Connectome-Based Individualized Prediction of Loneliness

Chunliang Feng; Li Wang; Ting Li; Pengfei Xu


Soc Cogn Affect Neurosci. 2019;14(4):353-365. 

In This Article


Loneliness is an increasingly prevalent condition associated with enhanced morbidity and premature mortality. Despite the increased recognition of loneliness as an important risk factor for many mental and physical health and recent proposal on medicalization of loneliness (Holt-Lunstad et al., 2017; Cacioppo and Cacioppo, 2018), so far no effort has been made to establish a model capable of predicting loneliness at the individual level. Such a model would be important for diagnosis and prognosis in future, and since the brain is thought to be the key organ of social connections and processes (Cacioppo et al., 2014; Cacioppo et al., 2015a), brain features provide promising candidates to establish predictive models. The current work utilized the intrinsic whole-brain functional connectivity in a machine-learning framework to establish a connectome-based model that is predictive of loneliness at the individual level. Notably, our findings further indicate that both loneliness and underlying neural substrates were modulated according to the levels of neuroticism and extraversion.

Our findings reveal intrinsic functional connectivity across multiple neural systems contributes to predicting individual loneliness. Specifically, inter-individual variations in loneliness were primarily accounted for by intrinsic functional connectivity within the prefrontal cortex as well as its connectivity with other networks, particularly the subcortical, limbic and temporal structures. The activity within these neural systems has been previously implicated in cognitive, affective and social components of loneliness (Cacioppo et al., 2009; Inagaki et al., 2015; Wong et al., 2016; Canli et al., 2018). In short, loneliness could be predicted by large-scale distributed functional network connectivity, suggesting that loneliness is characterized by interactive patterns across multiple brain systems. In line with this hypothesis, evidence from animal studies indicates that chronic social isolation has profound effects on brain chemistry and function across multiple neural systems (Zelikowsky et al., 2018).

We demonstrate that the predictive model of loneliness consisted of key nodes associated with emotion processing, including the vmPFC, caudate and amygdala. On the one hand, the vmPFC and caudate have been frequently involved in positive social interactions, such as cooperating with others (Rilling et al., 2002; Feng et al., 2015a), being fairly treated (Tabibnia et al., 2008; Feng et al., 2015b) and communicating one's own thoughts and feelings to others (Tamir and Mitchell, 2012). Therefore, it is plausible that altered functional connectivity in these regions might underlie the diminished pleasure derived from social interactions among lonely people (Hawkley et al., 2007). In line with our findings, loneliness is associated with lower striatal activation in response to positive social information (Cacioppo et al., 2009) as well as differential transcriptome expression in the ventral striatum (Canli et al., 2017). On the other hand, the amygdala is a key region in the limbic system associated with the encoding of threatening stimuli (LaBar et al., 1998; Adolphs et al., 2005; Adolphs, 2008). Accordingly, changes in functional connectivity of this region may be related to hypervigilance to negative social information and negative expectations of social interactions among lonely people (Yamada and Decety, 2009; Hawkley et al., 2010; Cacioppo et al., 2015b).

We further demonstrated MTG and ITG in the temporal lobe as key nodes of the predictive model of loneliness. These regions play critical roles in social perception, such as the processing of faces and eye gaze (Perrett et al., 1985; Critchley et al., 2000; Haxby et al., 2002). Other studies have identified the activations of these regions in the empathy and theory of mind tasks (Farrow et al., 2001; Völlm et al., 2006). In light of previous findings, our results suggest that loneliness is involved in altered social perception and communication mediated by the temporal lobe. This conjecture aligns with two lines of evidence. First, high-lonely people compared to low-lonely people gave less attention to others during communication and were less accurate at encoding nonverbal communications, implicating social skill deficits among lonely people (Gerson and Perlman, 1979; Jones et al., 1982). Second, loneliness was corrected with structural changes in the pSTS part of the temporal lobe, and the association was mediated by basic social perception skills (Kanai et al., 2012).

