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Alzheimer Disease ll: Pathophysiology01:23

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Alzheimer disease involves structural changes in the brain that begin long before symptoms appear. The most distinctive features are extracellular neuritic plaques and intracellular neurofibrillary tangles.Neuritic plaques form in the cerebral cortex and around blood vessels. These plaques contain a dense core of beta-amyloid (Aβ)—a toxic protein fragment that clumps outside neurons. The core is surrounded by damaged neuronal extensions, as well as reactive astrocytes and...
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Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
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Localized Sparse Code Gradient in Alzheimer's disease staging.

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    A novel Alzheimer's disease (AD) staging method accurately classifies individuals across the disease spectrum. This approach enhances diagnostic precision for early detection and intervention in dementia risk assessment.

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    Area of Science:

    • Neuroscience
    • Medical Imaging Analysis
    • Machine Learning in Healthcare

    Background:

    • Accurate Alzheimer's disease (AD) staging is crucial for dementia risk identification and timely intervention.
    • Current staging methods may lack precision across the full spectrum of cognitive decline.
    • Neuroimaging data holds significant potential for improving AD diagnosis.

    Purpose of the Study:

    • To propose and validate a novel staging method for Alzheimer's disease (AD) encompassing the entire spectrum from cognitive normal to AD.
    • To enhance the distinctiveness of feature representation by embedding high-dimensional multi-view neuroimaging features into a low-dimensional space.
    • To improve classification accuracy by updating testing data using Localized Sparse Code Gradients (LSCG).

    Main Methods:

    • Development of a new AD staging methodology integrating multi-view neuroimaging features.
    • Dimensionality reduction of high-dimensional features into a more distinctive low-dimensional space.
    • Application of the Localized Sparse Code Gradients (LSCG) algorithm for enhanced classification by updating testing data.
    • Validation using Magnetic Resonance Imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline cohort.

    Main Results:

    • The proposed method demonstrated a more distinctive feature representation compared to naive concatenated features.
    • Significant improvements in classification accuracy were observed across all diagnostic groups (AD, Mild Cognitive Impairment with/without conversion, Cognitive Normal).
    • The LSCG algorithm provided enhanced diagnostic performance over the original sparse coding method.

    Conclusions:

    • The novel AD staging method offers improved diagnostic accuracy across the spectrum of cognitive function.
    • The integration of dimensionality reduction and LSCG-based data updating enhances the classification of AD and related cognitive states.
    • This approach holds promise for more precise identification of individuals at risk of dementia, facilitating targeted management strategies.