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Basics of Multivariate Analysis in Neuroimaging Data
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Asymmetric canonical correlation analysis of Riemannian and high-dimensional data.

James Buenfil1, Eardi Lila2

  • 1Department of Statistics, University of Washington, Seattle.

Electronic Journal of Statistics
|March 30, 2026
PubMed
Summary

This study presents a new statistical model for analyzing brain connectivity and lifestyle data. The model reveals a key connection between dynamic functional connectivity and personal characteristics, capturing temporal patterns missed by other methods.

Keywords:
62H20Manifold data analysisPrimary 62R30data integrationdynamic functional connectivityfunctional data analysishigh-dimensional statisticssecondary 62G05

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Area of Science:

  • Statistics
  • Neuroscience
  • Data Science

Background:

  • Understanding the relationship between brain activity and personal factors is crucial.
  • Dynamic functional connectivity (DFC) offers insights into brain function over time.
  • Integrating DFC with high-dimensional data presents analytical challenges.

Purpose of the Study:

  • To develop a novel statistical model for the integrative analysis of Riemannian-valued functional data (DFC) and high-dimensional data.
  • To explore the dependence structure between DFC and lifestyle, demographic, and psychometric measures.
  • To identify dominant modes of covariation and their temporal patterns.

Main Methods:

  • A reformulated canonical correlation analysis (CCA) was employed.
  • Tangent space sieve approximations were used to control functional canonical direction complexity.
  • A sparsity-promoting penalty was applied to enforce group structure on high-dimensional canonical directions.

Main Results:

  • The proposed method demonstrated superior empirical performance compared to existing approaches.
  • Application to Human Connectome Project data identified a dominant mode of covariation.
  • This mode links DFC with lifestyle, demographic, and psychometric measures.

Conclusions:

  • The novel statistical model effectively integrates DFC and high-dimensional data.
  • A significant link exists between dynamic brain connectivity and individual characteristics.
  • The model captures unique temporal non-stationary patterns in brain function, unobservable in static analyses.