Alzheimer's Disease

A machine learning algorithm based on circulating metabolic biomarkers offers improved predictions of neurological diseases.

BACKGROUND AND AIMS: A machine learning algorithm based on circulating metabolic biomarkers for the predictions of neurological diseases (NLDs) is lacking. To develop a machine learning algorithm to compare the performance of a metabolic biomarker-based model with that of a clinical model based on conventional risk factors for predicting three NLDs: dementia, Parkinson's disease (PD), and Alzheime...

Positive Emotional Responses to Socially Assistive Robots in People With Dementia: Pilot Study.

BACKGROUND: Interventions and care that can evoke positive emotions and reduce apathy or agitation are important for people with dementia. In recent years, socially assistive robots used for better dementia care have been found to be feasible. However, the immediate responses of people with dementia when they are given multiple sensory modalities from socially assistive robots have not yet been su...

Perineuronal Net Microscopy: From Brain Pathology to Artificial Intelligence.

Perineuronal nets (PNN) are a special highly structured type of extracellular matrix encapsulating synapses on large populations of CNS neurons. PNN u...

Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning.

Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of ...

Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer's Disease.

Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly...

Uncovering Predictors of Low Hippocampal Volume: Evidence from a Large-Scale Machine-Learning-Based Study in the UK Biobank.

INTRODUCTION: Hippocampal atrophy is an established biomarker for conversion from the normal ageing process to developing cognitive impairment and dem...

Using Natural Language Processing to Identify Home Health Care Patients at Risk for Diagnosis of Alzheimer's Disease and Related Dementias.

This study aimed to: (1) validate a natural language processing (NLP) system developed for the home health care setting to identify signs and symptoms...

Data-driven normative values based on generative manifold learning for quantitative MRI.

In medicine, abnormalities in quantitative metrics such as the volume reduction of one brain region of an individual versus a control group are often ...

Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer's Disease Using Biophysical Modeling and Deep Learning.

Alzheimer's disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progre...

Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis.

The ordered assembly of Tau protein into filaments characterizes Alzheimer's and other neurodegenerative diseases, and thus, stabilization of Tau prot...

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.

BACKGROUND: Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cog...

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data.

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive s...

Utilizing Siamese 4D-AlzNet and Transfer Learning to Identify Stages of Alzheimer's Disease.

Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological disorder characterized by memory loss due to brain cel...

Can an AI-carebot be filial? Reflections from Confucian ethics.

This article discusses the application of artificially intelligent robots within eldercare and explores a series of ethical considerations, including ...

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.

BACKGROUND: The most common form of dementia, Alzheimer's Disease (AD), is challenging for both those affected as well as for their care providers, an...

Facilitators and barriers to codesigning social robots with older adults with dementia: a scoping review protocol.

INTRODUCTION: Social robots including telepresence robots have emerged as potential support in dementia care. However, the effectiveness of these robo...

Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.

BACKGROUND: Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportun...

Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.

As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. M...

Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease.

Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targe...

Patient portal messages to support an age-friendly health system for persons with dementia.

BACKGROUND: Patient portal secure messaging can support age-friendly dementia care, yet little is known about care partner use of the portal and how m...