Investigating the Progression of Alzheimer’s Disease Using Digital Volume Correlation Algorithm and Strain As a Metric

2018 ◽  
Author(s):  
Annastacia K. McCarty ◽  
Sarah A. Bentil
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
The United States ◽  
Digital Volume Correlation ◽  
Correlation Algorithm ◽  
New Information ◽  
Innovative Therapies ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

In the United States, Alzheimer’s disease (AD) affects one in ten people ages 65 and older. In most patients, the first indication of AD is the inability to remember new information, and symptoms grow to include behavior changes and increasing confusion and suspicions surrounding loved ones and daily events. As the disease progresses, the cortex and hippocampus regions of the brain decrease in size, allowing the fluid-filled ventricles within the brain to increase. New and innovative therapies to delay the onset of the disease and progression of the symptoms are being discovered. For example, the antibody solanezumab is undergoing clinical trials to determine its ability to reduce the levels of beta-amyloid in the brain, a known risk factor of AD. Consequently, the ability to identify patients who could benefit from the therapies will be invaluable. The purpose of this study is to determine if the digital volume correlation (DVC) algorithm can detect and track the onset and progression of AD using magnetic resonance imaging (MRI) scans of the head. DVC measures the deformation and strain of the volumetric MRI dataset by tracking the changes in its grey value pattern. A collection of MRI datasets of a patient’s head, which include scans from a baseline visit and visits at 6 months, 12 months, and every 12 months thereafter, is used in our analysis. A strain is applied to each set of MRI scans prior to implementation of the digital volume correlation algorithm. The DVC algorithm is then applied to the dataset and the resulting error between the expected and calculated strain is computed. A decrease in the contrast of the MRI dataset will correlate to additional error by the algorithm. As a result, an increase in the calculated strain error is anticipated to correlate with an increase in the ventricles in the brain, or progression of the disease, over the time period of interest.

scholarly journals A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Automated Segmentation ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri ◽  
Underlying Mechanisms ◽  
The Brain

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disease that leads to anatomical atrophy, as evidenced by magnetic resonance imaging (MRI). Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in AD, as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using automated segmentation methods in more than 60 areas between AD and healthy controls (HC).MethodsA total of 44 MRI images (22 AD and 22 HC, 50% females) were taken from the minimal interval resonance imaging in Alzheimer’s disease (MIRIAD) dataset. HIPS, volBrain, CAT and BrainSuite segmentation methods were used for the subfields of hippocampus, and the rest of the brain.ResultsWhile HIPS, volBrain and CAT showed strong conformity with the past literature, BrainSuite misclassified several brain areas. Additionally, the volume of the brain areas that successfully discriminated between AD and HC showed a correlation with mini mental state examination (MMSE) scores. The two methods of volBrain and CAT showed a very strong correlation. These two methods, however, did not correlate with BrainSuite.ConclusionOur results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of AD and HC. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.

scholarly journals The Diagnosis of Alzheimer’s Disease: An Ensemble Approach

2020 ◽  
Author(s):  
Jingyan Qiu ◽  
Linjian Li ◽  
Yida Liu ◽  
Yingjun Ou ◽  
Yubei Lin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Large Scale ◽  
Early Stage ◽  
Training Transfer ◽  
Detection And Diagnosis ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Model ◽  
The Brain

Alzheimer’s disease (AD) is one of the most common forms of dementia. The early stage of the disease is defined as Mild Cognitive Impairment (MCI). Recent research results have shown the prospect of combining Magnetic Resonance Imaging (MRI) scanning of the brain and deep learning to diagnose AD. However, the CNN deep learning model requires a large scale of samples for training. Transfer learning is the key to enable a model with high accuracy by using limited data for training. In this paper, DenseNet and Inception V4, which were pre-trained on the ImageNet dataset to obtain initialization values of weights, are, respectively, used for the graphic classification task. The ensemble method is employed to enhance the effectiveness and efficiency of the classification models and the result of different models are eventually processed through probability-based fusion. Our experiments were completely conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset. Only the ternary classification is made due to a higher demand for medical detection and diagnosis. The accuracies of AD/MCI/Normal Control (NC) of different models are estimated in this paper. The results of the experiments showed that the accuracies of the method achieved a maximum of 92.65%, which is a remarkable outcome compared with the accuracies of the state-of-the-art methods.

scholarly journals Magnetic Nanoparticles as In Vivo Tracers for Alzheimer’s Disease

