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Biology subjects

Luo, Y.-j.

Publications and source records attributed to Luo, Y.-j..

3 recordsLinked to original sources

Preserved self-other integration during social decision making among individuals with elevated autistic traits

Autistic people can find social interactions difficult to navigate, traditionally attributed to difficulties in taking others perspectives. However, we have a limited understanding of how autistic people integrate self and other information efficiently during social decision-making. We conducted four highly powered experiments (total N = 1,621) to determine whether autistic traits affect two aspects of self-other integration during social decision making: self-bias and social basis function use. Using Bayesian analyses, we found strong support for the absence of a relationship between autistic traits and either aspect of social decision making, even after controlling for potential confounds (BF01 = 32.13 for self-bias, BF01 = 7.04 for social basis function use). Our results indicate that variations along autistic traits do not impact how people prioritise self-relevant information (self-bias) or utilize compressed social patterns of interaction (social basis function use) to guide their decisions about oneself and other people. These findings nuance the conceptualisation of social-cognitive processes across autistic traits while highlighting the need for large samples to validate null effects.

neuroscience↗

Oscillatory mechanisms of intrinsic brain networks

Neuroimaging studies of hemodynamic fluctuations have shown specific network-based organization of the brain at rest, yet the neurophysiological underpinning of these networks in human brain remain unclear. Here, we recorded resting-state activities of neuronal populations in the key regions of default mode network (DMN, posterior cingulate cortex and medial prefrontal cortex), frontoparietal network (FPN, dorsolateral prefrontal cortex and inferior parietal lobule), and salience network (SN, anterior insula and dorsal anterior cingulate cortex) from 42 human participants using intracranial electroencephalogram (iEEG). We observed stronger within-network connectivity of the DMN, FPN and SN in broadband iEEG power, stronger phase synchronization within the DMN across theta and alpha bands, and weaker phase synchronization within the FPN in delta, theta and alpha band. We also found positive power correlations in high frequency band (70-170Hz) and negative power correlations in alpha and beta band for FPN-DMN and FPN-SN. Robust negative correlations in DMN-SN were found in alpha, beta and gamma band. These findings provide intracranial electrophysiological evidence in support of the network model for intrinsic organization of human brain and shed light on the way how the brain networks communicate at rest.

neuroscience↗

Resting-state functional connectivity of social brain regions predicts motivated dishonesty

Motivated dishonesty is a typical social behavior varying from person to person. Resting-state fMRI (rsfMRI) is capable of identifying unique patterns from functional connectivity (FC) between brain networks. To identify the relevant neural patterns and build an interpretable model to predict dishonesty, we scanned 8-min rsfMRI before an information-passing task. In the task, we employed monetary rewards to induce dishonesty. We applied both connectome-based predictive modeling (CPM) and region-of-interest (ROI) analysis to examine the association between FC and dishonesty. CPM indicated that the stronger FC between fronto-parietal and default mode networks can predict a higher dishonesty rate. The ROIs were set in the regions involving four cognitive processes (self-reference, cognitive control, reward valuation, and moral regulation). The ROI analyses showed that a stronger FC between these regions and the prefrontal cortex can predict a higher dishonesty rate. Our study offers an integrated model to predict dishonesty with rsfMRI, and the results suggest that the frequent motivated dishonest behavior may require a higher engagement of social brain regions.

neuroscience↗