Dementia

Novel Effective Connectivity Inference Using Ultra-Group Constrained Orthogonal Forward Regression and Elastic Multilayer Perceptron Classifier for MCI Identification.

Mild cognitive impairment (MCI) detection is important, such that appropriate interventions can be imposed to delay or prevent its progression to severe stages, including Alzheimer's disease (AD). Brain connectivity network inferred from the functional magnetic resonance imaging data has been prevalently used to identify the individuals with MCI/AD from the normal controls. The capability to detec...

Using telepresence for social connection: views of older people with dementia, families, and health professionals from a mixed methods pilot study.

To explore the acceptability of telepresence robots in dementia care from the perspectives of people with dementia, family carers, and health professionals/trainees, and investigate the utility of a social presence assessment tool, the Modified-Temple Presence Inventory (Modified-TPI), for people with dementia. A mixed-methods pilot study conducted in a social robotics laboratory. Three particip...

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using F-FDG PET of the Brain.

Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or n...

Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis.

In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic data for identifying Alzheimer's disease (AD) and its pro...

Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors.

Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one...

Deep learning based low-cost high-accuracy diagnostic framework for dementia using comprehensive neuropsychological assessment profiles.

BACKGROUND: The conventional scores of the neuropsychological batteries are not fully optimized for diagnosing dementia despite their variety and abun...

Alzheimer's disease diagnosis based on multiple cluster dense convolutional networks.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Structural magne...

Learning to Detect Cognitive Impairment through Digital Games and Machine Learning Techniques.

OBJECTIVE: Alzheimer's disease (AD) is one of the most prevalent diseases among the adult population. The early detection of Mild Cognitive Impairment...

Dialogue Systems and Conversational Agents for Patients with Dementia: The Human-Robot Interaction.

This study aimed to identify and describe the fundamental characteristics of spoken dialogue systems, and their role in supporting human-robot interac...

Instance-Based Representation Using Multiple Kernel Learning for Predicting Conversion to Alzheimer Disease.

The early detection of Alzheimer's disease and quantification of its progression poses multiple difficulties for machine learning algorithms. Two of t...

A Novel Deep Learning Framework on Brain Functional Networks for Early MCI Diagnosis.

Although alternations of brain functional networks (BFNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been conside...

Temporal Correlation Structure Learning for MCI Conversion Prediction.

In Alzheimer's research, Mild Cognitive Impairment (MCI) is an important intermediate stage between normal aging and Alzheimer's. How to distinguish M...

Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis.

In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain diseases and predicting clinical scores using magnetic re...

Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia.

Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we h...

Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer's disease across imaging databases.

INTRODUCTION: The main goal of this work is to investigate the feasibility of estimating an anatomical index that can be used as an Alzheimer's diseas...

Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data.

A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD...

Identification of novel immune-relevant drug target genes for Alzheimer's Disease by combining ontology inference with network analysis.

AIMS: Alzheimer's disease (AD) is one of the leading causes of death in elderly people. Its pathogenesis is greatly associated with the abnormality of...

Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles.

The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysi...

Improving well-being in patients with major neurodegenerative disorders: differential efficacy of brief social robot-based intervention for 3 neuropsychiatric profiles.

BACKGROUND: Behavioral and psychological symptoms of dementia (BPSD) affect patients' daily life and subjective well-being. International recommendati...

Multilevel Feature Representation of FDG-PET Brain Images for Diagnosing Alzheimer's Disease.

Using a single imaging modality to diagnose Alzheimer's disease (AD) or mild cognitive impairment (MCI) is a challenging task. FluoroDeoxyGlucose Posi...