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Related Concept Videos

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Philip Nkwam1, Ethan Draper2, Jasmine Cakmak3

  • 1University of Lagos, Lagos, Nigeria.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025
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Summary
This summary is machine-generated.

This study introduces a cloud-based resting-state functional MRI (rs-fMRI) analysis pipeline to overcome computational barriers for researchers in low- and middle-income countries (LMICs). The accessible pipeline enables reproducible neuroimaging research for Alzheimer's disease (AD) studies.

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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.