Alzheimer's Disease

Major depression disorder diagnosis and analysis based on structural magnetic resonance imaging and deep learning.

Major depression disorder is one of the diseases with the highest rate of disability and morbidity and is associated with numerous structural and functional differences in neural systems. However, it is difficult to analyze digital medical imaging data without computational intervention. A voxel-wise densely connected convolutional neural network, Three-dimensional Densenet (3D-DenseNet), is propo...

Educational Program Using Robots for Preventing Cognitive Decline of Elderly Persons.

An expected surge of dementia patients in Japan indicates a pressing need to establish countermeasures. As described herein, by developing an educational program for elderly people using robots, we performed a demonstration experiment. Results revealed that involvement of elderly people with robots enhances their enjoyment, indicating a future direction of cognitive decline prevention education fo...

Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data.

Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized by memory loss and cognitive decline. Early detection and acc...

[Retinal Imaging as Potential Biomarkers for Dementia].

Alzheimer's disease (AD) is a leading cause of dementia, and the current diagnostic methods of AD, such as positron emission tomography imaging, have ...

Deep Learning on SDF for Classifying Brain Biomarkers.

Biomarkers are one of the primary medical signs to facilitate the early detection of Alzheimer's disease. The small beta-amyloid (Aβ) peptide is an im...

Early Detection of Low Cognitive Scores from Dual-task Performance Data Using a Spatio-temporal Graph Convolutional Neural Network.

Detecting low cognitive scores at an early stage is important for delaying the progress of dementia. Investigations of early-stage detection have empl...

Federated Learning via Conditional Mutual Learning for Alzheimer's Disease Classification on T1w MRI.

Data-driven deep learning has been considered a promising method for building powerful models for medical data, which often requires a large amount of...

Data-Limited Deep Learning Methods for Mild Cognitive Impairment Classification in Alzheimer's Disease Patients.

Mild Cognitive Impairment (MCI) is the stage between the declining of normal brain function and the more serious decline of dementia. Alzheimer's dise...

Input Agnostic Deep Learning for Alzheimer's Disease Classification Using Multimodal MRI Images.

Alzheimer's disease (AD) is a progressive brain disorder that causes memory and functional impairments. The advances in machine learning and publicly ...

Nonlinear registration as an effective preprocessing technique for Deep learning based classification of disease.

A number of machine learning (ML), and particularly in recent years, deep learning (DL) approaches have been proposed for automatic classification of ...

Use of deep learning genomics to discriminate Alzheimer's disease and healthy controls.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Because gene is an impo...

A modified binary particle swarm optimization with a machine learning algorithm and molecular docking for QSAR modelling of cholinesterase inhibitors.

The acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitors play a key role in treating Alzheimer's disease. This study proposes an a...

Machine learning-based estimation of cognitive performance using regional brain MRI markers: the Northern Manhattan Study.

High dimensional neuroimaging datasets and machine learning have been used to estimate and predict domain-specific cognition, but comparisons with sim...

Robot-induced hallucinations in Parkinson's disease depend on altered sensorimotor processing in fronto-temporal network.

Hallucinations in Parkinson's disease (PD) are disturbing and frequent non-motor symptoms and constitute a major risk factor for psychosis and dementi...

Deep Learning-Based Image Classification in Differentiating Tufted Astrocytes, Astrocytic Plaques, and Neuritic Plaques.

This study aimed to develop a deep learning-based image classification model that can differentiate tufted astrocytes (TA), astrocytic plaques (AP), a...

PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning.

PURPOSE: This study was to develop a convolutional neural network (CNN) model with a residual learning framework to predict the full-time 18F-florbeta...

[A Preliminary Study of Applying Geometric Deep Learning in Brain Morphometry for Diagnosis of Alzheimer's Disease].

OBJECTIVE: A predictive model of Alzheimer's disease (AD) was established based on brain surface meshes and geometric deep learning, and its performan...

Converting disease maps into heavyweight ontologies: general methodology and application to Alzheimer's disease.

Omics technologies offer great promises for improving our understanding of diseases. The integration and interpretation of such data pose major challe...

In Vivo Assay of Cortical Microcircuitry in Frontotemporal Dementia: A Platform for Experimental Medicine Studies.

The analysis of neural circuits can provide crucial insights into the mechanisms of neurodegeneration and dementias, and offer potential quantitative ...

Prediction of Alzheimer's disease-specific phospholipase c gamma-1 SNV by deep learning-based approach for high-throughput screening.

Exon splicing triggered by unpredicted genetic mutation can cause translational variations in neurodegenerative disorders. In this study, we discover ...