Connectome-Based Individualized Prediction of Loneliness

Chunliang Feng; Li Wang; Ting Li; Pengfei Xu

Disclosures

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

In This Article

Results

Exploratory Correlation Analysis

As expected, loneliness showed significant positive association with neuroticism (β = 0.51, t = 3.99, P < 0.0005) and negative association with extraversion (β = −0.33, t = −3.28, P = 0.002), but not with conscientiousness (β = 0.06, t = 0.46, P = 0.65), openness (β = −0.06, t = −0.61, P = 0.55) or agreeableness (β = −0.18, t = −1.90, P = 0.06). Additionally, loneliness scores were not significantly correlated with mean frame-to-frame head motion (r = 0.0003, P = 1.00) or age (r = −0.04, P = 0.75) and did not differ as a function of gender (males vs females: t = −0.26, P = 0.80) or relationship status (single vs in a romantic relationship: t = 0.99, P = 0.33).

Regarding the correlation between RSFC and loneliness scores, across all participants, the average r value was 0.298 (range: 0.241 ~ 0.3400) in the positive tail that comprised 14 edges. The average r value was −0.292 (range: −0.239 ~ −0.508) in the negative tail that comprised 8163 edges. Because limited number of edges in the positive tail could not provide reliable predictions, the following analyses focused on the negative network.

The edges in the negative network represented <25% of the whole-brain 35 778 total edges defined in the current atlas. The negative network strength, computed by summing the edge strengths for all the edges in the negative tail, were correlated with loneliness scores (r = −0.488). These findings implicated the validity of negative network strength as a summary statistic.

Prediction Analysis Using Cross-validation

A LOOCV approach was implemented to examine whether the relevance between negative network strength and loneliness scores generalized to novel individuals. It was demonstrated that RSFC in the negative network was able to predict loneliness scores in the novel individuals (correlation between actual and predicted scores: r = 0.244, P = 0.019; MSE = 72.70, P = 0.019, permutatio tests, Figure 1). However, RSFC in the positive network could not reliably predict loneliness scores (correlation between actual and predicted scores: r = −0.30, P > 0.05; MSE = 97.72, P > 0.05).

Figure 1.

Performance of the prediction model. (A) Scores of loneliness across participants. (B) Correlation between actual and predicted loneliness scores. (C) Permutation distribution of the correlation coefficient (r) for the prediction analysis. The value obtained using the real scores are indicated by the blue dash line. (D) Consistency between actual and predicted loneliness scores. (E) Permutation distribution of the mean squared error for the prediction analysis. The value obtained using the real scores are indicated by the blue dash line. *P < 0.05.

Contributing Networks in the Prediction of Loneliness Scores

Across all folds of LOOCV, the numbers of edges that contributed to the prediction ranged from 2001 to 10 865. Notably, 1912 of these edges appeared in the every iterations of the LOOCV and were defined as the contributing network (Rosenberg et al., 2016; Shen et al., 2017).

Based on macroscale regions (Figure 2B), it was revealed that connections within prefrontal, temporal and occipital lobes; connections of the prefrontal lobe with subcortical, limbic and temporal lobes; and connections of the temporal with limbic, occipital and cerebellum lobes were primary predictors of loneliness scores (Figure 2C and D).

In addition, the top 20 most highly connected nodes were located in the dlPFC, lateral orbital frontal cortex (lOFC), ventromedial prefrontal cortex (vmPFC), caudate, amygdala, inferior temporal gyrus (ITG), middle temporal gyrus (MTG), supplementary motor area (SMA), precentral gyrus and cerebellum implicating the critical roles of these regions in predicting loneliness (Figure 3 and Table 1).

Figure 3.

Connectivity patterns of the top 20 nodes with the most connections. L, left; R, right; dlPFC, dorsolateral prefrontal cortex; lOFC, lateral orbital frontal cortex; ITG, inferior temporal gyrus; vmPFC, ventromedial prefrontal cortex; MTG, middle temporal gyrus; SMA, supplementary motor area; PFC, prefrontal cortex; Mot, motor; Ins, insula; Par, parietal; Tem, temporal; Occ, occipital; Lim, limbic; Cer, cerebellum; Sub, subcortical; Bsm, brainstem.

Validation With Different Cross-validation Schemes

Using different cross-validation schemes, the performance of predication was re-estimated. The resultant correlation coefficients between actual and predicted loneliness scores remained significant (Table 2), thus validating the main findings.

Control Analyses

After controlling for the potential confounds of head motion, age, gender, relationship state, head motion, neuroticism and extraversion, new predictive networks were constructed and used in the cross-validation schemes. These analyses indicated that predictive models could still significantly predict loneliness scores (i.e. correlation between actual and predicted loneliness scores remained significant), independent of age, gender, relationship state, head motion, neuroticism and extraversion (Table 2).

Personality Prediction Based on the Loneliness-related Network

To assess the association between personality (i.e. neuroticism and extraversion) and networks that contribute to the prediction of loneliness, we examined whether these networks were capable of predicting neuroticism and extraversion. It was demonstrated that the loneliness-related network was able to predict these personality scores in the novel individuals: neuroticism (correlation between actual and predicted scores: r = 0.45, P = 0.001; MSE = 143.01, P = 0.001, permutation tests, Figure 4A and C) and extraversion (correlation between actual and predicted scores: r = 0.22, P = 0.004; MSE = 110.10, P = 0.001, permutation tests, Figure 4B and D).

Figure 4.

Performance of the prediction model for personality scores. (A) Correlation between actual and predicted neuroticism scores. (B) Correlation between actual and predicted extraversion scores. (C) Permutation distribution of the correlation coefficient (r) for the prediction analysis of neuroticism scores. The value obtained using the real scores are indicated by the blue dash line. (D) Permutation distribution of the correlation coefficient (r) for the prediction analysis of extraversion scores. The value obtained using the real scores are indicated by the blue dash line.

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