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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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Normalizing flow based neural processes for Alzheimer's disease progression prediction.

Emad Al-Anbari1, Hossein Karshenas1, Bijan Shoushtarian1

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This study introduces a new Alzheimer's disease prediction model, combining Neural Processes (NPs) and Normalizing Flows (NFs). The model improves early detection and personalized treatment by effectively handling complex patient data and temporal changes.

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Medical Informatics

Background:

  • Alzheimer's disease (AD) is a leading cause of neurodegeneration globally, necessitating accurate prediction for effective interventions.
  • Traditional predictive models struggle with complex, multi-modal data and temporal dynamics inherent in AD progression.
  • Existing methods often overlook crucial data points, limiting diagnostic accuracy.

Purpose of the Study:

  • To develop a novel predictive model for Alzheimer's disease detection and classification.
  • To enhance the handling of complex, longitudinal patient data for improved diagnostic accuracy.
  • To leverage the strengths of Neural Processes (NPs) and Normalizing Flows (NFs) for robust AD prediction.

Main Methods:

  • Integration of Neural Processes (NPs) for modeling stochastic temporal dependencies and Normalizing Flows (NFs) for complex data distribution transformation.
  • Utilized the Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) dataset, incorporating cognitive, neuroimaging, genetic, and demographic features.
  • Compared the proposed SNP-NF model against established methods like SNP, deep geometric learning, and Manifold DCNN.

Main Results:

  • The proposed SNP-NF model demonstrated improved performance in predicting Alzheimer's disease across Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD classes.
  • Achieved approximately 3% improvement in mean Area Under the Curve (mAUC), 1% in Precision, and 0.7% in Recall compared to a previous NP-only model.
  • The model effectively captures temporal dependencies and adapts to individual patient trajectories, showing enhanced robustness and generalization.

Conclusions:

  • The novel SNP-NF model offers a significant advancement in Alzheimer's disease prediction, outperforming existing methods.
  • This approach facilitates earlier detection and the development of personalized treatment strategies for Alzheimer's patients.
  • The model's ability to handle complex, longitudinal data paves the way for more accurate and reliable neurodegenerative disease diagnostics.