scholarly journals The Integrated Voxel Analysis Method (IVAM) to Diagnose Onset of Alzheimer’s Disease and Identify Brain Regions through Structural MRI Images

2019 ◽  
Author(s):  
Matthew Hur ◽  
Armen Aghajanyan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Nearest Neighbor ◽  
Feature Vector ◽  
Three Dimensional ◽  
Structural Mri ◽  
Brain Regions ◽  
Brain Atlas ◽  
Analysis Method ◽  
K Nearest Neighbor

AbstractMagnetic Resonance Imaging (MRI) provides three-dimensional anatomical and physiological details of the human brain. We describe the Integrated Voxel Analysis Method (IVAM) which, through machine learning, classifies MRI images of brains afflicted with early Alzheimer’s Disease (AD). This fully automatic method uses an extra trees regressor model in which the feature vector input contains the intensities of voxels, whereby the effect of AD on a single voxel can be predicted. The resulting tree predicts based on the following two steps: a K-nearest neighbor (KNN) algorithm based on Euclidean distance with the feature vector to classify whole images based on their distribution of affected voxels and a voxel-by-voxel classification by the tree of every voxel in the image. An Ising model filter follows voxel-by-voxel tree-classification to remove artifacts and to facilitate clustering of classification results which identify significant voxel clusters affected by AD. We apply this method to T1-weighted MRI images obtained from the Open Access Series of Imaging Studies (OASIS) using images belonging to normal and early AD-afflicted individuals associated with a Client Dementia Rating (CDR) which we use as the target in the supervised learning. Furthermore, statistical analysis using a pre-labeled brain atlas automatically identifies significantly affected brain regions. While achieving 90% AD classification accuracy on 198 images in the OASIS dataset, the method reveals morphological differences caused by the onset of AD.

New Approaches for the Quantification and Targeting of Noradrenergic Dysfunction in Alzheimer’s Disease

2021 ◽  
Author(s):  
Michael David ◽  
Paresh A. Malhotra
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Locus Coeruleus ◽  
Neuropsychiatric Symptoms ◽  
Structural Mri ◽  
Brain Regions ◽  
Small Scale ◽  
Symptomatic Therapy ◽  
Pontine Nucleus ◽  
Individual Level

There is clear and early noradrenergic dysfunction in Alzheimer’s disease. This is likely secondary to pathological tau deposition in the locus coeruleus, the pontine nucleus that produces and releases noradrenaline, which occurs prior to involvement of cortical brain regions. Disruption of noradrenergic pathways affects cognition, especially attention, which impacts on memory and broader functioning in daily life. Additionally, it leads to the autonomic and neuropsychiatric symptoms that are frequently observed with disease progression.Despite the strong evidence of noradrenergic involvement in Alzheimer’s, there are no clear trial data supporting the clinical use of any noradrenergic treatments. Several approaches have been tried, including both proof-of-principle studies and randomised controlled trials, although most of the latter have been relatively small scale. Treatments have included drug therapies as well as stimulation modalities thought to modulate noradrenergic transmission. The lack of clear positive findings is likely secondary to limited capacity for gauging locus coeruleus integrity and noradrenergic dysfunction at an individual level. However, the recent development of several novel biomarkers holds potential and should allow quantification of dysfunction. This in turn may inform inclusion criteria and stratification for future trials. Imaging approaches in particular have improved greatly following the development of neuromelanin-sensitive sequences, enabling the use of structural MRI scans to estimate locus coeruleus integrity. Additionally, functional MRI and PET scanning have the potential to quantify network dysfunction. As well as neuroimaging, EEG and pupillometry techniques may prove useful in assessing noradrenergic tone and dynamic response to stimulation.Here we review the development of these biomarkers and discuss how they might augment clinical studies, particularly randomised trials, through stratification and identification of patients most likely to benefit from treatment. We outline the biomarkers with most potential, and how they hold promise as a means of transforming symptomatic therapy for people living with Alzheimer’s disease.

Three-Dimensional Eigenbrain for the Detection of Subjects and Brain Regions Related with Alzheimer’s Disease

2016 ◽  
Vol 50 (4) ◽  
pp. 1163-1179 ◽  
Author(s):  
Yudong Zhang ◽  
Shuihua Wang ◽  
Preetha Phillips ◽  
Jiquan Yang ◽  
Ti-Fei Yuan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Brain Regions

scholarly journals Early detection algorithm for alzheimer’s disease using autonomous learning

2021 ◽  
Vol 10 (11) ◽  
pp. 608-621
Author(s):  
Jorge Eduardo Aguilar Obregón ◽  
Octavio José Salcedo Parra ◽  
Juan Pablo Rodríguez Miranda
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Nearest Neighbor ◽  
Complete Information ◽  
Research Problem ◽  
Detection Algorithm ◽  
Autonomous Learning ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Information Bank

The current document describes the approach to a research problem that aims to generate an algorithm that allows detecting the probable appearance of Alzheimer’s disease in its first phase, using autonomous learning techniques or Machine Learning, more specifically KNN (K- nearest Neighbor) with which the best result was obtained. This development will be based on a complete information bank taken from ADNI (Alz- heimer’s Disease NeuroImaging Initiative), with all the necessary parameters to direct the inves- tigation to an algorithm that is as efficient as pos- sible, since it has biological, sociodemographic and medical history data, biological specimens, neural images, etc., and in this way the early de- tection of the aforementioned disease was con- figured. A complete guide to the process will be carried out to finally obtain the KNN algorithm whose efficiency is 99%, and then discuss the obtained results. 

scholarly journals Screening for Core Genes Related to Pathogenesis of Alzheimer’s Disease

2021 ◽  
Vol 9 ◽  
Author(s):  
Longxiu Yang ◽  
Yuan Qin ◽  
Chongdong Jian
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Nearest Neighbor ◽  
Core Gene ◽  
Middle Temporal Gyrus ◽  
Support Vector ◽  
Rna Expression ◽  
K Nearest Neighbor ◽  
System Disease ◽  
Core Genes

Alzheimer’s disease (AD), a nervous system disease, lacks effective therapies at present. RNA expression is the basic way to regulate life activities, and identifying related characteristics in AD patients may aid the exploration of AD pathogenesis and treatment. This study developed a classifier that could accurately classify AD patients and healthy people, and then obtained 3 core genes that may be related to the pathogenesis of AD. To this end, RNA expression data of the middle temporal gyrus of AD patients were firstly downloaded from GEO database, and the data were then normalized using limma package following a supplementation of missing data by k-Nearest Neighbor (KNN) algorithm. Afterwards, the top 500 genes of the most feature importance were obtained through Max-Relevance and Min-Redundancy (mRMR) analysis, and based on these genes, a series of AD classifiers were constructed through Support Vector Machine (SVM), Random Forest (RF), and KNN algorithms. Then, the KNN classifier with the highest Matthews correlation coefficient (MCC) value composed of 14 genes in incremental feature selection (IFS) analysis was identified as the best AD classifier. As analyzed, the 14 genes played a pivotal role in determination of AD and may be core genes associated with the pathogenesis of AD. Finally, protein-protein interaction (PPI) network and Random Walk with Restart (RWR) analysis were applied to obtain core gene-associated genes, and key pathways related to AD were further analyzed. Overall, this study contributed to a deeper understanding of AD pathogenesis and provided theoretical guidance for related research and experiments.

scholarly journals The three-dimensional landscape of chromatin accessibility in Alzheimer’s disease

2021 ◽  
Author(s):  
Jaroslav Bendl ◽  
Mads E. Hauberg ◽  
Kiran Girdhar ◽  
Eunju Im ◽  
James M. Vicari ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Regulatory Networks ◽  
3D Structure ◽  
Three Dimensional ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
Chromatin Accessibility ◽  
Regulatory Sequences ◽  
3D Genome

AbstractMuch is still unknown about the neurobiology of Alzheimer’s disease (AD). To better understand AD, we generated 636 ATAC-seq libraries from cases and controls to construct detailed genomewide chromatin accessibility maps of neurons and non-neurons from two AD-affected brain regions, the entorhinal cortex and superior temporal gyrus. By analyzing a total of 19.6 billion read pairs, we expanded the known repertoire of regulatory sequences in the human brain. Multi-omic data integration associated global patterns of chromatin accessibility with gene expression and identified cell-specific enhancer-promoter interactions. Using inter-individual variation in chromatin accessibility, we define cis-regulatory domains capturing the 3D structure of the genome. Multifaceted analyses uncovered disease associated perturbations impacting chromatin accessibility, transcription factor regulatory networks and the 3D genome, and implicated transcriptional dysregulation in AD. Overall, we applied a systematic approach to understand the role of the 3D genome in AD and to illuminate novel disease biology that can advance diagnosis and therapy.

Deep Learning for Alzheimer’s Disease Classification using Texture Features

2019 ◽  
Vol 15 (7) ◽  
pp. 689-698 ◽  
Author(s):  
Jae-Hong So ◽  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Boo-Kyeong Choi ◽  
Hyeon-Gyun Park
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Image Data ◽  
Magnetic Resonance Images ◽  
Disease Classification ◽  
Support Vector ◽  
K Nearest Neighbor

