Different Decision-Making Responses Occupy Different Brain Networks for Information Processing: A Study Based on EEG and TMS

Abstract

This study used large-scale time-varying network analysis to reveal the diverse network patterns during the different decision stages and found that the responses of rejection and acceptance involved different network structures. When participants accept unfair offers, the brain recruits a more bottom-up mechanism with a much stronger information flow from the visual cortex (O2) to the frontal area, but when they reject unfair offers, it displayed a more top-down flow derived from the frontal cortex (Fz) to the parietal and occipital cortices. Furthermore, we performed 2 additional studies to validate the above network models: one was to identify the 2 responses based on the out-degree information of network hub nodes, which results in 70% accuracy, and the other utilized theta burst stimulation (TBS) of transcranial magnetic stimulation (TMS) to modulate the frontal area before the decision-making tasks. We found that the intermittent TBS group demonstrated lower acceptance rates and that the continuous TBS group showed higher acceptance rates compared with the sham group. Similar effects were not observed after TBS of a control site. These results suggest that the revealed decision-making network model can serve as a potential intervention model to alter decision responses.

G-Causality Brain Connectivity Differences of Finger Movements between Motor Execution and Motor Imagery

“…And it can be used to explore the temporal relationship between regions of interest in order to reveal the directional information flow between brain regions. The Granger causality analysis model was already used for computing the brain connection of decision-making and motor recovery [25–29], which showed that the Granger causality analysis is effective for analyzing the brain.…”

“…Brain function is increasingly understood to be a result of extensively interconnected neurons which means the brain connection reflects the brain function such as decision-making [25–27] and motor function recovery [28–31]. Asymmetry also exists in the perspective of functional connectivity [32].…”

The description–experience gap: a challenge for the neuroeconomics of decision-making under uncertainty

“…Thereafter, the proposed model should be validated using lesions and assessing its generalizability. Methods such as trans-cranial magnetic stimulation and brain lesions can be used to test the alleged causal relationship between neural correlates and behavioural processes [46][47][48]. The model's ability to generalize can be assessed by generating predictions in tasks involving different decision problems and behavioural processes (out-of-sample validation).…”

Progresses and prospects of brain electromagnetic imaging

“…[36,37] 、相位同步(phase synchronization) [38] 、同步 似然(synchronization likelihood) [39,40] 、Granger因 果 [41,42] 、自适应直接传递函数(adaptive directed transfer function, ADTF) [43] 、跨频耦合(cross-frequency coupling) [44] 以及一些其他方法 [45~47] . 网络分析方法, 主要 有基于图论的分析方法 [48,49] 、时变脑网络分析 [50,51] 、 网络动态分析 [52~54] 、网络重组 [55,56] 、多层网络 [57,58] 、 高阶网络 [59] 等. 最近出现的网络分析处理算法则有Lp 范数Granger因果有向网络分析方法和Lp(p≤1)范数偏 有向相干(partial directed coherence)网络分析方法, 这 些方法能在噪声情况下比较准确地估计出网络连接模 式 [60,61] .…”