Alzheimer's Disease

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 and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer's disease neuroimaging initiative (ADNI) participants...

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 points can be reliably used to evaluate the progression of the disease clinically. In recent studies, machine learning techniques have shown promising results on the prediction of AD clinical scores. However, there are multiple limitations in the curr...

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...

Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer's Disease.

Understanding the progression of chronic diseases can empower the sufferers in taking proactive care. To predict the disease status in the future time...

HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework.

Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseas...

Significant Increase in C-Reactive Protein and Serum Amyloid A in the Early Hours of Paroxysmal Atrial Fibrillation.

BACKGROUND: A number of data have been accumulated on inflammation in persistent and permanent atrial fibrillation (AF). Our aim was to study the proc...

Use of machine learning for behavioral distinction of autism and ADHD.

Although autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) continue to rise in prevalence, together affecting >10% of...

Vision-Based Pose Estimation for Robot-Mediated Hand Telerehabilitation.

Vision-based Pose Estimation (VPE) represents a non-invasive solution to allow a smooth and natural interaction between a human user and a robotic sys...