3D-Deep Learning Based Automatic Diagnosis of Alzheimer’s Disease with Joint MMSE Prediction Using Resting-State fMRI
Autor: | Seungjun Ryu, Boreom Lee, Kun Ho Lee, Min Choi, Muhammad Naveed Iqbal Qureshi, Nguyen Thanh Duc |
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Rok vydání: | 2019 |
Předmět: |
Male
Support Vector Machine Computer science Convolutional neural network 050105 experimental psychology 03 medical and health sciences Deep Learning Imaging Three-Dimensional 0302 clinical medicine Lasso (statistics) Alzheimer Disease Feature (machine learning) medicine Humans 0501 psychology and cognitive sciences Default mode network Aged Aged 80 and over Mini–Mental State Examination medicine.diagnostic_test business.industry General Neuroscience Deep learning 05 social sciences Brain Pattern recognition Middle Aged Mental Status and Dementia Tests Magnetic Resonance Imaging Regression Support vector machine Female Neural Networks Computer Artificial intelligence business 030217 neurology & neurosurgery Software Information Systems |
Zdroj: | Neuroinformatics. 18:71-86 |
ISSN: | 1559-0089 1539-2791 |
Popis: | We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer’s disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task. MMSE scores were predicted using: linear least square regression (LLSR), support vector regression, bagging-based ensemble regression, and tree regression with group independent component analysis (gICA) features. To improve MMSE regression performance, we applied feature optimization methods including least absolute shrinkage and selection operator and support vector machine-based recursive feature elimination (SVM-RFE). The mean balanced test accuracy was 85.27% for the classification of AD versus healthy controls. The medial visual, default mode, dorsal attention, executive, and auditory related networks were mainly associated with AD. The maximum clinical MMSE score prediction accuracy with the LLSR method applied on gICA combined with SVM-RFE features had the lowest root mean square error (3.27 ± 0.58) and the highest R2 value (0.63 ± 0.02). Classification of AD and healthy controls can be successfully achieved using only rs-fMRI and MMSE scores can be accurately predicted using functional independent component features. In the absence of trained clinicians, AD disease status and clinical MMSE scores can be jointly predicted using 3-D deep learning and regression learning approaches with rs-fMRI data. |
Databáze: | OpenAIRE |
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