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Major depressive disorder on a neuromorphic continuum.

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Radiology

Background:

  • Major depressive disorder (MDD) exhibits significant heterogeneity, complicating clinical translation and the identification of reliable neuromarkers.
  • Current approaches like biotyping attempt to address heterogeneity by classifying patients into subgroups, but individual variations suggest a continuum model may be more appropriate.

Purpose of the Study:

  • To develop a data-driven approach to deconstruct MDD heterogeneity using structural magnetic resonance imaging (MRI) data.
  • To identify latent disease factors and their individual expression patterns in MDD patients.
  • To investigate the association of these factors with neurotransmitter systems and their predictive value for treatment response.

Main Methods:

  • Utilized a Bayesian model to analyze structural MRI data from a multisite, cross-sectional cohort of MDD patients.
  • Decomposed MRI features into three latent spatial disease factors and continuum factor compositions representing individual expression.
  • Correlated identified disease factors with neurotransmitter receptor/transporter densities from open PET data and assessed stability and predictive value in longitudinal and transdiagnostic cohorts.

Main Results:

  • Identified three distinct disease factors associated with specific patterns of cortical thickness changes and neurotransmitter densities (norepinephrine, 5-HT2A, 5-HTT).
  • Factor 1: Sensory/orbitofrontal changes linked to norepinephrine and 5-HT2A.
  • Factor 2: Cingulo-opercular/subcortical changes linked to norepinephrine and 5-HTT.
  • Factor 3: Social/affective network changes linked to 5-HTT.
  • Disease factor patterns predicted symptom improvement in a longitudinal cohort and showed stable individual expression over time.

Conclusions:

  • MDD can be conceptualized as a continuum rather than discrete categories, characterized by continuous dimensions affecting distinct brain regions.
  • The identified data-driven disease factors offer potential neuromarkers for MDD heterogeneity and treatment response.
  • This dimensional approach advances understanding of MDD pathophysiology and may guide personalized treatment strategies.