Normative modelling using deep autoencoders: a multi-cohort study on mild cognitive impairment and Alzheimer's disease

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 proces...

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Veröffentlicht in:bioRxiv
Hauptverfasser: Pinaya, Walter H L, Scarpazza, Cristina, Garcia-Dias, Rafael, Vieira, Sandra, Baecker, Lea, Da Costa, Pedro F, Redolfi, Alberto, Frisoni, Giovanni B, Pievani, Michela, Calhoun, Vince D, Sato, João R, Mechelli, Andrea, Alzheimer's Disease Neuroimaging Initiative, Australian Imaging Biomarkers And Lifestyle Flagship Study Of Ageing
Format: Paper
Sprache:Englisch
Veröffentlicht: Cold Spring Harbor Cold Spring Harbor Laboratory Press 11.02.2020
Cold Spring Harbor Laboratory
Ausgabe:1.1
Schlagworte:
Alzheimer's disease
Cognitive ability
Learning algorithms
Machine learning
Neural networks
Neurodegenerative diseases
Neuroimaging
Neuroscience
Regression analysis
Ventricle
Normative modelling
autoencoders
deep learning
Alzheimer’s disease
mild cognitive impairment
anomaly detection
neuroimaging
ISSN:2692-8205, 2692-8205
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Zusammenfassung: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. In order to promote open science, we are making all scripts and the trained models available to the wider research community. Footnotes * https://github.com/Warvito/Normative-modelling-using-deep-autoencoders
Bibliographie:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2692-8205
2692-8205
DOI:10.1101/2020.02.10.931824