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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 microglia. Abnormal...

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Microstructural mapping of neural pathways in Alzheimer's disease using macrostructure-informed normative

Yixue Feng1, Bramsh Q Chandio1, Julio E Villalon-Reina1

  • 1Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 31, 2024
PubMed
Summary

This study introduces a new framework (MINT) to analyze brain white matter changes in dementia and mild cognitive impairment. MINT jointly models tract shape and microstructure, offering a clearer understanding of neurodegenerative disease effects.

Keywords:
Alzheimer's diseaseanomaly detectiondeep generative modelsdiffusion magnetic resonance imagingnormative modelingtractometrytransfer learning

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Area of Science:

  • Neuroimaging
  • Brain Microstructure Analysis
  • White Matter Integrity

Background:

  • Diffusion-weighted MRI (dMRI) detects brain tissue changes relevant to neurodegenerative diseases.
  • Current methods often average diffusion measures, neglecting underlying white matter fiber geometry.

Purpose of the Study:

  • To introduce a novel framework, macrostructure-informed normative tractometry (MINT), for joint analysis of white matter (WM) microstructure and macrostructure.
  • To investigate alterations in MCI and dementia using MINT.
  • To compare MINT metrics with traditional diffusion tensor imaging (DTI) metrics to understand the impact of fiber geometry.

Main Methods:

  • Developed the MINT framework integrating macrostructure and microstructure.
  • Employed normative models to capture healthy variability.
  • Utilized variational autoencoders for tractography and fiber geometry patterns.
  • Applied multivariate methods for WM microstructure and macrostructure modeling.
  • Leveraged transfer learning for efficient model training.

Main Results:

  • Identified consistent microstructural and macrostructural anomalies in MCI and dementia across two large cohorts (North America, India).
  • Ranked the sensitivity of various diffusion metrics in detecting dementia.
  • Demonstrated that changes in DTI metrics can be influenced by macroscopic alterations.

Conclusions:

  • MINT enables joint modeling of tract shape and microstructure for improved interpretation of neurodegenerative disease effects.
  • The framework has the potential to better disentangle and understand alterations in neural pathways.
  • MINT offers a more comprehensive approach than univariate DTI analysis.