Dementia

Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer's dementia diagnosis using multi-measure rs-fMRI spatial patterns.

BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) is essential for timely treatment. Machine learning and multivariate pattern analysis (MVPA) for the diagnosis of brain disorders are explicitly attracting attention in the neuroimaging community. In this paper, we propose a voxel-wise discriminative framework applied to multi-measure resting-state fMRI (rs...

Random forest prediction of Alzheimer's disease using pairwise selection from time series data.

Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a random forest to learn the relationship between pairs of data points at different time separations. The input vector is ...

Detection of abnormal behaviour for dementia sufferers using Convolutional Neural Networks.

In recent years, there is a rapid increase in the population of elderly people. However, elderly people may suffer from the consequences of cognitive ...

New-Onset Alzheimer's Disease and Normal Subjects 100% Differentiated by P300.

Previous work has suggested that evoked potential analysis might allow the detection of subjects with new-onset Alzheimer's disease, which would be us...

Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Currently, m...

RNN-based longitudinal analysis for diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and other mental functions. Magnetic reso...

A Novel Protein Subcellular Localization Method With CNN-XGBoost Model for Alzheimer's Disease.

The disorder distribution of protein in the compartment or organelle leads to many human diseases, including neurodegenerative diseases such as Alzhei...

Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia.

Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 h...

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease.

Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's disease (AD), while other MCI types tend to remain stable ove...

Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling.

Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependenci...

Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome.

Early prediction of disease progression in patients with amnestic mild cognitive impairment (aMCI) is important for early diagnosis and intervention ...

Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI.

Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disea...

Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks.

We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who wil...

Antemortem CSF A42/A40 ratio predicts Alzheimer's disease pathology better than A42 in rapidly progressive dementias.

OBJECTIVE: Despite the critical importance of pathologically confirmed samples for biomarker validation, only a few studies have correlated CSF A42 va...

Probiotic and selenium co-supplementation, and the effects on clinical, metabolic and genetic status in Alzheimer's disease: A randomized, double-blind, controlled trial.

BACKGROUND AND AIMS: Combined probiotic and selenium supplementation may improve Alzheimer's disease (AD) by correcting metabolic abnormalities, and a...

Correlation-Aware Sparse and Low-Rank Constrained Multi-Task Learning for Longitudinal Analysis of Alzheimer's Disease.

Alzheimer's disease (AD), as a severe neurodegenerative disease, is now attracting more and more researchers' attention in the healthcare. With the de...

Pet robot intervention for people with dementia: A systematic review and meta-analysis of randomized controlled trials.

This study aims to systematically evaluate the efficacy of Pet robot intervention (PRI) for people with dementia. Two waves of electronic searches of ...

Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification.

We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease class...

Disease Trajectories and End-of-Life Care for Dementias: Latent Topic Modeling and Trend Analysis Using Clinical Notes.

Despite the increasing prevalence, growing costs, and high mortality of dementia in older adults in the U.S., little is known about the course of thes...

Machine learning studies on major brain diseases: 5-year trends of 2014-2018.

In the recent 5 years (2014-2018), there has been growing interest in the use of machine learning (ML) techniques to explore image diagnosis and progn...