Time-resolved connectome of the five-factor model of personality

Figure 1. Resting-state ‘large-scale’ networks identified via independent component analysis (ICA). By using group ICA, the Human Connectome Project consortium has identified fifteen distinct brain circuits that we used to study the time-resolved connectivity patterns. The complete list of the brain region in each resting-state network is reported in Supplementary Table 1.

Figure 2. Examples of high- and low- dynamicity (i.e. T-index) in time-resolved connectivity. Two types of time-resolved connections are depicted, one with high-dynamicity (high “T”-index, light brown line) and the second with low-level of dynamicity (i.e. high stationarity) (low “T”-index, blue line). The dotted lines represent the median values for each connection (i.e. static/time-averaged connectivity). The more dynamic connection exhibits more, higher, and longer-lasting excursions from the overall median. DCC, dynamic conditional correlation.

Figure 3. Dynamicity and strength of inter-network connectivity regardless of personality differences. The colour of the lines connecting each pair of ‘nodes’ (large scale-networks) reflects the degree of dynamicity (red: more dynamic, green: less dynamic, as assessed by the “T”-index, see Methods and Fig. 2). The thickness of each line represents the strength of the connection between each couple of nodes (with thicker lines reflecting stronger connections, as assessed by the HCP consortium in the ‘netmats2’ version). Both strength and dynamicity are represented through median values across all subjects. Only connections associated with positive strength (i.e. partial correlation, as provided by the HCP consortium) are shown.

Figure 4. Conscientiousness was the only personality trait that was linked to stationary connectivity patterns across large-scale networks after accounting for potentially confounding factors such as the remaining personality traits, age, sex, years of education, handedness, and intelligence. The colour of the lines connecting each pair of ‘nodes’ (large scale-networks) represents the statistical significance (−Log10−p-value, yellow colour representing lower p-values: e.g. −Log10(0.05) = 1.30 and −Log10(0.01) = 2) of the negative associations found between conscientiousness scores and the “T”-index of each connection. The thickness of each line represents the effect sizes for each connection with thicker lines reflecting higher effect sizes. All associations shown survive a threshold of P < 0.05, correction for multiple comparisons using false discovery rate across all possible 105 connections (see Table 2).

Figure 5. Example of excursions around the median value of a DCC estimate. Excursion length τ1 and τ2 and excursion magnitudes h1 and h2 are shown.

Figure 6. Image analysis workflow. The resting-state functional magnetic imaging (fMRI) data were initially pre-processed and next a set of 15 separate brain circuits were extracted via independent components analysis (ICA). Next, time-series from each ICA brain circuit were obtained from each individual and fed into dynamic conditional correlation (DCC) models. This led to 15 × 15 time-variant functional connectivity matrices at the single-subject level that were then used to estimate non-stationarity “T”-index as in Zalesky et al.27. Finally, these “T”-index values were analyzed in conjunction with the traits of the five-factor-model of personality at the group level. The generalization ability of the model was evaluated using a using a train/test split approach (see the statistical analyses section of the methods for further information).