Dementia

Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study.

BACKGROUND: Dementia is increasing in prevalence worldwide, yet frequently remains undiagnosed, especially in low- and middle-income countries. Population-based surveys represent an underinvestigated source to identify individuals at risk of dementia.

Altered Micro-RNA Regulation and Neuroprotection Activity of in Alzheimer's Disease Model.

BACKGROUND: Amyloid-β peptide (Aβ) is involved in the formation of senile plaques in Alzheimer's disease (AD), and causes neuronal cell death by inducing oxidative stress.

Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease.

Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical appli...

Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer's disease classification.

In recent years, machine learning approaches have been successfully applied to the field of neuroimaging for classification and regression tasks. Howe...

Factors associated with dementia in elderly.

We analyzed the factors associated with dementia in the elderly attended at a memory outpatient clinic of the University of Southern Santa Catarina (U...

Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine.

Different modalities such as structural MRI, FDG-PET, and CSF have complementary information, which is likely to be very useful for diagnosis of AD an...

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques.

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and ...

Localized instance fusion of MRI data of Alzheimer's disease for classification based on instance transfer ensemble learning.

BACKGROUND: Diagnosis of Alzheimer's disease (AD) is very important, and MRI is an effective imaging mode of Alzheimer's disease. There are many exist...

Does Cognitive Impairment and Agitation in Dementia Influence Intervention Effectiveness? Findings From a Cluster-Randomized-Controlled Trial With the Therapeutic Robot, PARO.

OBJECTIVES: To explore whether severity of cognitive impairment and agitation of older people with dementia predict outcomes in engagement, mood state...

Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model.

Brain-wide and genome-wide association (BW-GWA) study is presented in this paper to identify the associations between the brain imaging phenotypes (i....

Characteristics of Mild Cognitive Impairment Using the Thai Version of the Consortium to Establish a Registry for Alzheimer's Disease Tests: A Multivariate and Machine Learning Study.

BACKGROUND: The Consortium to Establish a Registry for Alzheimer's Disease (CERAD) developed a neuropsychological battery (CERAD-NP) to screen patient...

Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling.

Alzheimer's disease (AD) is a progressive brain disease. The goal of this study is to provide a new computer-vision based technique to detect it in an...

Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease.

Early diagnosis is critical for individuals with Alzheimer's disease (AD) in clinical practice because its progress is irreversible. In the existing l...

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitat...

Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer's disease patients.

Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) require differential management despite presenting with symptomatic overlap. Currently, t...

Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation.

To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed a...

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

BACKGROUND: There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's dise...

Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase whe...

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

BACKGROUND: The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning...

In Schizophrenia, Depression, Anxiety, and Physiosomatic Symptoms Are Strongly Related to Psychotic Symptoms and Excitation, Impairments in Episodic Memory, and Increased Production of Neurotoxic Tryptophan Catabolites: a Multivariate and Machine Learning Study.

The depression, anxiety and physiosomatic symptoms (DAPS) of schizophrenia are associated with negative symptoms and changes in tryptophan catabolite ...