Background: We propose a classification method for Alzheimer’s disease (AD) based on the texture of the hippocampus, which is the organ that is most affected by the onset of AD. Methods: We obtained magnetic resonance images (MRIs) of Alzheimer’s patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This dataset consists of image data for AD, mild cognitive impairment (MCI), and normal controls (NCs), classified according to the cognitive condition. In this study, the research methods included image processing, texture analyses, and deep learning. Firstly, images were acquired for texture analyses, which were then re-spaced, registered, and cropped with Gabor filters applied to the resulting image data. In the texture analyses, we applied the 3-dimensional (3D) gray-level co-occurrence (GLCM) method to evaluate the textural features of the image, and used Fisher’s coefficient to select the appropriate features for classification. In the last stage, we implemented a deep learning multi-layer perceptron (MLP) model, which we divided into three types, namely, AD-MCI, AD-NC, and MCI-NC. Results: We used this model to assess the accuracy of the proposed method. The classification accuracy of the proposed deep learning model was confirmed in the cases of AD-MCI (72.5%), ADNC (85%), and MCI-NC (75%). We also evaluated the results obtained using a confusion matrix, support vector machine (SVM), and K-nearest neighbor (KNN) classifier and analyzed the results to objectively verify our model. We obtained the highest accuracy of 85% in the AD-NC. Conclusion: The proposed model was at least 6–19% more accurate than the SVM and KNN classifiers, respectively. Hence, this study confirms the validity and superiority of the proposed method, which can be used as a diagnostic tool for early Alzheimer’s diagnosis.

Computer-aided methods for 3-D visualization of serial sections and thick biological specimens

1992 ◽  
Vol 50 (2) ◽  
pp. 1060-1061
Author(s):  
Mark Ellisman ◽  
Maryann Martone ◽  
Gabriel Soto ◽  
Eleizer Masliah ◽  
David Hessler ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Neuritic Plaque ◽  
Dimensional Structure ◽  
Data Sets ◽  
Molecular Physiology ◽  
Research Activities ◽  
Computer Aided ◽  
Dimensional Reconstruction

Structurally-oriented biologists examine cells, tissues, organelles and macromolecules in order to gain insight into cellular and molecular physiology by relating structure to function. The understanding of these structures can be greatly enhanced by the use of techniques for the visualization and quantitative analysis of three-dimensional structure. Three projects from current research activities will be presented in order to illustrate both the present capabilities of computer aided techniques as well as their limitations and future possibilities.The first project concerns the three-dimensional reconstruction of the neuritic plaques found in the brains of patients with Alzheimer's disease. We have developed a software package “Synu” for investigation of 3D data sets which has been used in conjunction with laser confocal light microscopy to study the structure of the neuritic plaque. Tissue sections of autopsy samples from patients with Alzheimer's disease were double-labeled for tau, a cytoskeletal marker for abnormal neurites, and synaptophysin, a marker of presynaptic terminals.

Three-Dimensional Chromatin Architecture Landscape of Aging and Alzheimer's Disease

2020 ◽  
Author(s):  
Guofeng Meng ◽  
Jialan Huang ◽  
Dong Lu ◽  
Zhenzhen Zhao ◽  
Feng Yu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Chromatin Architecture

Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

2019 ◽  
Vol 19 (4) ◽  
pp. 216-223 ◽  
Author(s):  
Tianyi Zhao ◽  
Donghua Wang ◽  
Yang Hu ◽  
Ningyi Zhang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Feature Vector ◽  
Mirna Gene ◽  
Interaction Network ◽  
Gene Interaction ◽  
Proteinprotein Interaction ◽  
Synaptic Structures

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

A Biomarker Combining Imaging and Neuropsychological Assessment for Tracking Early Alzheimer's Disease in Clinical Trials

2018 ◽  
Vol 15 (5) ◽  
pp. 429-442 ◽  
Author(s):  
Nishant Verma ◽  
S. Natasha Beretvas ◽  
Belen Pascual ◽  
Joseph C. Masdeu ◽  
Mia K. Markey ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Impairment ◽  
Latent Variable ◽  
Brain Regions ◽  
Disease Assessment ◽  
Latent Variable Analysis ◽  
The Relationship ◽  
Selection Of

Background: Combining optimized cognitive (Alzheimer's Disease Assessment Scale- Cognitive subscale, ADAS-Cog) and atrophy markers of Alzheimer's disease for tracking progression in clinical trials may provide greater sensitivity than currently used methods, which have yielded negative results in multiple recent trials. Furthermore, it is critical to clarify the relationship among the subcomponents yielded by cognitive and imaging testing, to address the symptomatic and anatomical variability of Alzheimer's disease. Method: Using latent variable analysis, we thoroughly investigated the relationship between cognitive impairment, as assessed on the ADAS-Cog, and cerebral atrophy. A biomarker was developed for Alzheimer's clinical trials that combines cognitive and atrophy markers. Results: Atrophy within specific brain regions was found to be closely related with impairment in cognitive domains of memory, language, and praxis. The proposed biomarker showed significantly better sensitivity in tracking progression of cognitive impairment than the ADAS-Cog in simulated trials and a real world problem. The biomarker also improved the selection of MCI patients (78.8±4.9% specificity at 80% sensitivity) that will evolve to Alzheimer's disease for clinical trials. Conclusion: The proposed biomarker provides a boost to the efficacy of clinical trials focused in the mild cognitive impairment (MCI) stage by significantly improving the sensitivity to detect treatment effects and improving the selection of MCI patients that will evolve to Alzheimer’s disease.

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