scholarly journals Early Recognition of Alzheimer’s Disease using Brain MRI

2017 ◽  
Vol 2 (1) ◽  
pp. 6
Author(s):  
Rania Ahmed Kadry Abdel Gawad Birry
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Euclidean Distance ◽  
Early Recognition ◽  
Brain Mri ◽  
Extraction Techniques ◽  
City Block ◽  
Cosine Distance ◽  
City Block Distance ◽  
The Brain

Abstract—Alzheimer’s disease (AD) is a brain disease that causes a slow decline in memory, thinking and reasoning skills. It represents a major public health problem.  Magnetic Resonance Imaging (MRI) have shown that the brains of people with (AD) shrink significantly as the disease progresses. This shrinkage appears in specific brain regions such as the hippocampus which is a small, curved formation in the brain that plays an important role in the limbic system also involved in the formation of new memories and is also associated with learning and emotions.  Medical information on brain MRI is used in detecting the abnormalities in physiological structures. Structural MRI measurements can detect and follow the evolution of brain atrophy which is a marker of the disease progression; therefore, it allows diagnosis and prediction of AD.  The research’s main target is the early recognition of Alzheimer’s disease automatically, which will thereby avoid deterioration of the case resulting in complete brain damage stage.  Alzheimer’s disease yields visible changes in the brain structures. The aim is to recognize if the patient belongs to Alzheimer’s disease category or a normal healthy person at an early stage. Initially, image pre-processing and features extraction techniques are applied including data reduction using Discrete Cosine Transform (DCT) and Cropping, then traditional classification techniques like Euclidean Distance, Chebyshev Distance, Cosine Distance, City Block Distance, and Black pixel counter, were applied on the resulting vectors for classification. Image pre-processing includes noise reduction, Gray-scale conversion and binary scale conversion were applied for the MRI images. Feature extraction techniques follow including cropping and low spatial frequency components (DCT). This paper aims to automatically recognize and detect Alzheimer’s infected brain using MRI, without the need of clinical expert. This early recognition would be helpful to postpone the disease progression and maintain it at an almost steady stage. It was concluded after collecting a dataset of 50 MRI , 25 for normal MRI and  25 for AD MRI that Chebyshev Distance classifier yielded the highest success rate in the recognition of Alzheimer’s disease with accuracy 94% compared to other classification techniques used where, Euclidean Distance is 91.6%,  Cosine Distance is 86.8%, City block Distance is 89.6%, Correlation Distance is 86.4% and Black pixels counter is 90%.

scholarly journals Understanding and Using Common Similarity Measures for Text Analysis

2020 ◽  
Author(s):  
John R. Ladd
Keyword(s):  
Text Analysis ◽  
Euclidean Distance ◽  
Similarity Measures ◽  
City Block ◽  
Cosine Distance ◽  
City Block Distance

This lesson introduces three common measures for determining how similar texts are to one another: city block distance, Euclidean distance, and cosine distance. You will learn the general principles behind similarity, the different advantages of these measures, and how to calculate each of them using the SciPy Python library.

scholarly journals Alzheimer's Disease: A Survey

2021 ◽  
Vol 8 (1) ◽  
pp. 33-39
Author(s):  
Harshitha ◽  
Gowthami Chamarajan ◽  
Charishma Y
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Neural Networks ◽  
Brain Area ◽  
Brain Mri ◽  
Three Dimensional ◽  
The Other ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Brain

Alzheimer's Diseases (AD) is one of the type of dementia. This is one of the harmful disease which can lead to death and yet there is no treatment. There is no current technique which is 100% accurate for the treatment of this disease. In recent years, Neuroimaging combined with machine learning techniques have been used for detection of Alzheimer's disease. Based on our survey we came across many methods like Convolution Neural Network (CNN) where in each brain area is been split into small three dimensional patches which acts as input samples for CNN. The other method used was Deep Neural Networks (DNN) where the brain MRI images are segmented to extract the brain chambers and then features are extracted from the segmented area. There are many such methods which can be used for detection of Alzheimer’s Disease.

scholarly journals Alzheimer’s Disease: Classification and Detection using MRI Dataset

2021 ◽  
Vol 10 (5) ◽  
pp. 70-72
Author(s):  
V P Suhaira ◽  
Sita S ◽  
Joby George
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Heart Function ◽  
Brain Mri ◽  
Memory Loss ◽  
Disease Classification ◽  
Data Set ◽  
Mri Scans ◽  
Disease Experience ◽  
The Brain

Alzheimer's disease (AD) is a hereditary brain condition that is incurable and progresses over time. Patients with Alzheimer's disease experience memory loss, uncertainty, and difficulty speaking, reading, and writing as a result of this condition. Alzheimer's disease eventually affects the portion of the brain that controls breathing and heart function, leading to death. This framework proposes the OASIS (Open Access Series of Imaging Studies) dataset, which contains the existing MRI data set, which is comprised of a longitudinal sample of 150 subjects aged 60 to 96 who were all acquired on the same scanner using similar sequences. This paper uses a combination of brain MRI scans and psychological parameters to predict disease with high accuracy using various classifier algorithms, and the results can be compared to improve performance.

scholarly journals MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3243 ◽  
Author(s):  
Nagaraj Yamanakkanavar ◽  
Jae Young Choi ◽  
Bumshik Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Brain Structure ◽  
State Of The Art ◽  
Brain Mri ◽  
Mri Segmentation ◽  
Learning Methods ◽  
The Brain

Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer’s disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.

Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques

2019 ◽  
Vol 43 (9) ◽  
Author(s):  
U. Rajendra Acharya ◽  
Steven Lawrence Fernandes ◽  
Joel En WeiKoh ◽  
Edward J. Ciaccio ◽  
Mohd Kamil Mohd Fabell ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Brain Mri ◽  
Automated Detection ◽  
Extraction Techniques

scholarly journals Compensatory reconfiguration of functional networks between white matter and grey matter in Alzheimer’s disease

2021 ◽  
Author(s):  
Xingxing Zhang ◽  
Qing Guan ◽  
Debo Dong ◽  
Fuyong Chen ◽  
Jing Yi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Euclidean Distance ◽  
Grey Matter ◽  
Temporal Cortex ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Compensatory Mechanisms ◽  
The Brain

AbstractThe temporal synchronization of BOLD signals within white matter (WM) and between WM and grey matter (GM) exhibited intrinsic architecture and cognitive relevance. However, few studies examined the network property within- and between-tissue in Alzheimer’s disease (AD). The hub regions with high weighted degree (WD) were prone to the neuropathological damage of AD. To systematically investigate the changes of hubs within- and between-tissue functional networks in AD patients, we used the resting-state fMRI data of 30 AD patients and 37 normal older adults (NC) from the ADNI open database, and obtained four types of voxel-based WD metrics and four types of distant-dependent WD metrics (ddWD) based on a series of Euclidean distance ranges with a 20mm increment. We found that AD patients showed decreased within-tissue ddWD in the thalamic nucleus and increased between-tissue ddWD in the occipito-temporal cortex, posterior thalamic radiation, and sagittal stratum, compared to NC. We also found that AD patients showed the increased between-tissue FCs between the posterior thalamic radiation and occipito-temporal cortex, and between the sagittal stratum and the salience and executive networks. The dichotomy of decreased and increased ddWD metrics and their locations were consistent with previous studies on the neurodegnerative and compensatory mechanisms of AD, indicating that despite the disruptions, the brain still strived to compensate for the neural inefficiency by reorganizing functional circuits. Our findings also suggested the short-to-medium ranged ddWD metrics between WM and GM as useful biomarker to detect the compensatory changes of functional networks in AD.

Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

GeroPsych ◽  
2012 ◽  
Vol 25 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Katja Franke ◽  
Christian Gaser
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthy Aging ◽  
Brain Aging ◽  
Elderly Subjects ◽  
Additional Increase ◽  
Longitudinal Changes ◽  
Novel Method ◽  
The Brain

We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longitudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer’s disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive functioning in the future.

The Weak Combined Magnetic Fields Reduce the Brain β-Amyloid in an Animal Model of Sporadic Alzheimer's Disease

PIERS Online ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 311-315 ◽  
Author(s):  
Natalia V. Bobkova ◽  
Vadim V. Novikov ◽  
Natalia I. Medvinskaya ◽  
Irina Yu. Aleksandrova ◽  
Eugenii E. Fesenko
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Animal Model ◽  
Magnetic Fields ◽  
Sporadic Alzheimer’S Disease ◽  
Combined Magnetic Fields ◽  
The Brain

SPECIFIC PROPERTIES OF TUBULIN IN THE PREFRONTAL CORTEX IN CONTROL, SCHIZOPHRENIA AND VASCULAR DEMENTIA

2020 ◽  
pp. 65-71
Author(s):  
Burbaeva G.Sh. ◽  
Androsova L.V. ◽  
Vorobyeva E.A. ◽  
Savushkina O.K.
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prefrontal Cortex ◽  
Vascular Dementia ◽  
Binding Activity ◽  
Nephelometric Method ◽  
Rate Of Polymerization ◽  
Mental Diseases ◽  
And Control ◽  
The Brain

The aim of the study was to evaluate the rate of polymerization of tubulin into microtubules and determine the level of colchicine binding (colchicine-binding activity of tubulin) in the prefrontal cortex in schizophrenia, vascular dementia (VD) and control. Colchicine-binding activity of tubulin was determined by Sherlinе in tubulin-enriched extracts of proteins from the samples. Measurement of light scattering during the polymerization of the tubulin was carried out using the nephelometric method at a wavelength of 450-550 nm. There was a significant decrease in colchicine-binding activity and the rate of tubulin polymerization in the prefrontal cortex in both diseases, and in VD to a greater extent than in schizophrenia. The obtained results suggest that not only in Alzheimer's disease, but also in other mental diseases such as schizophrenia and VD, there is a decrease in the level of tubulin in the prefrontal cortex of the brain, although to a lesser extent than in Alzheimer's disease, and consequently the amount of microtubules.

Electromagnetic field in Alzheimer’s disease: a literature review of recent preclinical and clinical studies

2020 ◽  
Vol 17 ◽  
Author(s):  
Reem Habib Mohamad Ali Ahmad ◽  
Marc Fakhoury ◽  
Nada Lawand
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
In Vivo Studies ◽  
Key Factors ◽  
Potential Benefits ◽  
Preclinical And Clinical Studies ◽  
The Brain

: Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by the progressive loss of neurons leading to cognitive and memory decay. The main signs of AD include the irregular extracellular accumulation of amyloidbeta (Aβ) protein in the brain and the hyper-phosphorylation of tau protein inside neurons. Changes in Aβ expression or aggregation are considered key factors in the pathophysiology of sporadic and early-onset AD and correlate with the cognitive decline seen in patients with AD. Despite decades of research, current approaches in the treatment of AD are only symptomatic in nature and are not effective in slowing or reversing the course of the disease. Encouragingly, recent evidence revealed that exposure to electromagnetic fields (EMF) can delay the development of AD and improve memory. This review paper discusses findings from in vitro and in vivo studies that investigate the link between EMF and AD at the cellular and behavioural level, and highlights the potential benefits of EMF as an innovative approach for the treatment of AD.

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