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Basics of Multivariate Analysis in Neuroimaging Data
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Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and

Jianfeng Wu1, Wenhui Zhu1, Yi Su2

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

Proceedings of Spie--The International Society for Optical Engineering
|December 28, 2021
PubMed
Summary

This study introduces novel MRI-based methods, Multivariate Morphometry Statistics (MMS) and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP), to predict tau deposition in Alzheimer's disease (AD). These techniques show improved accuracy over existing methods for early AD detection.

Keywords:
Alzheimer’s diseaseBraak12Braak34Dictionary and Correntropy-induced Sparse CodingHippocampal Multivariate Morphometry Statistics (MMS)Tau deposition

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Area of Science:

  • Neuroimaging
  • Biomarker Discovery
  • Alzheimer's Disease Research

Background:

  • Alzheimer's disease (AD) diagnosis and intervention rely on biomarkers, with tau pathology accumulation being a key hallmark.
  • Current tau detection methods like lumbar puncture and Tau PET are invasive, costly, or not widely accessible.
  • Previous research highlighted the potential of MRI-based Multivariate Morphometry Statistics (MMS) for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) for amyloid prediction.

Purpose of the Study:

  • To apply the MMS and PASCS-MP framework with ridge regression models to predict tau deposition in specific Braak regions (Braak1-2 and Braak3-4) in Alzheimer's disease.
  • To evaluate the predictive power of these novel MRI-based representations against traditional morphometric features.

Main Methods:

  • Utilized structural MRI and PET scans from 925 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.
  • Applied Multivariate Morphometry Statistics (MMS) and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) for feature extraction.
  • Employed ridge regression models to predict tau deposition in Braak1-2 and Braak3-4 regions.

Main Results:

  • The MMS and PASCS-MP derived representations demonstrated superior predictive power for tau deposition compared to other methods.
  • Predicted Braak1-2 and Braak3-4 scores using the novel framework were closer to actual values.
  • Outperformed traditional measures like hippocampal surface area, volume, and spherical harmonics (SPHARM) based morphometry.

Conclusions:

  • The integrated MMS and PASCS-MP framework offers a more accurate and potentially more accessible approach for predicting tau pathology in Alzheimer's disease.
  • These findings suggest a promising non-invasive biomarker strategy for early AD detection and monitoring, potentially improving upon current diagnostic limitations.