Examining real-world Alzheimer’s disease heterogeneity using neuroanatomical normative modelling
Bibliographic record
Abstract
Abstract Alzheimer’s disease (AD) has been traditionally associated with episodic memory impairment and medial temporal lobe atrophy. However, recent literature has highlighted the existence of atypical forms of AD, presenting with different cognitive and radiological profiles. Failure to appreciate the heterogeneity of AD in the past has led to misdiagnoses, diagnostic delays, clinical trial failures and risks limiting our understanding of the disease. AD research requires the incorporation of new analytic methods that are as free as possible from the intragroup homogeneity assumption underlying case-control approaches according to which patients belonging to the same group are comparable to each other. Neuroanatomical normative modelling is a promising technique allowing for modelling the variation in neuroimaging profiles and then assessing individual deviations from the respective distribution. Here, neuroanatomical normative modelling was applied for the first time to a real-world clinical cohort of Alzheimer’s disease patients (n=86) who had a positive amyloid PET scan and a T1-weighted MR performed as part of their diagnostic workup. The model indexed normal cortical thickness distributions using a separate healthy reference dataset (n= 33,072), employing hierarchical Bayesian regression to predict cortical thickness per region using age and sex. Transfer learning was used to recalibrate the normative model on a validation cohort (n=20) of scanner-matched cognitively normal individuals. Brain heterogeneity was quantified as z-scores at each of the 148 ROIs generated within each AD patient. Z-scores < -1.96 defined as outliers. Clinical features including disease severity, presenting phenotypes and comorbidities were collected from health records to explore their association with outlier profiles. Amyloid quantification was performed using an automated PET-only driven method to examine the association between amyloid burden and outliers. The total number of individual outliers ( total outlier count ) in biomarker-confirmed AD clinical patients ranged between 1 and 120 out of 148 (median 21.5). The superior temporal sulcus was the region with the highest count of outliers (60%) in AD patients. The mean proportion of outliers was higher in the temporal (31.5%) than in the extratemporal (19.1%) regions and up to 20% of patients had no temporal outliers. We found higher mean outlier count in patients with non-amnestic phenotypes, at more advanced disease stages and without depressive symptoms. Amyloid burden was negatively associated with outlier count. This study corroborates the heterogeneity of brain atrophy in AD and provides evidence that this approach can be used to explore anatomo-clinical correlations at an individual level.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".