Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer's dementia diagnosis using multi-measure rs-fMRI spatial patterns

PloS One
Duc Thanh NguyenBoreom Lee

Abstract

Early diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) is essential for timely treatment. Machine learning and multivariate pattern analysis (MVPA) for the diagnosis of brain disorders are explicitly attracting attention in the neuroimaging community. In this paper, we propose a voxel-wise discriminative framework applied to multi-measure resting-state fMRI (rs-fMRI) that integrates hybrid MVPA and extreme learning machine (ELM) for the automated discrimination of AD and MCI from the cognitive normal (CN) state. We used two rs-fMRI cohorts: the public Alzheimer's disease Neuroimaging Initiative database (ADNI2) and an in-house Alzheimer's disease cohort from South Korea, both including individuals with AD, MCI, and normal controls. After extracting three-dimensional (3-D) patterns measuring regional coherence and functional connectivity during the resting state, we performed univariate statistical t-tests to generate a 3-D mask that retained only voxels showing significant changes. Given the initial univariate features, to enhance discriminative patterns, we implemented MVPA feature reduction using support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection...Continue Reading

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Citations

Dec 18, 2020·Frontiers in Neuroinformatics·Ali Noroozi, Mansoor Rezghi
Oct 24, 2020·APL Bioengineering·Alice SegatoElena De Momi
May 15, 2021·PloS One·Kyungwon KimBoreom Lee

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Methods Mentioned

BETA
feature measure
PCA

Software Mentioned

Resting
MVPA
State fMRI Data Analysis Toolkit ( REST )
SVM
fMRI
AlphaSim
REST
Statistical Parametric Mapping SPM8
LASSO
LOO

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