• Li Geng, Jie Meng, Qiuyang Feng, Yu Li, and Jiang Qiu.
• Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China; Faculty of Psychology, Southwest University (SWU), Chongqing, China.
• Neuroscience. 2025 Jan 26; 565: 431439431-439.

AbstractLoneliness is intricately connected to social cognition, yet the precise brain mechanisms that underscore their relationship need further exploration. The present study employed a theory of mind processing task that engaged participants in assessing the trajectories of geometric shapes while undergoing fMRI scans. The comprehensive data pool encompassed loneliness assessments and brain imaging data from a cohort of 157 participants. Utilizing a machine learning approach, task-induced functional connectivity data was used to forecast individuals' loneliness scores. The findings unveil that specific patterns of task-induced alterations in brain functional connectivity hold a remarkable capability to anticipate loneliness scores. Further dissection of the data disclosed pivotal nodes, including the prefrontal cortex, temporoparietal junction, and amygdala, among other cerebral regions. Furthermore, functional connectivity among the social network, the default mode network, and somatomotor networks emerged as crucial factors in prediction. Brain regions contributed strongly in prediction are involved in a variety of social cognitive processes, including intention inference, empathy, and information integration. The results illuminate the association between brain functional connectivity induced by social cognition and loneliness, which enhance the comprehensive understanding of this complex emotional state and may have implications for its diagnosis and intervention.Copyright © 2024 International Brain Research Organization (IBRO). Published by Elsevier Inc. All rights reserved.

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