<i>Comprehensive Neuroimaging Analysis Experience In Resource Constrained settings (CONNExIN): An Approach to Advance Dementia Neuroimaging Training in LMICs</i>
Bibliographic record
Abstract
BACKGROUND: Positron emission tomography (PET) and magnetic resonance imaging (MRI) are established imaging technologies for dementia management in both clinical (e.g., diagnosis) and research (e.g., identifying biomarkers) settings. Though still far below global rates, access to PET and MRI in low- and middle-income countries (LMICs) is improving. However, there remains a dearth of research personnel in LMICs trained to use neuroimaging data for research. METHOD: CONNExIN (COmprehensive Neuroimaging aNalysis Experience In resource constraiNed settings) is a hybrid initiative led by Montreal Neurological Institute, McGill University, in collaboration with AFRICA-FINGERS [1], and the Consortium for Advancement of MRI Education and Research in Africa (CAMERA) [2]. CONNExIN [3] implements RAD-AID's Teach-Try-Use strategy, previously applied by CAMERA to improve MRI and PET analysis competencies through seminars and hands-on skills development [4] (Figure 1). Multi-scanner (0.55T, 1.5T, and 3T) brain MRI data were acquired on seven healthy volunteers at six sites including at AFRICA-FINGERS sites (Lagos and Nairobi) and used strictly for training (Table 1). RESULT: This 16-week program began on August 26th, 2024 (Figure 1). Following three weeks of self-paced virtual content and weekend tutorials, 34 African students and clinicians from 6 countries completed a hybrid bootcamp. The one-week bootcamp included on-site training hosted at Crestview Radiology (Lagos, Nigeria) and Aga Khan University (Nairobi, Kenya) and virtual participation. Participants analyzed local (Table 1) and open-access brain scans (PREVENT-AD) [5] in groups, self-selecting one of six modalities (i.e., MRI and PET). At the end of the program in December 2024, a total of 120 hours of no-cost neuroimaging analysis and dementia research training was provided including design, implementation, and dissemination. Participants are currently being guided on science communication through drafting and submitting conference abstracts including to AAIC. All training materials will be shared on protocols.io for wider dissemination. CONCLUSION: CONNExIN is training a cohort to analyze neuroimaging data and become local experts who can train others, thereby improving dementia imaging research capacity in LMICs. [1] Udeh-Momoh CT, et al. Alzheimers Dement. 2024 [2] https://www.cameramriafrica.org/ [3] event.fourwaves.com/connexin [4] Mumuni AN, et al. J Am Coll Radiol. 2024 [5] Tremblay-Mercier J, et al. Neuroimage Clin. 2021.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".