Topological properties of brain functional networks are associated with individual risk tolerance

889 

Abstract Submission 

Wi Hoon Jung¹

¹Gachon University, Seongnam-si, Gyeonggi-do

Wi Hoon Jung  
Gachon University
Seongnam-si, Gyeonggi-do

Risk tolerance refers to the extent to which an individual is willing to take risks to achieve greater expected returns. Individual differences in the risk tolerance are associated with real-life outcomes, such as financial choices and health behaviors (Krain et al., 2008). In the brain, these individual differences are functionally associated with areas belonging to the valuation and salience networks (such as the medial prefrontal cortex, ventral striatum, anterior insula, and anterior cingulate cortex) and areas that process emotions (such as amygdala) (Levy et al., 2010; De Martino et al., 2010; Knutson and Huettel, 2015; Jung et al., 2018). However, it remains unknown how individual risk tolerance is associated with whole-brain functional network topological properties. Thus, this study investigates whether the topological properties of individual brain functional networks are associated with individual risk tolerance using resting-state fMRI data in conjunction with a graph theoretical analysis approach (Rubinov and Sporns, 2010).

A total of 67 healthy young adults were included in the analysis (37 males/30 females; age = 24.00±1.41 years; risk tolerance = 0.60±0.25). While performing the risk preference task, participants were asked to make a series of 120 choices between a smaller-but-certain reward and a larger-but-risky rewards (Glimcher, 2008; Levy et al., 2010; Jung et al., 2018). The smaller-but-certain reward was fixed at a 100% chance of 10,000 Korean won (KRW, approximately USD 8~9) and the larger-but-risky rewards ranged from KRW 11,000 to KRW 63,000 and their probability varied from 13% to 98%. Individual behavioral data were fitted using a logistic regression function with maximum likelihood estimate to capture the probability of choosing the larger-but-risky reward as a stochastic function of the difference in subjective value (SV) between the two options. To estimate risk tolerance, the SV was followed by the function form of expected utility. A detail description of the risk tolerance estimation method can be found elsewhere (Jung et al., 2018).
After preprocessing the resting-fMRI data, brain areas (i.e., nodes) for these data were divided using the atlas with 160 areas functionally defined by Dosenbach et al. (2010). Next, Pearson correlation coefficients (i.e., edges) between a pair of these divided areas were calculated to construct a functional brain network for each participant. Then, several global topological properties were estimated with different sparsity thresholds (0.10 < S < 0.40), including small-world parameters (i.e., clustering coefficient, characteristic path length, small-worldness, global efficiency, and local efficiency), and a regional topological property, including betweenness centrality.
Finally, Spearman's rank correlation analyses were performed to examine the association between (log-transformed) individual risk tolerance and each of the network topological properties.

Individual risk tolerance was positively associated with global topological properties, including the normalized clustering coefficient, related to the degree of information segregation (r-value = 0.29, p-value = 0.02), and small-worldness, related to the balance between information segregation and integration in a network (r-value = 0.27, p-value = 0.03). We also found that individuals with higher risk tolerance exhibited greater centrality in the ventromedial prefrontal cortex (vmPFC), associated with the subjective value of the given options (r-value = 0.35, p-value = 0.01).

Consistent with previous studies, we confirmed that the whole-brain functional network had a small-world architecture. These results extend our understanding of how individual differences in risk tolerance are associated with functional brain organization, particularly regarding the balance between segregation and integration in functional networks, and highlight the important role of the connections of the vmPFC as the hub.

Reward and Punishment ²

Decision Making ¹

BOLD fMRI

Computational Neuroscience

FUNCTIONAL MRI

NORMAL HUMAN