The psychological and neuro-morphological predictors of resilience in healthy adults: The whole is more than the sum of its parts

The psychological and neuro-morphological predictors of resilience in healthy adults: The whole is more than the sum of its parts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The psychological and neuro-morphological predictors of resilience in healthy adults: The whole is more than the sum of its parts Carlo Fabrizio, Eleonora Picerni, Daniela Laricchiuta, Davide Decandia, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4485591/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Research in neuroscience has shifted the focal point from a pathological orientation (responses to recover from trauma or stress) to an emphasis on the role of resilience in health (protective factors to maintain health levels despite eventual adversities). Even if multiple single factors impact resilience capacities, an integrative predictive model including psychological constructs, personality traits and brain structural features may offer a deeper knowledge on trait resilience. We examined the associations between Resilience Scale-10 scores with numerous psychological dimensions, personality traits, and neuro-morphological features (brain volumes and thickness) in healthy adults of both sexes. Furthermore, we investigated the predictors potentially associated to resilience by regression modeling. Resilience values were predicted: positively by some personality characteristics ( Conscientiousness, Openness, Resourcefulness, Enlightened second nature), psychological dimensions (Self-efficacy, Positive affect, Confidence), and brain morphological aspects (volumes of amygdala and hippocampus, and cortical thickness of temporal pole); and negatively by other personality traits (Fear of uncertainty) and psychological dimensions (Anxiety, Depression, Need for Approval). The identification of the multiple psychological and personality features and neuro-morphological aspects associated to resilience represents a critical step to understand the factors that predispose individuals to be resilient and eventually to develop novel approaches for resilience promotion. Biological sciences/Neuroscience/Emotion Biological sciences/Neuroscience/Social neuroscience Psychological constructs personality traits brain volumes cortical thickness Resilience Scale-10 elastic net. Introduction Resilience can be defined as a positive outcome despite adversity 1–4 . Resilient individuals are able to face life adversities by implementing successful coping responses, enjoy intimate bonds and a wider social circle, express empathy to others, promote autonomous goals, live a creative and significant life, and succeed in being free of distressing symptoms in front of trauma or grief. The majority of researches on resilience has up to now focused on the outcomes of trauma, regarding resilience as absence of symptoms or maintenance of homeostasis following trauma, limiting the scope of resilience to an observable phenomenon after an adverse event 5,6 . Investigating the response to adversity as a “proxy” of resilience, many studies have examined the associations between psychological constructs and resilience in response to stressful events. These studies indicated that multiple factors, such as personality traits, self-efficacy, flexibility, optimism, positive affect, may promote adaptive responses to adverse situations 5,7,8 . Similarly, many neuroimaging studies have reported resilience-related differences in response to different kinds of trauma in the volumes 9,10 and resting-state activity 11–14 of some brain areas. However, a new scientific approach on resilience suggested to consider what are the biological and psychological underpinnings of such a construct per se and not as reaction in the face of trauma or stress 4,15,16 . This shifting in operationalizing resilience has led to take into account resilience-promoting factors, in an attempt to gain insights into aspects of resilience not captured by models focused on the responses following adversity 17 . For example, the relationships between resilience and positive affect, emotion regulation, or personality traits have evidenced that individuals who effectively regulate their emotions, experience positive affect or have constructive personality traits display greater resilience 7,18,19 . Although it is difficult to capture the biologically dynamic nature of resilience, some studies demonstrated that resilience properties are determined by the adaptive responses of brain networks controlling the behaviors related to emotional regulation, coping, and cognitive flexibility 13,20–26 . Even if a lot of single factors (whether psychological or neuronal) have been shown to efficiently impact resilience capacities, an integrative predictive model of resilience including multiple psychological constructs, personality traits and brain structural features seems more appropriate to obtain a deeper knowledge on trait resilience. Given the close interplay between psychological constructs, personality and morphological characteristics of specific brain regions 19,27,28 , it seemed interesting to explore the potential associations between resilience measures (assessed by the self-reported Resilience Scale-10 (RS-10) 29 with numerous psychological dimensions (assessed by 14 tests), personality traits (assessed by 2 questionnaires), and brain morphological features (cortical and subcortical volumes and thickness) in healthy adult subjects of both sexes through correlational analyses. In addition, we investigated the predictors potentially associated to resilience by regression modeling. The identification of the multiple psychological features and neuro-morphological aspects associated to resilience is a critical step to better understand what factors predispose individuals to be resilient and eventually to develop novel approaches for resilience promotion. Results Frequency distribution of RS-10 scores is summarized in figure S1 of Supplementary Materials. No effect of the sex was found on RS-10 scores (Mann-Whitney U Test: U=478.5, p=0.245; RS-10 mean ± SD score: Males: 58.8 ± 7.4; Females: 55.2 ± 11.3). Male and female participants did not differ in age (Mann-Whitney U Test: U=453, p=0.141), in education (Mann-Whitney U Test: U=565, p=0.916), or numerosity (Chi-Square=2.45, p=0.118). Correlation tests were performed between RS-10 measures and all other variables of interest for this study. Out of the 279 variables examined, 46 exhibited a statistically significant correlation (p-value < 0.05), as summarized in Fig. 1 (and Table S2). Abbreviations: TCI-HA1: TCI-Harm Avoidance Anticipatory worry; TCI-HA: TCI-Harm Avoidance; TCI-HA2; TCI-Harm Avoidance Fear of uncertainty; TCI-HA4: TCI-Harm Avoidance Fatigability; TCI-SD5: TCI-Self-directedness Enlightened second nature; TCI-NS3: TCI-Novelty Seeking Extravagance; TCI-SD: TCI-Self-directedness; TCI-SD2: TCI-Self-directedness Purposefulness; TCI-SD3: TCI-Self-directedness Resourcefulness; Cx: Cortex. These 46 variables were selected as candidate predictors for the regression analysis, regardless of the p-value obtained through the application of the False Discovery Rate (FDR) correction. In particular, 25 correlations were deemed significant (p-adjusted < 0.05) after FDR correction. Our focus was to select variables with a numerical association with the resilience measure, and we were not interested in identifying potential false positive correlations when selecting the features for the Elastic Net. The Elastic Net model, which was fitted using repeated k-fold cross-validation, exhibited satisfactory performance in identifying factors associated with resilience. Model performance was assessed using the root mean square error (RMSE) and the coefficient of determination (R 2 ). The model’s RMSE and R 2 values were 8.129 and 0.400, respectively, with corresponding standard deviations of 3.010 and 0.233. Given that the RS-10 scale ranges from 10 to 70, a prediction that falls within approximately ± 8 points of the actual value can be considered reasonable. Several predictors (n=15) were found to significantly contribute to resilience, with positive or negative estimates indicating the direction and intensity of change in the dependent variable (Fig. 2). Discussion As Feldman (2020) 16 says, the flexible regulation and integration of multiple psychological processes and brain systems allow them not only to coexist but also to dynamically coalesce into a functionally resilient whole. In the same line but with a major emphasis on the neurobiological processes, Cathomas et al. (2019) 30 have recently proposed viewing resilience as a process that requires the integration of multiple central (hippocampal neurogenesis, dopaminergic systems, transcriptional and epigenetic pathways) and peripheral (cellular and humoral factors of the immune system, gut microbiota, and blood-brain barrier) systems. A number of studies have analyzed the psycho-social factors contributing to resilience, among them emotion regulation, executive function, dispositional optimism, coping strategies, cognitive reappraisal, and social support 1 . Many of these protective factors are interlinked: for example, greater emotional regulation is associated with strengthened executive function and cognitive flexibility 31 . In accordance with this integrative approach, the present paper provides a model of factors spanning psychological domains and cortical and subcortical morphological variables that allow predicting the resilient phenotype. By analyzing a lot of psychological constructs and brain structural features, we found that (regardless of the occurrence of stressful events within the last 3 months, as assessed by HR-SS) resilience scores assessed by RS-10 were predicted: with positive coefficients by some personality characteristics (Conscientiousness, Openness, Resourcefulness, Enlightened second nature), psychological dimensions (Self-efficacy, Positive affect, Confidence), and brain morphological aspects (volumes of amygdala and hippocampus, and cortical thickness of temporal pole); and with negative coefficients by other personality traits (Fear of uncertainty) and psychological dimensions (Anxiety, Depression, Need for Approval). Self-efficacy Interestingly, we found that the factor with the most predictive value was linked to the self-efficacy by GSES, defined as individuals' belief in their capacity to succeed in specific situations or to accomplish a task. The very definition of self-efficacy emphasizes how much it is related to resilience. By determining the beliefs people hold regarding their power to affect situations, self-efficacy strongly influences both the power to competently face challenges and the most likely made choices. People with high self-efficacy values view challenges as things to be mastered rather than threats to avoid. Self-efficacy not only affects our lives in highly stressful situations but also helps one to develop motivation and envision challenging high goals in life. Although self-efficacy and resilience are distinct psychological resources, independent from each other, they are highly related since both of them share the ability to persevere in the face of difficulty and have a positive self-concept. By activating affective, motivational, and behavioral mechanisms in demanding situations, self-efficacy beliefs can promote resilience so much that sometimes self-efficacy has been conceptualized as one component of resilience 32 . Remarkably, high self-efficacy levels have been linked to low levels of anxiety and low vulnerability to depression 33 . Note that within the predictors of resilience found in the present research, the values of anxiety and depression scales were the factors with the highest negative coefficients associated with resilience*. Personality It is worth noting that within personality traits, Conscientiousness and Openness – factors of the Big Five model – and Resourcefulness and Enlightened second nature – subscales of TCI character dimension Self-directedness – predicted resilience values with positive coefficients, while Fear of Uncertainty – subscale of the TCI Harm Avoidance temperamental dimension – predicted resilience values with negative coefficients. These outcomes fully agree with literature findings on healthy individuals of different ages 8,19,34 , although such associations have been more frequently described in patients with various pathologies. Briefly, Conscientiousness is the personality trait that implies being careful and diligent, efficient and organized, with tendency to show self-discipline, act dutifully, and aim for achievement. Although with less predictive impact, resilience values were also predicted by the trait Openness, which assesses how open-minded, imaginative, creative, and insightful a person is. Those who are more broadminded tend more willing to listen to multiple viewpoints or try new things (cognitive flexibility). Not very differently, the most distinctive characteristics of self-directed individuals are that they are effective, able to adapt their behavior according to voluntary goals. Finally, we found that resilience displayed a small negative relationship with Fear of Uncertainty which is associated with a tendency to be sensitive to cues that signal punishment, with behavioral inhibition, and with avoidance of aversive situations, poor coping, and proneness to ne gative emotions. Furthermore, the TCI subscales Resourcefulness and Enlightened second nature were associated with RS-10 scores with positive coefficients. As a final note, it has to be underlined that the further subscales of TCI (Purposefulness, Extravagance, Anticipatory worry) and BFQ-2 (Energy/Extraversion, Emotional Stability) were significantly correlated to resilience scale, even if not resulting as predictors in the Elastic Net model. This ensemble of personality-related predictors of resilience reflects the benefits of having a hard-working and positive affective style as well as abilities of interpersonal closeness and social interaction. In fact, on one hand the meticulous and careful approach of conscientious individuals may lend itself well to effectively coping with negative life experiences resulting in a sense of self-efficacy. On the other hand, the positive emotions and close social interactions contribute to resilience because they broaden the “thought-action repertoires”. In fact, having more flexible thinking, expanded behavioral options, and networks of social support increases the personal resources and thus the resilience 35 . Anxiety and depression The identification of the profile that characterizes the resilience and has predictive value as to whether or not anxiety and depression symptoms will be present is an important issue in the resilience literature. In close agreement with previous reports 36,37 , we found that state and trait anxiety, as well as depression were the highest negative coefficients associated with resilience. Predictably, as resilience reflects the ability to cope with life adversities, and therefore adaptively acts against psychological distress, we found significant inverse relationships between resilience values and anxiety and depression scores. These negative predictors suggest that resilience resources may turn the triggers for anxiety and depression into opportunities to improve performances and overcome difficulties, suggesting that highly resilient individuals have better coping mechanisms buffering against the development of anxiety and depression. These findings are consistent with previous studies reporting inverse associations between resilience and psychological distress among patients with chronic diseases 37 , and older people 38 . Positive affect Research has repeatedly demonstrated that experiencing positive emotions in the face of adversity is one of the most important processes involved in resilience 35,39 . In accordance with these reports, we found that resilience values were associated positively with positive mood scores and negatively with negative mood scores, assessed by the PANAS. Notably, positive emotions resulted as predictors in the present Elastic Net model for resilience. This result is not surprising, given that positive emotions play the role of a buffer between the distressing situation and the emotional elicitation and appraisal of that situation (Paquette et al., 2023; Philippe et al., 2009). Individuals with high positive emotionality are better skilled at self-generating positive emotions and at coping with adversities 35 . The role of positive emotions in resilient behaviors has been explained by the “Broaden-and-Build Theory” 35 positing that positive emotions facilitate resilience by broadening one’s attention and effective coping strategies 40 . According to this view, as opposed to negative emotions, positive emotions broaden thought and allow for flexible attention. Repeated experiences of positive emotions would render this broadened mindset habitual and result in increased personal resources that can be drawn on in times of need and facilitate resilient behaviors and adaptive coping strategies 40 . In addition, positive emotions would have an undoing effect, given they counteract the deleterious after-effects of negative emotions and stress. The longitudinal study by Cohn et al. (2009) 41 has shown that, over a one-month period, daily positive emotions buffered against the effect of negative emotions and were related to growth in resilience. Attachment interactions with the primary caregiver 42 . The initial experiences have an impact of moderate to high degree of stability over time (trait component) and influence the way the later relationships are processed, the ability to cope with hardships, and the overall functioning and mental health. Attachment styles have broadly been categorized as secure - characterized by positive sense of self and others, comfort with intimacy and independence, and adaptive resources to recruit help, when needed - or insecure - inability to engage in intimacy, struggling to form healthy relationships, tendency to inconsistent behaviors with others. Research has shown that secure attachment style can foster resilience by means of the implementation of effective problem-focused coping strategies , and coping strategies may in turn shape resilience 43,44 . It has been suggested that secure attachment and resilience are complementary concepts which share similar developmental circumstances, stemming from a healthy childhood and leading to the emergence of adaptive self-esteem and empathy, through positive relations with others.Marriner and colleagues (2014) 45 reported that individuals with a secure attachment exhibit high levels of resilience, and both these variables in turn correlate positively with proactive coping strategies, and negatively with avoidant coping strategies. Intriguingly, in the present research we found that among the predictors of resilience there were the positively predicting ASQ dimension Confidence and the negatively predicting ASQ dimension Need for Approval. The other ASQ dimensions Relationships as Secondary and Preoccupation with Relationships as well as the coping strategy Problem Solving assessed by COPE inventory were positively correlated with the RS-10, although not resulting as predictors in the Elastic Net model. Specifically, the active coping strategies are intentional efforts aimed at minimizing the physical, psychological, or social harm of a stressor. They are associated with actual or perceived control over the stressor and lead to changes facilitating an adaptive and resilient response 46 . In addition, we found that the IRI subscale Personal Distress, which measures an affective dimension of empathy, was negatively associated with the RS-10 scores, although it was not resulting as a predictor in the model for resilience. Note that empathic people may promote prosocial behavior, upgrade interpersonal relationships, be considerate of others and put aside their concerns, and at same time have high self-esteem, reduced loneliness, and a strong sense of self. In doing so, they become more resilient. Structural brain correlates of resilience The above-described ensemble of the predictors of resilience allowed us to describe the constructs shaping the psychological profile of the resilient people. In order to achieve an even more multifaceted profile of the resilient phenotype, in addition to the psychological and personality factors, within the present regression model we inserted several brain morphological features. Human cross-sectional studies have focused on neural structures and neuroendocrine markers of resilience, and the animal models provided data on the behavioral, genetic, molecular, and hormonal bases of resilience, showing that in resilient animals there is an absence of the key molecular abnormalities found in susceptible individuals as well as distinct epigenetic and cellular adaptations 47–49 . In brief, neuronal architecture of resilience largely overlaps with the neuronal structures related to cognitive and emotional regulation, as the executive control network (including prefrontal, frontal and parietal regions), and the emotional arousal network (including cingulate cortex subregions, amygdala, hippocampus and insula). However, up to now these brain structures have been mainly implicated in the vulnerability, rather than in the resilience, to stress or trauma 50 . In fact, since the brain is continuously adapting to the perturbations in bodily homeostasis, most information regards the neurobiology of resilience as response to disease or traumatic adversities, and not to trait resilience we were conversely mainly interested in. However, since the maladaptive responses to stress/trauma are the flipside of resilience, it is appropriate to take into account even literature data analyzing the brain morphological responses to stress/trauma. Interestingly, in the present model of resilience the volumes of the amygdala and hippocampus, as well as the cortical thickness of the temporal pole predicted the resilience values with positive coefficients. Additionally, even if not resulting as a predictor, we found that the volume of the medial orbitofrontal cortex, structure which mediates emotional regulation, cognitive control, social cognition 51 , was positively correlated with the resilience scores, in agreement with previous findings 22,52,53 . Let’s analyze in detail the single neuronal predictors of resilience. Amygdala We found that the volume of left amygdala positively predicted enhanced resilience scores. This finding is consistent with the larger amygdala volume associated with the increased resilience scores described by Gupta et al. (2017) 24 in healthy subjects, and with the evidence provided by Reynaud et al. (2013) 52 that the larger amygdala and OFC activation responses to stressful events, the greater the resilience. In close accordance with the present findings, Morey et al. (2016) 54 described larger left amygdala and right hippocampal volumes in resilient maltreated children. In literature, conflicting studies reported amygdala volumes larger 55 , unmodified 56 , or smaller 57 in individuals who had experienced stressful social adversity. Recently, a structural connectivity study by Ohashi et al. (2019) 58 showed that amygdala nodal efficiency was lower in resilient than in susceptible to maltreatment individuals, suggesting that the decreased efficiency of amygdala node in propagating information throughout the network might mitigate the effects of adversities and lead to enhanced resilience. However, it has to be noted that these contradictory findings have been attributed to the amygdala vulnerability to the type, magnitude, and timing of stress. Conversely, the present research was aimed at finding the neuronal predictors of resilience in the absence of any stressful event. Hippocampus The second most significant positive neuronal predictor of resilience of the present model was the volume of the right hippocampus. This finding fully fits with the reduction of hippocampal volume repeatedly described in individuals affected by trauma-related psychopathologies or mood disorders or living in poverty 59–61 , given that one of the core symptoms of these conditions is the altered regulation of emotions induced by traumatic memories. Numerous data indicate that smaller hippocampal volumes might be the result of exposure to severe stress (and perhaps also a vulnerability factor) 62,63 . Animal studies report that the exposure to traumatic events damages hippocampal neurons, inhibits neurogenesis, and suppresses the production of new granule neurons in the dentate gyrus 64 . Notably, the opposite description of increased hippocampal volumes associated with increased resilience is less established 65 . A study by Vermetten et al. (2003) 66 which reports that psychopharmacological treatment