Effective connectivity of brain regions underlying third-party punishment: Functional MRI and Granger causality evidence

Abstract

Third-party punishment (TPP) for norm violations is an essential deterrent in large-scale human societies, and builds on two essential cognitive functions: evaluating legal responsibility and determining appropriate punishment. Despite converging evidence that TPP is mediated by a specific set of brain regions, little is known about their effective connectivity (direction and strength of connections). Applying parametric event-related functional MRI in conjunction with multivariate Granger causality analysis, we asked healthy participants to estimate how much punishment a hypothetical perpetrator deserves for intentionally committing criminal offenses varying in levels of harm. Our results confirmed that TPP legal decisions are based on two domain-general networks: the mentalizing network for evaluating legal responsibility and the central-executive network for determining appropriate punishment. Further, temporal pole (TP) and dorsomedial prefrontal cortex (PFC) emerged as hubs of the mentalizing network, uniquely generating converging output connections to ventromedial PFC, temporo-parietal junction, and posterior cingulate. In particular, dorsomedial PFC received inputs only from TP and both its activation and its connectivity to dorsolateral PFC correlated with degree of punishment. This supports the hypothesis that dorsomedial PFC acts as the driver of the TPP activation pattern, leading to the decision on the appropriate punishment. In conclusion, these results advance our understanding of the organizational elements of the TPP brain networks and provide better insights into the mental states of judges and jurors tasked with blaming and punishing legal wrongs.

Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma

“…Recent simulations [Ryali et al, 2011;Wen et al, 2013] as well as experimental results [David et al, 2008;Katwal et al, 2013;Ryali et al, 2016] suggest that GC applied after deconvolving the HRF from fMRI data (as we have done), is reliable for making inferences about directional influences between brain regions. This method for obtaining SEC and DEC has also been employed in several recent fMRI studies [Bellucci et al, 2017;Deshpande et al, 2013;Feng et al, 2015;Grant et al, 2014Grant et al, , 2015Hutcheson et al, 2015;Lacey et al, 2014;Sathian et al, 2013;Wheelock et al, 2014]. Variance of DEC (vDEC) was taken as the measure of variability in directional connectivity over time (125 3 125 matrix per participant), which, along with SEC, was used further in identifying disease foci.…”

Effective connectivity of the large-scale brain networks for procrastination: A Granger causality analysis

“…入、流出, 对这种平稳时间序列性数据进行稳健的有 向性估计 [29] . 已有研究大量地运用GCA检验个体的神 经效用有向连接, 并揭示其异常交互对于个体精神健 康的影响, 同时表现出了高度的可重复性和有效 [33,34] .…”

Investigating Focal Connectivity Deficits in Alzheimer's Disease Using Directional Brain Networks Derived from Resting-State fMRI

“…Recent works suggests that connectivity varies over time, and that the temporal variability of connectivity is sensitive to human behavior in health and disease (Garrett et al, 2013; Jia et al, 2014; Rashid et al, 2016; Rangaprakash et al, 2017). Therefore, in addition to studying the conventional static effective connectivity (SEC), we also estimated dynamic effective connectivity (DEC; Grant et al, 2015; Hutcheson et al, 2015; Bellucci et al, 2016; Feng et al, 2016; Hampstead et al, 2016) from the resting-state fMRI data acquired from participants with AD as well as healthy controls (HC).…”