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Tensor response quantile regression with neuroimaging data.

Bo Wei1, Limin Peng1, Ying Guo1

  • 1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA.

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Summary
This summary is machine-generated.

This study introduces a new tensor response quantile regression method for analyzing neuroimaging data in mental health. The method reveals complex associations between brain connectivity and PTSD symptoms, offering deeper insights than traditional approaches.

Keywords:
canonical polyadic decompositionheterogeneous associationquantile regressiontensor response

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Area of Science:

  • Neuroscience
  • Statistics
  • Psychiatry

Background:

  • Neuroimaging data, often in tensor format, is increasingly used in mental health research.
  • Understanding associations between neuroimaging phenotypes and clinical conditions like PTSD is crucial.

Purpose of the Study:

  • To develop a novel tensor response quantile regression framework for analyzing complex neuroimaging data.
  • To investigate the heterogeneous associations between neuroimaging phenotypes and clinical predictors.

Main Methods:

  • Proposed a low-rank structure to model associations in tensor data.
  • Developed a simple, two-step estimation procedure with theoretical justifications.
  • Validated the method using simulations with realistic sample sizes.

Main Results:

  • The proposed method effectively analyzes neuroimaging data, revealing non-homogeneous effects.
  • Applied to a PTSD study, it uncovered detailed associations between fMRI connectivity and symptom severity.
  • Demonstrated superior ability to capture complex effects compared to existing tensor methods.

Conclusions:

  • The tensor response quantile regression framework offers a powerful new tool for mental health neuroimaging studies.
  • This method provides a more nuanced understanding of brain-symptom relationships in conditions like PTSD.
  • The findings highlight the potential for uncovering previously undetected heterogeneity in clinical associations.