Individual variation in brain network topology predicts emotional intelligence
Ling, George Chun-Bong
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BACKGROUND: Social cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits. Using ‘resting-state’ fMRI (functional magnetic resonance imaging) and a trans-diagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition. METHODS: Subjects included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants from three different sites: Beth Israel Deaconess Medical Center, Boston, MA, McLean Hospital, Belmont, MA, and University of Pittsburgh, Pittsburgh, PA. All participants underwent a structural T1/MPRAGE and resting-state fMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR). RESULTS: We identified a region in the left superior parietal lobule (SPL) where individual network topology predicted emotional intelligence. Specifically, the association of this region with the Default Mode Network (DMN) predicted higher emotional intelligence (r = 0.424, p < 0.001) and association with the Dorsal Attention Network (DAN) predicted lower emotional intelligence (r = -0.504, p < 0.001). This correlation was observed in both schizophrenia and healthy comparison participants. These results held true despite corrections for sex, age, race, medication dosage (chlorpromazine equivalents), and full scale IQ (FSIQ), and was replicable per site. Post-hoc analyses showed that membership of the left SPL was entirely within the DMN in high scorers and within the DAN in low scorers. This relationship was also shown to be specific to the identified left SPL region when compared to adjacent regions. Sulcal depth analysis of the left SPL revealed a correlation to emotional intelligence (r = 0.269, p = 0.0075). CONCLUSIONS: Previous studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts emotional intelligence. This is the first demonstration of a clinical phenotype in individual brain network topology.