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Multivariate brain-behaviour associations in psychiatric disorders.

S Vieira1,2,3, T A W Bolton4,5, M Schöttner4

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Doubly multivariate methods like CCA and PLS reveal shared brain-behavior links in psychiatric disorders. Future research must address biases from small sample sizes and in-sample testing for reliable brain-behavior association mapping.

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Data Science

Background:

  • Understanding brain-behavior associations is crucial for psychiatric disorder diagnosis and treatment.
  • Traditional univariate and single multivariate approaches have limitations in capturing complex relationships.
  • Emerging doubly multivariate methods, such as Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS), offer advanced tools for simultaneous analysis of brain and behavior.

Purpose of the Study:

  • To provide an overview of doubly multivariate methods (CCA and PLS) for brain-behavior association studies.
  • To review existing literature on these methods in psychiatric disorders.
  • To discuss challenges and biases in predictive modeling from a machine learning perspective.

Main Methods:

  • Systematic literature review of studies employing CCA and PLS for brain-behavior associations in psychiatric disorders.
  • Analysis of 39 studies across attention deficit and hyperactive disorder (ADHD), autism spectrum disorders (ASD), major depressive disorder (MDD), psychosis spectrum disorders (PSD), and transdiagnostic (TD) groups.
  • Focus on brain measures (morphology, connectivity, white matter integrity) and behavioral variables (symptoms, cognition, physical health, clinical history).

Main Results:

  • Most studies (67%) utilized CCA, focusing on brain morphology, resting-state functional connectivity, or fractional anisotropy.
  • Common findings across diagnoses include links between clinical/cognitive symptoms and frontal brain morphology/activity, and white matter association fibers.
  • Physical health and clinical history emerged as significant, yet less investigated, behavioral predictors.

Conclusions:

  • Doubly multivariate approaches effectively identify complex brain-behavior associations in psychiatric disorders.
  • Frontal brain regions and white matter tracts are consistently implicated across various diagnoses.
  • Studies are susceptible to bias from low sample size-to-feature ratios and in-sample testing, necessitating careful methodological considerations and validation.