Significance of Normalization on Anatomical MRI Measures in Predicting Alzheimer’s Disease

This study establishes a new approach for combining neuroimaging and neuropsychological measures for an optimal decisional space to classify subjects with Alzheimer’s disease (AD). This approach relies on a multivariate feature selection method with different MRI normalization techniques. Subcortical volume, cortical thickness, and surface area measures are obtained using MRIs from 189 participants (129 normal controls and 60 AD patients). Statistically significant variables were selected for each combination model to construct a multidimensional space for classification. Different normalization approaches were explored to gauge the effect on classification performance using a support vector machine classifier. Results indicate that the Mini-mental state examination (MMSE) measure is most discriminative among single-measure models, while subcortical volume combined with MMSE is the most effective multivariate model for AD classification. The study demonstrates that subcortical volumes need not be normalized, whereas cortical thickness should be normalized either by intracranial volume or mean thickness, and surface area is a weak indicator of AD with and without normalization. On the significant brain regions, a nearly perfect symmetry is observed for subcortical volumes and cortical thickness, and a significant reduction in thickness is particularly seen in the temporal lobe, which is associated with brain deficits characterizing AD.

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An Embedded Feature Selection and Multi-Class Classification Method for Detection of the Progression from Mild Cognitive Impairment to Alzheimer's Disease

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.2888 ◽

2020 ◽

Vol 10 (2) ◽

pp. 370-379 ◽

Cited By ~ 1

Author(s):

Jie Cai ◽

Lingjing Hu ◽

Zhou Liu ◽

Ke Zhou ◽

Huailing Zhang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Selection ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Treatment Plan ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Feature Subset

Background: Mild cognitive impairment (MCI) patients are a high-risk group for Alzheimer's disease (AD). Each year, the diagnosed of 10–15% of MCI patients are converted to AD (MCI converters, MCI_C), while some MCI patients remain relatively stable, and unconverted (MCI stable, MCI_S). MCI patients are considered the most suitable population for early intervention treatment for dementia, and magnetic resonance imaging (MRI) is clinically the most recommended means of imaging examination. Therefore, using MRI image features to reliably predict the conversion from MCI to AD can help physicians carry out an effective treatment plan for patients in advance so to prevent or slow down the development of dementia. Methods: We proposed an embedded feature selection method based on the least squares loss function and within-class scatter to select the optimal feature subset. The optimal subsets of features were used for binary classification (AD, MCI_C, MCI_S, normal control (NC) in pairs) based on a support vector machine (SVM), and the optimal 3-class features were used for 3-class classification (AD, MCI_C, MCI_S, NC in triples) based on one-versus-one SVMs (OVOSVMs). To ensure the insensitivity of the results to the random train/test division, a 10-fold cross-validation has been repeated for each classification. Results: Using our method for feature selection, only 7 features were selected from the original 90 features. With using the optimal subset in the SVM, we classified MCI_C from MCI_S with an accuracy, sensitivity, and specificity of 71.17%, 68.33% and 73.97%, respectively. In comparison, in the 3-class classification (AD vs. MCI_C vs. MCI_S) with OVOSVMs, our method selected 24 features, and the classification accuracy was 81.9%. The feature selection results were verified to be identical to the conclusions of the clinical diagnosis. Our feature selection method achieved the best performance, comparing with the existing methods using lasso and fused lasso for feature selection. Conclusion: The results of this study demonstrate the potential of the proposed approach for predicting the conversion from MCI to AD by identifying the affected brain regions undergoing this conversion.

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Identification of Alzheimer’s Disease on the Basis of a Voxel-Wise Approach

Applied Sciences ◽

10.3390/app9153063 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3063

Author(s):

Iman Beheshti ◽

Hadi Mahdipour Hossein-Abad ◽

Hiroshi Matsuda ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Spatial Resolution ◽

