Reproducible Neuroimaging Analysis Pipelines for Dementia Research in Resource‐Limited Settings: Experience with fMRI Processing and Analysis
Bibliographic record
Abstract
BACKGROUND: Resting-state functional MRI (rs-fMRI) allows us to investigate disruptions in brain connectivity associated with Alzheimer's disease (AD) progression. However, researchers in Low- and Middle-Income Countries (LMICs) face significant barriers in analyzing fMRI data due to limited computational resources, and lack of standardized preprocessing pipelines tailored towards limited resource environments. We created a reproducible, resource-efficient cloud-based rs-fMRI analysis pipeline specifically designed from open-source preprocessing tools to address these challenges and empower LMIC researchers to conduct comparable neuroimaging research. METHOD: This work was performed as part of the CONNExIN (COmprehensive Neuroimaging aNalysis Experience In resource-constraiNed Settings) Program, a neuroimage analysis training program for African neuroscience researchers. A team of CONNExIN students utilized the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD) dataset to develop an open-access analysis pipeline. The dataset included 75 subjects, each with high-resolution anatomical T1-weighted images and rsfMRI datasets acquired at baseline and four annual follow-ups (12- 48 months). Our approach used Neurodesk (1) running on Google Colab to process and analyze the data using Google Cloud Engine instance (Intel(R) Xeon(R) CPU 3.10 GHz and 32GB RAM) running on a standard internet connection. Standard fMRI preprocessing steps (slice timing correction, motion correction, skull stripping and spatial normalization) were implemented to process the data. Functional connectivity metrics (Amplitude of Low-Frequency Fluctuations (ALFF), fractional ALFF (fALFF), and Regional Homogeneity (ReHo)) were estimated on a subset of the data to evaluate the functionality of our approach. RESULT: Preliminary analysis on data from 17 subjects taking around 20 minutes/timepoint/subject to generate reliable ALFF, fALFF, and ReHo maps (Figure 1), across all timepoints). Documentation of the full analysis pipeline will be made available on Protocol.io, ensuring transparency, version control, and facilitating reproducibility for LMIC researchers. CONCLUSION: 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.
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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 | 0.036 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".