AI Medical Compendium Topic:
Alzheimer Disease

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

NeuroImage
Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification ...

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

Psychiatry research. Neuroimaging
Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) are important diffusion MRI techniques for detecting microstructure abnormities in diseases such as Alzheimer's. The advantages of DKI over DTI have been reported generally; however,...

DES-ncRNA: A knowledgebase for exploring information about human micro and long noncoding RNAs based on literature-mining.

RNA biology
Noncoding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long ncRNAs (lncRNAs), are important players in diseases and emerge as novel drug targets. Thus, unraveling the relationships between ncRNAs and other biomedical entities in cells are criti...

Distinguishing age-related cognitive decline from dementias: A study based on machine learning algorithms.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND AND AIM: This study aims to examine the distinguishability of age-related cognitive decline (ARCD) from dementias based on some neurocognitive tests using machine learning.

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

Journal of food and drug analysis
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 AD. Deposits of insoluble Aβ are found in the brains of patients with AD and are one of the pathologi...

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

Journal of neuroscience methods
BACKGROUND: Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can trans...

A novel hypothesis-unbiased method for Gene Ontology enrichment based on transcriptome data.

PloS one
Gene Ontology (GO) classification of statistically significantly differentially expressed genes is commonly used to interpret transcriptomics data as a part of functional genomic analysis. In this approach, all significantly expressed genes contribut...

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

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
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 cognitive impairment (MCI) is considered to be a prodromal stage of AD. The ability to identify subjects wit...

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

Behavioural neurology
Subjects with Alzheimer's disease (AD) show loss of cognitive functions and change in behavioral and functional state affecting the quality of their daily life and that of their families and caregivers. A neuropsychological assessment plays a crucial...

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

Medical image analysis
Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effecti...