AI Medical Compendium Topic:
Alzheimer Disease

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CSF YKL-40 and pTau181 are related to different cerebral morphometric patterns in early AD.

Neurobiology of aging
Cerebrospinal fluid (CSF) concentrations of YKL-40 that serve as biomarker of neuroinflammation are known to be altered along the clinico-biological continuum of Alzheimer's disease (AD). The specific structural cerebral correlates of CSF YKL-40 were...

Graph-guided joint prediction of class label and clinical scores for the Alzheimer's disease.

Brain structure & function
Accurate diagnosis of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, is very important for early treatment. Over the last decade, various machine learning methods have been proposed to predict disease status and clinica...

Computer-Aided Diagnosis System for Alzheimer's Disease Using Different Discrete Transform Techniques.

American journal of Alzheimer's disease and other dementias
The different discrete transform techniques such as discrete cosine transform (DCT), discrete sine transform (DST), discrete wavelet transform (DWT), and mel-scale frequency cepstral coefficients (MFCCs) are powerful feature extraction techniques. Th...

A novel method for early diagnosis of Alzheimer's disease based on pseudo Zernike moment from structural MRI.

Neuroscience
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia among older people. The number of patients with AD will grow rapidly each year and AD is the fifth leading cause of death for those aged 65 and ...

Exploring multifractal-based features for mild Alzheimer's disease classification.

Magnetic resonance in medicine
PURPOSE: Multifractal applications to resting state functional MRI (rs-fMRI) time series for diagnosing Alzheimer's disease (AD) are still limited. We aim to address two issues: (I) if and what multifractal features are sufficiently discriminative to...

Multiple kernel learning with random effects for predicting longitudinal outcomes and data integration.

Biometrics
Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although, kernel-based st...

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

Brain structure & function
Recently, neuroimaging-based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis has attracted researchers in the field, due to the increasing prevalence of the diseases. Unfortunately, the unfavorable high-dimensional nature of neu...

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

IEEE journal of biomedical and health informatics
Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many rese...

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

Behavioural brain research
OBJECTIVE: Boosting accuracy in automatically discriminating patients with Alzheimer's disease (AD) and normal controls (NC), based on multidimensional classification of longitudinal whole brain atrophy rates and their intermediate counterparts in an...