Connectome-based individualized prediction of loneliness

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.

The default network of the human brain is associated with perceived social isolation

“…Among the scant human neuroscience studies on loneliness, visual, attention, and limbic regions have been repeatedly emphasized 21,22,27 . These neural findings align with the Supplementary Fig.…”

“…Among the scant human neuroscience studies on loneliness, visual, attention, and limbic regions have been repeatedly emphasized 21 , 22 , 27 . These neural findings align with the behavioral sequelae of loneliness, which involves an attentional bias for negative social cues in the context of reduced cognitive control 22 – 24 , 30 .…”

“…Its cognitive and affective characteristics also include heightened emotional reactivity to social stimuli, often in the context of reduced cognitive control 25,26 . Accordingly, brain differences related to the experience of loneliness have been reported in visual cortices, as well as visual attention networks, limbic structures, and prefrontal cortex [27][28][29][30][31] .…”

Connectome-based predictive modeling of individual anxiety

“…Within the temporal gyrus, medial (MTG) and inferior temporal gyrus (ITG) were highly connected brain nodes in anxiety-predictive networks. MTG plays a crucial role in social perception to uncertain threats (Haxby et al, 2002;Geng et al, 2018;Feng et al, 2019). ITG is associated with the ventral visual pathway (Baddeley et al, 1997).…”

Intrinsic organization of cortical networks predicts state anxiety: an functional near-infrared spectroscopy (fNIRS) study

“…Our work provides evidence for prediction of emotion states, besides prediction of personality trait or psychopathology. The connectivity-based predictive model characterizes individual-specific trait profiles in a way as fingerprint 59 , which has been widely applied to cognitive functions 24,26 , personality trait 28,60 , and clinical disorders 61 . However, extensive evidence also shows indispensable contributions of state-dependent functional architecture to Individualized brain organization 62,63 .…”