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Alzheimer's Imaging Consortium.

Ethan Draper1, Jasmine Cakmak2, Kesavi Kanagasabai3

  • 1Montreal Neurological Institute, Montreal, QC, Canada.

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Summary
This summary is machine-generated.

CONNExIN is a hybrid initiative training African students and clinicians in neuroimaging analysis for dementia research. This program enhances local expertise and capacity in low- and middle-income countries (LMICs).

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Area of Science:

  • Neuroimaging
  • Dementia Research
  • Medical Education

Background:

  • Positron emission tomography (PET) and magnetic resonance imaging (MRI) are crucial for dementia diagnosis and biomarker identification.
  • Access to PET and MRI is increasing in low- and middle-income countries (LMICs).
  • A significant gap exists in trained research personnel in LMICs for neuroimaging data analysis.

Purpose of the Study:

  • To address the shortage of trained personnel for neuroimaging research in LMICs.
  • To build local capacity for analyzing neuroimaging data in dementia research.
  • To implement a hybrid training initiative using the Teach-Try-Use strategy.

Main Methods:

  • CONNExIN (COmprehensive Neuroimaging aNalysis Experience In resource constraiNed settings) is a hybrid initiative.
  • Utilized a 16-week program including virtual self-paced content, weekend tutorials, and a one-week hybrid bootcamp.
  • Trained 34 participants from 6 countries using multi-scanner brain MRI data and open-access datasets.

Main Results:

  • 34 students and clinicians completed the hybrid bootcamp, analyzing MRI and PET brain scans.
  • A total of 120 hours of no-cost neuroimaging analysis and dementia research training was delivered.
  • Participants are currently being guided on science communication, including abstract submission for conferences.

Conclusions:

  • CONNExIN successfully trained a cohort in neuroimaging data analysis.
  • The initiative aims to establish local experts who can further train others in LMICs.
  • This program significantly improves dementia imaging research capacity in resource-constrained settings.