Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns.
Cell Rep Med
; 2(12): 100467, 2021 12 21.
Article
en En
| MEDLINE
| ID: mdl-35028609
ABSTRACT
Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date, subtyping of MCI has been based primarily on cognitive measures, often resulting in indistinct boundaries between subgroups and limited validity. Here, we introduce a subtyping method for MCI based solely upon brain atrophy. We train a deep learning model to differentiate between Alzheimer's disease (AD) and cognitively normal (CN) subjects based on whole-brain MRI features. We then deploy the trained model to classify MCI subjects based on whole-brain gray matter resemblance to AD-like or CN-like patterns. We subsequently validate the subtyping approach using cognitive, clinical, fluid biomarker, and molecular imaging data. Overall, the results suggest that atrophy patterns in MCI are sufficiently heterogeneous and can thus be used to subtype individuals into biologically and clinically meaningful subgroups.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Encéfalo
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Disfunción Cognitiva
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Aprendizaje Profundo
Tipo de estudio:
Etiology_studies
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Aged
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Female
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Humans
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Male
Idioma:
En
Revista:
Cell Rep Med
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos