The neurocognitive impact of loneliness and social networks on social adaptation

doi:10.21203/rs.3.rs-1969074/v1

Download PDF

Article

The neurocognitive impact of loneliness and social networks on social adaptation

https://doi.org/10.21203/rs.3.rs-1969074/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 25 Jul, 2023

Read the published version in Scientific Reports →

You are reading this latest preprint version

Social adaptation arises from the interaction between the individual and the social environment. However, there is little empirical evidence regarding the relationship between social contact and social adaptation. We propose that loneliness and social networks are key factors explaining social adaptation. Here we recruited 64 subjects to analyze the effects of loneliness, social network and their brain functional correlates on social adaptation. A hierarchical regression model confirmed that loneliness and social network have negative and positive effects on social adaptation respectively. Functional connectivity (FC) analysis showed that loneliness was associated with decreased FC between fronto-amygdalar and fronto-parietal regions, while the social network was positively associated with FC between the fronto-temporo-parietal network. Finally, a multidimensional path model examined the combined effects of behavioral and brain predictors of social adaptation. The model revealed that social networks mediated the effects of loneliness on social adaptation. Further, loneliness-related abnormal brain FC (associated with difficulties in cognitive control, emotion regulation, and sociocognitive processes) emerged as the strongest predictor of poor social adaptation. Findings offer insights into the brain indicators of social adaptation and highlight the role of social networks as a buffer against the maladaptive effects of loneliness. These findings may aid social development approaches.

Social adaptation

loneliness

social network

social neuroscience

Social adaptation refers to the extent to which individuals are motivated to take part in their various roles within society and interact with others under societal norms and expectations ^1,2. One crucial factor negatively affecting social adaptation is the experience of loneliness, defined as the emotional distress resulting from perceived isolation ^3,4. The maladaptive effect of loneliness is associated with poor well-being, disruptive social behaviour ⁵, and abnormal function in networks involved in cognitive control, self-regulation, and social cognition ^6,7. However, these maladaptive indicators can be attenuated by the social support obtained from the social network ^8,9. Despite the above, little is known about the effect of loneliness, and social networks on social adaptation, and even less is known about the neural aspects underlying such relationships. To approach this issue, we analyzed the effects of multilevel measures (i.e. self-report, resting-state FC data) of loneliness and social network on social adaptation. A better understanding of the behavioral and neurophysiologic indicators of social adaptation may constitute a valuable input for public policymaking and interventions aimed at promoting development.

Social adaptation is intricately related to social engagement ¹⁰. Interaction with other members of society and social institutions allows individuals to gain knowledge about the roles, values, rights, and obligations within their society ^11,12. The degree to which subjects accommodate these social conventions determines social adaptation ^11,13. Despite the importance of social interaction for social adaptation, there is no empirical evidence evaluating the relationship between these constructs. Consequently, the neurophysiological aspects associated with this relationship remain unknown.

One factor that can predict social adaptation is the experience of loneliness. First, loneliness is associated with strong emotional distress that is linked to maladaptive adjustment ^14–16. Longitudinal research shows that loneliness predicts poor physical and mental health ^17–19. Poor mental health can affect social adaptation through diminished wellbeing ^5,20,21 and limited ability to fulfill societal roles ²². Second, loneliness has a negative impact on social cognition (i.e. hostile attributions, maladaptive encoding of social cues, aggression, isolation) ^21,23 and ultimately affects the ability to bond with other members of society ²¹. Despite the above, the direct link between loneliness and social adaptation has not been evaluated. At the neurophysiological level, loneliness is associated with altered intrinsic functional connectivity (FC) in attentional (i.e. Dorsal and ventral networks) ^24,25 and cognitive control networks (Cingulo-opercular network and right middle/superior frontal gyrus) ^24,26. Alterations in attentional and impulse control networks might hinder social adaptation through antisocial behavior ²⁷ and the inability to accommodate contextual demands ²⁸. However, there is no evidence of the contribution of the FC in these networks to social adaption.

One important factor that may protect against the maladaptive effects of loneliness is the social network ^8,29. The social network provides support in the form of emotional, instrumental, and informational resources that facilitate social adjustment, and coping ability while reducing stress ^8,30. In fact, people with better and more complex social networks report fewer daily hassles and lower stress ^9,31−33. The stress-buffering effects of social support are known to be modulated by frontal lobe function and self-regulatory processes ³⁴. However, to date, there is no evidence supporting the effects of social networks and frontal lobe function on social adaptation.

Considering this background, this work aims to test the predictive value of self-report measures of loneliness, social network and their FC correlates to social adaptation. We also evaluated the mediating effects of the social network in the relationship between loneliness and social adaptation. Based on previous research on the negative effects of loneliness on mental health and social functioning ^5,21, we expect that loneliness will negatively predict social adaptation. On the other hand, considering previous accounts of the stress-buffering hypothesis ^9,34,35, we expect that social networks will facilitate social adaptation and minimize the effect of loneliness on social adaptation. Regarding the resting-state FC predictors of social adaptation, we expect that loneliness-related abnormal function in attentional and cognitive control networks would negatively predict social adaptation. We also expect FC associated with social networks will positively predict social adaptation. This is the first effort to examine the behavioral and neural predictors of social adaptation. This study also provides empirical evidence that can aid public policy and serve as a basis for interventions aimed at promoting social development.

Participants

Sixty-four healthy subjects between 20 and 73 years old (M = 36.88; SD = 13.62; female (n = 38) were enrolled in this study. They have an average of 11 years of education (M = 11.1, SD = 3.09, range 2 – 20 years of education) and reported no history of psychiatric or neurological conditions. Participants were recruited by accessibility from the general population.

To control for individual differences in executive function, all participants were screened for executive functioning using the INECO frontal screening ³⁶. This is a sensitive instrument for assessing executive dysfunction (see supplementary instruments). The mean score for our sample was 20.92 points, just above the 18-point cut-off for the Chilean population ³⁷.

Every procedure of this research was approved by the Universidad Diego Portales ethics committee. All participants signed an informed consent according to the principles of the Declaration of Helsinki and received a payment as retribution for their collaboration and time.

Procedure

Participants were contacted via telephone and/or social network media and invited to attend the laboratory to complete various scales tapping on social adaptation, loneliness, and social network. fMRI scanning session was carried out on a separate day using a Siemens Avanto 1.5 T scanner equipped with a standard head coil.

Self-report assessment

Social Adaptation Self-administered Scale (SASS)

The SASS ³⁸ is a 21-item scale that explores social adjustment and motivation. The items tap levels of engagement with the environment (e.g., “Do you feel able to organize your environment according to your wishes and needs?”), family relationships (e.g., “How frequently do you seek contact with your family members?”), friendships (e.g., “What value do you attach to the relationship with others?”), and engagement to work (“How interested are you in your occupation?”), among others. Responses are recorded via a 4-point Likert scale (from 0 to 3). The total score ranges from 0 to 60, corresponding to minimal and maximal social adjustment. Scores within 35 and 52 are considered normal ³⁸. The instrument showed good reliability in our sample (α = .73).

University of California Loneliness Scale (UCLA)

The UCLA ⁴ is a widely used measure of the subject’s feelings of loneliness and levels of satisfaction with social experiences. For the present study, an abbreviated version of the UCLA was used ³⁹, comprising the following 8 items: (1) “I can find companionship when I want it”; (2) “I feel left out”; (3) “I feel isolated from others”; (4) “I lack companionship”; (5) “There is no one I can turn to”; (6) “I’m unhappy with being so withdrawn”; (7) “People are around me but not with me”; (8)“I am an outgoing person”. Responses were recorded via a 4-point Likert scale, ranging from 1 (never) to 4 (always). The total score is obtained by inverting positive items and summarizing the score of all items. Thus, larger scores indicate a more pronounced experience of loneliness. The UCLA has shown good levels of reliability as evidenced by an α coefficient of .89 ⁴. We used a shorter 8-item version that showed good reliability levels in our sample (α = .85).

The revised Lubben Social Network Scale (LSNS-R)

The LSNS-R is a 12-item scale that measures the size and complexity of social relationships ⁴⁰. It consists of two scales, one tapping on kinship/family ties (e.g., “How many relatives do you see or hear from at least once a month?”), and other evaluating non-kin / friendship ties (e.g., “How many friends do you feel close to such that you could call on them for help?”). Items are rated on a scale from 0 to 5, with 0 indicating the lowest frequency and number of contact with others and 5 indicating the highest frequency/number of contacts with others. The total score is obtained by summarizing all items’ scores. The maximum total score is 60; with higher scores reflecting bigger and stronger social ties. The scale shows high reliability in old adults (α = 0.90) ⁴¹ and young populations (α = 0.83) ⁴². A similar level of reliability was obtained in our sample with a Cronbach coefficient of α = 0.85.

Images data collection

Images for this study were obtained from a Siemens Avanto 1.5 T scanner equipped with a standard head coil. We obtained 10-minute resting-state fMRI recordings from 61 participants, (data from 3 participants was excluded during pre-processing because of the low quality of their recordings). Functional spin-echo volumes were acquired in sequentially ascending order, parallel to the anterior-posterior commissures, covering the whole brain. The following parameters were used: TR = 3.3 sec; TE = 50 ms; flip angle = 90°; 36 slices, matrix dimension 4 x 64; voxel size in plane RL 3.59mm; AP 3.59mm; slice thickness = 4 mm; number of volumes = 190. Participants were instructed to lay still, keep their eyes closed, and not to think about anything in particular.

Data analyses

Self-report data

Descriptive data analysis for social adaptation, loneliness, social network, and executive functions are displayed in Table 1. We also conducted correlation analyses including sociodemographic data and variables of interest (see supplementary table 1)

Behavioral data were analyzed with R studio ⁴³. We first conducted a hierarchical multiple regression to evaluate the predictive value of loneliness and social network on social adaptation. Hierarchical multiple regression models are useful to evaluate and compare the predictability of groups of independent variables that are entered at different steps of the analysis ⁴⁴. In other words, the main idea of the analysis is to test whether variables entered in subsequent steps, have better predictive value compared to those entered in a former step of the analysis. As for the present analysis, we first specified a base model including our control variables (executive functions, age, education, and gender). These variables did not have any significant effect on social adaptation. In a subsequent step, our measure of loneliness was incorporated into the group of variables. For the last step, we specified a model that also included the social network measure (LSNS scores).

Table 1. Sample’s performance in the behavioral assessment

	N = 64 (37 female; 27 male)
	M (SD)	Range
SASS	42.27 (7.12)	27 - 55
UCLA	8.65 (5.27)	0 - 24
LSNS-R	34.53 (10.47)	8 - 51
IFS total score	20.92 (3.80)	8.5 - 28.0

Abbreviations: SASS: Social Adaptation Self-evaluation Scale); UCLA: University of California Loneliness Scale; LSNS-R: Lubben Social Network Scale; IFS: INECO Frontal Screening.

Resting-state fMRI data

Pre-processing

First, we discarded the first three volumes of each subject’s resting-state recording to ensure that magnetization achieved a steady state. Images were then pre-processed using the Data Processing Assistant for Resting-State fMRI (DPARSF V2.3) ⁴⁵ an open-access toolbox that generates an automatic pipeline for fMRI analysis. The DPARFS processes the data by recruiting the Statistical Parametric Mapping (SPM12) and the Resting-State fMRI Data Analysis Toolkit (REST V.1.7). In line with recommendations ⁴⁶, pre-processing included slice-timing correction (using the middle slice of each volume as the reference scan) and realignment to the first scan of the session to correct head movement (SPM functions). To reduce the effect of motion during image acquisition as well as physiological artifacts ⁴⁷, we controlled two motion parameters (i.e. Translation, rotation; See supplementary table 2), CFS, and WM signals (REST V1.7 toolbox). Motion parameters were estimated during realignment, and data from three subjects were discarded due to exceeding the maximum head movement (3mm and 3^o). CFS and WM masks were derived from the tissue segmentation of each subject’s T1 scan in native space with SPM12 (after co-registration of each subject’s structural image with the functional image). Then, images were normalized to the MNI space using the echo-planar imaging (EPI) template from SPM ⁴⁸, smoothed using an 8-mm full-width-at-half-maximum isotropic Gaussian kernel (SPM functions), and bandpass filtered between 0.01 and 0.08 Hz. None of the participants showed movements greater than 3 mm (M = 0.05, SD = 0.04) and/or rotations higher than 3° (M = 0.05, SD = 0.03).

Functional connectivity analyses

We explored associations between resting-state functional connectivity data and scores from our predictor variables, loneliness (UCLA scores), and social network (LSNS-R score). First, for each subject, we extracted the mean time course of the BOLD signal in each of the 116 regions of the Automated Anatomical Labelling Atlas (AAL) ⁴⁹, by averaging the signal in all voxels comprising each region. Second, we constructed a connectivity matrix for each subject indicating the strength of association between all pairs of regions (Pearson’s correlation coefficient; DPARSF toolbox). Third, we performed a Fisher z-transformation. The resulting functional connectivity (FC) correlation coefficients between all pairs of regions (AAL atlas) were used to perform Spearman’s correlations with the scores of each predictor: loneliness (UCLA score), and social network (LSNS-R score). Following procedures from recent research ^50–52 significance thresholding of neuroimaging results was set to p ≤ .001 (uncorrected). Uncorrected statistical threshold has been used previously to avoid the detrimental effects of liberal primary thresholds on false positives (especially in modest sample sizes such as ours) ^53–55.

Principal component analyses (PCA) of fMRI data

To reduce the dimensionality of the FC correlates of each variable of interest (loneliness, social network, and social adaptation) to include in path analyses, we performed three PCA in RStudio software. We retained the first component for each variable (i.e., the one that accounted for most of the variation in FC correlates) to include in a posterior path analysis model.

Multilevel Path analysis

To evaluate the combined effect of self-report and brain indicators of social adaptation, we performed a path analysis using the laavan package ⁵⁶ in JASP statistical software. This technique aims to test a theoretical model characterized by hypothesized relationships between a set of variables. These patterns are specified a priori (following a given theoretical criterion), and the fit of said model is evaluated with the data obtained. Goodness of fit is indicated by various parameters, including the X² statistic (non-significant), NFI(>0.95), GFI (>0.95), CFI (0.95-1.00), RMSEA (<0.08), IC(≤ 0.05), SRMR (<.08). Path models can be used as an extension of the multiple regression model but with the virtue of simultaneously analyzing the relationships between the independent and dependent variables ⁵⁷. Based on this approach, we proposed a model incorporating brain variables with purely behavioral ones to generate an integrated model (multi-level perspective).

Self-report measures

Hierarchical multiple regression

To evaluate the predictive value of loneliness and social network on social adaptation we performed a hierarchical multiple regression (see table 2). Before the hypothesized model, we conducted the first step of analysis by considering sociodemographic control variables. Results showed that gender (β = .13, p = .41), age (β = .04, p = .81) years of education (β =.09, p = .67) and executive function (β = -.04, p = .82) did not have significant effects over social adaptation. In a second step, after controlling the intervening variables, only loneliness (β = -.58, p<.001) emerged as a significant predictor of Social adaptation (SASS score) [F(1, 38) = 17.82, p<.001]. In a third step social network was added to the existing predictors, this model revealed that both loneliness (β = -.41, p<.005) and social network (β =.43, p<.005) significantly predicted social adaptation. Interestingly, this model shows that after including the social network measure, the effect of loneliness decreases by 0.18 standardized units. After comparing the R²_adj. values for the last two steps, the third step explains 13% more variance associated with social adaptation than the second step. Finally, regarding the effects of potentially confounding variables, we confirmed that the associations amongst the variables of interest remained after controlling for gender, age, years of education, and IFS.

Table 2. Multiple hierarchical regression output.

Demographic				Loneliness			Social network
	std. Beta	Standardized CI	p	std. Beta	Standardized CI	p	std. Beta	Standardized CI	p
(Intercept)	0.00	-0.32 – 0.32	<0.001*	0.00	-0.26 – 0.26	<0.001*	0.00	-0.24 – 0.24	0.002*
Gender	0.13	-0.19 – 0.46	0.411	-0.02	-0.30 – 0.26	0.884	-0.04	-0.29 – 0.22	0.783
Age	0.04	-0.33 – 0.42	0.818	0.16	-0.16 – 0.47	0.325	0.25	-0.05 – 0.54	0.099
Years of education	0.09	-0.33 – 0.51	0.673	0.12	-0.23 – 0.47	0.500	0.26	-0.07 – 0.59	0.123
Executive functions	-0.04	-0.42 – 0.33	0.828	0.04	-0.28 – 0.35	0.803	-0.08	-0.38 – 0.22	0.596
Loneliness				-0.59	-0.87 – -0.31	<0.001*	-0.41	-0.70 – -0.13	0.005*
Social network							0.43	0.14 – 0.71	0.005*
R²/R² adjusted	0.025/ -0.075			0.336/ 0.249			0.466 / 0.380

Note: The table shows standardized coefficients and p-values for each of the steps comprising the hierarchical multiple regression model. The demographic column shows parameter estimates for our control variables (i.e. sociodemographic data and executive functions). The middle column showed the model evaluating the effects of loneliness on social adaptation after controlling for non-interest variables. The final column shows the model evaluating the effects of loneliness and social network on social adaptation after controlling for non-interest variables. Asterisks indicate significant effects. For more details, refer to supplementary table 3

Functional connectivity results

We found significant correlations between our predictor variables (loneliness [UCLA score], and social network [LSNS-R score]) and the functional connectivity between key areas of the social brain (see Figure 1 and supplementary tables 4 and 5). Briefly, loneliness (UCLA score) was negatively associated with the functional connectivity of a more circumscribed set of areas, mainly comprising functional connections between the amygdala and frontal regions, as well as the amygdala and parietal regions (supramarginal gyrus). We also found fronto-insular connections and connectivity within frontal areas (see Figure 1A and supplementary table 4). Lastly, social network (LSNS-R score) was associated with increased functional connectivity mainly between the fronto-temporal, and temporo-parietal areas as well as connections between the insula, frontal areas, and basal ganglia (Figure 1B and supplementary table 5).

Multidimensional path analysis

We proposed a pattern of associations (see figure 2) that integrates the self-report and brain dimensions of our predictor variables. To reduce the dimensionality of our neurophysiological variables, we conducted a principal component analysis on our functional connectivity correlates. We then retained the component that captured the most variance and used it as a predictor in the multidimensional model (see supplementary results). For loneliness, the principal component reflected the functional connectivity between fronto-amygdalar and parietal areas. As for the social network, the component reflected the functional connectivity between fronto-temporo-parietal areas. The fit of the model to the data was excellent. The χ2 test statistic was not significant, suggesting that the model had a good fit (χ² [4, N = 47] = 2.16, p = 0.71). Overall, the fit indices were very good; NFI = 0.99 (values above 0.95 is good); GFI = 0.99 (values above 0.95 is good); CFI = 1.00 (values in the range of 0.95-0.99 considered as excellent fit, and a value of 1 considered as exact fit); RMSEA = 0.00, IC = 0.00 – 0.16 (values ≤ 0.05 considered as good fit); SRMR = 0.04 (a value less than .08 is generally considered a good fit). All paths among variables were significant (see Figure 2)

This is the first study to assess the behavioral and brain functional network effects of loneliness and social network on social adaptation. We hypothesized that loneliness and its brain functional connectivity (FC) correlates would negatively predict social adaptation while social network and its brain correlates would buffer against such maladaptive effects. Our hierarchical multiple regression confirmed that loneliness had negative effects on social adaptation. The model also revealed that the maladaptive effect of loneliness decreased after including the social network measure, which had positive effects on social adaptation. Functional connectivity analysis revealed that loneliness was negatively associated with resting-state FC between the amygdala and fronto-parietal areas known to be key for social emotion regulation, cognitive control, and mentalizing abilities. This FC was later found to be the strongest predictor of social adaptation (amongst self-report and FC variables). On the other hand, the social network was positively associated with intrinsic FC between areas of the contextual social cognition network. However, this FC did not have predictive value over social adaptation. The final integrative path analysis model showed that at the behavioral level, social network mediated the effects of loneliness over social adaptation. At a neurophysiological level, social adaptation was predicted solely by the resting-state FC correlates of loneliness. As above, these FC correlates of loneliness explained most of the variance associated with social adaptation when considering behavioral and brain factors. These findings offer new insight into behavioral, affective, socio-cognitive, and more importantly neurophysiological predictors of social adaptation. Our findings may also serve as an empirical basis for interventions aimed at improving wellbeing and development.

Previous research has highlighted the maladaptive impact of loneliness on health, cognitive functioning, and social behaviour ^5,21,23. Decreased contact with others limits support resources that are important to cope with stress and life demands ^8,34,35. Consistent with the above, we found that loneliness harms social adaptation, while the social network plays a key role in protecting against these negative effects and facilitating adaptation. Our findings suggest that loneliness affects social adaptation through isolation from the social group, decreased mental health, and leads to maladaptive social cognition. However, contact with the social network may modulate these deficits and promote social adaption by minimizing emotional distress, fomenting self-regulation and coping ^8,30. These findings highlight previously unaddressed affective, cognitive, and behavioral factors that predict social adaptation.

At the Neurophysiological level, we found that loneliness-related decreased activity in fronto-amygdalar and fronto-parietal networks predicted low social adaptation. Past research shows that the strength of fronto-amygdalar coupling modulates emotion regulation processes ⁵⁸, affective aspects of social perception and affiliative behaviors ⁵⁹. The inability of frontal cortical regions to modulate amygdala reactivity results in emotion dysregulation, anxious and depressive symptomatology, and isolation exhibited by lonely individuals ²³. In line with the above, our results suggest that decreased fronto-amygdalar connectivity may index poor social adaptation through emotion regulation impairments and poor mental health. On the other hand, fronto-parietal areas are part of a widely distributed cognitive control network ⁶⁰ . Cognitive control enable the appropriate selection of thoughts, emotions, and behaviors based on contextual demands ⁶¹. Another aspect associated with fronto-parietal function is the modulation of socio-cognitive abilities (i.e. mentalizing, empathy) ^62,63. Taken together, our results suggest that dampened fronto-amygdalar and fronto-parietal activity in loneliness may hinder social adaptation through emotion regulation impairments, inability to cope with daily life stressors, and affected social cognition (i.e. Negative appraisal of social stimuli, poor mentalizing and empathic abilities, etc.).

Interestingly, these maladaptive patterns of FC could be reversed by social support-related increases in oxytocin neuropeptide. Research shows that oxytocin neuropeptide modulates emotion regulation by increasing amygdala-prefrontal cortex connectivity ⁶⁴. Oxytocin also enhances trust and social approaching through modulation of insula reactivity to positive social stimuli ⁶⁵. Regarding sociocognitive impairments, evidence shows that oxytocin weakens negativity bias within regions involved in theory of mind (temporoparietal junction), and identification of emotional cues in social perception (right fusiform) ⁶⁶. Future studies should test oxytocin's effects on social adaptation and how it differs from self-reports of social networks.

Regarding possible limitations, we acknowledge that self-report variables may not entirely reflect the construct under investigation. However, such variables showed a pattern of expected relationships according to the theory. Further, to obtain a bigger picture of our construct, we considered the resting-state functional connectivity associated with our predictor variables. This action permitted the results of this research to be more robust. However, future research should include objective measures of the variables in this study.

Little has been done to evaluate the aspects that predict and facilitate social adaptation from a socio-affective and neural perspective. This work aimed to predict the effects of socio-affective, cognitive, and brain predictors of social adaptation. Our findings suggest that the negative impact of loneliness on social adaptation involves impairments in social functioning; affective disorders and decreased functional connectivity in networks associated with cognitive control, self-regulation, and social cognition. However, support obtained from the social network is a key factor that protects against these maladaptive behavioral and brain patterns and foments adequate social adaptation. Considering this, future research should aim at two main goals. One is to incorporate measures of the above processes as indicators of social adaptation. The second is to further explore the various contributions of social integration to the social adaptation processes. The findings here reported contributing to the understanding of mechanisms involved in social adaptation. A better understanding of these mechanisms can inform public policymaking, and as promote social development interventions.

Funding

David Huepe is supported by ANID/FONDECYT Regular (1201486, 1171200). Agustin Ibañez is supported by CONICET; FONCYT-PICT (2017-1818, 2017-1820); ANID/FONDECYT Regular (1210195, 1210176, 1220995) ANID/FONDAP (15150012); ANID/PIA/ANILLOS ACT210096; and the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat), funded by the National Institutes of Aging of the National Institutes of Health under award number R01AG057234, and Alzheimer’s Association grant (SG-20-725707-ReDLat), the Rainwater Foundation, and the Global Brain Health Institute. The content is solely the responsibility of the authors and does not represent the official views of these institutions.

Acknowledgments

Compliance with Ethical Standards

Ethics approval

Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

Author Contributions

Contributors DF, JPMS, DH, SF, and AI conceived and designed the project, contributed analysis tools, performed the analysis, and participated in the drafting of the final manuscript. DF, DHA, RM, and JA collected the data. DF, DH, JPMS, SF, and AI conducted data analysis and interpretation and participated in the drafting of the final manuscript. All authors approved the final version of the manuscript.

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Supplementary Information

The online version contains supplementary material available at submission

Herrera López, M., Romera Félix, E. M., Ortega Ruiz, R. & Ortiz, O. G. Influence of social motivation, self-perception of social efficacy and normative adjustment in the peer setting. Psicothema (2016). doi:10.7334/psicothema2015.135
Samadi, M. & Sohrabi, N. Mediating Role of the Social Problem Solving for Family Process, Family Content, and Adjustment. Procedia - Soc. Behav. Sci. (2016). doi:10.1016/j.sbspro.2016.02.141
Jeste, Dilip V., Ellen E. Lee, and S. C. Battling the modern behavioral epidemic of loneliness: suggestions for research and interventions. JAMA 77, 553 (2020).
Russell, D., Peplau, L. A. & Cutrona, C. E. The revised UCLA Loneliness Scale: Concurrent and discriminant validity evidence. J. Pers. Soc. Psychol. (1980). doi:10.1037/0022-3514.39.3.472
Cacioppo, J. T. & Cacioppo, S. Social relationships and health: The toxic effects of perceived social isolation. Soc. Personal. Psychol. Compass (2014). doi:10.1111/spc3.12087
Lam, J. A. et al. Neurobiology of loneliness: a systematic review. Neuropsychopharmacol. 2021 4611 46, 1873–1887 (2021).
Tomova, L., Tye, K., & Saxe, R. The neuroscience of unmet social needs. Soc. Neurosci. 16, 221–231 (2021).
Berkman, L. F. & Glass, T. Social Integration, Social Networks, Social Support, and Health. in Social Epidemiology (2000).
Cohen, S. & Wills, T. A. Stress, Social Support, and the Buffering Hypothesis. Psychological Bulletin (1985). doi:10.1037/0033-2909.98.2.310
Londono, Y., Mcmillan, D. E., Rn, M. & Candidate, P. Psychosocial adaptation: an evolutionary concept analysis exploring a common multidisciplinary language. J. Adv. Nurs. 71, 2504–2519 (2015).
Reker, G. T. & Woo, L. C. Personal meaning orientations and psychosocial adaptation in older adults. SAGE Open 1, 1–10 (2011).
Terziev, V. Factors influencing employment and unemployment policies in bulgaria. Acad. Russ. Acad. Nat. Hist. 14, (2019).
Wong, P. T. P. The Human Quest for Meaning: Theories, Research, and Applications. (2012).
Cacioppo, J. T., Norris, C. J., Decety, J., Monteleone, G. & Nusbaum, H. Loneliness Affects How the Brain Operates. ScienceDaily (2009). doi:10.1162/jocn.2009.21007
Cacioppo, J. T. & Hawkley, L. C. Perceived social isolation and cognition. Trends in Cognitive Sciences (2009). doi:10.1016/j.tics.2009.06.005
Cristofori, I. et al. The lonely brain: evidence from studying patients with penetrating brain injury. Soc. Neurosci. (2019). doi:10.1080/17470919.2018.1553798
Cacioppo, J. T., Hawkley, L. C. & Thisted, R. A. Perceived social isolation makes me sad: 5-year cross-lagged analyses of loneliness and depressive symptomatology in the Chicago Health, Aging, and Social Relations Study. Psychol. Aging 25, 453–463 (2010).
Hawkley, L. C., Thisted, R. A., Masi, C. M. & Cacioppo, J. T. Loneliness predicts increased blood pressure: 5-year cross-lagged analyses in middle-aged and older adults. Psychol. Aging 25, 132–141 (2010).
Hawkley, L. C., Thisted, R. A. & Cacioppo, J. T. Loneliness predicts reduced physical activity: cross-sectional & longitudinal analyses. Heal. Psychol. Off. J. Div. Heal. Psychol. Am. Psychol. Assoc. 28, 354–363 (2009).
Cacioppo, J. T., Cacioppo, S. & Boomsma, D. I. Evolutionary mechanisms for loneliness. Cognition and Emotion (2014). doi:10.1080/02699931.2013.837379
Hawkley, L. C. & Cacioppo, J. T. Loneliness matters: A theoretical and empirical review of consequences and mechanisms. Ann. Behav. Med. (2010). doi:10.1007/s12160-010-9210-8
Tokac, U. & Razon, S. Nursing professionals’ mental well‐being and workplace impairment during the COVID‐19 crisis: A Network analysis. J. Nurs. Manag. 29, 1653–1659 (2021).
Cacioppo, S., Capitanio, J. P. & Cacioppo, J. T. Toward a neurology of loneliness. Psychological bulletin 140, 1464–1504 (2014).
Layden, E. A. et al. Perceived social isolation is associated with altered functional connectivity in neural networks associated with tonic alertness and executive control. Neuroimage (2017). doi:10.1016/j.neuroimage.2016.09.050
Mwilambwe-Tshilobo, Laetitia, Tian Ge, Minqi Chong, Michael A. Ferguson, Bratislav Misic, Anthony L. Burrow, Richard M. Leahy, and R. N. S. Loneliness and meaning in life are reflected in the intrinsic network architecture of the brain. Soc. Cogn. Affect. Neurosci. 14, 423–433 (2019).
Feng, Chunliang, Li Wang, Ting Li, and P. X. Connectome-based individualized prediction of loneliness. Soc. Cogn. Affect. Neurosci. 14, 353–365 (2019).
DeWall, C. Nathan, and S. B. R. Social exclusion and the desire to reconnect. Soc. Personal. Psychol. Compass 5, 919–932 (2011).
Woolgar, Alexandra, Soheil Afshar, Mark A. Williams, and A. N. R. Flexible coding of task rules in frontoparietal cortex: an adaptive system for flexible cognitive control. J. Cogn. Neurosci. 27, 1895–1911 (2015).
Nguyen, A. W., Chatters, L. M., Taylor, R. J. & Mouzon, D. M. Social Support from Family and Friends and Subjective Well-Being of Older African Americans. J. Happiness Stud. (2016). doi:10.1007/s10902-015-9626-8
French, K. A., Dumani, S., Allen, T. D. & Shockley, K. M. A Meta-Analysis of Work-Family Conflict and Social Support. Psychol. Bull. 144, 284 (2018).
Janicki Deverts, D., Cohen, S. & Doyle, W. J. Dispositional Affect Moderates the Stress-Buffering Effect of Social Support on Risk for Developing the Common Cold. J. Pers. (2017). doi:10.1111/jopy.12270
McQuaid, R. J. et al. Relations between plasma oxytocin and cortisol: The stress buffering role of social support. Neurobiol. Stress (2016). doi:10.1016/j.ynstr.2016.01.001
Szkody, E. & McKinney, C. Appraisal and social support as moderators between stress and physical and psychological quality of life. Stress Heal. (2020). doi:10.1002/smi.2957
Hostinar, C. E. & Gunnar, M. R. Social Support Can Buffer against Stress and Shape Brain Activity. AJOB Neurosci. 6, 34 (2015).
Cohen, S. Social relationships and health. American Psychologist (2004). doi:10.1037/0003-066X.59.8.676
Torralva, Roca, Gleichgerrcht, López, M. INECO Frontal Screening (IFS): a brief, sensitive, and specific tool to assess executive functions in dementia. J Int Neuropsychol Soc 15, 777–786 (2009).
Jory, J. I., Bruna, A. A., Muñoz-Neira, C., & Chonchol, A. S. Chilean version of the INECO Frontal Screening (IFS-Ch): psychometric properties and diagnostic accuracy. Dement. Neuropsychol. 7, 40–47 (2013).
Bosc, M., Dubini, A. & Polin, V. Development and validation of a social functioning scale, the social adaptation self-evaluation scale. in European Neuropsychopharmacology (1997). doi:10.1016/S0924-977X(97)00420-3
Hays, R. D. & Dimatteo, M. R. A Short-Form Measure of Loneliness. J. Pers. Assess. (1987). doi:10.1207/s15327752jpa5101_6
Lubben, J. E. Assessing social networks among elderly populations. Fam. Community Heal. (1988). doi:10.1097/00003727-198811000-00008
Wells, M. Resilience in Older Adults Living in Rural, Suburban, and Urban Areas. Online J. Rural Nurs. Heal. Care (2010). doi:10.14574/ojrnhc.v10i2.55
Villas-Boas, S., Lima De Oliveira, A., Ramos, N. & Montero, I. Social support and generational diversity: The potential of the LSNS-6. Pedagog. Soc. (2018). doi:10.7179/PSRI_2018.31.14
R core team. A language and environment for statistical computing. (2020).
Petrocelli, J. V. Hierarchical multiple regression in counseling research: Common problems and possible remedies. Meas. Eval. Couns. Dev. 36, 9–22 (2003).
Chao-Gan, Y. & Yu-Feng, Z. DPARSF: A MATLAB toolbox for ‘pipeline’ data analysis of resting-state fMRI. Front. Syst. Neurosci. (2010). doi:10.3389/fnsys.2010.00013
Parker, D. B. & Razlighi, Q. R. The benefit of slice timing correction in common fMRI preprocessing pipelines. Front. Neurosci. (2019). doi:10.3389/fnins.2019.00821
Goto, M. et al. Head motion and correction methods in resting-state functional MRI. Magnetic Resonance in Medical Sciences (2016). doi:10.2463/mrms.rev.2015-0060
Ashburner, J. & Friston, K. J. Nonlinear spatial normalization using basis functions. Hum. Brain Mapp. (1999). doi:10.1002/(SICI)1097-0193(1999)7:4<254::AID-HBM4>3.0.CO;2-G
Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage (2002). doi:10.1006/nimg.2001.0978
Garcia-Cordero, I. et al. Metacognition of emotion recognition across neurodegenerative diseases. Cortex 137, 93–107 (2021).
Birba, A. et al. Multimodal Neurocognitive Markers of Naturalistic Discourse Typify Diverse Neurodegenerative Diseases. Cereb. Cortex (2021). doi:10.1093/CERCOR/BHAB421
Ibañez, A. et al. Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes. J. Alzheimer’s Dis. 83, 227–248 (2021).
Jabbi, M., Bastiaansen, J., & Keysers, C. A Common Anterior Insula Representation of Disgust Observation, Experience and Imagination Shows Divergent Functional Connectivity. PLoS One 3, (2008).
Kanske, P., Böckler, A., Trautwein, F. M., Parianen Lesemann, F. H., & Singer, T. Are strong empathizers better mentalizers? Evidence for independence and interaction between the routes of social cognition. Soc. Cogn. Affect. Neurosci. 11, 1383–1392 (2016).
Zang, Y.-F. et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 29, 83–91 (2007).
Rosseel, Y., Oberski, D., Byrnes, J., Vanbrabant, L., Savalei, V., Merkle, E., ... & Rosseel, M. Y. Package ‘lavaan’. (2017).
Garson, D. G. Path analysis. (NC: Statistical Associates Publishing, 2013).
Xie, X. et al. How do you make me feel better? Social cognitive emotion regulation and the default mode network. Neuroimage 134, 270–280 (2016).
Bickart, K. C., Dickerson, B. C. & Barrett, L. F. The amygdala as a hub in brain networks that support social life. Neuropsychologia 63, 235 (2014).
Harding, Ian H., Murat Yücel, Ben J. Harrison, Christos Pantelis, & M. B. Effective connectivity within the frontoparietal control network differentiates cognitive control and working memory. Neuroimage 106, 144–153 (2015).
Dixon, M. L. Cognitive control, emotional value, and the lateral prefrontal cortex. Front. Psychol. 6, 758 (2015).
Schurz, M., Maliske, L. & Kanske, P. Cross-network interactions in social cognition: A review of findings on task related brain activation and connectivity. Cortex 130, 142–157 (2020).
Dumas, G., Moreau, Q., Tognoli, E. & Kelso, J. A. S. The Human Dynamic Clamp Reveals the Fronto-Parietal Network Linking Real-Time Social Coordination and Cognition. Cereb. Cortex 30, 3271–3285 (2020).
Xin, F. et al. Oxytocin Differentially Modulates Amygdala Responses during Top-Down and Bottom-Up Aversive Anticipation. Adv. Sci. 7, 2001077 (2020).
Yao, Shuxia, Weihua Zhao, Yayuan Geng, Yuanshu Chen, Zhiying Zhao, Xiaole Ma, Lei Xu, Benjamin Becker, and K. M. K. Oxytocin Facilitates Approach Behavior to Positive Social Stimuli via Decreasing Anterior Insula Activity. Int. J. Neuropsychopharmacol. 21, 918–925 (2018).
Gozzi, M., Dashow, E. M., Thurm, A., Swedo, S. E. & Zink, C. F. Effects of Oxytocin and Vasopressin on Preferential Brain Responses to Negative Social Feedback. Neuropsychopharmacology 42, 1409–1419 (2016).

No competing interests reported.

Supplementarymaterial.docx

Download PDF

Journal Publication

published 25 Jul, 2023

Read the published version in Scientific Reports →

Editorial decision: Major revision
08 Dec, 2022
Reviews received at journal
13 Oct, 2022
Reviewers agreed at journal
19 Sep, 2022
Reviews received at journal
08 Sep, 2022
Reviewers agreed at journal
02 Sep, 2022
Reviewers invited by journal
28 Aug, 2022
Editor assigned by journal
20 Aug, 2022
Editor invited by journal
18 Aug, 2022
Submission checks completed at journal
18 Aug, 2022
First submitted to journal
16 Aug, 2022

You are reading this latest preprint version

The neurocognitive impact of loneliness and social networks on social adaptation

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials And Methods

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1