2020 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Bhargy Sharma ◽  
Konstantin Pervushin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Contrast Agents ◽  
Neurological Diseases ◽  
Pulse Sequences ◽  
Experimental Conditions ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Drug formulations and suitable methods for their detection play a very crucial role in the development of therapeutics towards degenerative neurological diseases. For diseases such as Alzheimer’s disease, magnetic resonance imaging (MRI) is a non-invasive clinical technique suitable for early diagnosis. In this review, we will discuss the different experimental conditions which can push MRI as the technique of choice and the gold standard for early diagnosis of Alzheimer’s disease. Here, we describe and compare various techniques for administration of nanoparticles targeted to the brain and suitable formulations of nanoparticles for use as magnetically active therapeutic probes in drug delivery targeting the brain. We explore different physiological pathways involved in the transport of such nanoparticles for successful entry in the brain. In our lab, we have used different formulations of iron oxide nanoparticles (IONPs) and protein nanocages as contrast agents in anatomical MRI of an Alzheimer’s disease (AD) brain. We compare these coatings and their benefits to provide the best contrast in addition to biocompatibility properties to be used as sustainable drug-release systems. In the later sections, the contrast enhancement techniques in MRI studies are discussed. Examples of contrast-enhanced imaging using advanced pulse sequences are discussed with the main focus on important studies in the field of neurological diseases. In addition, T1 contrast agents such as gadolinium chelates are compared with the T2 contrast agents mainly made of superparamagnetic inorganic metal nanoparticles.

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 VBM-Based Alzheimer’s Disease Detection from the Region of Interest of T1 MRI with Supportive Gaussian Smoothing and a Bayesian Regularized Neural Network

2021 ◽  
Vol 11 (13) ◽  
pp. 6175
Author(s):  
Bijen Khagi ◽  
Kun Ho Lee ◽  
Kyu Yeong Choi ◽  
Jang Jae Lee ◽  
Goo-Rak Kwon ◽  
...  
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Statistical Parametric Mapping ◽  
Region Of Interest ◽  
Extraction Process ◽  
Dementia Patients ◽  
Mri Features ◽  
Mri Scans ◽  
The Brain

This paper presents an efficient computer-aided diagnosis (CAD) approach for the automatic detection of Alzheimer’s disease in patients’ T1 MRI scans using the voxel-based morphometry (VBM) analysis of the region of interest (ROI) in the brain. The idea is to generate a normal distribution of feature vectors from ROIs then later use for classification via Bayesian regularized neural network (BR-NN). The first dataset consists of the magnetic resonance imaging (MRI) of 74 Alzheimer’s disease (AD), 42 mild cognitive impairment (MCI), and 74 control normal (CN) from the ADNI1 dataset. The other dataset consists of the MRI of 42 Alzheimer’s disease dementia (ADD), 42 normal controls (NCs), and 39 MCI due to AD (mAD) from our GARD2 database. We aim to create a generalized network to distinguish normal individuals (CN/NC) from dementia patients AD/ADD and MCI/mAD. Our performance relies on our feature extraction process and data smoothing process. Here the key process is to generate a Statistical Parametric Mapping (SPM) t-map image from VBM analysis and obtain the region of interest (ROI) that shows the optimistic result after two-sample t-tests for a smaller value of p < 0.001(AD vs. CN). The result was overwhelming for the distinction between AD/ADD and CN/NC, thus validating our idea for discriminative MRI features. Further, we compared our performance with other recent state-of-the-art methods, and it is comparatively better in many cases. We have experimented with two datasets to validate the process. To validate the network generalization, BR-NN is trained from 70% of the ADNI dataset and tested on 30% of the ADNI, 100% of the GARD dataset, and vice versa. Additionally, we identified the brain anatomical ROIs that may be relatively responsible for brain atrophy during the AD diagnosis.

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.

Role of Forkhead Transcription Factors of the O Class (FoxO) in Development and Progression of Alzheimer’s Disease

2020 ◽  
Vol 19 (9) ◽  
pp. 709-721
Author(s):  
Shikha Goswami ◽  
Ozaifa Kareem ◽  
Ramesh K. Goyal ◽  
Sayed M. Mumtaz ◽  
Rajiv K. Tonk ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transcription Factors ◽  
Neurological Disorders ◽  
The United States ◽  
Forkhead Transcription Factors ◽  
The Central Nervous System ◽  
Degenerative Disorder ◽  
The Brain

: In the central nervous system (CNS), a specific loss of focal neurons leads to mental and neurological disorders like dementia, Alzheimer’s disease (AD), Huntington’s disease, Parkinson’s disease, etc. AD is a neurological degenerative disorder, which is progressive and irreversible in nature and is the widely recognized reason for dementia in the geriatric populace. It affects 10% of people above the age of 65 and is the fourth driving reason for death in the United States. Numerous evidence suggests that the neuronal compartment is not the only genesis of AD, but transcription factors also hold significant importance in the occurrence and advancement of the disease. It is the need of the time to find the novel molecular targets and new techniques for treating or slowing down the progression of neurological disorders, especially AD. In this article, we summarised a conceivable association between transcriptional factors and their defensive measures against neurodegeneration and AD. The mammalian forkhead transcription factors of the class O (FoxO) illustrate one of the potential objectives for the development of new methodologies against AD and other neurocognitive disorders. The presence of FoxO is easily noticeable in the “cognitive centers” of the brain, specifically in the amygdala, hippocampus, and the nucleus accumbens. FoxO proteins are the prominent and necessary factors in memory formation and cognitive functions. FoxO also assumes a pertinent role in the protection of multiple cells in the brain by controlling the involving mechanism of autophagy and apoptosis and also modulates the process of phosphorylation of the targeted protein, thus FoxO must be a putative target in the mitigation of AD. This review features the role of FoxO as an important biomarker and potential new targets for the treatment of AD.

scholarly journals Altered d-glucose in brain parenchyma and cerebrospinal fluid of early Alzheimer’s disease detected by dynamic glucose-enhanced MRI

2020 ◽  
Vol 6 (20) ◽  
pp. eaba3884 ◽  
Author(s):  
Jianpan Huang ◽  
Peter C. M. van Zijl ◽  
Xiongqi Han ◽  
Celia M. Dong ◽  
Gerald W. Y. Cheng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Glucose Uptake ◽  
Brain Parenchyma ◽  
Protein Accumulation ◽  
Altered Glucose ◽  
Early Alzheimer’S Disease ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Altered cerebral glucose uptake is one of the hallmarks of Alzheimer’s disease (AD). A dynamic glucose-enhanced (DGE) magnetic resonance imaging (MRI) approach was developed to simultaneously monitor d-glucose uptake and clearance in both brain parenchyma and cerebrospinal fluid (CSF). We observed substantially higher uptake in parenchyma of young (6 months) transgenic AD mice compared to age-matched wild-type (WT) mice. Notably lower uptakes were observed in parenchyma and CSF of old (16 months) AD mice. Both young and old AD mice had an obviously slower CSF clearance than age-matched WT mice. This resembles recent reports of the hampered CSF clearance that leads to protein accumulation in the brain. These findings suggest that DGE MRI can identify altered glucose uptake and clearance in young AD mice upon the emergence of amyloid plaques. DGE MRI of brain parenchyma and CSF has potential for early AD stratification, especially at 3T clinical field strength MRI.

scholarly journals Cognitive and MRI trajectories for prediction of Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Samaneh A. Mofrad ◽  
Astri J. Lundervold ◽  
Alexandra Vik ◽  
Alexander S. Lundervold
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Normal Function ◽  
Cognitive Changes ◽  
Data Set ◽  
Ensemble Machine Learning ◽  
Mri Features ◽  
Trajectories Of Change ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

AbstractThe concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer’s disease (AD), and identification and treatment before further decline is an important clinical task. We selected longitudinal data from the ADNI database to investigate how well normal function (HC, n= 134) vs. conversion to MCI (cMCI, n= 134) and stable MCI (sMCI, n=333) vs. conversion to AD (cAD, n= 333) could be predicted from cognitive tests, and whether the predictions improve by adding information from magnetic resonance imaging (MRI) examinations. Features representing trajectories of change in the selected cognitive and MRI measures were derived from mixed effects models and used to train ensemble machine learning models to classify the pairs of subgroups based on a subset of the data set. Evaluation in an independent test set showed that the predictions for HC vs. cMCI improved substantially when MRI features were added, with an increase in $$F_1$$ F 1 -score from 60 to 77%. The $$F_1$$ F 1 -scores for sMCI vs. cAD were 77% without and 78% with inclusion of MRI features. The results are in-line with findings showing that cognitive changes tend to manifest themselves several years after the Alzheimer’s disease is well-established in the brain.

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