TY - JOUR

T1 - Connectome-based individualized prediction of loneliness

AU - Feng, Chunliang

AU - Wang, Li

AU - Li, Ting

AU - Xu, Pengfei

PY - 2019/4

Y1 - 2019/4

N2 - 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.

AB - 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.

KW - loneliness

KW - connectome-based predictive modeling

KW - resting-state functional connectivity

KW - PERCEIVED SOCIAL-ISOLATION

KW - CROSS-LAGGED ANALYSES

KW - RESTING-STATE

KW - FUNCTIONAL CONNECTIVITY

KW - ENVIRONMENTAL CONTRIBUTIONS

KW - HUMAN BRAIN

KW - NETWORK

KW - SELF

KW - ATTENTION

KW - CORTEX

U2 - 10.1093/scan/nsz020

DO - 10.1093/scan/nsz020

M3 - Article

VL - 14

SP - 353

EP - 365

JO - Social Cognitive and Affective Neuroscience

JF - Social Cognitive and Affective Neuroscience

SN - 1749-5016

IS - 4

ER -