IC‐P‐137: Combining Two Large MRI Data Sets (AddNeuroMed and ADNI) Using Multivariate Data Analysis to Distinguish between Patients with Alzheimer's Disease and Healthy Controls
Bibliographic record
Abstract
The European Union AddNeuroMed project and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-centre initiatives designed to analyse and validate biomarkers for AD. This study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and multivariate data analysis. A total of 664 subjects were included in this study (AddNeuroMed: 126 AD, 115 CTL, ADNI: 194 AD, 229 CTL) Data acquisition for the AddNeuroMed project was set up to be compatible with the ADNI study and the high resolution sagital 3D T1w MP-RAGE datasets used for image analysis. Regional segmentation of the brain was carried out using the multi-scale ANIMAL image analysis technique (Automated Non-linear Image Matching and Anatomical Labeling). Cortical thickness measurements were performed using CLASP. A total of 24 measures were pooled together for multivariate analysis using the OPLS method (orthogonal partial least squares). Models were created for the two cohorts and for the combined cohorts to discriminate between AD patients and controls. Finally the ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort. Using cross-validation, we achieved the following values: AddNeuroMed cohort: sensitivity = 79%, specificity = 86%; ADNI cohort: sensitivity = 79%, specificity = 87%; both cohorts combined: sensitivity = 83%, specificity = 83%. Using the AddNeuroMed cohort as a training set and validating the model with the ADNI cohort resulted in a sensitivity of 78% and specificity of 87%. All three models created showed very similar results. Examples of important variables for discriminating between AD and CTL included temporal lobe grey matter volume, total CSF volume and mean cortical thickness. Multivariate data analysis is a powerful tool for distinguishing between different patient groups. The AddNeuroMed, ADNI and combined cohorts showed similar patterns of atrophy and the predictive power was very similar. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".