scholarly journals Machine Learning for Diagnosis of Alzheimer’s Disease and Early Stages

2021 ◽  
Vol 1 (3) ◽  
pp. 182-200
Author(s):  
Julio José Prado ◽  
Ignacio Rojas
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Early Stages ◽  
Wide Range ◽  
Different Types ◽  
Selection Algorithms ◽  
Available Information ◽  
The Brain

According to the WHO, approximately 50 million people worldwide have dementia and there are nearly 10 million new cases every year. Alzheimer’s disease is the most common form of dementia and may contribute to 60–70% of cases. It has been proved that early diagnosis is key to promoting early and optimal management. However, the early stage of dementia is often overlooked and patients are typically diagnosed when the disease progresses to a more advanced stage. The objective of this contribution is to predict Alzheimer’s early stages, not only dementia itself. To carry out this objective, different types of SVM and CNN machine learning classifiers will be used, as well as two different feature selection algorithms: PCA and mRMR. The different experiments and their performance are compared when classifying patients from MRI images. The newness of the experiments conducted in this research includes the wide range of stages that we aim to predict, the processing of all the available information simultaneously and the Segmentation routine implemented in SPM12 for preprocessing. We will make use of multiple slices and consider different parts of the brain to give a more accurate response. Overall, excellent results have been obtained, reaching a maximum F1 score of 0.9979 from the SVM and PCA classifier.

A Comparative Study of Alzheimer Detection Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2877-2883
Author(s):  
Adwait Patil
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Comparative Study ◽  
Fuzzy System ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

scholarly journals Microglia-Based Sex-Biased Neuropathology in Early-Stage Alzheimer’s Disease Model Mice and the Potential Pharmacologic Efficacy of Dioscin

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3261
Author(s):  
Xiao Liu ◽  
Qian Zhou ◽  
Jia-He Zhang ◽  
Xiaoying Wang ◽  
Xiumei Gao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Disease Model ◽  
Amyloid Β ◽  
Brain Lesions ◽  
Synaptic Dysfunction ◽  
Synaptic Loss ◽  
And Function ◽  
The Brain

Alzheimer’s disease (AD), the most common form of dementia, is characterized by amyloid-β (Aβ) accumulation, microglia-associated neuroinflammation, and synaptic loss. The detailed neuropathologic characteristics in early-stage AD, however, are largely unclear. We evaluated the pathologic brain alterations in young adult App knock-in model AppNL-G-F mice at 3 and 6 months of age, which corresponds to early-stage AD. At 3 months of age, microglia expression in the cortex and hippocampus was significantly decreased. By the age of 6 months, the number and function of the microglia increased, accompanied by progressive amyloid-β deposition, synaptic dysfunction, neuroinflammation, and dysregulation of β-catenin and NF-κB signaling pathways. The neuropathologic changes were more severe in female mice than in male mice. Oral administration of dioscin, a natural product, ameliorated the neuropathologic alterations in young AppNL-G-F mice. Our findings revealed microglia-based sex-differential neuropathologic changes in a mouse model of early-stage AD and therapeutic efficacy of dioscin on the brain lesions. Dioscin may represent a potential treatment for 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 S-nitrosation of proteins relevant to Alzheimer’s disease during early stages of neurodegeneration

2016 ◽  
Vol 113 (15) ◽  
pp. 4152-4157 ◽  
Author(s):  
Uthpala Seneviratne ◽  
Alexi Nott ◽  
Vadiraja B. Bhat ◽  
Kodihalli C. Ravindra ◽  
John S. Wishnok ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Function ◽  
Amyloid Precursor Protein Processing ◽  
Amyloid Formation ◽  
Early Stages ◽  
Model Of Alzheimer’S Disease ◽  
Liquid Chromatography Tandem Mass ◽  
Cysteine Thiols ◽  
The Brain

Protein S-nitrosation (SNO-protein), the nitric oxide-mediated posttranslational modification of cysteine thiols, is an important regulatory mechanism of protein function in both physiological and pathological pathways. A key first step toward elucidating the mechanism by which S-nitrosation modulates a protein’s function is identification of the targeted cysteine residues. Here, we present a strategy for the simultaneous identification of SNO-cysteine sites and their cognate proteins to profile the brain of the CK-p25–inducible mouse model of Alzheimer’s disease-like neurodegeneration. The approach—SNOTRAP (SNO trapping by triaryl phosphine)—is a direct tagging strategy that uses phosphine-based chemical probes, allowing enrichment of SNO-peptides and their identification by liquid chromatography tandem mass spectrometry. SNOTRAP identified 313 endogenous SNO-sites in 251 proteins in the mouse brain, of which 135 SNO-proteins were detected only during neurodegeneration. S-nitrosation in the brain shows regional differences and becomes elevated during early stages of neurodegeneration in the CK-p25 mouse. The SNO-proteome during early neurodegeneration identified increased S-nitrosation of proteins important for synapse function, metabolism, and Alzheimer’s disease pathology. In the latter case, proteins related to amyloid precursor protein processing and secretion are S-nitrosated, correlating with increased amyloid formation. Sequence analysis of SNO-cysteine sites identified potential linear motifs that are altered under pathological conditions. Collectively, SNOTRAP is a direct tagging tool for global elucidation of the SNO-proteome, providing functional insights of endogenous SNO proteins in the brain and its dysregulation during neurodegeneration.

