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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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Related Experiment Video

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Alzheimer's disease risk assessment using large-scale machine learning methods.

Ramon Casanova1, Fang-Chi Hsu, Kaycee M Sink

  • 1Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.

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|November 20, 2013
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Summary

New Alzheimer's disease (AD) risk metrics, AD Pattern Similarity (AD-PS) scores, show promise. Derived from MRI and cognitive data, these scores aid in predicting conversion from mild cognitive impairment to AD.

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Area of Science:

  • Neuroimaging
  • Biostatistics
  • Neurology

Background:

  • Alzheimer's disease (AD) poses a significant global health challenge.
  • Accurate risk assessment is crucial for early intervention and management.
  • Existing metrics for AD risk prediction have limitations.

Purpose of the Study:

  • To introduce novel Alzheimer's disease Pattern Similarity (AD-PS) scores for risk assessment.
  • To evaluate the performance of AD-PS scores using structural MRI and cognitive data.
  • To compare AD-PS scores against established AD risk assessment metrics.

Main Methods:

  • Development of AD-PS scores using large-scale regularized logistic regression.
  • Utilizing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.
  • Employing Cox proportional hazards regression to analyze conversion times from mild cognitive impairment to AD.
  • Systematic characterization of classifier performance across different brain tissue types and cognitive statuses.
  • Exploration of composite scores integrating anatomical and cognitive-anatomical data.

Main Results:

  • AD-PS scores were computed across participant groups stratified by cognitive, age, and functional status.
  • The study systematically evaluated classifier performance based on various data types.
  • Performance of AD-PS scores was compared against SPARE-AD index and hippocampal volume.
  • Composite scores demonstrated potential by combining different data modalities.

Conclusions:

  • AD-PS scores represent a promising new tool for assessing Alzheimer's disease risk.
  • These scores, derived from MRI and cognitive data, offer valuable insights into disease progression.
  • Further validation and application of AD-PS scores could enhance early detection and management strategies for AD.