Extraversion and neuroticism relate to topological properties of resting-state brain networks

Abstract

With the advent and development of modern neuroimaging techniques, there is an increasing interest in linking extraversion and neuroticism to anatomical and functional brain markers. Here, we aimed to test the theoretically derived biological personality model as proposed by Eysenck using graph theoretical analyses. Specifically, the association between the topological organization of whole-brain functional networks and extraversion/neuroticism was explored. To construct functional brain networks, functional connectivity among 90 brain regions was measured by temporal correlation using resting-state functional magnetic resonance imaging (fMRI) data of 71 healthy subjects. Graph theoretical analysis revealed a positive association of extraversion scores and normalized clustering coefficient values. These results suggested a more clustered configuration in brain networks of individuals high in extraversion, which could imply a higher arousal threshold and higher levels of arousal tolerance in the cortex of extraverts. On a local network level, we observed that a specific nodal measure, i.e., betweenness centrality (BC), was positively associated with neuroticism scores in the right precentral gyrus (PreCG), right caudate nucleus, right olfactory cortex, and bilateral amygdala. For individuals high in neuroticism, these results suggested a more frequent participation of these specific regions in information transition within the brain network and, in turn, may partly explain greater regional activation levels and lower arousal thresholds in these regions. In contrast, extraversion scores were positively correlated with BC in the right insula, while negatively correlated with BC in the bilateral middle temporal gyrus (MTG), indicating that the relationship between extraversion and regional arousal is not as simple as proposed by Eysenck.

Network Neuroscience and Personality

“…In other words, the metric uses whole brain connectivity information to infer a single region's importance to network communication. A highly interesting aspect of the Gao et al (2013) paper is the attempt to predict individual personality scores from topological aspects of the brain network. A separate prediction model was set up for each participant by estimating a regression function from the data of all other participants (leave-one-out-validation).…”

“…And Kyeong, Kim, Park, and Hwang (2014) examined functional connectivity between all cortical and subcortical brain regions in a whole brain parcellation scheme and showed that the intrinsic organization of the functional connectome into large-scale network is different depending on approach-and avoidance-related personality traits. Whole brain connectomic data were also analyzed by Gao et al (2013). In a network analysis of functional connectivity between 90 brain regions from the automatic anatomical labeling atlas, they found that extraversion relates positively to the global clustering coefficient, a measure of the clustering in a network (see Figure 4C).…”

Functional neuroimaging of extraversion-introversion

“…Moreover, some researchers have linked personality traits with graph theory analyses, and found a positive association between extraversion and clustering coefficient values [36] .…”

“…Previous studies have suggested that extraversion is associated with activities in the frontal and tem poral areas [8,25] . Recent studies have linked extraversion with functional connectivity, which examines the inter-regional temporal correlations between predefi ned seed regions and related functional regions [35,36] . One remarkable property of spontaneous brain activity is that it can be consistently segregated into multiple resting-state networks (RSNs) [37] , which provides new insights into the large-scale neuronal representation of extraversion [12] .…”

Connectomics and Neuroticism: An Altered Functional Network Organization

“…The latter coefficient measures the degree to which a node is connected to other nodes that are part of a different functional subnetwork (Rubinov and Sporns, 2010). To our knowledge, only one graph theoretical study (based on the whole-brain network) has been conducted on neuroticism so far (Gao et al, 2013). An important finding of this study was that the amygdala functions more as a hub in high neurotic individuals compared with low neurotic individuals.…”