fonds de Recherche du Québec FRQ/Canada-Cuba-China Axis (246117). The team of Malaysia was funded from the Translational Research Grant Scheme, Ministry of Higher Education (TRGS/1/2015/USM/01/6/3) and the Research University Grant (RUI), Universiti Sains Malaysia (1001/PPSP/8012307). The dataset from Russia was collected with the support of the Russian Foundation of Basic Research, grant No. 18–29–13027. The Barbados dataset was funded by the Grants R01 HD060986 (JRG). Publisher Copyright: © 2022

PY - 2022/8/1

Y1 - 2022/8/1

N2 - 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.

AB - 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.

KW - Batch effects

KW - Covid induced brain dysfunction

KW - Developmental Brain Chart

KW - EEG cross-spectrum

KW - Harmonization

KW - Malnutrition

KW - Quantitative EEG

KW - Riemannian geometry

KW - Z-score

UR - http://www.scopus.com/inward/record.url?scp=85129522110&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2022.119190

DO - 10.1016/j.neuroimage.2022.119190

M3 - Article

C2 - 35398285

AN - SCOPUS:85129522110

VL - 256

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

M1 - 119190

ER -