of PTSD symptoms resulted in increased hippocampal volumes suggests that larger hippocampal volumes are related to higher resilience. In the same vein, the pharmacological treatment with antidepressants is reported to reverse the decreased hippocampal volumes by increasing neural progenitor cells 67 . Furthermore, the deleterious effect of poverty on hippocampal volume was alleviated in subjects with high self-esteem, a finding suggesting that positive psychological resources may provide protection against the hippocampal atrophy in adversity 68 . Of note, several studies found larger hippocampal volumes in resilient individuals in comparison to PTSD subjects 69 . In healthy volunteers greater functional coupling between hippocampus and ventromedial prefrontal cortex is suggested to be linked to greater extinction recall, a capacity thought to promote resilience 70 . Subjects with high adversity level but high resilience scores show reduced reward-related activation of the ventral striatum and increased activation of the ventral tegmental area and hippocampus 65 . In conclusion, literature data support the idea that larger hippocampal volumes may confer resilience to trauma and be stress-protective, as found in the present research. Temporal pole A further positive neuronal predictor of resilience of the present model was the cortical thickness of the temporal pole, which constitutes the most rostral part of the temporal lobe. Cortical thickness reflects the size, density and arrangement of neurons, neuroglia and nerve fibers, and also axon and dendrite remodeling and myelination. Because of its distributed anatomical connections with limbic structures and neocortical regions, the temporal pole has been associated with several high-level cognitive processes, such as visual processing for complex objects, face recognition and visual memory, autobiographical memory, semantic processing 71 . Moreover, it has been involved in several emotional (positive or negative) or affective circumstances, such as recalling emotionally intense autobiographical memories or watching an emotion-inducing movie 72 . Notably, on the bases of impaired recognition of facial and musical emotions found to be associated with atrophy of the right temporal pole 73 , the temporal pole is argued to be a conduit for integrating visceral information, sensory representations and memories for emotionally or socially-relevant concepts 72 . Such emotional processing and multimodal sensory integration may contribute to stable emotional and social behavior. Notably, stability in behavior is an important component of resilience. Furthermore, the temporal pole is involved in higher-level social cognition functions relating to the perception and comprehension of others’ thoughts and actions 72 , and it is activated during theory of mind and empathy tasks 74 . Interestingly, temporal pole volume it positively correlates with scores in trait modesty 75 . Note that trait modesty has been associated with the social information processing and interpersonal perception that enables and motivates prosocial behavior and stable interpersonal relationships, and it is beneficial to adaptive psychological functioning 75 , all components of resilience. Consistently with previous studies in non-older groups 23,24 , and in full agreement with the present results, a recent study on older people reported that resilience capacities were positively related to the cortical thickness of the left temporal pole 26 . Specularly, a meta-analysis on brain volumes in subjects with PTSD showed reduced volume of temporal pole 76 . Considered in this context, it is remarkable that the morphometric features of the temporal pole may predict affective regulation and hence resilience, as occurring in the present Elastic Net model. Foot note *As Bandura wrote (2006), “ self-efficacy beliefs influence the challenges and goals people set for themselves and their commitment to them, how much effort they put forth in given endeavors, the outcomes they expect their efforts to produce, how long they persevere in the face of obstacles, their resilience to adversity, the quality of their emotional life and how much stress and depression they experience in coping with taxing environmental demands”. (Bandura, A. Guide for constructing self-efficacy scales . In Self-efficacy beliefs of adolescents, Vol. 5, pp. 307-337 (eds. Pajares F. & Urdan T.), Greenwich, CT: Information Age Publishing, 2006). Conclusions The profile of resilient people derived from the present model on one hand encompasses what has been inventively termed the ‘ordinary magic’ 77 of strongly adaptive fundamental systems, such as positive personality characteristics, high cognitive abilities, psychological well-being linked to high self-efficacy and conscientiousness, low anxiety and depression, secure attachment, positive emotional experience, wider associative thinking, adaptive coping, broad and affective social support. On the other hand, the resilient profile includes brain structural correlates, indicating that resilient subjects are characterized by neural substrates reflecting efficient arousal modulation and emotional/cognitive regulation in a flexible interplay with the psychological and environmental factors. Interestingly, these multiple variables belonging to different domains are intertwined. One of the main future challenges will be to gain a holistic model of resilience that encompasses key circuits in the brain, peripheral systems, psychological dimensions, and personality features. The present multidimensional predictive model can lead to a novel framework useful to study the assorted interconnections among psychological and neuronal variables able to confer resilience. Hopefully, the same predictors could be applied to research on the interplay between genetic predisposition and environmental factors in determining resilience or vulnerability to psychiatric disorders. Methods Participants A sample of 69 healthy right-handed subjects (28 males: mean age ±SD: 38.21 ±11.87 years (y), 41 females: 41.88 ±12.69 y) reporting no history of psychiatric or neurological diseases, participated in this study, which was part of a large research investigating the relations between brain and psychological dimensions 78–80 . Data were filtered to keep only the participants who could contribute data for all variables included in the final analyses. Educational level ranged from an eighth grade to a post-graduate degree (mean education years ± SD: 15.75 ± 2.99 y). All participants underwent MRI scanning and completed questionnaires. Inclusion and exclusion criteria are described in detail in Supplementary Materials. The investigation was carried out in accordance with the latest version of the Declaration of Helsinki. Both behavioral and MRI protocols were approved by the local ethic committee of the Santa Lucia Foundation IRCCS. Written informed consent of all participants was obtained before the study. Psychological assessment Participants’ psychological profile was assessed by means of the Resilience Scale-10 (RS-10), Attachment Style Questionnaire (ASQ), Beck’s Depression Inventory Scale (BDI), Coping Orientation to Problems and Experiences (COPE), Emotion Regulation Questionnaire (ERQ), General Self-Efficacy Scale (GSES), Hamilton Anxiety Rating Scale (HAM-A), Hamilton Depression Rating Scale (HAM-D), Holmes and Rahe Stressful Event Scale (HR-SS), Interpersonal Reactivity Index (IRI), Positive and Negative Affect Schedule (PANAS), State-Trait Anger Expression Inventory (STAXI), State-Trait Anxiety Inventory-Form Y (STAI-Y), Toronto Alexithymia Scale (TAS-20), Raven's Progressive Matrices (RPM). Participants’ personality traits were assessed by means of the Big Five Questionnaire-2 (BFQ-2) and Temperament and Character Inventory (TCI). Italian versions of psychological and personality scales were used. Tests and questionnaires are described in Supplementary Materials, in which descriptive statistics for socio-demographic and psychological variables are also reported (Table S1). The Resilience Scale (RS-10) Resilience was measured by using the Italian version of RS-10 81,82 , which is a 10 item-version of the psychometrically sound Resilience Scale (RS) 29 . A previous study 83 demonstrated the equivalence between the unifactorial 10-item