Alzheimer's Disease

Effects on Symptoms of Agitation and Depression in Persons With Dementia Participating in Robot-Assisted Activity: A Cluster-Randomized Controlled Trial.

OBJECTIVES: To examine effects on symptoms of agitation and depression in nursing home residents with moderate to severe dementia participating in a robot-assisted group activity with the robot seal Paro.

A brief method for preparation of gintonin-enriched fraction from ginseng.

BACKGROUND: Ginseng has been used as a tonic for invigoration of the human body. In a previous report, we identified a novel candidate responsible for the tonic role of ginseng, designated gintonin. Gintonin induces [Ca(2+)]i transient in animal cells via lysophosphatidic acid receptor activation. Gintonin-mediated [Ca(2+)]i transient is linked to anti-Alzheimer's activity in transgenic Alzheimer'...

Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis.

Recently, neuroimaging-based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis has attracted researchers in the field, due to the ...

Feature Selection Based on the SVM Weight Vector for Classification of Dementia.

Computer-aided diagnosis of dementia using a support vector machine (SVM) can be improved with feature selection. The relevance of individual features...

A Robust Deep Model for Improved Classification of AD/MCI Patients.

Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preven...

Discriminative analysis of Parkinson's disease based on whole-brain functional connectivity.

Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagn...

Boosting diagnosis accuracy of Alzheimer's disease using high dimensional recognition of longitudinal brain atrophy patterns.

OBJECTIVE: Boosting accuracy in automatically discriminating patients with Alzheimer's disease (AD) and normal controls (NC), based on multidimensiona...

Identification of a small set of plasma signalling proteins using neural network for prediction of Alzheimer's disease.

MOTIVATION: Alzheimer's disease (AD) is a dementia that gets worse with time resulting in loss of memory and cognitive functions. The life expectancy ...

Lokomat training in vascular dementia: motor improvement and beyond!

Vascular dementia (VaD) is a general term describing problems with reasoning, planning, judgment, memory, and other thought processes caused by brain ...

Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM.

Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related t...

Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data.

BACKGROUND: Alzheimer's disease (AD) patients show early changes in white matter (WM) structural integrity. We studied the use of diffusion tensor ima...

Diabetes knowledge in young adults: associations with hemoglobin A1C.

The purpose of this study was to quantify associations between hemoglobin A1C (A1C) and diabetes knowledge score using an assessment tool developed to...

Autonomous unobtrusive detection of mild cognitive impairment in older adults.

The current diagnosis process of dementia is resulting in a high percentage of cases with delayed detection. To address this problem, in this paper, w...

Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.

Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatmen...

Predictive Models Based on Support Vector Machines: Whole-Brain versus Regional Analysis of Structural MRI in the Alzheimer's Disease.

Decision-making systems trained on structural magnetic resonance imaging data of subjects affected by the Alzheimer's disease (AD) and healthy control...

Manifold regularized multitask feature learning for multimodality disease classification.

Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive...

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis.

Recently, there have been great interests for computer-aided diagnosis of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment ...

Creating a place for caregivers in personal health: the iHealthSpace copilot program and diabetes care.

BACKGROUND: As America's baby boom generation reaches retirement, the number of elders, and, in turn, the number of lay individuals who support them, ...

Validation of clinical diagnosis and machine learning classification of cognitive impairment

INTRODUCTION: Cognitive syndrome diagnosis (Normal, Mild Cognitive Impairment (MCI), Dementia) is important for summarizing disease status and predict...

A Multimodal Multiomics Machine Learning (MMM) approach for biomarker discovery and acceleration of clinical trial readiness for childhood-onset neurological disorders

Background Childhood neurodegenerative disorders are usually rare, genetic, and life-limiting. Whilst targeted approaches present huge potential, sign...