Alzheimer's Disease

Diagnosis of Dementia by Machine learning methods in Epidemiological studies: a pilot exploratory study from south India.

BACKGROUND: There are limited data on the use of artificial intelligence methods for the diagnosis of dementia in epidemiological studies in low- and middle-income country (LMIC) settings. A culture and education fair battery of cognitive tests was developed and validated for population based studies in low- and middle-income countries including India by the 10/66 Dementia Research Group.

Identifying incipient dementia individuals using machine learning and amyloid imaging.

Identifying individuals destined to develop Alzheimer's dementia within time frames acceptable for clinical trials constitutes an important challenge to design studies to test emerging disease-modifying therapies. Although amyloid-β protein is the core pathologic feature of Alzheimer's disease, biomarkers of neuronal degeneration are the only ones believed to provide satisfactory predictions of cl...

Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review.

BACKGROUND: Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is...

A Pilot Randomized Trial of a Companion Robot for People With Dementia Living in the Community.

OBJECTIVES: To investigate the affective, social, behavioral, and physiological effects of the companion robot Paro for people with dementia in both a...

Diagnosis of Alzheimer's Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network.

. Error-free diagnosis of Alzheimer's disease (AD) from healthy control (HC) patients at an early stage of the disease is a major concern, because inf...

Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing.

Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroenceph...

Diagnosis of Alzheimer's Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features.

Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain f...

Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry.

INTRODUCTION: Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implic...

Low-Cost Robotic Assessment of Visuo-Motor Deficits in Alzheimer's Disease.

A low-cost robotic interface was used to assess the visuo-motor performance of patients with Alzheimer's disease (AD). Twenty AD patients and twenty a...

Quad-phased data mining modeling for dementia diagnosis.

BACKGROUND: The number of people with dementia is increasing along with people's ageing trend worldwide. Therefore, there are various researches to im...

Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning.

Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In parti...

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages.

Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these ...

Automated detection of pathologic white matter alterations in Alzheimer's disease using combined diffusivity and kurtosis method.

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) are important diffusion MRI techniques for detecting microstructure abnormities in...

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-Learning Predictive Model.

With rapid progress in high-throughput genotyping and neuroimaging, imaging genetics has gained significant attention in the research of complex brain...

Amino acid composition predicts prion activity.

Many prion-forming proteins contain glutamine/asparagine (Q/N) rich domains, and there are conflicting opinions as to the role of primary sequence in ...

Protective effect of Nelumbo nucifera extracts on beta amyloid protein induced apoptosis in PC12 cells, in vitro model of Alzheimer's disease.

Alzheimer's disease (AD) is the most common cause of dementia in the elderly. β-Amyloid (Aβ) has been proposed to play a role in the pathogenesis of A...

Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS Transistors.

Neurotransmitter release in chemical synapses is fundamental to diverse brain functions such as motor action, learning, cognition, emotion, perception...

Early identification of mild cognitive impairment using incomplete random forest-robust support vector machine and FDG-PET imaging.

Alzheimer's disease (AD) is the most common type of dementia and will be an increasing health problem in society as the population ages. Mild cognitiv...

Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study.

Subjects with Alzheimer's disease (AD) show loss of cognitive functions and change in behavioral and functional state affecting the quality of their d...

Deep ensemble learning of sparse regression models for brain disease diagnosis.

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease d...