Alzheimer's Disease

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 of the most suitable application domains for these techniques since they help model diagnostic information based on causal and/or statistical data and therefore reveal hidden dependencies between symptoms and illnesses. In this paper we give a detai...

3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI.

Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia. Automated quantification of EPVS would greatly help to advance research into its etiology and its potential as a risk indicator of disease. We propose a convolutional n...

The Rational Discovery of a Tau Aggregation Inhibitor.

Intrinsically disordered proteins play vital roles in biology, and their dysfunction contributes to many major disease states. These proteins remain c...

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...

Adaptive Neuro-Fuzzy Inference System-Based Exploration of the Interrelationships of 25-Hydroxyvitamin D, Calcium, Phosphorus with Parathyroid Hormone Production.

The rationale of the current study was to assess the prevalence of 25-hydroxyvitamin D (25-OHD) deficiency and hyperparathyroidism in South Indian pop...

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...

Deep learning-based quantification of abdominal fat on magnetic resonance images.

Obesity is increasingly prevalent and associated with increased risk of developing type 2 diabetes, cardiovascular diseases, and cancer. Magnetic reso...

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...

Lipid-apolipoprotein interactions in amyloid fibril formation and relevance to atherosclerosis.

The apolipoprotein family is a set of highly conserved proteins characterized by the presence of amphipathic α-helical sequences that mediate lipid bi...

A deep convolutional neural network approach for astrocyte detection.

Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer's and Parkinson's disease...

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...

Structural neuroimaging as clinical predictor: A review of machine learning applications.

In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain c...