Alzheimer's Disease

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 over-time and do not progress to AD. To identify and choose effective and personalized strategies to prevent or slow the progression of AD, we need to develop objective measures that are able to discriminate the MCI patients who are at risk of AD from t...

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 dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multiple biomarkers jointly, and need an alignment of subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these ...

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...

Ultra-Low-Dose F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.

Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Me...

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...

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 seve...

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 profess...

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...

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 t...

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...