Multimodal Discrimination of Alzheimer’s Disease Based on Regional Cortical Atrophy and Hypometabolism

Abstract

Structural MR image (MRI) and 18F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) have been widely employed in diagnosis of both Alzheimer’s disease (AD) and mild cognitive impairment (MCI) pathology, which has led to the development of methods to distinguish AD and MCI from normal controls (NC). Synaptic dysfunction leads to a reduction in the rate of metabolism of glucose in the brain and is thought to represent AD progression. FDG-PET has the unique ability to estimate glucose metabolism, providing information on the distribution of hypometabolism. In addition, patients with AD exhibit significant neuronal loss in cerebral regions, and previous AD research has shown that structural MRI can be used to sensitively measure cortical atrophy. In this paper, we introduced a new method to discriminate AD from NC based on complementary information obtained by FDG and MRI. For accurate classification, surface-based features were employed and 12 predefined regions were selected from previous studies based on both MRI and FDG-PET. Partial least square linear discriminant analysis was employed for making diagnoses. We obtained 93.6% classification accuracy, 90.1% sensitivity, and 96.5% specificity in discriminating AD from NC. The classification scheme had an accuracy of 76.5% and sensitivity and specificity of 46.5% and 89.6%, respectively, for discriminating MCI from AD. Our method exhibited a superior classification performance compared with single modal approaches and yielded parallel accuracy to previous multimodal classification studies using MRI and FDG-PET.

Isothiocyanate from Moringa oleifera seeds mitigates hydrogen peroxide-induced cytotoxicity and preserved morphological features of human neuronal cells

“…However, accumulation of ROS begins in the neuros prior to clinical detections of signs and symptoms of NDDs particularly AD and PD [ 10 , 11 ]. When that happened, apoptotic mechanism usually switches on to eliminate neurons deemed unbearable [ 12 , 13 ], resulting to severe morphological and functional deficit, leading to progressive decline in cognitive and memory well-being [ 14 , 15 ].…”

Reproducible evaluation of classification methods in Alzheimer’s disease: framework and application to MRI and PET data

“…A large number of machine learning approaches have been proposed to classify and predict AD stages (see (Falahati et al, 2014;Haller et al, 2011;Rathore et al, 2017) for reviews). Some of them make use of a single imaging modality (usually anatomical MRI) (Cuingnet et al, 2011;Fan et al, 2008;Klöppel et al, 2008;Liu et al, 2012;Tong et al, 2014) and others have proposed to combine multiple modalities (MRI and PET images, fluid biomarkers) (Gray et al, 2013;Jie et al, 2015;Teipel et al, 2015;Young et al, 2013;Yun et al, 2015;Zhang et al, 2011) . Validation and comparison of such approaches require a large number of patients followed over time.…”

Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers

“…Previous studies have shown that neuroimaging biomarkers are potential predictors of cognitive impairment (Shi et al, 2010 ; Cuingnet et al, 2011 ; Davatzikos et al, 2011 ; Falahati et al, 2014 ; Trzepacz et al, 2014 ; Bron et al, 2015 ; Jung et al, 2016 ; Lebedeva et al, 2017 ). Many researchers have developed and implemented machine learning systems which use neuroimaging biomarkers for more accurate identification of individuals with MCI or dementia (Cui et al, 2012a ; Shao et al, 2012 ; Lebedev et al, 2014 ; Min et al, 2014 ; Moradi et al, 2015 ; Yun et al, 2015 ; Cai et al, 2017 ; Guo et al, 2017 ). Early diagnosis is an essential step in the prevention and early treatment of MCI and dementia.…”