[IC‐P‐079]: MULTIPLE DISTINCT ATROPHY PATTERNS FOUND IN GENETIC FRONTOTEMPORAL DEMENTIA USING SUBTYPE AND STAGE INFERENCE (SUSTAIN)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».