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The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds

Authors :
Pavel Prado
Vicente Medel
Raul Gonzalez-Gomez
Agustín Sainz-Ballesteros
Victor Vidal
Hernando Santamaría-García
Sebastian Moguilner
Jhony Mejia
Andrea Slachevsky
Maria Isabel Behrens
David Aguillon
Francisco Lopera
Mario A. Parra
Diana Matallana
Marcelo Adrián Maito
Adolfo M. Garcia
Nilton Custodio
Alberto Ávila Funes
Stefanie Piña-Escudero
Agustina Birba
Sol Fittipaldi
Agustina Legaz
Agustín Ibañez
Source :
Scientific Data, Vol 10, Iss 1, Pp 1-13 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

Abstract The Latin American Brain Health Institute (BrainLat) has released a unique multimodal neuroimaging dataset of 780 participants from Latin American. The dataset includes 530 patients with neurodegenerative diseases such as Alzheimer’s disease (AD), behavioral variant frontotemporal dementia (bvFTD), multiple sclerosis (MS), Parkinson’s disease (PD), and 250 healthy controls (HCs). This dataset (62.7 ± 9.5 years, age range 21–89 years) was collected through a multicentric effort across five Latin American countries to address the need for affordable, scalable, and available biomarkers in regions with larger inequities. The BrainLat is the first regional collection of clinical and cognitive assessments, anatomical magnetic resonance imaging (MRI), resting-state functional MRI (fMRI), diffusion-weighted MRI (DWI), and high density resting-state electroencephalography (EEG) in dementia patients. In addition, it includes demographic information about harmonized recruitment and assessment protocols. The dataset is publicly available to encourage further research and development of tools and health applications for neurodegeneration based on multimodal neuroimaging, promoting the assessment of regional variability and inclusion of underrepresented participants in research.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20524463
Volume :
10
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
edsdoj.7faa9efaed4ab48f3b0387d0e2997c
Document Type :
article
Full Text :
https://doi.org/10.1038/s41597-023-02806-8