[IC‐P‐079]: MULTIPLE DISTINCT ATROPHY PATTERNS FOUND IN GENETIC FRONTOTEMPORAL DEMENTIA USING SUBTYPE AND STAGE INFERENCE (SUSTAIN)
Bibliographic record
Abstract
Genetic frontotemporal dementia (FTD) is heterogeneous in its clinical syndromes and pathology, with substantial variation existing both between and within different genetic groups. Here we use Subtype and Stage Inference (SuStaIn) – a novel data-driven model of heterogeneous disease progression - to identify subtypes of genetic FTD with distinct patterns of regional brain volume loss. This allows us to investigate how phenotypic heterogeneity relates to FTD genotype. SuStaIn evaluates the optimal grouping of individuals into disease subtypes, where each subtype consists of a sequence in which biomarkers transition between different z-scores. We applied SuStaIn to cross-sectional volumetric MRI data from all mutation carriers in the Genetic Frontotemporal dementia Initiative (GENFI) study to find the best stratification of the data into subtypes, and the temporal progression of each subtype. We included 324 GENFI participants: 144 non-carriers, 129 unaffected carriers (64 GRN, 41 C9orf72, 24 MAPT), and 51 affected carriers (14 GRN, 26 C9orf72, 11 MAPT). We performed 10-fold cross-validation to assess the reproducibility of the SuStaIn subtypes and to determine the optimal number of subtypes. We further used SuStaIn to assign affected carriers to the different subtypes, allowing us to associate the different subtypes with the genetic mutations. SuStaIn modelling reveals the three FTD genetic types are best described as four subtypes with distinct atrophy patterns (Figure 1), which we describe as A. asymmetric frontal, B. temporal, C. frontotemporal, D. subcortical. Figure 2 shows the average probability the affected carriers belong to the four subtypes. We found that the GRN and MAPT mutation carriers are relatively homogeneous, with a high probability of belonging to the asymmetric frontal subtype and the temporal subtype respectively. The C9orf72 mutation carriers, however, are highly heterogeneous, being associated with all four subtypes, but predominantly the frontotemporal and subcortical subtypes. Subtype and Stage Inference (SuStaIn) modelling of GENFI dataset. Subfigures (A)-(D) show the progression pattern of each of the four subtypes estimated by SuStaIn. Each progression pattern consists of a sequence in which regional brain volumes in mutation carriers (affected and unaffected) reach different z-scores relative to non-carriers. The cumulative probability each region has reached a particular z-score is shown for different stages along the progression; the cumulative probability of a region going from a z-score of 0-sigma to 1-sigma ranges from 0 in white to 1 in red, the cumulative probability of a region going from a z-score of 1-sigma to 2-sigma ranges from 0 in red to 1 in magenta, and the cumulative probability of a region going from a z-score of 2-sigma to 3-sigma ranges from 0 in magenta to 1 in blue. The circle labelled ‘A’ indicates the asymmetry of the atrophy pattern (absolute value of the difference in volume between the left and right hemispheres divided by the total volume of the left and right hemispheres) at each stage for each subtype. CVS is the model cross- validation similarity: the average similarity of the subtype progression patterns across cross-validation folds, measured using the Bhattacharyya coefficient. The CVS ranges from 0 (no similarity) to 1 (maximum similarity). Probability affected mutation carriers in GENFI belong to each of the four subtypes in Figure1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".