Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood

Rebecca B. Price, Stephanie Lane, Kathleen Gates, Thomas E. Kraynak, Michelle S. Horner, Michael E. Thase, Greg J. Siegle

Research output: Contribution to journalArticlepeer-review

Abstract

Background There is well-known heterogeneity in affective mechanisms in depression that may extend to positive affect. We used data-driven parsing of neural connectivity to reveal subgroups present across depressed and healthy individuals during positive processing, informing targets for mechanistic intervention. Methods Ninety-two individuals (68 depressed patients, 24 never-depressed control subjects) completed a sustained positive mood induction during functional magnetic resonance imaging. Directed functional connectivity paths within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation (GIMME), a method shown to accurately recover the direction and presence of connectivity paths in individual participants. During model selection, individuals were clustered using community detection on neural connectivity estimates. Subgroups were externally tested across multiple levels of analysis. Results Two connectivity-based subgroups emerged: subgroup A, characterized by weaker connectivity overall, and subgroup B, exhibiting hyperconnectivity (relative to subgroup A), particularly among ventral affective regions. Subgroup predicted diagnostic status (subgroup B contained 81% of patients; 50% of control subjects; χ2 = 8.6, p =.003) and default mode network connectivity during a separate resting-state task. Among patients, subgroup B members had higher self-reported symptoms, lower sustained positive mood during the induction, and higher negative bias on a reaction-time task. Symptom-based depression subgroups did not predict these external variables. Conclusions Neural connectivity-based categorization travels with diagnostic category and is clinically predictive, but not clinically deterministic. Both patients and control subjects showed heterogeneous, and overlapping, profiles. The larger and more severely affected patient subgroup was characterized by ventrally driven hyperconnectivity during positive processing. Data-driven parsing suggests heterogeneous substrates of depression and possible resilience in control subjects in spite of biological overlap.

Original languageEnglish (US)
Pages (from-to)347-357
Number of pages11
JournalBiological psychiatry
Volume81
Issue number4
DOIs
StatePublished - Feb 15 2017

Keywords

  • Community detection
  • Depression
  • Neural network connectivity
  • Positive mood
  • S-GIMME
  • fMRI

ASJC Scopus subject areas

  • Biological Psychiatry

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