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Multi-Group Tensor Canonical Correlation Analysis.

Zhuoping Zhou1, Boning Tong1, Davoud Ataee Tarzanagh1

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Summary
This summary is machine-generated.

Multi-Group TCCA (MG-TCCA) addresses data heterogeneity in tensor analysis. This new method improves the identification of sex-specific brain imaging correlations in Alzheimer's disease research.

Keywords:
Alzheimer’s DiseaseCanonical Correlation AnalysisNeuroimagingTensor Decomposition

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Area of Science:

  • Neuroscience
  • Biostatistics
  • Medical Imaging

Background:

  • Traditional Tensor Canonical Correlation Analysis (TCCA) struggles with heterogeneous tensor data, potentially leading to biased results in group analyses.
  • Real-world data, like brain imaging from diverse populations (sex, race), exhibit heterogeneity not adequately handled by existing TCCA models.
  • This limitation hinders accurate analysis of complex datasets, particularly in neurodegenerative disease research.

Purpose of the Study:

  • To introduce Multi-Group TCCA (MG-TCCA), a novel method for joint analysis of multiple subgroups within tensor datasets.
  • To address data heterogeneity and leverage cross-group information for identifying consistent signals in tensor data.
  • To quantify shared and individual structures, reduce dimensionality, and enable visual exploration of complex tensor data.

Main Methods:

  • Developed Multi-Group TCCA (MG-TCCA) incorporating a dual sparsity structure.
  • Employed a block coordinate ascent algorithm for efficient computation within the MG-TCCA framework.
  • Applied MG-TCCA to analyze correlations between AV-45 and FDG PET imaging modalities in an Alzheimer's disease cohort.

Main Results:

  • MG-TCCA effectively addresses heterogeneity in tensor data by enabling joint analysis of multiple subgroups.
  • The method successfully identified sex-specific cross-modality imaging correlations between AV-45 and FDG PET data in Alzheimer's disease patients.
  • MG-TCCA demonstrated superior performance compared to traditional TCCA in detecting these nuanced correlations.

Conclusions:

  • MG-TCCA offers a robust approach for analyzing heterogeneous tensor data, outperforming traditional methods.
  • The findings highlight the utility of MG-TCCA in uncovering group-specific patterns, such as sex-specific imaging correlations in Alzheimer's disease.
  • This method provides valuable insights for characterizing multimodal imaging biomarkers and understanding disease heterogeneity.