version (RS-10) and the original version of RS (encompassing 25 items and measuring five essential characteristics of resilience). The unifactorial structure of RS-10 facilitated the definition of the resilience measure, which was our dependent variable. The 10 items of the test are rated on a 7-point Likert scale (from 1 = strongly disagree to 7 = strongly agree ). The RS-10 gives total scores ranging from 10 to 70, with higher scores reflecting greater levels of resilience. MRI Acquisition and Processing Participants underwent a neuroimaging protocol including standard clinical sequences (FLAIR, DP-T2-weighted) and a volumetric whole-brain 3D high-resolution T1-weighted sequence, performed with a 3T Allegra M in Supplementary Materials. Volumetric whole-brain T1-weighted images were obtained in the sagittal plane using a Modified Driven Equilibrium Fourier Transform (MDEFT) sequence (Echo Time/Repetition Time-TE/TR- = 2.4/7.92 ms, flip angle 15, voxel size 1 x 1 x 1 mm 3 ). All planar sequence acquisitions were obtained in the plane of the AC-PC line. The FreeSurfer imaging analysis suite (v5.1.3, http://surfer.nmr.mgh.harvard.edu/, accessed on 26 January 2020) was used for reconstructing volumes and cortical thickness of brain regions 84,85 . Cerebellum parcellation was performed through a freely available patch-based multi-atlas segmentation tool called CERES (CEREbellum Segmentation) able to automatically parcellate the cerebellar lobules. CERES 86 is part of broader software pipeline for volumetric brain analysis, namely volBrain (https://www.volbrain.net/). Neuroimaging data acquisition and processing is detailed in Supplementary Materials. Data preparation and regression analysis Before regression modeling, a Spearman correlation filter was used (Table S2) to select independent variables significantly correlated to the dependent variable 87 .The 279 variables taken into account for correlations were: socio-demographic variables (n=2); psychological (n=34) and personality (n=37) variables from the 16 tests detailed in Supplementary Materials; cortical volume variables (n=66); sub-cortical volume variables (n=26); cortical thickness variables (n=66), cerebellar volume and thickness variables (n=48). Sex was used as between-subjects factor in non-parametric statistical analyses. Selected predictors, along with socio-demographics as covariates (namely, age, education and sex), were used in a regression analysis to predict the resilience score. In order to manage the presence of several predictors, the regression analysis was performed with the Elastic Net method. The Elastic Net modulates regression coefficients to penalize complex models implementing implicit feature selection through a regularization approach that combines ridge and LASSO regression 88,89 . The model was trained using repeated k-fold cross-validation, with 5 folds repeated 5 times, to avoid overfitting and ensure results reliability. Repeated k-fold cross-validation was preferred to single-run k-fold cross-validation because it ensures a more accurate estimate of results by reporting the mean result across all folds from all runs. Repeated cross-validation reduces the error in the estimate of mean model performance. On each run of this cross-validation procedure, data were centered and scaled. The model was evaluated with the R 2 and the Root Mean Squared Error (RMSE) metrics. Feature importance was investigated by evaluating the model's regression coefficients. Declarations ACKNOWLEDGEMENTS The authors sincerely thank all the participants in this study. FUNDING This work was partially supported by the Italian Ministry of Health, Ricerca Corrente 2024. AUTHOR CONTRIBUTIONS All authors conceptualized and designed the study. EP, FP and GS gathered and analyzed neuroimaging data. EP, DL and DC gathered and analyzed psychological and personality data. CF, DD and AT performed correlation analyses and regression modeling. All authors contributed to the interpretation of data and were involved in writing and critically revising the manuscript, which all authors approved for publication. COMPETING INTERESTS All authors have no conflict of interest to declare. 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Journal of the Royal Statistical Society Series B: Statistical Methodology 67 , 301–320 (2005). Kuhn, M. & Johnson, K. Applied Predictive Modeling . (Springer New York, New York, NY, 2013). Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYMATERIALS.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4485591","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":312848344,"identity":"6fdd2c21-fd44-4fc6-85ba-f028ebc581ad","order_by":0,"name":"Carlo Fabrizio","email":"","orcid":"","institution":"IRCCS Santa Lucia Foundation","correspondingAuthor":false,"prefix":"","firstName":"Carlo","middleName":"","lastName":"Fabrizio","suffix":""},{"id":312848345,"identity":"ba1f6a35-384a-43d3-89e6-3afa86effb4f","order_by":1,"name":"Eleonora 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02:27:23","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":872363,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIALS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4485591/v1/94c2d4e760ad1c3f422c2fdd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The psychological and neuro-morphological predictors of resilience in healthy adults: The whole is more than the sum of its parts","fulltext":[{"header":"Introduction","content":"\u003cp\u003eResilience can be defined as a positive outcome despite adversity\u003csup\u003e1\u0026ndash;4\u003c/sup\u003e. Resilient individuals are able to face life adversities by implementing successful coping responses, enjoy intimate bonds and a wider social circle, express empathy to others, promote autonomous goals, live a creative and significant life, and succeed in being free of distressing symptoms in front of trauma or grief. The majority of researches on resilience has up to now focused on the outcomes of trauma, regarding resilience as absence of symptoms or maintenance of homeostasis following trauma, limiting the scope of resilience to an observable phenomenon after an adverse event\u003csup\u003e5,6\u003c/sup\u003e. Investigating the response to adversity as a \u0026ldquo;proxy\u0026rdquo; of resilience, many studies have examined the associations between psychological constructs and resilience in response to stressful events. These studies indicated that multiple factors, such as personality traits, self-efficacy, flexibility, optimism, positive affect, may promote adaptive responses to adverse situations\u003csup\u003e5,7,8\u003c/sup\u003e. Similarly, many neuroimaging studies have reported resilience-related differences in response to different kinds of trauma in the volumes\u003csup\u003e9,10\u003c/sup\u003e and resting-state activity\u003csup\u003e11\u0026ndash;14\u003c/sup\u003e of some brain areas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, a new scientific approach on resilience suggested to consider what are the biological and psychological underpinnings of such a construct \u003cem\u003eper se\u003c/em\u003e and not as reaction in the face of trauma or stress\u003csup\u003e4,15,16\u003c/sup\u003e. This shifting in operationalizing resilience has led to take into account resilience-promoting factors, in an attempt to gain insights into aspects of resilience not captured by models focused on the responses following adversity\u003csup\u003e17\u003c/sup\u003e. For example, the relationships between resilience and positive affect, emotion regulation, or personality traits have evidenced that individuals who effectively regulate their emotions, experience positive affect or have constructive personality traits display greater resilience\u003csup\u003e7,18,19\u003c/sup\u003e. Although it is difficult to capture the biologically dynamic nature of resilience, some studies demonstrated that resilience properties are determined by the adaptive responses of brain networks controlling the behaviors related to emotional regulation, coping, and cognitive flexibility\u003csup\u003e13,20\u0026ndash;26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eEven if a lot of single factors (whether psychological or neuronal) have been shown to efficiently impact resilience capacities, an integrative predictive model of resilience including multiple psychological constructs, personality traits and brain structural features seems more appropriate to obtain a deeper knowledge on trait resilience.