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

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.

scholarly journals Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer’s Disease With High Performance Computing

2021 ◽  
Vol 4 ◽  
Author(s):  
Fan Zhang ◽  
Melissa Petersen ◽  
Leigh Johnson ◽  
James Hall ◽  
Sid E. O’Bryant
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
High Performance ◽  
Support Vector ◽  
Massive Datasets ◽  
Complexity Of Algorithms ◽  
Magnetic Resonance Imaging Mri ◽  
Research Challenge ◽  
Improved Accuracy

Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer’s disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.

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

2020 ◽  
Vol 10 (2) ◽  
pp. 370-379 ◽  
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.

scholarly journals Prediction of Alzheimer’s Disease Using Machine Learning Classifiers

2020 ◽  
Vol 9 (3) ◽  
pp. 116-120
Author(s):  
Mansour Rezaei ◽  
Ehsan Zereshki ◽  
Soodeh Shahsavari ◽  
Mohammad Gharib Salehi ◽  
Hamid Sharini
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Correct Diagnosis ◽  
Medical Science ◽  
Screening Tests ◽  
Support Vector ◽  
Svm Classifier ◽  
Rbf Kernel ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Background: Alzheimer’s disease (AD) is the most common brain failure for which no cure has yet been found. The disease starts with a disturbance in the brain structure and then it manifests itself clinically. Therefore, by timely and correct diagnosis of changes in the structure of the brain, the occurrence of this disease or at least its progression can be prevented. Due to the fact that magnetic resonance imaging (MRI) can be used to obtain very useful information from the brain, and also because it is non-invasive, this method has been considered by researchers. Materials and Methods: The data were obtained from an MRI database (MIRIAD) of 69 subjects including 46 AD patients and 23 healthy controls (HC). Individuals were categorized based on two criteria including NINCDS-ADRAD and MMSE, as the gold standard. In this paper, we used the support vector machine (SVM) and Bayesian SVM classifiers. Results: Using the SVM classifier with Gaussian radial basis function (RBF) kernel, we distinguished AD and HC with an accuracy of 88.34%. The most important regions of interest (ROIs) in this study included right para hippocampal gyrus, left para hippocampal gyrus, right hippocampus, and left hippocampus. Conclusion: This study showed that the SVM model with Gaussian RBF kernel can distinguish AD from HC with high accuracy. These studies are of great importance in medical science. Based on the results of this study, MRI centers and neurologists can perform AD screening tests in people over the age of 50 years.

scholarly journals Biomarkers for Alzheimer’s Disease Early Diagnosis

2020 ◽  
Vol 10 (3) ◽  
pp. 114 ◽  
Author(s):  
Eva Ausó ◽  
Violeta Gómez-Vicente ◽  
Gema Esquiva
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neuronal Loss ◽  
Diagnostic Tools ◽  
Csf Biomarkers ◽  
Diagnosis And Prognosis ◽  
Positron Emission ◽  
The Central Nervous System ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Alzheimer’s disease (AD) is the most common cause of dementia, affecting the central nervous system (CNS) through the accumulation of intraneuronal neurofibrillary tau tangles (NFTs) and β-amyloid plaques. By the time AD is clinically diagnosed, neuronal loss has already occurred in many brain and retinal regions. Therefore, the availability of early and reliable diagnosis markers of the disease would allow its detection and taking preventive measures to avoid neuronal loss. Current diagnostic tools in the brain, such as magnetic resonance imaging (MRI), positron emission tomography (PET) imaging, and cerebrospinal fluid (CSF) biomarkers (Aβ and tau) detection are invasive and expensive. Brain-secreted extracellular vesicles (BEVs) isolated from peripheral blood have emerged as novel strategies in the study of AD, with enormous potential as a diagnostic evaluation of therapeutics and treatment tools. In addition; similar mechanisms of neurodegeneration have been demonstrated in the brain and the eyes of AD patients. Since the eyes are more accessible than the brain, several eye tests that detect cellular and vascular changes in the retina have also been proposed as potential screening biomarkers. The aim of this study is to summarize and discuss several potential markers in the brain, eye, blood, and other accessible biofluids like saliva and urine, and correlate them with earlier diagnosis and prognosis to identify individuals with mild symptoms prior to dementia.

scholarly journals Predictive Models Based on Support Vector Machines: Whole-Brain versus Regional Analysis of Structural MRI in the Alzheimer's Disease

2014 ◽  
Vol 25 (4) ◽  
pp. 552-563 ◽  
Author(s):  
Alessandra Retico ◽  
Paolo Bosco ◽  
Piergiorgio Cerello ◽  
Elisa Fiorina ◽  
Andrea Chincarini ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machines ◽  
Predictive Models ◽  
Regional Analysis ◽  
Structural Mri ◽  
Support Vector ◽  
Whole Brain ◽  
Vector Machines

scholarly journals Machine Learning and DWI Brain Communicability Networks for Alzheimer’s Disease Detection

2020 ◽  
Vol 10 (3) ◽  
pp. 934 ◽  
Author(s):  
Eufemia Lella ◽  
Angela Lombardi ◽  
Nicola Amoroso ◽  
Domenico Diacono ◽  
Tommaso Maggipinto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Imaging Modality ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Whole Brain ◽  
Feature Importance ◽  
Importance Analysis ◽  
The Brain

