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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Related Experiment Video

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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TEMPORALLY ADAPTIVE-DYNAMIC SPARSE NETWORK FOR MODELING DISEASE PROGRESSION.

Jie Zhang1, Yalin Wang1

  • 1School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|May 20, 2021
PubMed
Summary

This study introduces TADsNet, a novel method for Alzheimer's disease (AD) diagnosis using longitudinal brain imaging. TADsNet captures temporal-subject sparse features for earlier and more accurate AD detection.

Keywords:
LongitudinalRNNSparse Coding

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biomedical Data Analysis

Background:

  • Alzheimer's disease (AD) is a progressive neurodegenerative disorder impacting memory and cognition.
  • Sparse coding (SC) shows promise for AD diagnosis and prognosis.
  • Existing SC methods often overlook longitudinal features and process image patches independently.

Purpose of the Study:

  • To develop a novel supervised sparse coding network (TADsNet) for Alzheimer's disease (AD) diagnosis.
  • To capture temporal-subject sparse features from longitudinal brain images for improved AD discriminability.
  • To address limitations of previous SC methods by incorporating longitudinal data and subject-level feature learning.

Main Methods:

  • Proposed Temporally Adaptive-Dynamic Sparse Network (TADsNet), a supervised sparse coding approach.
  • Adaptively updated sparse codes to enforce temporal regularized correlation.
  • Dynamically mined dictionary atoms for comprehensive subject-level feature utilization from longitudinal brain images.

Main Results:

  • TADsNet demonstrated superior performance in AD diagnosis compared to existing methods.
  • The approach effectively captured sequential correlations and subject-specific codes.
  • Validated superiority on the Alzheimer's Disease Neuroimaging Initiative (ADNI-I) cohort.

Conclusions:

  • TADsNet offers a more effective approach for AD diagnosis by leveraging longitudinal brain imaging data.
  • The method enhances early detection and discriminability of Alzheimer's disease.
  • The findings highlight the importance of temporal and subject-level feature integration in neurodegenerative disease analysis.