\u0026nbsp;Given the close interplay between psychological constructs, personality and morphological characteristics of specific brain regions\u003csup\u003e19,27,28\u003c/sup\u003e, it seemed interesting to explore the potential associations between resilience measures (assessed by the self-reported Resilience Scale-10 (RS-10)\u003csup\u003e29\u003c/sup\u003e with numerous psychological dimensions (assessed by 14 tests), personality traits (assessed by 2 questionnaires), and brain morphological features (cortical and subcortical volumes and thickness) in healthy adult subjects of both sexes through correlational analyses. In addition, we investigated the predictors potentially associated to resilience by regression modeling. The identification of the multiple psychological features and neuro-morphological aspects associated to resilience is a critical step to better understand what factors predispose individuals to be resilient and eventually to develop novel approaches for resilience promotion.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFrequency distribution of RS-10 scores is summarized in figure S1 of Supplementary Materials. No effect of the sex was found on RS-10 scores (Mann-Whitney U Test: U=478.5, p=0.245; RS-10 mean \u0026plusmn; SD score: Males: 58.8 \u0026plusmn; 7.4; Females: 55.2 \u0026plusmn; 11.3). Male and female participants did not differ in age (Mann-Whitney U Test: U=453, p=0.141), in education (Mann-Whitney U Test: U=565, p=0.916), or numerosity (Chi-Square=2.45, p=0.118).\u003c/p\u003e\n\u003cp\u003eCorrelation tests were performed between RS-10 measures and all other variables of interest for this study. Out of the 279 variables examined, 46 exhibited a statistically significant correlation (p-value \u0026lt; 0.05), as summarized in Fig. 1 (and Table S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviations: TCI-HA1: TCI-Harm Avoidance Anticipatory worry; TCI-HA: TCI-Harm Avoidance; TCI-HA2; TCI-Harm Avoidance Fear of uncertainty; TCI-HA4: TCI-Harm Avoidance Fatigability; TCI-SD5: TCI-Self-directedness Enlightened second nature; TCI-NS3: TCI-Novelty Seeking Extravagance; TCI-SD: TCI-Self-directedness; TCI-SD2: TCI-Self-directedness Purposefulness; TCI-SD3: TCI-Self-directedness Resourcefulness; Cx: Cortex.\u003c/p\u003e\n\u003cp\u003eThese 46 variables were selected as candidate predictors for the regression analysis, regardless of the p-value obtained through the application of the False Discovery Rate (FDR) correction. In particular, 25 correlations were deemed significant (p-adjusted \u0026lt; 0.05) after FDR correction. Our focus was to select variables with a numerical association with the resilience measure, and we were not interested in identifying potential false positive correlations when selecting the features for the Elastic Net.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Elastic Net model, which was fitted using repeated k-fold cross-validation, exhibited satisfactory performance in identifying factors associated with resilience. Model performance was assessed using the root mean square error (RMSE) and the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e). The model\u0026rsquo;s RMSE and R\u003csup\u003e2\u003c/sup\u003e values were 8.129 and 0.400, respectively, with corresponding standard deviations of 3.010 and 0.233. Given that the RS-10 scale ranges from 10 to 70, a prediction that falls within approximately \u0026plusmn; 8 points of the actual value can be considered reasonable. Several predictors (n=15) were found to significantly contribute to resilience, with positive or negative estimates indicating the direction and intensity of change in the dependent variable (Fig. 2).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs Feldman (2020)\u003csup\u003e16\u003c/sup\u003e says, the flexible regulation and integration of multiple psychological processes and brain systems allow them not only to coexist but also to dynamically coalesce into a functionally resilient whole. In the same line but with a major emphasis on the neurobiological processes, Cathomas et al. (2019)\u003csup\u003e30\u003c/sup\u003e have recently proposed viewing resilience as a process that requires the integration of multiple central (hippocampal neurogenesis, dopaminergic systems, transcriptional and epigenetic pathways) and peripheral (cellular and humoral factors of the immune system, gut microbiota, and blood-brain barrier) systems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA number of studies have analyzed the psycho-social factors contributing to resilience, among them emotion regulation, executive function, dispositional optimism, coping strategies, cognitive reappraisal, and social support\u003csup\u003e1\u003c/sup\u003e. Many of these protective factors are interlinked: for example, greater emotional regulation is associated with strengthened executive function and cognitive flexibility\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn accordance with this integrative approach, the present paper provides a model of factors spanning psychological domains and cortical and subcortical morphological variables that allow predicting the resilient phenotype.\u003c/p\u003e\n\u003cp\u003eBy analyzing a lot of psychological constructs and brain structural features, we found that (regardless of the occurrence of stressful events within the last 3 months, as assessed by HR-SS) resilience scores assessed by RS-10 were predicted: with positive coefficients by some personality characteristics (Conscientiousness, Openness, Resourcefulness, Enlightened second nature), psychological dimensions (Self-efficacy, Positive affect, Confidence), and brain morphological aspects (volumes of amygdala and hippocampus, and cortical thickness of temporal pole); and with negative coefficients by other personality traits (Fear of uncertainty) and psychological dimensions (Anxiety, Depression, Need for Approval).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-efficacy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInterestingly, we found that the factor with the most predictive value was linked to the self-efficacy by GSES, defined as individuals\u0026apos; belief in their capacity to succeed in specific situations or to accomplish a task. The very definition of self-efficacy emphasizes how much it is related to resilience. By\u0026nbsp;determining the beliefs people hold regarding their power to affect situations, self-efficacy strongly influences both the power to competently face challenges and the most likely made choices. People with high self-efficacy values\u0026nbsp;view challenges as things to be mastered rather than threats to avoid. Self-efficacy not only affects our lives in highly stressful situations but also helps one to develop motivation and envision challenging high goals in life. Although\u0026nbsp;self-efficacy and resilience are distinct psychological resources, independent from each other, they are highly related since both of them share the ability to persevere in the face of difficulty and have a positive self-concept. By activating affective, motivational, and behavioral mechanisms in demanding situations, self-efficacy beliefs can promote resilience so much that sometimes self-efficacy has been conceptualized as one component of resilience\u003csup\u003e32\u003c/sup\u003e. Remarkably, high self-efficacy levels have been linked to low levels of anxiety and low vulnerability to depression\u003csup\u003e33\u003c/sup\u003e. Note that within the predictors of resilience found in the present research, the values of anxiety and depression scales were the factors with the highest negative coefficients associated with resilience*.