Feature Selection Method ◽

Neuronal Loss ◽

Support Vector ◽

Whole Brain ◽

Mri Features ◽

Magnetic Resonance Imaging Mri ◽

The Impact

Robust prediction of Alzheimer’s disease (AD) helps in the early diagnosis of AD and may support the treatment of AD patients. In this study, for early detection of AD and prediction of mild cognitive impairment (MCI) conversion, we develop an automatic computer-aided diagnosis (CAD) framework based on a merit-based feature selection method through a whole-brain voxel-wise analysis using baseline magnetic resonance imaging (MRI) data. We also explore the impact of different MRI spatial resolution on the voxel-wise metric AD classification and MCI conversion prediction. We assessed the proposed CAD framework using the whole-brain voxel-wise MRI features of 507 J-ADNI participants (146 healthy controls [HCs], 102 individuals with stable MCI [sMCI], 112 with progressive MCI [pMCI], and 147 with AD) among four clinically relevant pairs of diagnostic groups at different imaging resolutions (i.e., 2, 4, 8, and 16 mm). Using a support vector machine classifier through a 10-fold cross-validation strategy at a spatial resolution of 2 mm, the proposed CAD framework yielded classification accuracies of 91.13%, 74.77%, 81.12%, and 81.78% in identifying AD/healthy control, sMCI/pMCI, sMCI/AD, and pMCI/HC, respectively. The experimental results show that a lower spatial resolution (i.e., 2 mm) may provide more robust information to trace the neuronal loss-related brain atrophy in AD.

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Classification of Alzheimer’s Disease Based on Cortical Thickness Using AdaBoost and Combination Feature Selection Method

Computing and Intelligent Systems - Communications in Computer and Information Science ◽

10.1007/978-3-642-24091-1_51 ◽

2011 ◽

pp. 392-401 ◽

Cited By ~ 3

Author(s):

Zhiwei Hu ◽

Zhifang Pan ◽

Hongtao Lu ◽

Wenbin Li

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Selection ◽

Cortical Thickness ◽

Feature Selection Method ◽

Selection Method

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Study of brain morphology change in Alzheimer’s disease and amnestic mild cognitive impairment compared with normal controls

General Psychiatry ◽

10.1136/gpsych-2018-100005 ◽

2019 ◽

Vol 32 (2) ◽

pp. e100005 ◽

Cited By ~ 1

Author(s):

Huanqing Yang ◽

Hua Xu ◽

Qingfeng Li ◽

Yan Jin ◽

Weixiong Jiang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Surface Area ◽

Cortical Thickness ◽

Right Hemisphere ◽

Brain Regions ◽

Left And Right ◽

Normal Controls

BackgroundWith an aggravated social ageing level, the number of patients with Alzheimer’s disease (AD) is gradually increasing, and mild cognitive impairment (MCI) is considered to be an early form of Alzheimer’s disease. How to distinguish diseases in the early stage for the purposes of early diagnosis and treatment is an important topic.AimsThe purpose of our study was to investigate the differences in brain cortical thickness and surface area among elderly patients with AD, elderly patients with amnestic MCI (aMCI) and normal controls (NC).Methods20 AD patients, 21 aMCIs and 25 NC were recruited in the study. FreeSurfer software was used to calculate cortical thickness and surface area among groups.ResultsThe patients with AD had less cortical thickness both in the left and right hemisphere in 17 of the 36 brain regions examined than the patients with aMCI or NC. The patients with AD also had smaller cerebral surface area both in the left and right hemisphere in 3 of the 36 brain regions examined than the patients with aMCI or NC. Compared with the NC, the patients with aMCI only had slight atrophy in the inferior parietal lobe of the left hemisphere, and no significant difference was found.ConclusionAD, as well as aMCI (to a lesser extent), is associated with reduced cortical thickness and surface area in a few brain regions associated with cognitive impairment. These results suggest that cortical thickness and surface area could be used for early detection of AD.

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Cortical Thickness and Surface Area Networks in Healthy Aging, Alzheimer’s Disease and Behavioral Variant Fronto-Temporal Dementia

International Journal of Neural Systems ◽

10.1142/s0129065718500557 ◽

2019 ◽

Vol 29 (06) ◽

pp. 1850055 ◽

Cited By ~ 6

Author(s):

