Biomarkers

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Global Health

Background:

• Resting-state functional MRI (rs-fMRI) is crucial for understanding Alzheimer's disease (AD) brain connectivity.
• Low- and Middle-Income Countries (LMICs) face significant hurdles in fMRI data analysis due to limited resources and standardized pipelines.
• A novel, resource-efficient cloud-based rs-fMRI analysis pipeline was developed to address these challenges.

Purpose of the Study:

• To create a reproducible and accessible rs-fMRI analysis pipeline for LMIC researchers.
• To enable comparable neuroimaging research in resource-constrained settings.
• To facilitate the study of Alzheimer's disease (AD) progression in diverse global populations.

Main Methods:

• Developed an open-access, cloud-based rs-fMRI analysis pipeline using Neurodesk on Google Colab.
• Utilized the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD) dataset.
• Implemented standard fMRI preprocessing steps and estimated functional connectivity metrics (ALFF, fALFF, ReHo).

Main Results:

• Preliminary analysis on 17 subjects demonstrated the pipeline's efficiency, generating reliable functional connectivity maps in approximately 20 minutes per subject per timepoint.
• The full analysis pipeline documentation will be publicly available on Protocol.io to ensure transparency and reproducibility.
• The pipeline is designed for resource-efficient rs-fMRI analysis.

Conclusions:

• A cloud-based, low-resource rs-fMRI analysis pipeline has been successfully introduced, addressing computational limitations in LMICs.
• The pipeline will be applied to the PREVENT-AD dataset and an ongoing African dementia study.
• Future reliability analyses will assess the pipeline's generalizability across different LMIC research groups.