AI to automatically analyze MRI brain images and diagnose early dementia
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. It 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.
(Extreme learning machine (ELM) is a network that drastically improves the slow learning speed which is a disadvantage of general neural network.)
Alzheimer’s disease (AD) is the most common neurodegenerative disease and is the main cause of 60% to 70% of dementia cases in aging societies. It is characterized by cognitive decline and short-term memory loss. Mild cognitive impairment (MCI) is referred to as the prodromal stage of AD, and subjects with MCI are at high risk of developing AD. Because AD/MCI are neurodegenerative diseases and progressively attack memory cells, the development of early diagnostic tools is undoubtedly important.
In recent years a, machine learning (ML) technique known as multivariate pattern analysis (MVPA) has been promisingly applied to classify individual subjects using neuroimaging scans. Multivariate methods such as support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) investigate the mutual relationships between multiple voxels and spatial patterns. Thus, the combination of univariate t-test and multivariate MVPA approaches is expected to enhance the prediction performance as compared to each individual approach used alone.
It 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 operator (LASSO), in combination with the univariate t-test. Classifications were performed by an ELM, and its efficiency was compared to linear and nonlinear (radial basis function) SVMs.
Photo shows a comparison of Intra-cerebral network activity between dementia and mild cognitive impairments. Among the networks used in the analysis, the activity of a specific network was diagnosed and compared, and the relatively high activity was indicated in red yellow, and the low activity in cyan.(Left) Activity of dementia patient group: It showed lower activity in all cerebral areas compared to normal people.(Right) Activity of patients with mild cognitive impairment: The activity of the frontal lobe was higher than that of normal patients.
In conclusion, It proved the possibility of using rs-fMRI scans for AD/MCI prediction in individual subjects. Using a standard Alzheimer’s disease Neuroimaging Initiative cohort and an in-house AD cohort from South Korea, the proposed framework extracts the maximum amount of information changes due to AD/MCI from concatenations of multiple rs-fMRI biomarkers which lead to maximal classification accuracies as compared to all other recent researches. The combination of t-test-based univariate, and RFE-based multivariate feature selection techniques performed on the concatenated measure extracted from rs-fMRI data provided the best discriminative performance when the features thus selected were used by the ELM classifier, superior to that of linear and non-linear SVM classifiers. These results may direct future studies using rs-fMRI scans for the classification of patients with preclinical AD or MCI.
