Alzheimer's Disease

Modelling disease risk for amyloid A (AA) amyloidosis in non-human primates using machine learning.

Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk prior to clinical symptoms is challenging. We applied machine learning to identify the best predictors of amyloidosis in rhesus macaques from available clinical and pathology records. To explore potential biomarkers, we also assessed whether changes in circulating serum amyloid A (SAA) or lipoprotei...

Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders.

Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the arrival of the deep learning paradigm, it has become possible to extract high-level abstract features directly from MRI images that internally describe...

Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss.

PURPOSE: Our goal was to use a generative adversarial network (GAN) with feature matching and task-specific perceptual loss to synthesize standard-dos...

The Classifying Autoencoder: Gaining Insight into Amyloid Assembly of Peptides and Proteins.

Despite the importance of amyloid formation in disease pathology, the understanding of the primary structure?activity relationship for amyloid-forming...

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.

INTRODUCTION: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimatel...

Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning.

Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression a...

A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer's Disease.

BACKGROUND: Hippocampus is one of the first structures affected by neurodegenerative diseases such as Alzheimer's disease (AD) and mild cognitive impa...

Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly.

Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed ...

Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors.

Human activity recognition has become an active research field over the past few years due to its wide application in various fields such as health-ca...

Cerebral microbleed detection using Susceptibility Weighted Imaging and deep learning.

Detecting cerebral microbleeds (CMBs) is important in diagnosing a variety of diseases including dementia, stroke and traumatic brain injury. However,...

Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles.

BACKGROUND AND OBJECTIVES: Clathrin is an adaptor protein that serves as the principal element of the vesicle-coating complex and is important for the...

Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer's Disease Based on an Extreme Learning Machine Method from the ADNI cohort.

Computer-aided diagnosis has become a widely-used auxiliary tool for the diagnosis of Alzheimer's disease (AD). In this study, we developed an extreme...

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, h...

Engagement and experience of older people with socially assistive robots in home care.

Social isolation is one of the most common consequences of older people with dementia, especially for those who live at their own dwellings alone due ...

Identifying Children With Clinical Language Disorder: An Application of Machine-Learning Classification.

In this study, we identified child- and family-level characteristics most strongly associated with clinical identification of language disorder for pr...

Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models.

Magnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection of incipient Alzheimer's Disease (AD) dementia in m...

Systems Pharmacological Approach to Investigate the Mechanism of for Application to Alzheimer's Disease.

(OC)-a traditional Chinese medicine (TCM)-has been reported to have large numbers of flavonoids, alkaloids, and triterpenoids. The previous studies o...

Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia.

OBJECTIVES: The overall aim of the present study was to explore the role of cognitive reserve (CR) in the conversion from mild cognitive impairment (M...

Application of Artificial Neural Networks to Identify Alzheimer's Disease Using Cerebral Perfusion SPECT Data.

The aim of this study was to demonstrate the usefulness of artificial neural networks in Alzheimer disease diagnosis (AD) using data of brain single p...

Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease.

BACKGROUND: In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, th...