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Alzheimer's Imaging Consortium.

Philip Nkwam1, Ethan Draper2, Jasmine Cakmak3

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Summary
This summary is machine-generated.

This study introduces a cloud-based resting-state functional MRI (rs-fMRI) pipeline to enable Alzheimer's disease research in low-resource settings. The accessible tool empowers researchers by overcoming computational barriers in neuroimaging analysis.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Alzheimer's Disease Research

Background:

  • Resting-state functional MRI (rs-fMRI) is crucial for studying brain connectivity in Alzheimer's disease (AD).
  • Low- and Middle-Income Countries (LMICs) face significant challenges in fMRI data analysis due to limited computational resources and lack of tailored preprocessing pipelines.
  • This work addresses these barriers by developing a reproducible, resource-efficient cloud-based rs-fMRI analysis pipeline.

Purpose of the Study:

  • To create an open-access, cloud-based rs-fMRI analysis pipeline.
  • To empower researchers in LMICs to conduct comparable neuroimaging research.
  • To overcome computational and standardization limitations in fMRI data analysis for AD research.

Main Methods:

  • Developed an open-access analysis pipeline using open-source tools on Neurodesk via Google Colab.
  • Utilized the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD) dataset (75 subjects, 12-48 months follow-up).
  • Implemented standard fMRI preprocessing steps and estimated functional connectivity metrics (ALFF, fALFF, ReHo) on a subset of data.

Main Results:

  • Preliminary analysis on 17 subjects generated reliable ALFF, fALFF, and ReHo maps in approximately 20 minutes per subject per timepoint.
  • The full analysis pipeline documentation will be available on Protocol.io to ensure transparency and reproducibility.
  • The pipeline is designed for resource-constrained environments, utilizing Google Cloud Engine with standard specifications.

Conclusions:

  • Introduced a cloud-based, low-resource rs-fMRI analysis pipeline to address computational constraints in LMIC research settings.
  • The pipeline will be applied to the PREVENT-AD dataset and an ongoing African dementia study.
  • Future reliability analysis will assess the pipeline's generalizability for resource-efficient rs-fMRI analysis across LMIC groups.