Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study.
Sci Rep
; 11(1): 15746, 2021 08 03.
Article
en En
| MEDLINE
| ID: mdl-34344910
ABSTRACT
Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer's disease (n = 206) and mild cognitive impairment (n = 354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. To promote open science, we are making all scripts and the trained models available to the wider research community.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Encéfalo
/
Modelos Estadísticos
/
Redes Neurales de la Computación
/
Enfermedad de Alzheimer
/
Disfunción Cognitiva
/
Aprendizaje Automático
Tipo de estudio:
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Prevalence_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Sci Rep
Año:
2021
Tipo del documento:
Article
País de afiliación:
Reino Unido