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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Localizing Sources of Brain Disease Progression with Network Diffusion Model.

Chenhui Hu1, Xue Hua2, Jun Ying3

  • 1Microsoft, Cambridge, MA, 02142 USA.

IEEE Journal of Selected Topics in Signal Processing
|May 16, 2017
PubMed
Summary

This study introduces a novel diffusion model to pinpoint dementia origins by analyzing brain atrophy. Findings reveal distinct source concentrations in Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients, suggesting evolving disease mechanisms.

Keywords:
Alzheimer’s diseaseMRISources of dementiabrain morphologyinverse problemnetwork diffusion

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Area of Science:

  • Neurodegenerative diseases
  • Brain imaging analysis
  • Computational neuroscience

Background:

  • Effective treatment of neurodegenerative diseases requires precise identification of dementia sources.
  • Brain atrophy progression is a key indicator in diseases like Alzheimer's disease (AD).
  • Existing methods may lack the precision to pinpoint early-stage disease origins.

Purpose of the Study:

  • To develop and validate a diffusion model for localizing dementia sources within the brain's connectivity network.
  • To model the progression of brain atrophy and its origins in neurodegenerative diseases.
  • To investigate differences in dementia source distribution between Alzheimer's disease (AD) and mild cognitive impairment (MCI) subjects.

Main Methods:

  • A diffusion model with impulsive sources was applied to brain connectivity networks.
  • Sparse regularization and gradient descent methods were used to solve the inverse problem for source estimation.
  • Analysis utilized a large dataset of repeated magnetic resonance imaging (MRI) scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

Main Results:

  • The model successfully localized potential origins of Alzheimer's disease (AD).
  • Significant differences in dementia source concentrations were observed across brain lobes for AD and MCI patients.
  • The proposed model demonstrated effectiveness in predicting future brain atrophy patterns.

Conclusions:

  • The findings suggest a potential shift in the underlying mechanisms driving dementia progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD).
  • The developed model offers a valuable tool for understanding dementia dynamics and origins.
  • This approach can aid in early-stage monitoring and management of neurodegenerative diseases.