Alzheimer's Disease

Discriminative margin-sensitive autoencoder for collective multi-view disease analysis.

Medical prediction is always collectively determined based on bioimages collected from different sources or various clinical characterizations described from multiple physiological features. Notably, learning intrinsic structures from multiple heterogeneous features is significant but challenging in multi-view disease understanding. Different from existing methods that separately deal with each si...

How people with dementia perceive a therapeutic robot called PARO in relation to their pain and mood: A qualitative study.

BACKGROUND: Interacting with social robots, such as the robotic seal PARO, has been shown to improve mood and acute pain for people with dementia. Little attention has been paid to the effect of PARO on people with dementia and chronic pain.

Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches.

BACKGROUND: Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ul...

Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review.

Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. From a research point of view, impressive results have been rep...

Synthetic review of financial capacity in cognitive disorders: Foundations, interventions, and innovations.

PURPOSE OF REVIEW: Financial capacity (FC) is a complex, multi-dimensional construct that changes over the lifespan and commonly becomes impaired as i...

Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer's disease to Parkinson's disease.

PURPOSE: Although functional brain imaging has been used for the early and objective assessment of cognitive dysfunction, there is a lack of generaliz...

Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease.

Parkinson's disease is the second most common neurodegenerative disease in the elderly after Alzheimer's disease. The aetiology and pathogenesis of Pa...

A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study.

Predicting the progression of Alzheimer's Disease (AD) has been held back for decades due to the lack of sufficient longitudinal data required for the...

Single-slice Alzheimer's disease classification and disease regional analysis with Supervised Switching Autoencoders.

BACKGROUND: Alzheimer's disease (AD) is a difficult to diagnose pathology of the brain that progressively impairs cognitive functions. Computer-assist...

A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data.

BACKGROUND: Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer's disease (A...

Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer's disease continuum.

OBJECTIVE: The aim of this study was to evaluate the effect of electroencephalographic (EEG) volume conduction in different measures of functional con...

MCADNNet: Recognizing Stages of Cognitive Impairment through Efficient Convolutional fMRI and MRI Neural Network Topology Models.

Mild cognitive impairment (MCI) represents the intermediate stage between normal cerebral aging and dementia associated with Alzheimer's disease (AD)....

Review of outcome measures in PARO robot intervention studies for dementia care.

The aim of this study was to describe interventions for PARO, as well as the outcomes evaluated and found following use of PARO, and to identify outco...

Hippocampal segmentation for brains with extensive atrophy using three-dimensional convolutional neural networks.

Hippocampal volumetry is a critical biomarker of aging and dementia, and it is widely used as a predictor of cognitive performance; however, automated...

Person transfer assist systems: a literature review.

OBJECTIVE: Novel developments in the robotics field have produced systems that can support person wheelchair transfers, maximize safety and reduce car...

Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction.

One of the main technical challenges of PET/MRI is to achieve an accurate PET attenuation correction (AC) estimation. In current systems, AC is accomp...

Developing assistive robots for people with mild cognitive impairment and mild dementia: a qualitative study with older adults and experts in aged care.

OBJECTIVES: This research is part of an international project to design and test a home-based healthcare robot to help older adults with mild cognitiv...

Using path signatures to predict a diagnosis of Alzheimer's disease.

The path signature is a means of feature generation that can encode nonlinear interactions in data in addition to the usual linear terms. It provides ...

Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer's dementia score.

F-fluorodeoxyglucose positron emission tomography (FDG-PET) enables in-vivo capture of the topographic metabolism patterns in the brain. These images...

The benefits of and barriers to using a social robot PARO in care settings: a scoping review.

BACKGROUND: Given the complexity of providing dementia care in hospitals, integrating technology into practice is a high challenge and an important op...