Reproducible Neuroimaging Analysis Pipelines for Dementia Research in Resource‐Limited Settings: Experience with fMRI Processing and Analysis
We introduced a cloud-based low-resource rs-fMRI analysis pipeline to address computational constraints in LMIC research settings. Our approach will be applied to the whole PREVENT-AD dataset and an ongoing African dementia study. A reliability analysis across LMIC groups, running the same pipeline on the same dataset, will assess its generalizability for resource-efficient rs-fMRI analysis.
Authors
- Kesavi Kanagasabai
- Farouk Dako (ORCID: https://orcid.org/0000-0003-4765-9358)
- Abdalla Z. Mohamed (ORCID: https://orcid.org/0000-0003-4499-672X)
- Alfonso Fajardo
- Jasmine D. Cakmak (ORCID: https://orcid.org/0000-0002-5197-234X)
- Confidence Raymond (ORCID: https://orcid.org/0000-0003-3927-9697)
- Olusola Aremu (ORCID: https://orcid.org/0009-0004-9882-379X)
- Channelle Tham
- Ethan C Draper
- Charity Umoren
- Nsiah Donkor Anita
- Philip Nkwam
- Oluwateniola Akinwale
- Udunna Anazodo
Institutions
- New York University Abu Dhabi (AE)
- Western University (CA)
- Lagos State University (NG)
- Montreal Neurological Institute and Hospital (CA)
- Johns Hopkins University (US)
- Radboud University Nijmegen (NL)
- University of Lagos (NG)
- Artificial Intelligence in Medicine (Canada) (CA)
- McGill University (CA)
- University of Pennsylvania (US)
- University for Development Studies (GH)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70856_104116
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00