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.

Details

Title
Connectome-based individualized prediction of loneliness
Author
Chunliang Feng 1 ; Wang, Li 2 ; Li, Ting 2 ; Xu, Pengfei 3 

 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 
 Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, Beijing, China 
 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 
Pages
353-365
Publication year
2019
Publication date
Apr 2019
Publisher
Oxford University Press
ISSN
17495016
e-ISSN
17495024
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3171538059
Copyright
© The Author(s) 2019. Published by Oxford University Press. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.