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Abstract
Loneliness is an increasingly prevalent condition linking with enhanced morbidity and premature mortality. Despite recent proposal on medicalization of loneliness, so far no effort has been made to establish a model capable of predicting loneliness at the individual level. Here, we applied a machine-learning approach to decode loneliness from whole-brain resting-state functional connectivity (RSFC). The relationship between whole-brain RSFC and loneliness was examined in a linear predictive model. The results revealed that individual loneliness could be predicted by within- and between-network connectivity of prefrontal, limbic and temporal systems, which are involved in cognitive control, emotional processing and social perceptions and communications, respectively. Key nodes that contributed to the prediction model comprised regions previously implicated in loneliness, including the dorsolateral prefrontal cortex, lateral orbital frontal cortex, ventromedial prefrontal cortex, caudate, amygdala and temporal regions. Our findings also demonstrated that both loneliness and associated neural substrates are modulated by levels of neuroticism and extraversion. The current data-driven approach provides the first evidence on the predictive brain features of loneliness based on organizations of intrinsic brain networks. Our work represents initial efforts in the direction of making individualized prediction of loneliness that could be useful for diagnosis, prognosis and treatment.
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Details
1 Guangdong Provincial Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China
2 Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, Beijing, China
3 Shenzhen Key Laboratory of Affective and Social Neuroscience, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China; Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China; Department of Neuroscience, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands