Alzheimer's Disease

A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease.

BACKGROUND AND OBJECTIVES: Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the disease from the deluge of available data. Machine learning has been used successfully for many different tasks, including neuroimaging related problems....

Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease.

Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent progression to AD. Brain connectivity networks inferred from medical imaging data have been commonly used to distinguish MCI patients from normal controls (NC). However, existing methods still suffer from limited performance, and classification rema...

Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder.

Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain h...

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Com...

Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis.

Neurodegenerative diseases are excessively affecting millions of patients, especially elderly people. Early detection and management of these diseases...

A Classification Algorithm by Combination of Feature Decomposition and Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image Classification and AD Diagnosis.

Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzhei...

Serum amyloid A4 is a procoagulant apolipoprotein that it is elevated in venous thrombosis patients.

BACKGROUND: Serum amyloid A4 (SAA4) is an apolipoprotein that is in the SAA family and it is constitutively translated. Previously, acute-phase SAA1 a...

Automatic identification of crossovers in cryo-EM images of murine amyloid protein A fibrils with machine learning.

Detecting crossovers in cryo-electron microscopy images of protein fibrils is an important step towards determining the morphological composition of a...

Dual-functional neural network for bilateral hippocampi segmentation and diagnosis of Alzheimer's disease.

PURPOSE: Knowing the course of Alzheimer's disease is very important to prevent the deterioration of the disease, and accurate segmentation of sensiti...

Hypergraph based multi-task feature selection for multimodal classification of Alzheimer's disease.

Multi-modality based classification methods are superior to the single modality based approaches for the automatic diagnosis of the Alzheimer's diseas...

Ethical concerns with the use of intelligent assistive technology: findings from a qualitative study with professional stakeholders.

BACKGROUND: Advances in artificial intelligence (AI), robotics and wearable computing are creating novel technological opportunities for mitigating th...

Shall I compare thee…to a robot? An exploratory pilot study using participatory arts and social robotics to improve psychological well-being in later life.

Research suggests that the use of creative, artistic activities in later life may positively impact the psychological well-being of older adults. Soci...

A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. Mild cognitive impairment (MCI) is a clinical precursor of AD....

A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia.

Electroencephalographic (EEG) recordings generate an electrical map of the human brain that are useful for clinical inspection of patients and in biom...

Exploring the perceptions of people with dementia about the social robot PARO in a hospital setting.

New technology, such as social robots, opens up new opportunities in hospital settings. PARO, a robotic pet seal, was designed to provide emotional an...

Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach.

Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be...

Comprehensive Exploration of Target-specific Ligands Using a Graph Convolution Neural Network.

Machine learning approaches are widely used to evaluate ligand activities of chemical compounds toward potential target proteins. Especially, explorat...

The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases.

PURPOSE: Although most deep learning (DL) studies have reported excellent classification accuracy, these studies usually target typical Alzheimer's di...

Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches.

Aβ-amyloid deposition is a key feature of Alzheimer's disease, but Consortium to Establish a Registry for Alzheimer's Disease (CERAD) assessment, base...

Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs.

Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are appl...