Causal interactions in resting-state networks predict perceived lonelinessReport as inadecuate

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Loneliness is broadly described as a negative emotional response resulting from the differences between the actual and desired social relations of an individual, which is related to the neural responses in connection with social and emotional stimuli. Prior research has discovered that some neural regions play a role in loneliness. However, little is known about the differences among individuals in loneliness and the relationship of those differences to differences in neural networks. The current study aimed to investigate individual differences in perceived loneliness related to the causal interactions between resting-state networks RSNs, including the dorsal attentional network DAN, the ventral attentional network VAN, the affective network AfN and the visual network VN. Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from AfN to VN is also related to higher loneliness scores. Our results clearly support the hypothesis that there is a connection between loneliness and neural networks. It is envisaged that neural network features could play a key role in characterizing the loneliness of an individual.

Author: Yin Tian , Li Yang, Sifan Chen, Daqing Guo, Zechao Ding, Kin Yip Tam, Dezhong Yao



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