scholarly journals The potential role for sphingolipids in neuropathogenesis of Alzheimer’s disease

2013 ◽  
Vol 59 (1) ◽  
pp. 25-50 ◽  
Author(s):  
A.V. Alessenko
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Role ◽  
Potential Role ◽  
Early Stage ◽  
New Drugs ◽  
Neuronal Function ◽  
Sphingosine 1 Phosphate ◽  
The Brain

The review discusses the functional role of sphingolipids in the pathogenesis of Alzheimer's disease. Certain evidence exist that the imbalance of sphingolipids such as sphingomyelin, ceramide, sphingosine, sphingosine-1-phosphate and galactosylceramide in the brain of animals and humans, in the cerebrospinal fluid and blood plasma of patients with Alzheimer's disease play a crucial role in neuronal function by regulating growth, differentiation and cell death in CNS. Activation of sphingomyelinase, which leads to the accumulation of the proapoptotic agent, ceramide, can be considered as a new mechanism for AD and may be a prerequisite for the treatment of this disease by using drugs that inhibit sphingomyelinase activity. The role of sphingolipids as biomarkers for the diagnosis of the early stage of Alzheimer's disease and monitoring the effectiveness of treatment with new drugs is discussed.

scholarly journals Imaging asparaginyl endopeptidase (AEP) in the live brain as a biomarker for Alzheimer’s disease

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Shan-Shan Wang ◽  
Zi-Kai Liu ◽  
Jing-Jing Liu ◽  
Qing Cheng ◽  
Yan-Xia Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Senile Plaques ◽  
Disease Stage ◽  
Assay Method ◽  
Imaging Probe ◽  
Asparaginyl Endopeptidase ◽  
Potential Biomarker ◽  
The Brain

Abstract Background Discovery of early-stage biomarkers is a long-sought goal of Alzheimer’s disease (AD) diagnosis. Age is the greatest risk factor for most AD and accumulating evidence suggests that age-dependent elevation of asparaginyl endopeptidase (AEP) in the brain may represent a new biological marker for predicting AD. However, this speculation remains to be explored with an appropriate assay method because mammalian AEP exists in many organs and the level of AEP in body fluid isn’t proportional to its concentration in brain parenchyma. To this end, we here modified gold nanoparticle (AuNPs) into an AEP-responsive imaging probe and choose transgenic APPswe/PS1dE9 (APP/PS1) mice as an animal model of AD. Our aim is to determine whether imaging of brain AEP can be used to predict AD pathology. Results This AEP-responsive imaging probe AuNPs-Cy5.5-A&C consisted of two particles, AuNPs-Cy5.5-AK and AuNPs-Cy5.5-CABT, which were respectively modified with Ala–Ala–Asn–Cys–Lys (AK) and 2-cyano-6-aminobenzothiazole (CABT). We showed that AuNPs-Cy5.5-A&C could be selectively activated by AEP to aggregate and emit strong fluorescence. Moreover, AuNPs-Cy5.5-A&C displayed a general applicability in various cell lines and its florescence intensity correlated well with AEP activity in these cells. In the brain of APP/PS1 transgenic mice , AEP activity was increased at an early disease stage of AD that precedes formation of senile plaques and cognitive impairment. Pharmacological inhibition of AEP with δ-secretase inhibitor 11 (10 mg kg−1, p.o.) reduced production of β-amyloid (Aβ) and ameliorated memory loss. Therefore, elevation of AEP is an early sign of AD onset. Finally, we showed that live animal imaging with this AEP-responsive probe could monitor the up-regulated AEP in the brain of APP/PS1 mice. Conclusions The current work provided a proof of concept that assessment of brain AEP activity by in vivo imaging assay is a potential biomarker for early diagnosis of AD. Graphical abstract

scholarly journals Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning

2019 ◽  
Author(s):  
Vipul K. Satone ◽  
Rachneet Kaur ◽  
Anant Dadu ◽  
Hampton Leonard ◽  
Hirotaka Iwaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Disease Onset ◽  
Machine Learning Techniques ◽  
Progression Rate ◽  
Early Stages ◽  
Age Related ◽  
Learning Techniques

