Alzheimer's Disease

Deep learning based pipelines for Alzheimer's disease diagnosis: A comparative study and a novel deep-ensemble method.

BACKGROUND: Alzheimer's disease is a chronic neurodegenerative disease that destroys brain cells, causing irreversible degeneration of cognitive functions and dementia. Its causes are not yet fully understood, and there is no curative treatment. However, neuroimaging tools currently offer help in clinical diagnosis, and, recently, deep learning methods have rapidly become a key methodology applied...

Identification of Alzheimer associated differentially expressed gene through microarray data and transfer learning-based image analysis.

Major factors contribute to mental stress and enhance the progression of late-onset Alzheimer's disease (AD). The factors that lead to neurodegeneration, such as tau protein hyperphosphorylation and increased amyloid-beta production, can be mimicked in animal stress models. The present study identifies differentially expressed genes (DEGs) data and its corresponding predictive image analysis in ra...

Effect of data leakage in brain MRI classification using 2D convolutional neural networks.

In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging ...

A Bio-inspired trajectory planning method for robotic manipulators based on improved bacteria foraging optimization algorithm and tau theory.

In this paper, a novel bio-inspired trajectory planning method is proposed for robotic systems based on an improved bacteria foraging optimization alg...

Plasma neurofilament light chain protein is not increased in treatment-resistant schizophrenia and first-degree relatives.

OBJECTIVE: Schizophrenia, a complex psychiatric disorder, is often associated with cognitive, neurological and neuroimaging abnormalities. The process...

Generation of Interconnected Neural Clusters in Multiscale Scaffolds from Human-Induced Pluripotent Stem Cells.

The development of in vitro neural networks depends to a large extent on the scaffold properties, including the scaffold stiffness, porosity, and dime...

Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia.

We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting...

Recurrent Wheeze Exacerbations Following Acute Bronchiolitis-A Machine Learning Approach.

Acute bronchiolitis is one of the most common respiratory infections in infancy. Although most infants with bronchiolitis do not get hospitalized, in...

A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer's disease.

Structural magnetic resonance imaging (sMRI) can capture the spatial patterns of brain atrophy in Alzheimer's disease (AD) and incipient dementia. Rec...

Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data.

Parkinson's disease (PD), Alzheimer's disease (AD) are common neurodegenerative disease, while mild cognitive impairment (MCI) may be happened in the...

Deep learning reveals disease-specific signatures of white matter pathology in tauopathies.

Although pathology of tauopathies is characterized by abnormal tau protein aggregation in both gray and white matter regions of the brain, neuropathol...

Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network.

High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer's disease diagnosis. However...

Classification of beta-site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods.

The beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a transmembrane aspartyl-protease, that cleaves amyloid precursor protein (APP) a...

Feasibility evaluation of PET scan-time reduction for diagnosing amyloid-β levels in Alzheimer's disease patients using a deep-learning-based denoising algorithm.

PURPOSE: To shorten positron emission tomography (PET) scanning time in diagnosing amyloid-β levels thus increasing the workflow in centers involving ...

Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network.

Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual pro...

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.

PURPOSE: Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients' qu...

Self-Supervised Longitudinal Neighbourhood Embedding.

Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases. Analyz...

Long-term effect of tocilizumab on left ventricular hypertrophy and systolic dysfunction in AA amyloidosis with rheumatoid arthritis.

Because cardiac involvement of amyloid A (AA) is not frequent, little is known about the effects of tocilizumab (TCZ; a humanized monoclonal anti-inte...

PARROT is a flexible recurrent neural network framework for analysis of large protein datasets.

The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test...

Dementia care, robot pets, and aliefs.

Studies have shown that using robot pets in dementia care contributes to a reduction in loneliness and anxiety, and other benefits. Studies also show ...