Alzheimer's Disease

Early Diagnosis of Alzheimer's Disease by Joint Feature Selection and Classification on Temporally Structured Support Vector Machine.

The diagnosis of Alzheimer's disease (AD) from neuroimaging data at the pre-clinical stage has been intensively investigated because of the immense social and economic cost. In the past decade, computational approaches on longitudinal image sequences have been actively investigated with special attention to Mild Cognitive Impairment (MCI), which is an intermediate stage between normal control (NC)...

Robots to assist daily activities: views of older adults with Alzheimer's disease and their caregivers.

BACKGROUND: Robots have the potential to both enable older adults with dementia to perform daily activities with greater independence, and provide support to caregivers. This study explored perspectives of older adults with Alzheimer's disease (AD) and their caregivers on robots that provide stepwise prompting to complete activities in the home.

Combined thioflavin T-Congo red fluorescence assay for amyloid fibril detection.

Fluorescence represents one of the most powerful tools for the detection and structural characterization of the pathogenic protein aggregates, amyloid...

Change in quality of life in older people with dementia participating in Paro-activity: a cluster-randomized controlled trial.

AIM: The aim of this study was to investigate effects of robot-assisted group activity with Paro on quality of life in older people with dementia.

Making use of longitudinal information in pattern recognition.

Longitudinal designs are widely used in medical studies as a means of observing within-subject changes over time in groups of subjects, thereby aiming...

Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer's with Visual Support.

Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability...

Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest.

Alzheimer's disease (AD) is an irreversible neurodegenerative disease and affects a large population in the world. Cognitive scores at multiple time p...

NeuroRDF: semantic integration of highly curated data to prioritize biomarker candidates in Alzheimer's disease.

BACKGROUND: Neurodegenerative diseases are incurable and debilitating indications with huge social and economic impact, where much is still to be lear...

Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI.

Brain network alterations in patients with Alzheimer's disease (AD) has been the subject of much investigation, but the biological mechanisms underlyi...

A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging.

Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer's disease (AD). Non-invasive diagnostic methods are...

Effectiveness of a social robot, "Paro," in a VA long-term care setting.

Interest in animal assisted interventions (AAI) has grown over the years, but acceptance of AAI by the clinical and research community has been hamper...

Classification of amyloid status using machine learning with histograms of oriented 3D gradients.

Brain amyloid burden may be quantitatively assessed from positron emission tomography imaging using standardised uptake value ratios. Using these rati...

Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia.

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progre...

Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease.

Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer's disease and its prodromal stage, i.e....

Service innovation through social robot engagement to improve dementia care quality.

Assistive technologies, such as robots, have proven to be useful in a social context and to improve the quality of life for people with dementia (PwD)...

An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer's Disease.

Alzheimer's disease is a degenerative brain disease that results in cardinal memory deterioration and significant cognitive impairments. The early tre...

Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease.

Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the applicati...

Group activity with Paro in nursing homes: systematic investigation of behaviors in participants.

BACKGROUND: A variety of group activities is promoted for nursing home (NH) residents with dementia with the aim to reduce apathy and to increase enga...

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modal...

Voxel-Based Diagnosis of Alzheimer's Disease Using Classifier Ensembles.

Functional magnetic resonance imaging (fMRI) is one of the most promising noninvasive techniques for early Alzheimer's disease (AD) diagnosis. In this...