Vesna Vuksanović ◽

Roger T Staff ◽

Trevor Ahearn ◽

Alison D Murray ◽

Claude M Wischik

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Surface Area ◽

Cortical Thickness ◽

Healthy Aging ◽

Large Population ◽

Structural Basis ◽

Elderly Subjects ◽

Modular Organization ◽

Area Network

Models of the human brain as a complex network of inter-connected sub-units are important in helping to understand the structural basis of the clinical features of neurodegenerative disorders. The aim of this study was to characterize in a systematic manner the differences in the structural correlation networks in cortical thickness (CT) and surface area (SA) in Alzheimer’s disease (AD) and behavioral variant Fronto-Temporal Dementia (bvFTD). We have used the baseline magnetic resonance imaging (MRI) data available from a large population of patients from three clinical trials in mild to moderate AD and mild bvFTD and compared this to a well-characterized healthy aging cohort. The study population comprised 202 healthy elderly subjects, 213 with bvFTD and 213 with AD. We report that both CT and SA network architecture can be described in terms of highly correlated networks whose positive and inverse links map onto the intrinsic modular organization of the four cortical lobes. The topology of the disturbance in structural network is different in the two disease conditions, and both are different from normal aging. The changes from normal are global in character and are not restricted to fronto-temporal and temporo-parietal lobes, respectively, in bvFTD and AD, and indicate an increase in both global correlational strength and in particular nonhomologous inter-lobar connectivity defined by inverse correlations. These inverse correlations appear to be adaptive in character, reflecting coordinated increases in CT and SA that may compensate for corresponding impairment in functionally linked nodes. The effects were more pronounced in the cortical thickness atrophy network in bvFTD and in the surface area network in AD. Although lobar modularity is preserved in the context of neurodegenerative disease, the hub-like organization of networks differs both from normal and between the two forms of dementia. This implies that hubs may be secondary features of the connectivity adaptation to neurodegeneration and may not be an intrinsic property of the brain. However, analysis of the topological differences in hub-like organization CT and SA networks, and their underlying positive and negative correlations, may provide a basis for assisting in the differential diagnosis of bvFTD and AD.

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Diagnosis of Alzheimer’s Disease Using Brain Network

Frontiers in Neuroscience ◽

10.3389/fnins.2021.605115 ◽

2021 ◽

Vol 15 ◽

Author(s):

Ramesh Kumar Lama ◽

Goo-Rak Kwon

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Connectivity ◽

Feature Selection Method ◽

Brain Network ◽

Classification Performance ◽

Support Vector ◽

Early Event ◽

Learning Machine ◽

The Brain

Recent studies suggest the brain functional connectivity impairment is the early event occurred in case of Alzheimer’s disease (AD) as well as mild cognitive impairment (MCI). We model the brain as a graph based network to study these impairment. In this paper, we present a new diagnosis approach using graph theory based features from functional magnetic resonance (fMR) images to discriminate AD, MCI, and healthy control (HC) subjects using different classification techniques. These techniques include linear support vector machine (LSVM), and regularized extreme learning machine (RELM). We used pairwise Pearson’s correlation-based functional connectivity to construct the brain network. We compare the classification performance of brain network using Alzheimer’s disease neuroimaging initiative (ADNI) datasets. Node2vec graph embedding approach is employed to convert graph features to feature vectors. Experimental results show that the SVM with LASSO feature selection method generates better classification accuracy compared to other classification technique.

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Effects of Moderate Intensity Exercise on the Cortical Thickness and Subcortical Volumes of Preclinical Alzheimer’s Disease Patients: A Pilot Study

Psychiatry Investigation ◽

10.30773/pi.2020.0214 ◽

2020 ◽

Vol 17 (6) ◽

pp. 613-619

Author(s):

Yoo Hyun Um ◽

Sheng-Min Wang ◽

Nak-Young Kim ◽

Dong Woo Kang ◽

Hae-Ran Na ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Magnetic Resonance ◽

Cortical Thickness ◽

Exercise Group ◽

Moderate Intensity ◽

Preclinical Alzheimer’S Disease ◽

Moderate Intensity Exercise ◽

Preclinical Ad ◽

Subcortical Volumes

Objective We aimed to explore the impact of moderate intensity exercise on the cortical thickness and subcortical volumes of preclinical Alzheimer’s disease (AD) patients.Methods Sixty-three preclinical AD patients with magnetic resonance imaging (MRI) and 18-florbetaben positron emission tomography (PET) data were enrolled in the study. Information on demographic characteristics, cognitive battery scores, self-reported exercise habits were attained. Structural magnetic resonance images were analyzed and processed using Freesurfer v6.0.Results Compared to Exercise group, Non-Exercise group demonstrated reduced cortical thickness in left parstriangularis, rostral middle frontal, entorhinal, superior frontal, lingual, superior parietal, lateral occipital, inferior parietal gyrus, temporal pole, precuneus, insula, fusiform gyrus, right precuneus, superiorparietal, lateral orbitofrontal, rostral middle frontal, medial orbitofrontal, superior frontal, lingual, middle temporal gyrus, insula, supramarginal, parahippocampal, paracentral gyrus. Volumes of right thalamus, caudate, putamen, pallidum, hippocampus, amygdala were also reduced in Non-Exercise group.Conclusion Moderate intensity exercise affects cortical and subcortical structures in preclinical AD patients. Thus, physical exercise has a potential to be an effective intervention to prevent future cognitive decline in those at high risk of AD.

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