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 Horner, Michael E. Thase, Greg J. Siegle

Research output: Contribution to journalArticle

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)
JournalBiological Psychiatry
DOIs
StateAccepted/In press - Apr 6 2016
Externally publishedYes

Fingerprint

Depression
Brain
Reaction Time
Magnetic Resonance Imaging
Direction compound

Keywords

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

ASJC Scopus subject areas

  • Biological Psychiatry

Cite this

Price, R. B., Lane, S., Gates, K., Kraynak, T. E., Horner, M., Thase, M. E., & Siegle, G. J. (Accepted/In press). Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood. Biological Psychiatry. https://doi.org/10.1016/j.biopsych.2016.06.023

Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood. / Price, Rebecca B.; Lane, Stephanie; Gates, Kathleen; Kraynak, Thomas E.; Horner, Michelle; Thase, Michael E.; Siegle, Greg J.

In: Biological Psychiatry, 06.04.2016.

Research output: Contribution to journalArticle

Price, Rebecca B. ; Lane, Stephanie ; Gates, Kathleen ; Kraynak, Thomas E. ; Horner, Michelle ; Thase, Michael E. ; Siegle, Greg J. / Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood. In: Biological Psychiatry. 2016.
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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.",
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