Intrinsic brain activity patterns across large‐scale networks predict reciprocity propensity

Abstract

Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation. The brain regions contributing to the prediction were then analyzed for functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that patterns of resting-state brain activity across multiple brain systems were capable of predicting individual reciprocity propensity, with the contributing regions distributed across the salience (e.g., ventrolateral prefrontal cortex), fronto-parietal (e.g., dorsolateral prefrontal cortex), default mode (e.g., ventromedial prefrontal cortex), and sensorimotor (e.g., supplementary motor area) networks. Those contributing brain networks are implicated in emotion and cognitive control, mentalizing, and motor-based processes, respectively. Collectively, these findings provide novel evidence on the neural signatures underlying the individual differences in reciprocity, and lend support the assertion that reciprocity emerges from interactions among regions embodied in multiple large-scale brain networks.

Relationship between understanding of gender and LGBTQ and brain health with a focus on the triple network

“…Alternatively, data-driven quantitative reasoning studies suggest that SN and CEN facilitate individuals to build long-term social relationships through emotional processing and cognitive control and that DMN facilitates individuals to develop long-term social relationships through mentalizing processes. It has been suggested that it predicts the experiences, beliefs, and intentions of others and facilitates interaction (Li et al, 2022). In particular, DMN has been implicated in both functional and structural studies as prosocial personality traits such as extraversion and agreeableness (Mars et al, 2012;Sampaio et al, 2014;Coutinho et al, 2013), and it is associated with self-cognitive empathy (Silva et al, 2018;Esménio et al, 2019).…”

Emotion Recognition from EEG Network Connectivity Using Low-Dimensional Discriminant Analysis on Riemannian Manifolds

“…On the other hand, recent neuroscience findings have shown that emotion states are not only encoded in spatial-spectral-temporal EEG features but also encoded in functional network connectivity such as the covariance and coherence among different brain signal channels [22], [23]. Moreover, the brain circuits encoding emotions distribute across multiple frontal-limbic brain regions that have reciprocal functional interactions [24]- [27]. Therefore, directly using whole-brain EEG network connectivity for emotion recognition can be a promising approach.…”