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

Abstract and Introduction


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.


Loneliness is a negative emotional state induced by subjective perception of social isolation even when among other people (Weiss, 1973; Cacioppo and Cacioppo, 2018). Susceptibility to loneliness is a trait-like phenotype that is moderately heritable, stable across time and varied across individuals (McGuire and Clifford, 2000; Boomsma et al., 2005; Boomsma et al., 2007; Canli et al., 2018). People high on loneliness experience less reward from daily social interactions, exhibit hypersensitivity to negative social information, show impaired social skills and have poor self-regulation (Jones et al., 1982; Hawkley et al., 2007; Bangee et al., 2014; Yildiz, 2016; Cacioppo et al., 2017). Loneliness has also been linked to big five personality dimensions, especially neuroticism and extraversion (Atak, 2009; Abdellaoui et al., 2018a).

Loneliness is a risk factor for a variety of mental and physical health conditions (House et al., 1988), ranging from depression and anxiety to Alzheimer's disease, cardiovascular disease and cancer (Antoni et al., 2006; Cacioppo et al., 2006; Wilson et al., 2007; Cacioppo et al., 2010; Hawkley et al., 2010). Due to the increasing prevalence of loneliness and its detrimental effects in modern societies, many researchers have advocated the medical solution of loneliness as a public health problem (Holt-Lunstad et al., 2017; Cacioppo and Cacioppo, 2018). In this context, models that can be used to predict loneliness severity at the individual level may provide clinical utility in terms of diagnosis and prognosis in future. The current work presents initial efforts in this direction by making individualized prediction of loneliness from intrinsic whole-brain functional connectivity.

Recent brain imaging studies on loneliness have demonstrated links between loneliness and changes in brain functions and structures important for affective, social and cognitive processing. First, loneliness has been linked to attenuated ventral striatum responses to positive social information (Cacioppo et al., 2009; Inagaki et al., 2015), and enhanced insular responses to negative social information (Lindner et al., 2014), as well as aberrant fronto-limbic functional connectivity when processing negative stimuli (Wong et al., 2016). Second, loneliness is associated with altered structural morphometry and integrity in brain regions that are important for social perception, particularly the posterior superior temporal sulcus (pSTS) and temporoparietal junction (TPJ; Kanai et al., 2012; Nakagawa et al., 2015). Lastly, altered gray matter volume in the prefrontal system [e.g. dorsolateral prefrontal cortex (dlPFC)] (Kong et al., 2015) as well as its within- and between-network organizations have been associated with diminished self-regulation in lonely people (Tian et al., 2014; Layden et al., 2017; Tian et al., 2017). Taken together, previous neuroimaging evidence indicates diverse manifestations of loneliness in multiple neuropsychological processes (Cacioppo and Hawkley, 2009; Cacioppo et al., 2014). Intriguingly, preliminary evidence has shown that associations between loneliness and altered brain functions and structures are mediated by the neuroticism and extraversion (Kong et al., 2015).

Building on recent brain imaging findings (Rosenberg et al., 2016; Smith et al., 2017; Beaty et al., 2018; Hsu et al., 2018), here we implemented a connectome-based predictive modeling approach (Shen et al., 2017) to predict individual loneliness from whole-brain resting-state functional connectivity (RSFC). The RSFC allows for examining interplay between large-scale neural systems associated with loneliness (Braun et al., 2018), which is a complex construct rooted in the functional and structural integrity of distributed networks (e.g. Tian et al., 2014; Nakagawa et al.,2015; Layden et al., 2017; Smith et al., 2017; Tian et al., 2017; Smith et al., 2018). Furthermore, the machine-learning approach typically implements cross-validation procedures to estimate the model with training samples and to test the performance of the model with independent samples (i.e. test samples). Therefore, the predictive model enables subject-specific predictions, which are of help in clinical practice where doctors require for individualized assessment of symptom severity (Paulus, 2015; Huys et al., 2016; Paulus, 2017). Moreover, predictive models integrate all available brain features (i.e. RSFC in the present study) to predict outcomes (i.e. loneliness), which enhance statistical power and avoid multiple comparisons and provide more practical utility compared to commonly used group statistics (see also Woo et al., 2017). Finally, predictive features adopted by the model implicate neural correlates of the loneliness (Rosenberg et al., 2016; Cui et al., 2018).

Based on previous findings, we expected that individual differences in loneliness would be predicted by characteristics of intrinsic connectivity across distributed networks, particularly those implicated in emotional (e.g. the amygdala, insula, striatum), social (e.g. the pSTS and TPJ) and cognitive (e.g. the dlPFC) processing. We also expected that both loneliness and associated network connectivity would be modulated by neuroticism and extraversion.