Utilizing [18F]-AV-1451 tau positron emission tomography (PET) as an Alzheimer disease (AD) biomarker will require identification of brain regions that are most important in detecting elevated tau pathology in preclinical AD. Here, we utilized an uns...
Computer methods and programs in biomedicine
Jul 18, 2017
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...
Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electr...
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Jul 14, 2017
OBJECTIVE: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among each other. Thus, feature selection (FS) algorithms are often considered to find relevant/non-redunda...
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 co...
. 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 information about the condition's severity and developmental risks present allows AD sufferer to take p...
Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its sig...
INTRODUCTION: Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task m...
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 26, 2017
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 age-matched controls participated in this work. The battery of tests included simple reaction times, ...
Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is base...