GloCal brain health and open science

Bridging diversity gaps for better scientific models of brain health and dementia
GloCal brain health and open science
Like

The hidden side of diversity: uncovering real world complexities

In brain health research, multilevel disparities (exposome, socioeconomic inequality, lifespan threats) substantially affect multiple levels of biology and disease. However, much of these data are neglected in current models of brain health and dementia, due to the predominance of data sourced from the global north. This geographic bias poses substantial limitations in understanding dementia and neurodegenerative diseases, as it fails to encompass the global diversity of human populations.

The limitations of a universal approach in aging, brain health and dementia research are becoming increasingly evident. Diseases manifest differently across populations due to genetic, environmental, and cultural factors. Therefore, applying generalized models to diverse groups can lead to misdiagnosis and ineffective treatments, highlighting the urgent need for research models considering these diverse factors. 

In brain health research, one part of the solution involves a Glocal approach, which refers to research that combines global methodologies with a focus on local, specific populations. It consists of adapting global research standards and techniques to study and understand neurodegenerative diseases within Latin American populations' unique genetic and environmental contexts. This approach may help create more accurate and culturally sensitive models for understanding brain health, ensuring that findings are globally relevant yet locally applicable.  

Bridging Latin American diversity

 Latin American countries exhibit unique genetic and environmental characteristics that significantly impacting brain health research. The genetic diversity in these regions, resulting from a mix of indigenous, European, and African ancestries, leads to varied disease manifestations and responses to treatments. Additionally, environmental factors, such as socioeconomic status, diet, lifestyle, and exposure to specific pathogens, and caregiver burden, play a crucial role in health outcomes. These disparities highlight the importance of including Latin American populations in brain health research to ensure a more comprehensive understanding of neurodegenerative diseases and effective, tailored treatments.

Creating and analyzing diverse datasets is not without challenges. These include logistical difficulties in data collection, the need for sophisticated analysis techniques, and ensuring cultural sensitivity in research methodologies. Overcoming these barriers to facilitate more inclusive and representative studies remains a critical step.

The significance of the BrainLat Project

Addressing these gaps, the BrainLat project emerges as a novel initiative. Coordinated by the Latin American Brain Health Institute and the ReDLat project, it collects clinical and neuroimaging data from Latin American populations. The BrainLat dataset is a comprehensive collection featuring neuroimaging data (EEG, MRI, fMRI, DTI) from 780 participants, including 530 with neurodegenerative diseases (Alzheimer's disease, behavioral variant frontotemporal dementia, multiple sclerosis, and Parkinson's disease), along with healthy controls (Figure 1). This rich dataset, gathered across various Latin American countries, encompasses clinical and cognitive assessments. It aims to address underrepresentation in dementia research, providing crucial data from diverse backgrounds to enhance the understanding of neurodegenerative diseases.

 

Figure 1. The BrainLat multimodal dataset of neurodegenerative diseases. The figure summarizes the entire protocol, encompassing various centers, participant groups, diagnostic criteria, cognitive assessments, and EEG and MRI recordings. The activities carried out by the participants during their three visits to the clinical center are also depicted. For the EEG session, the figure illustrates the key steps in the processing pipeline. Session three summarizes the different MRI recordings (anatomical, functional, and diffusion MRI). The recruitment sites included the INNN: Instituto Nacional de Neurología y neurocirugía, Ciudad de México, Mexico; INCMN: Geriatrics Department, Instituto Nacional de Ciencias médicas y nutrición Salvador Zubirán, Mexico City, Mexico; AI-PUJB: Aging Institute, Pontificia Universidad Javeriana, Bogotá, Colombia; UCIDP-IPN: Unit Cognitive Impairment and Dementia Prevention, Peruvian Institute of Neurosciences, Lima, Peru; CICA: Centro de Investigación Clínica Avanzada (CICA) Hospital Clínico Universidad de Chile, Chile: GERO: Neurology Department, Geroscience Center for Brain Health and Metabolism, Santiago, Chile; CNC-UdeSA Centro de Neurociencia Cognitiva, Universidad de San Andrés, Argentina. AD: Alzheimer’s disease, bvFTD: behavioral variant frontotemporal dementia, PD: Parkinson’s disease, MS: Multiple sclerosis, HCs: older healthy controls. Source: https://www.nature.com/articles/s41597-023-02806-8

A call for global and coordinated efforts

There is a critical need for inclusive, diverse research in brain health. The BrainLat project is a small step forward, but it also serves as a call to action for more global and coordinated efforts in the open science of brain health. By embracing diversity in our scientific endeavors, we can develop more effective strategies for understanding and combating neurodegenerative diseases worldwide.

For access to the dataset of the BrainLat project, we recommend first reading the article here: https://www.nature.com/articles/s41597-023-02806-8.

Note: The poster image was designed using a generative artificial intelligence program supervised by Pavel Prado, Vicente Medel, and Agustín Ibanez.

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Subscribe to the Topic

Research Data
Research Communities > Community > Research Data
Public Health
Life Sciences > Health Sciences > Public Health
Neuroscience
Life Sciences > Biological Sciences > Neuroscience
Biodiversity
Life Sciences > Biological Sciences > Ecology > Biodiversity

Related Collections

With collections, you can get published faster and increase your visibility.

Remote sensing data for changes in land use

This Collection comprises a series of articles presenting data on changes to land use in urban areas, farmland, forests, and natural environments, as determined using remote sensing techniques.

Publishing Model: Open Access

Deadline: Jan 31, 2024

Ecological data for tracking biological diversity and environmental change

This collection presents data contributions addressing topics in biodiversity and ecology.

Publishing Model: Open Access

Deadline: Jan 31, 2024