Data-driven neuroanatomical subtypes of primary progressive aphasia
Notice bibliographique
Résumé
The primary progressive aphasias are rare, language-led dementias, with three main variants: semantic, non-fluent/agrammatic and logopenic. Although the semantic variant has a clear neuroanatomical profile, the non-fluent/agrammatic and logopenic variants are difficult to discriminate from neuroimaging. Previous phenotype-driven studies have characterized neuroanatomical profiles of each variant on MRI. In this work, we used a machine learning algorithm known as SuStaIn to discover data-driven neuroanatomical 'subtype' progression profiles and performed an in-depth subtype-phenotype analysis to characterize the heterogeneity of primary progressive aphasia. Our study included 270 participants with primary progressive aphasia seen for research in the UCL Queen Square Institute of Neurology Dementia Research Centre, with follow-up scans available for 137 participants. This dataset included individuals diagnosed with all three main variants (semantic, n = 94; non-fluent/agrammatic, n = 109; logopenic, n = 51) and individuals with unspecified primary progressive aphasia (n = 16). A dataset of 66 patients (semantic, n = 37; non-fluent/agrammatic, n = 29) from the ARTFL LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Research Study was used to validate our results. MRI scans were segmented, and SuStaIn was used on 19 regions of interest to identify neuroanatomical profiles independent of the diagnosis. We assessed the assignment of subtypes and stages, in addition to their longitudinal consistency. We discovered four neuroanatomical subtypes of primary progressive aphasia, labelled S1 (left temporal), S2 (insula), S3 (temporoparietal) and S4 (frontoparietal), exhibiting robustness to statistical scrutiny. S1 was correlated strongly with the semantic variant, whereas S2, S3 and S4 showed mixed associations with the logopenic and non-fluent/agrammatic variants. Notably, S3 displayed a neuroanatomical signature akin to a logopenic-only signature, yet a significant proportion of logopenic cases were allocated to S2. The non-fluent/agrammatic variant demonstrated diverse associations with S2, S3 and S4. No clear relationship emerged between any of the neuroanatomical subtypes and the unspecified cases. At first follow-up, subtype assignment was stable for 84% of patients, and stage assignment was stable for 91.9% of patients. We partially validated our findings in the ALLFTD dataset, finding comparable qualitative patterns. Our study, leveraging machine learning on a large primary progressive aphasia dataset, delineated four distinct neuroanatomical patterns. Our findings suggest that separable spatiotemporal neuroanatomical phenotypes do exist within the primary progressive aphasia spectrum, but that these are noisy, particularly for the non-fluent/agrammatic non-fluent/agrammatic and logopenic variants. Furthermore, these phenotypes do not always conform to standard formulations of clinico-anatomical correlation. Understanding the multifaceted profiles of the disease, encompassing neuroanatomical, molecular, clinical and cognitive dimensions, has potential implications for clinical decision support.
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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 | 0,001 |
| 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 | 0,001 | 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 ».