We also revealed dlPFC and lOFC as key nodes in the prediction of loneliness. These regions have been implicated in many high-order control processes, ranging from task-set maintaining to long-term planning and response suppression and selection (Miller and Cohen, 2001; Cole and Schneider, 2007; Seeley et al., 2007; Menon, 2011). Notably, they have also been involved in emotion regulation through modulations of limbic and subcortical regions (Wager et al., 2008; Kober et al., 2010; Lee et al., 2012). Accordingly, the current findings provide a potential neural mechanism on the impaired self-regulation and cognitive functions among lonely people (Baumeister et al., 2005; Campbell et al., 2006; Hawkley et al., 2009). In line with our findings, loneliness has been found related to changes in brain structures of the dlPFC (Kong et al., 2015) and its functional connectivity with arousal systems (Layden et al., 2017).

Taken together, the multiple neural systems identified in the current study might underlie the affective, social and cognitive processing deficits related to loneliness. Notably, our findings provide the first evidence showing that these seemingly distinct processes do not work separately, but extensively interact with each other to maintain loneliness. In this regard, the whole-brain functional connectivity approach provides more holistic measures of loneliness as a complex construct.

Our findings finally indicate that loneliness and associated neural substrates are modulated according to neuroticism and extraversion. These findings complement several lines of evidence. First, previous studies report the strongest correlations between loneliness and neuroticism or extraversion (Atak, 2009; Teppers et al., 2013; Mund and Neyer, 2016), although several studies also identify correlations of loneliness with openness, agreeableness and conscientiousness (Lopes et al., 2003; Abdellaoui et al., 2018b). Second, loneliness and neuroticism exhibited a considerable genetic overlap measured by both genetic variants and familial relationships (Abdellaoui et al., 2018a). Third, neuroticism and extraversion mediated the associations between loneliness and altered brain structures in the dlPFC (Kong et al.,2015). These findings together indicate that loneliness and neuroticism/extraversion are highly relevant constructs, and they might share common underpinnings at both psychological and biological levels. In particular, neuroticism is characterized by enhanced sensitivity to aversive stimuli, whereas extraversion is characterized by increased sensitivity to positive social stimuli, and both personality characteristics are closely related to core features of loneliness (Cacioppo et al., 2009; Cacioppo et al.,2015b). Nevertheless, the current study revealed that the RSFC-based model could still predict individual loneliness scores after controlling for neuroticism and extraversion. These findings indicate that the predictive model can account for variance in loneliness that is not explained by the personality traits.

Notably, the current study represents advances in neuroscience advocating the applications of brain features in a machine-learning framework to establish neuroimaging-based predictions (Fu and Costafreda, 2013; Paulus, 2015; Woo et al.,2017). This approach aims to reveal predictive brain features that can be used to facilitate diagnosis, prognosis and treatment of individual patients in clinical practice. Within this framework, an accumulating body of research has developed predictive models based on brain imaging features to discriminate patients from health controls or to predict symptom severity (for reviews, see also Fu and Costafreda, 2013; Woo et al., 2017). In this regard, a potential application of the current approach would be the use of RSFC measures in predicting severity of loneliness either among general population or among patients (e.g. anxiety or mood disorder), considering that loneliness is a critical risk factor for many health problems.

Several limitations should be noted as they relate to the current study. First, although the current study controlled for potential major confounds such as age, gender, relationship status and motion, other measures of objective social isolation (e.g. the objective levels of social contact) should be collected and controlled for in future studies. Similarly, loneliness could be related to transient mood states and could be temporary, future studies may also consider controlling for those confounding factors. Second, one may not interpret the predictive network as a 'neuromarker' of loneliness, since the current study did not completely examine the specificity of the predictive model. Indeed, the relationship between RSFC and loneliness could be explained by their common associations with a third variable. Third, our prediction was obtained in a relatively small sample, and the generalization of the current findings requires further validation using an independent larger sample and other cross-validation methods. Fourth, it is noteworthy that the current prediction model of loneliness was based on the negative network (i.e. connections negatively associated with loneliness). The large negative but small positive predictive network of loneliness may reveal a dis-connectivity pattern as the increase of loneliness. Given the positive predictive model failed and was not stable, we should be cautious about drawing any conclusions based on the non-significant findings.

Despite these limitations, we first demonstrate that functional connectivity of distributed networks effectively predicts loneliness at the individual level. Notably, nodes and edges of the predictive network have been frequently implicated in affective, social and cognitive processing required by developing and maintaining social connections. The current data-driven approach provides a novel tool to characterize neural mechanisms of loneliness and might have potential applications in clinical practice.