Neuroanatomical normative modelling in frontotemporal lobar degeneration: higher heterogeneity in the behavioural variant
Bibliographic record
Abstract
INTRODUCTION: Frontotemporal lobar degeneration (FTLD) includes heterogenous diseases: behavioural variant frontotemporal dementia (bvFTD), primary progressive aphasias (PPA), progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). We applied neuroanatomical normative modelling to quantify individual atrophy patterns and heterogeneity within and between FTLD forms. METHODS: We included 160 participants across FTLDNI and 4RTNI studies: controls (n = 15), bvFTD (n = 22), nfvPPA (n = 14), svPPA (n = 21), CBS (n = 43) and PSP (n = 45). Using cortical thickness and subcortical volumes from 3T MRIs, we applied normative modelling with a large healthy reference dataset (n = 58,836), further accounting for age, sex, and scanner. Outlier regions (z < - 1.96) were used to compute total outlier counts (tOC) and Hamming distances, capturing individual atrophy patterns and inter-subject dissimilarity. RESULTS: bvFTD, svPPA, CBS and PSP showed significantly higher cortical tOC than controls, with all groups showing higher subcortical tOC than controls, especially svPPA and PSP. bvFTD, svPPA, CBS and PSP had significantly higher cortical Hamming distance scores than controls, with higher scores in bvFTD and svPPA than nfvPPA and PSP. svPPA and PSP had significantly higher subcortical scores than controls and CBS. Greater disease severity (measured using the Clinical Dementia Rating-CDR for PSP and CBS, and the CDR® plus NACC-FTLD global scores for FTD variants) was associated with increased tOC and dissimilarity, highlighting the link between clinical progression and neuroanatomical heterogeneity. CONCLUSIONS: The pronounced heterogeneity within and between FTLD subtypes (particularly in bvFTD) increases with disease progression and may reflect distinct underlying pathologies. This supports the development of subtype-specific biomarkers and emphasize the need for personalized diagnostic and therapeutic strategies.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".