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePersonality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is worth noting that within personality traits, Conscientiousness and Openness \u0026ndash; factors of the Big Five model \u0026ndash; and Resourcefulness and Enlightened second nature \u0026ndash; subscales of TCI character dimension Self-directedness \u0026ndash; predicted resilience values with positive coefficients, while Fear of Uncertainty \u0026ndash; subscale of the TCI Harm Avoidance temperamental dimension \u0026ndash; predicted resilience values with negative coefficients. These outcomes fully agree with literature findings on healthy individuals of different ages\u003csup\u003e8,19,34\u003c/sup\u003e, although such associations have been more frequently described in patients with various pathologies. Briefly, Conscientiousness is the personality trait that implies being careful and diligent, efficient and organized, with tendency to show self-discipline, act dutifully, and aim for achievement. Although with less predictive impact, resilience values were also predicted by the trait Openness, which assesses how open-minded, imaginative, creative, and insightful a person is. Those who are more broadminded tend more willing to listen to multiple viewpoints or try new things (cognitive flexibility). Not very differently, the most distinctive characteristics of self-directed individuals are that they are effective, able to adapt their behavior according to voluntary goals. Finally, we found that resilience displayed a small negative relationship with Fear of Uncertainty which is associated with a tendency to be sensitive to cues that signal punishment, with behavioral inhibition, and with avoidance of aversive situations, poor coping, and proneness to ne \u0026nbsp;gative emotions. Furthermore, the TCI subscales Resourcefulness and Enlightened second nature were associated with RS-10 scores with positive coefficients. As a final note, it has to be underlined that the further subscales of TCI (Purposefulness, Extravagance, Anticipatory worry) and BFQ-2 (Energy/Extraversion, Emotional Stability) were significantly correlated to resilience scale, even if not resulting as predictors in the Elastic Net model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis ensemble of personality-related predictors of resilience reflects the benefits of having a hard-working and positive affective style as well as abilities of interpersonal closeness and social interaction. In fact, on one hand the meticulous and careful approach of conscientious individuals may lend itself well to effectively coping with negative life experiences resulting in a sense of self-efficacy. On the other hand, the positive emotions and close social interactions contribute to resilience because they broaden the \u0026ldquo;thought-action repertoires\u0026rdquo;. In fact, having more flexible thinking, expanded behavioral options, and networks of social support increases the personal resources and thus the resilience\u003csup\u003e35\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnxiety and depression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe identification of the profile that characterizes the resilience and has predictive value as to whether or not anxiety and depression symptoms will be present is an important issue in the resilience literature. In close agreement with previous reports\u003csup\u003e36,37\u003c/sup\u003e, we found that state and trait anxiety, as well as depression were the highest negative coefficients associated with resilience. Predictably, as resilience reflects the ability to cope with life adversities, and therefore adaptively acts against psychological distress, we found significant inverse relationships between resilience values and anxiety and depression scores. These negative predictors suggest that resilience resources may turn the triggers for anxiety and depression into opportunities to improve performances and overcome difficulties, suggesting that highly resilient individuals have better coping mechanisms buffering against the development of anxiety and depression. These findings are consistent with previous studies reporting inverse associations between resilience and psychological distress among patients with chronic diseases\u003csup\u003e37\u003c/sup\u003e, and older people\u003csup\u003e38\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePositive affect\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch has repeatedly demonstrated that experiencing positive emotions in the face of adversity is one of the most important processes involved in resilience\u003csup\u003e35,39\u003c/sup\u003e. In accordance with these reports, we found that resilience values were associated positively with positive mood scores and negatively with negative mood scores, assessed by the PANAS. Notably, positive emotions resulted as predictors in the present Elastic Net model for resilience. This result is not surprising, given that positive emotions play the role of a buffer between the distressing situation and the emotional elicitation and appraisal of that situation (Paquette et al., 2023; Philippe et al., 2009). Individuals\u0026nbsp;with high positive emotionality are better skilled at self-generating positive emotions and at coping with adversities\u003csup\u003e35\u003c/sup\u003e. The role of positive emotions in resilient behaviors has been explained by the \u0026ldquo;Broaden-and-Build Theory\u0026rdquo;\u003csup\u003e35\u003c/sup\u003e positing that positive emotions facilitate resilience by broadening one\u0026rsquo;s attention and effective coping strategies\u003csup\u003e40\u003c/sup\u003e. According to this view, as opposed to negative emotions, positive emotions broaden thought and allow for flexible attention. Repeated experiences of positive emotions would render this broadened mindset habitual and result in increased personal resources that can be drawn on in times of need and facilitate resilient behaviors and adaptive coping strategies\u003csup\u003e40\u003c/sup\u003e. In addition, positive emotions would have an undoing effect, given they counteract the deleterious after-effects of negative emotions and stress. The longitudinal study by\u0026nbsp;Cohn et al. (2009)\u003csup\u003e41\u003c/sup\u003e has shown that, over a one-month period, daily positive emotions buffered against the effect of negative emotions and were related to growth in resilience.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAttachment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003einteractions with the primary caregiver\u003csup\u003e42\u003c/sup\u003e. The initial experiences have an impact of moderate to high degree of stability over time (trait component) and influence the way the later relationships are processed, the ability to cope with hardships, and the overall functioning and mental health. Attachment styles have broadly been categorized as secure - characterized by positive sense of self and others, comfort with intimacy and independence, and adaptive resources to recruit help, when needed - or insecure - inability to engage in intimacy, struggling to form healthy relationships, tendency to inconsistent behaviors with others. Research has shown that secure attachment style can foster resilience by means of the implementation of effective problem-focused coping strategies\u003cem\u003e,\u0026nbsp;\u003c/em\u003eand coping strategies may in turn shape resilience\u003csup\u003e43,44\u003c/sup\u003e\u003cem\u003e.\u003c/em\u003e It has been suggested that secure attachment and resilience are complementary concepts which share similar developmental circumstances, stemming from a healthy childhood and leading to the emergence of adaptive self-esteem and empathy, through positive relations with others.Marriner and colleagues (2014)\u003csup\u003e45\u003c/sup\u003e reported that individuals with a secure attachment exhibit high levels of resilience, and both these variables in turn correlate positively with proactive coping strategies, and negatively with avoidant coping strategies.