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

Prado, Pavel and Medel, Vicente and Gonzalez-Gomez, Raul and Sainz-Ballesteros, Agustín and Vidal, Victor and Santamaría-García, Hernando and Moguilner, Sebastian and Mejía, Jhony and Slachevsky, Andrea and Beherens, Maria Isabel and Aguillon, David and Lopera, Francisco and Parra, Mario A. and Matallana, Diana and Maito, Marcelo Adrián and Garcia, Adolfo M and Custodio, Nilton and Funes, Alberto Ávila and Piña-Escudero, Stefanie and Birba, Agustina and Fittipaldi, Sol and Legaz, Agustina and Ibañez, Agustín (2023) The BrainLat project : a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds. Scientific Data, 10 (1). 889. ISSN 2052-4463 (https://doi.org/10.1038/s41597-023-02806-8)

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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.

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