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

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 92 individuals (68 depressed patients, 24 never-depressed controls) completed a sustained positive mood induction during fMRI. Directed functional connectivity paths within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation, 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 controls;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 controls 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 controls in spite of biological overlap.

Focusing Personality Assessment on the Person: Modeling General, Shared, and Person Specific Processes in Personality and Psychopathology

“…Although the current sample was highly heterogeneous by design, we anticipate based on past experience using GIMME in several data sets of different sorts (Beltz et al, 2016;Price et al, 2017) that massive heterogeneity is likely to be the rule rather than the exception.…”

Connecting Theory and Methods in Adolescent Brain Research

“…These analyses are conducted after GIMME using appropriate statistical tests (e.g., analyses of variance (ANOVAs), growth curves), so effects of covariates and other predictors can be included using standard procedures. Finally, GIMME analyses could even be used to identify data‐driven subgroups that cluster individuals based upon the similarity of their network features (e.g., Price et al., ).…”

“…These individual-level networks, however, contain group-level information, reflecting sample homogeneity and facilitating inference and generalization, when they are implemented in an algorithm called group iterative multiple model estimation (GIMME; Gates & Molenaar, 2012). The utility of GIMME for the analysis of data from heterogeneous neuropsychiatric samples has been established in studies on substance use, depression, and traumatic brain injury (Beltz et al, 2013;Hillary, Medaglia, Gates, Molenaar, & Good, 2014;Hillary et al, 2011;Nichols, Gates, Molenaar, & Wilson, 2014;Price et al, 2017). Thus, euSEMs implemented in GIMME provide a unique opportunity to ask and answer questions about individual differences in adolescent brain function.…”

Network Mapping with GIMME

“…GIMME detected more true edges and fewer spurious edges than 38 other undirected and directed functional connectivity approaches, ranging from partial correlations and coherence analyses to Granger causality inferred from autoregressive models and a variety of Bayesian net methods (for details on data simulation, see Smith et al, 2011). Since those simulations, GIMME has provided novel insights into the brain and behavioral processes underlying substance use (Beltz, Gates, et al, 2013; Nichols, Gates, Molenaar, & Wilson, 2014; Zelle, Gates, Fiez, Sayette, & Wilson, 2016), psychopathology (Beltz, Wright, Sprague, & Molenaar, 2016; Gates, Molenaar, Iyer, Nigg, & Fair, 2014; Price et al, 2017), cognition (Grant, Fang, & Li, 2015), language acquisition (Yang, Gates, Molenaar, & Li, 2015), and olfaction (Karunanayaka et al, 2014), among other areas of inquiry. Moreover, GIMME has been fully-automated and boasts multiple features and extensions that make it suitable for a plethora of research questions and data sets (Beltz & Molenaar, 2016; Gates, Lane, Varangis, Giovanello, & Guskiewicz, 2017; Gates & Molenaar, 2012; Lane, Gates, & Molenaar, 2017).…”

“…This algorithm, called “subgrouping GIMME” (or S-GIMME; Gates et al, 2017), clusters individuals into subgroups using information available after the group-level search; thus, individuals are clustered based solely on their network patterns. Previous work using this algorithm differentiated clinically depressed individuals from controls based on brain connectivity during an emotion task and also differentiated individuals within the same diagnostic category (Price et al, 2017). Alternatively, researchers can provide a priori subgroups (e.g., determined by task performance, diagnosis, or sex).…”