Signal processing and machine learning techniques are changing the clinical practice based on medical imaging from many perspectives. A major topic is related to (i) the development of computer aided diagnosis systems to provide clinicians with novel, non-invasive and low-cost support-tools, and (ii) to the development of new methodologies for the analysis of biomedical data for finding new disease biomarkers. Advancements have been recently achieved in the context of Alzheimer’s disease (AD) diagnosis through the use of diffusion weighted imaging (DWI) data. When combined with tractography algorithms, this imaging modality enables the reconstruction of the physical connections of the brain that can be subsequently investigated through a complex network-based approach. A graph metric particularly suited to describe the disruption of the brain connectivity due to AD is communicability. In this work, we develop a machine learning framework for the classification and feature importance analysis of AD based on communicability at the whole brain level. We fairly compare the performance of three state-of-the-art classification models, namely support vector machines, random forests and artificial neural networks, on the connectivity networks of a balanced cohort of healthy control subjects and AD patients from the ADNI database. Moreover, we clinically validate the information content of the communicability metric by performing a feature importance analysis. Both performance comparison and feature importance analysis provide evidence of the robustness of the method. The results obtained confirm that the whole brain structural communicability alterations due to AD are a valuable biomarker for the characterization and investigation of pathological conditions.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Qi Zhou ◽  
Mohammed Goryawala ◽  
Mercedes Cabrerizo ◽  
Warren Barker ◽  
Ranjan Duara ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Surface Area ◽  
Cortical Thickness ◽  
Feature Selection Method ◽  
Support Vector ◽  
Intracranial Volume ◽  
Single Measure ◽  
Subcortical Volumes ◽  
Subcortical Volume

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.

scholarly journals Classification of Alzheimer’s Disease Patients Using Texture Analysis and Machine Learning

2021 ◽  
Vol 4 (3) ◽  
pp. 49
Author(s):  
Sumit Salunkhe ◽  
Mrinal Bachute ◽  
Shilpa Gite ◽  
Nishad Vyas ◽  
Saanil Khanna ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complex Disease ◽  
Texture Classification ◽  
Region Of Interest ◽  
Structural Data ◽  
Support Vector ◽  
Magnetic Resonance Imaging Mri ◽  
The Many ◽  
Occurrence Matrix

Alzheimer’s disease (AD) has been studied extensively to understand the nature of this complex disease and address the many research gaps concerning prognosis and diagnosis. Several studies based on structural and textural characteristics have already been conducted to aid in identifying AD patients. In this work, an image processing methodology was used to extract textural information and classify the patients into two groups: AD and Cognitively Normal (CN). The Gray Level Co-occurrence Matrix (GLCM) was employed since it is a strong foundation for texture classification. Various textural parameters derived from the GLCM aided in deciphering the characteristics of a Magnetic Resonance Imaging (MRI) region of interest (ROI). Several commonly used image classification algorithms were employed. MATLAB was used to successfully derive 20 features based on the GLCM of the MRI dataset. Based on the data analysis, 8 of the 20 features were determined as significant elements. Ensemble (90.2%), Decision Trees (88.5%), and Support Vector Machine (SVM) (87.2%) were the best performing classifiers. It was observed in GLCM that as the distance (d) between pixels increased, the classification accuracy decreased. The best result was observed for GLCM with d = 1 and direction (d, d, −d) with age and structural data.

scholarly journals Non-invasive imaging of tau-targeted probe uptake by whole brain multi-spectral optoacoustic tomography

2021 ◽  
Author(s):  
Patrick Vagenknecht ◽  
Artur Luzgin ◽  
Maiko Ono ◽  
BIN JI ◽  
Makoto Higuchi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Spatial Resolution ◽  
Tissue Homogenate ◽  
Fluorescence Signal ◽  
Post Mortem ◽  
Whole Brain ◽  
Optoacoustic Tomography ◽  
Non Invasive ◽  
The Brain

Abstract Background Abnormal tau accumulation within the brain plays an important role in tauopathies such as Alzheimer’s disease and Frontotemporal dementia. High-resolution imaging of tau deposits at the whole-brain scale in animal disease models are highly desired. Herein, we approach this challenge by non-invasively imaging the brain of P301L mice of 4-repeat tau with concurrent volumetric multi-spectral optoacoustic tomography (vMSOT) at ~ 115 µm spatial resolution using tau-targeted pyridinyl-butadienyl-benzothiazole derivative PBB5 (i.v.). Results PBB5 showed specific binding to recombinant K18 tau fibrils by fluorescence assay, to post-mortem Alzheimer’s disease brain tissue homogenate by competitive binding against [11C]PBB3, and to tau deposits (AT-8 positive) in post-mortem corticobasal degeneration and progressive supranuclear palsy brains. Concurrent vMSOT and epi-fluorescence imaging of in vivo PBB5 targeting (i.v.) was performed in P301L and non-transgenic littermate mice. A dose dependent optoacoustic and fluorescence signal intensity was observed in the mouse brains with i.v. administration of different concentrations of PBB5. i.v. administration of PBB5 in P301L mice showed higher retention in tau-laden cortex and hippocampus compared to wild-type, confirmed by ex vivo vMSOT, epi-fluorescence, multiphoton microscopy, immunofluorescence staining using AT-8 antibody for phosphorylated tau. Conclusions We demonstrated non-invasive 3D whole-brain imaging of tau in P301L mice with a vMSOT system using PBB5 at a previously unachieved ~ 115 µm spatial resolution. This platform provides new tool to study tau spreading and clearance in tauopathy mouse model, foreseeable in monitoring of tau targeting putative therapeutics.

Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI

2008 ◽  
Vol 51 (2) ◽  
pp. 73-83 ◽  
Author(s):  
Benoît Magnin ◽  
Lilia Mesrob ◽  
Serge Kinkingnéhun ◽  
Mélanie Pélégrini-Issac ◽  
Olivier Colliot ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Support Vector ◽  
Whole Brain ◽  
Anatomical Mri
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