Alzheimer's Disease

Brain Network Classification for Accurate Detection of Alzheimer's Disease via Manifold Harmonic Discriminant Analysis.

Mounting evidence shows that Alzheimer's disease (AD) manifests the dysfunction of the brain network much earlier before the onset of clinical symptoms, making its early diagnosis possible. Current brain network analyses treat high-dimensional network data as a regular matrix or vector, which destroys the essential network topology, thereby seriously affecting diagnosis accuracy. In this context, ...

LCGNet: Local Sequential Feature Coupling Global Representation Learning for Functional Connectivity Network Analysis With fMRI.

Analysis of functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has greatly advanced our understanding of brain diseases, including Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). Advanced machine learning techniques, such as convolutional neural networks (CNNs), have been used to learn high-level feature ...

Psychosocial effects of a humanoid robot on informal caregivers of people with dementia: A randomised controlled trial with nested interviews.

BACKGROUND: Dementia rates are rising globally, impacting healthcare systems and society. The care of people with dementia is largely provided by info...

Developing a prediction model for cognitive impairment in older adults following critical illness.

BACKGROUND: New or worsening cognitive impairment or dementia is common in older adults following an episode of critical illness, and screening post-d...

Estimating global phase synchronization by quantifying multivariate mutual information and detecting network structure.

In neuroscience, phase synchronization (PS) is a crucial mechanism that facilitates information processing and transmission between different brain re...

Use of Artificial Intelligence in Imaging Dementia.

Alzheimer's disease is the most common cause of dementia in the elderly population (aged 65 years and over), followed by vascular dementia, Lewy body ...

Time Efficiency and Ergonomic Assessment of a Robotic Wheelchair Transfer System.

: Caregivers experience high rates of occupational injuries, especially during wheelchair transfers, which often result in back pain and musculoskelet...

BPEN: Brain Posterior Evidential Network for trustworthy brain imaging analysis.

The application of deep learning techniques to analyze brain functional magnetic resonance imaging (fMRI) data has led to significant advancements in ...

Effective Alzheimer's disease detection using enhanced Xception blending with snapshot ensemble.

Alzheimer's disease (AD), a prevalent neurodegenerative disorder, leads to progressive dementia, which impairs decision-making, problem-solving, and c...

Graph Convolutional Network for AD and MCI Diagnosis Utilizing Peripheral DNA Methylation: Réseau de neurones en graphes pour le diagnostic de la MA et du TCL à l'aide de la méthylation de l'ADN périphérique.

OBJECTIVE: Blood DNA methylation (DNAm) alterations have been widely reported in the onset and progression of mild cognitive impairment (MCI) and Alzh...

Identification and cognitive function prediction of Alzheimer's disease based on multivariate pattern analysis of hippocampal volumes.

BACKGROUND: Alzheimer's disease (AD) is strongly associated with slowly progressive hippocampal atrophy. Elucidating the relationships between local m...

Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging.

OBJECTIVE: To apply machine learning approaches on EEG event-related oscillations (ERO) to discriminate preclinical Alzheimer's disease (AD) from age-...

Identifying shared diagnostic genes and mechanisms in vascular dementia and Alzheimer's disease via bioinformatics and machine learning.

BACKGROUND: Alzheimer's disease (AD) and vascular dementia (VaD) share overlapping pathophysiological characteristics, yet comparative genetic studies...

Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils.

Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and degradation mechanisms of abnormal proteins implicate...

Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis.

Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary information ab...

Establishing a machine learning dementia progression prediction model with multiple integrated data.

OBJECTIVE: Dementia is a significant medical and social issue in most developed countries. Practical tools for predicting the progression of degenerat...

Multiclass classification of Alzheimer's disease prodromal stages using sequential feature embeddings and regularized multikernel support vector machine.

The detection of patients in the cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) stages of neurodegeneration is c...

A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer's Disease.

The clinical spectrum of Alzheimer's disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or sever...

Exploring the Perspectives of Older Adults on a Digital Brain Health Platform Using Natural Language Processing: Cohort Study.

BACKGROUND: Although digital technology represents a growing field aiming to revolutionize early Alzheimer disease risk prediction and monitoring, the...

Optimizing early diagnosis by integrating multiple classifiers for predicting brain stroke and critical diseases.

Machine learning has gained attention in the medical field. Continuous efforts are being made to develop robust models for early prognosis purposes. T...