Key result
Whole-brain resting-state functional connectivity predicted individual loneliness using within- and between-network connectivity of prefrontal, limbic, and temporal systems.
Why the study?
Loneliness is linked to enhanced morbidity and premature mortality, but no effort had been made to establish a model capable of predicting loneliness at the individual level.
Can a machine-learning approach using whole-brain resting-state functional connectivity predict individual loneliness?
Cross-Sectional
Can a machine-learning approach using whole-brain resting-state functional connectivity predict individual loneliness?
A machine-learning model using resting-state functional connectivity can predict individual levels of loneliness, providing potential neuroimaging biomarkers for diagnosis and prognosis.
May support RSFC-based biomarkers for loneliness; leaves open prospective validation before any clinical application.
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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Feng et al. (2019) conducted a cross-sectional in Loneliness. Whole-brain resting-state functional connectivity (RSFC) was evaluated on Individualized prediction of loneliness. Whole-brain resting-state functional connectivity predicted individual loneliness using within- and between-network connectivity of prefrontal, limbic, and temporal systems.
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