AbstractBackgroundAlzheimer’s disease (AD) is a common, age-related, neurodegenerative disease that impairs a person’s ability to perform day-to-day activities. Diagnosing AD is challenging, especially in the early stages. Many patients still go undiagnosed, partly due to the complex heterogeneity in disease progression. This highlights a need for early prediction of the disease course to assist its treatment and tailor therapy options to the disease progression rate. Recent developments in machine learning techniques provide the potential to not only predict disease progression and trajectory of AD but also to classify the disease into different etiological subtypes.Methods and findingsThe work shown here clusters participants in distinct and multifaceted progression subgroups of AD and discusses an approach to predict the progression rate from baseline diagnosis. We observed that the myriad of clinically reported symptoms summarized in the proposed AD progression space corresponds directly with memory and cognitive measures, which are routinely used to monitor disease onset and progression. Our analysis demonstrated accurate prediction of disease progression after four years from the first 12 months of post-diagnosis clinical data (Area Under the Curve of 0.96 (95% confidence interval (CI), 0.92-1.0), 0.81 (95% CI, 0.74-0.88) and 0.98 (95% CI, 0.96-1.0) for slow, moderate and fast progression rate patients respectively). Further, we explored the long short-term memory (LSTM) neural networks to predict the trajectory of an individual patient’s progression.ConclusionThe machine learning techniques presented in this study may assist providers in identifying different progression rates and trajectories in the early stages of the disease, hence allowing for more efficient and personalized healthcare deliveries. With additional information about the progression rate of AD at hand, providers may further individualize the treatment plans. The predictive tests discussed in this study not only allow for early AD diagnosis but also facilitate the characterization of distinct AD subtypes relating to trajectories of disease progression. These findings are a crucial step forward for early disease detection. These models can be used to design improved clinical trials for AD research.

scholarly journals Comparison of machine learning approaches for enhancing Alzheimer’s disease classification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10549
Author(s):  
Qi Li ◽  
Mary Qu Yang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Spatial Information ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Disease Classification ◽  
Support Vector ◽  
The Brain

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

scholarly journals Rapid Biomarker Screening of Alzheimer's Disease by Machine Learning and Graphene-Assisted Raman Spectroscopy

2021 ◽  
Author(s):  
Ziyang Wang ◽  
Jiarong Ye ◽  
Li Ding ◽  
Tomotaroh Granzier-Nakajima ◽  
Shubhang Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Raman Spectroscopy ◽  
Brain Slices ◽  
Rapid Screening ◽  
Monolayer Graphene ◽  
Accurate Identification ◽  
Machine Learning Classification ◽  
The Brain

As the most common cause of dementia, Alzheimer's disease (AD) faces challenges in terms of understanding of pathogenesis, developing early diagnosis and developing effective treatment. Rapid and accurate identification of AD biomarkers in the brain will be critical to provide novel insights of AD. To this end, in the current work, we developed a system that can enable a rapid screening of AD biomarkers by employing Raman spectroscopy and machine learning analyses in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD, and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, we achieved significantly increased accuracy from 77% to 98% in machine learning classification. Further, we identified the Raman signature bands that are most important in classifying AD and non-AD samples. Based on these, we managed to identify AD-related biomolecules, which have been confirmed by biochemical studies. Our Raman-machine learning integrated method is promising to greatly accelerate the study of AD and can be potentially extended to human samples and various other diseases.

Cholinesterase Inhibitors for Alzheimer's Disease: Multitargeting Strategy Based on Anti-Alzheimer's Drugs Repositioning

2019 ◽  
Vol 25 (33) ◽  
pp. 3519-3535 ◽  
Author(s):  
Md. Tanvir Kabir ◽  
Md. Sahab Uddin ◽  
Mst. Marium Begum ◽  
Shanmugam Thangapandiyan ◽  
Md. Sohanur Rahman ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cholinesterase Inhibitors ◽  
Treatment Approach ◽  
Symptomatic Treatment ◽  
Symptomatic Improvement ◽  
Ache Inhibition ◽  
Different Types ◽  
Brain Acetylcholine ◽  
The Brain

: In the brain, acetylcholine (ACh) is regarded as one of the major neurotransmitters. During the advancement of Alzheimer's disease (AD) cholinergic deficits occur and this can lead to extensive cognitive dysfunction and decline. Acetylcholinesterase (AChE) remains a highly feasible target for the symptomatic improvement of AD. Acetylcholinesterase (AChE) remains a highly viable target for the symptomatic improvement in AD because cholinergic deficit is a consistent and early finding in AD. The treatment approach of inhibiting peripheral AChE for myasthenia gravis had effectively proven that AChE inhibition was a reachable therapeutic target. Subsequently tacrine, donepezil, rivastigmine, and galantamine were developed and approved for the symptomatic treatment of AD. Since then, multiple cholinesterase inhibitors (ChEIs) have been continued to be developed. These include newer ChEIs, naturally derived ChEIs, hybrids, and synthetic analogues. In this paper, we summarize the different types of ChEIs which are under development and their respective mechanisms of actions.

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