Alzheimer's Disease

Multi-modal discriminative dictionary learning for Alzheimer's disease and mild cognitive impairment.

BACKGROUND AND OBJECTIVE: The differentiation of mild cognitive impairment (MCI), which is the prodromal stage of Alzheimer's disease (AD), from normal control (NC) is important as the recent research emphasis on early pre-clinical stage for possible disease abnormality identification, intervention and even possible prevention.

Applying an artificial neural network model for developing a severity score for patients with hereditary amyloid polyneuropathy.

Hereditary (familial) amyloid polyneuropathy (FAP) is a systemic disease that includes a sensorimotor polyneuropathy related to transthyretin (TTR) mutations. So far, a scale designed to classify the severity of this disease has not yet been validated. This work proposes the implementation of an artificial neural network (ANN) in order to develop a severity scale for monitoring the disease progres...

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease.

Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-in...

Feature selection before EEG classification supports the diagnosis of Alzheimer's disease.

OBJECTIVE: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among e...

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 ...

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 ...

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...