Dementia

How Can Anomalous-Diffusion Neural Networks Under Connectomics Generate Optimized Spatiotemporal Dynamics.

Spatiotemporal dynamics in the brain have been recognized as strongly related to the formation of perceived and cognitive diseases, such as delusions and hallucinations in Alzheimer's disease. However, two practical considerations are rarely mentioned in related mechanism research: the connectomics networking and the anomalous diffusion generated by the complex medium between neurons and the compl...

The Impact of Telepresence Robots on Family Caregivers and Residents in Long-Term Care.

Telepresence robots can enhance social connection and support person-centered care in long-term care (LTC) homes. This study evaluates their impact in facilitating virtual visits between family caregivers and older residents in Canadian LTC homes. Telepresence robots were placed in residents' rooms, allowing virtual visits at mutual convenience. A total of 18 residents and 17 family caregivers par...

Using Machine Learning to Predict Cognitive Decline in Older Adults From the Chinese Longitudinal Healthy Longevity Survey: Model Development and Validation Study.

BACKGROUND: Cognitive impairment, indicative of Alzheimer disease and other forms of dementia, significantly deteriorates the quality of life of older...

Alzheimer's disease knowledge graph enhances knowledge discovery and disease prediction.

OBJECTIVE: To construct an Alzheimer's Disease Knowledge Graph (ADKG) by extracting and integrating relationships among Alzheimer's disease (AD), gene...

AI in Home Care-Evaluation of Large Language Models for Future Training of Informal Caregivers: Observational Comparative Case Study.

BACKGROUND: The aging population presents an accomplishment for society but also poses significant challenges for governments, health care systems, an...

A novel diagnosis method utilizing MDBO-SVM and imaging genetics for Alzheimer's disease.

Alzheimer's disease (AD) is the most common neurodegenerative disorder, yet its underlying mechanisms remain elusive. Early and accurate diagnosis is ...

Therapeutic potential of enzymes, neurosteroids, and synthetic steroids in neurodegenerative disorders: A critical review.

Neurodegenerative disorders present a significant challenge to healthcare systems, mainly due to the limited availability of effective treatment optio...

Generative artificial intelligence, integrative bioinformatics, and single-cell analysis reveal Alzheimer's genetic and immune landscape.

The research aims to understand Alzheimer's genetic and immune landscapes using the amalgamation of three technologies: artificial intelligence (GenAI...

Enhanced EEG-based Alzheimer's disease detection using synchrosqueezing transform and deep transfer learning.

The most prevalent type of dementia and a progressive neurodegenerative disease, Alzheimer's disease has a major influence on day-to-day functioning d...

A review of multimodal fusion-based deep learning for Alzheimer's disease.

Alzheimer's Disease (AD) as one of the most prevalent neurodegenerative disorders worldwide, characterized by significant memory and cognitive decline...

Cerebrospinal fluid inflammatory cytokines as prognostic indicators for cognitive decline across Alzheimer's disease spectrum.

BackgroundNeuroinflammation actively contributes to the pathophysiology of Alzheimer's disease (AD); however, the value of neuroinflammatory biomarker...

The clinical significance of an AI-based assumption model for neurocognitive diseases using a novel dual-task system.

Dual-task composed of gait or stepping tasks combined with cognitive tasks has been well-established as valuable tools for detecting neurocognitive di...

Revolutionizing Alzheimer's disease detection with a cutting-edge CAPCBAM deep learning framework.

Early and accurate diagnosis of Alzheimer's disease (AD) is crucial for effective treatment. While the integration of deep learning techniques for AD ...

A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer's Disease With Fusion Network Using Eye Movements and Speech.

Detecting Alzheimer's disease (AD) in its earliest stages, particularly during an onset of Mild Cognitive Impairment (MCI), remains challenging due to...

Social robot PIO intervention for improving cognitive function and depression in older adults with mild to moderate dementia in day care centers: A randomized controlled trial.

The increases in the older population, the prevalence of dementia, and the resulting social costs are burdensome to individuals, families, and the nat...

Alzheimer's Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis.

A growing body of literature has examined the impact of neighborhood characteristics on Alzheimer's disease (AD) dementia, yet the spatial variability...

Computational modelling for risk assessment of neurological disorder in diabetes using Hodgkin-Huxley model.

BACKGROUND: Diabetes mellitus, characterized by chronic glucose dysregulation, significantly increases the risk of neurological disorders such as cogn...

Transcriptomic analyses of human brains with Alzheimer's disease identified dysregulated epilepsy-causing genes.

BACKGROUND & OBJECTIVE: Alzheimer's Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, wheth...

c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that requires early diagnosis for effective management. However, issues with currently availa...

Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images.

In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with a convolutional neural network (CNN) model de...