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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: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; ?2 = 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.

SUBMITTER: Price RB

PROVIDER: S-EPMC5215983 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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

Price Rebecca B RB Lane Stephanie S Gates Kathleen K Kraynak Thomas E TE Horner Michelle S MS Thase Michael E ME Siegle Greg J GJ

Biological psychiatry 20160705 4

<h4>Background</h4>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.<h4>Methods</h4>Ninety-two individuals (68 depressed patients, 24 never-depressed control subjects) completed a sustained positive mood induction during functional magnetic reson ...[more]

PMID: 27712830

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Project description:Very preterm birth (VPT; ≤32 weeks' gestation) is associated with altered brain development and cognitive and behavioral difficulties across the lifespan. However, heterogeneity in outcomes among individuals born VPT makes it challenging to identify those most vulnerable to neurodevelopmental sequelae. Here, we aimed to stratify VPT children into distinct behavioral subgroups and explore between-subgroup differences in neonatal brain structure and function. 198 VPT children (98 females) previously enrolled in the Evaluation of Preterm Imaging Study (EudraCT 2009-011602-42) underwent Magnetic Resonance Imaging at term-equivalent age and neuropsychological assessments at 4-7 years. Using an integrative clustering approach, we combined neonatal socio-demographic, clinical factors and childhood socio-emotional and executive function outcomes, to identify distinct subgroups of children based on their similarity profiles in a multidimensional space. We characterized resultant subgroups using domain-specific outcomes (temperament, psychopathology, IQ and cognitively stimulating home environment) and explored between-subgroup differences in neonatal brain volumes (voxel-wise Tensor-Based-Morphometry), functional connectivity (voxel-wise degree centrality) and structural connectivity (Tract-Based-Spatial-Statistics). Results showed two- and three-cluster data-driven solutions. The two-cluster solution comprised a 'resilient' subgroup (lower psychopathology and higher IQ, executive function and socio-emotional scores) and an 'at-risk' subgroup (poorer behavioral and cognitive outcomes). No neuroimaging differences between the resilient and at-risk subgroups were found. The three-cluster solution showed an additional third 'intermediate' subgroup, displaying behavioral and cognitive outcomes intermediate between the resilient and at-risk subgroups. The resilient subgroup had the most cognitively stimulating home environment and the at-risk subgroup showed the highest neonatal clinical risk, while the intermediate subgroup showed the lowest clinical, but the highest socio-demographic risk. Compared to the intermediate subgroup, the resilient subgroup displayed larger neonatal insular and orbitofrontal volumes and stronger orbitofrontal functional connectivity, while the at-risk group showed widespread white matter microstructural alterations. These findings suggest that risk stratification following VPT birth is feasible and could be used translationally to guide personalized interventions aimed at promoting children's resilience.

Dataset Information

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

Publications

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

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