\u003c/p\u003e\n\u003cp\u003eIntriguingly, in the present research we found that among the predictors of resilience there were the positively predicting ASQ dimension Confidence and the negatively predicting ASQ dimension Need for Approval. The other ASQ dimensions Relationships as Secondary and Preoccupation with Relationships as well as the coping strategy\u0026nbsp;Problem Solving assessed by COPE\u0026nbsp;inventory were positively correlated with the RS-10, although not resulting as predictors in the Elastic Net model. Specifically, the active coping strategies are intentional efforts aimed at minimizing the physical, psychological, or social harm of a stressor. They are associated with actual or perceived control over the stressor and lead to changes facilitating an adaptive and resilient response\u003csup\u003e46\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn addition, we found that the IRI subscale Personal Distress, which measures an affective dimension of empathy, was negatively associated with the RS-10 scores, although it was not resulting as a predictor in the model for resilience. Note that empathic people may promote prosocial behavior, upgrade interpersonal relationships, be considerate of others and put aside their concerns, and at same time have high self-esteem, reduced loneliness, and a strong sense of self. \u0026nbsp;In doing so, they become more resilient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural brain correlates of resilience\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe above-described ensemble of the predictors of resilience allowed us to describe the constructs shaping the psychological profile of the resilient people. In order to achieve an even more multifaceted profile of the resilient phenotype, in addition to the psychological and personality factors, within the present regression model we inserted several brain morphological features.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHuman cross-sectional studies have focused on neural structures and neuroendocrine markers of resilience, and the animal models provided data on the behavioral, genetic, molecular, and hormonal bases of resilience, showing that in resilient animals there is an absence of the key molecular abnormalities found in susceptible individuals as well as distinct epigenetic and cellular adaptations\u003csup\u003e47\u0026ndash;49\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn brief, neuronal architecture of resilience largely overlaps with the neuronal structures related to cognitive and emotional regulation, as the executive control network (including prefrontal, frontal and parietal regions), and the emotional arousal network (including cingulate cortex subregions, amygdala, hippocampus and insula). However, up to now these brain structures have been mainly implicated in the vulnerability, rather than in the resilience, to stress or trauma\u003csup\u003e50\u003c/sup\u003e. In fact, since the brain is continuously adapting to the perturbations in bodily homeostasis, most information regards the neurobiology of resilience as response to disease or traumatic adversities, and not to trait resilience we were conversely mainly interested in. However, since the maladaptive responses to stress/trauma are the flipside of resilience, it is appropriate to take into account even literature data analyzing the brain morphological responses to stress/trauma. Interestingly, in the present model of resilience the volumes of the amygdala and hippocampus, as well as the cortical thickness of the temporal pole predicted the resilience values with positive coefficients. Additionally, even if not resulting as a predictor, we found that the volume of the medial orbitofrontal cortex, structure which mediates emotional regulation, cognitive control, social cognition\u003csup\u003e51\u003c/sup\u003e, was positively correlated with the resilience scores, in agreement with previous findings\u003csup\u003e22,52,53\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLet\u0026rsquo;s analyze in detail the single neuronal predictors of resilience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAmygdala\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found that the volume of left amygdala positively predicted enhanced resilience scores. This finding is consistent with the larger amygdala volume associated with the increased resilience scores described by\u0026nbsp;Gupta et al. (2017)\u003csup\u003e24\u003c/sup\u003e in healthy subjects, and with the evidence provided by Reynaud et al. (2013)\u003csup\u003e52\u003c/sup\u003e that the larger amygdala and OFC activation responses to stressful events, the greater the resilience. In close accordance with the present findings, Morey et al. (2016)\u003csup\u003e54\u003c/sup\u003e described larger left amygdala and right hippocampal volumes in resilient maltreated children.\u003c/p\u003e\n\u003cp\u003eIn literature, conflicting studies reported amygdala volumes\u0026nbsp;larger\u003csup\u003e55\u003c/sup\u003e, unmodified\u003csup\u003e56\u003c/sup\u003e, or smaller\u003csup\u003e57\u003c/sup\u003e in individuals who had experienced stressful social adversity. Recently, a structural connectivity study by Ohashi et al. (2019)\u003csup\u003e58\u003c/sup\u003e showed that amygdala nodal efficiency was lower in resilient than in susceptible to maltreatment individuals, suggesting that the decreased efficiency of amygdala node in propagating information throughout the network might mitigate the effects of adversities and lead to enhanced resilience. However, it has to be noted that these contradictory findings have been attributed to the amygdala vulnerability to the type, magnitude, and timing of stress. Conversely, the present research was aimed at finding the neuronal predictors of resilience in the absence of any stressful event.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHippocampus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe second most significant positive neuronal predictor of resilience of the present model was the volume of the right hippocampus. This finding fully fits with the reduction of hippocampal volume repeatedly described in individuals affected by trauma-related psychopathologies or mood disorders or living in poverty\u003csup\u003e59\u0026ndash;61\u003c/sup\u003e, given that one of the core symptoms of these conditions is the altered regulation of emotions induced by traumatic memories. Numerous data indicate that smaller hippocampal volumes might be the result of exposure to severe stress (and perhaps also a vulnerability factor)\u003csup\u003e62,63\u003c/sup\u003e. Animal studies report that the exposure to traumatic events damages hippocampal neurons, inhibits neurogenesis, and suppresses the production of new granule neurons in the dentate gyrus\u003csup\u003e64\u003c/sup\u003e. Notably, the opposite description of increased hippocampal volumes associated with increased resilience is less established\u003csup\u003e65\u003c/sup\u003e. A study by\u0026nbsp;Vermetten et al. (2003)\u003csup\u003e66\u003c/sup\u003e which reports that psychopharmacological treatment of PTSD symptoms resulted in increased hippocampal volumes suggests that larger hippocampal volumes are related to higher resilience. In the same vein, the pharmacological treatment with antidepressants is reported to reverse the decreased hippocampal volumes by increasing neural progenitor cells\u003csup\u003e67\u003c/sup\u003e. Furthermore, the deleterious effect of poverty on hippocampal volume was alleviated in subjects with high self-esteem, a finding suggesting that positive psychological resources may provide protection against the hippocampal atrophy in adversity\u003csup\u003e68\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOf note, several studies found larger hippocampal volumes in resilient individuals in comparison to PTSD subjects\u003csup\u003e69\u003c/sup\u003e. In healthy volunteers greater functional coupling between hippocampus and ventromedial prefrontal cortex is suggested to be linked to greater extinction recall, a capacity thought to promote resilience\u003csup\u003e70\u003c/sup\u003e. Subjects with high adversity level but high resilience scores show reduced reward-related activation of the ventral striatum and increased activation of the ventral tegmental area and hippocampus\u003csup\u003e65\u003c/sup\u003e.\u0026nbsp;In conclusion, literature data support the idea that larger hippocampal volumes may confer resilience to trauma and be stress-protective, as found in the present research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal pole\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA further positive neuronal predictor of resilience of the present model was the cortical thickness of the temporal pole, which constitutes the most rostral part of the temporal lobe. Cortical thickness reflects the size, density and arrangement of neurons, neuroglia and nerve fibers, and also axon and dendrite remodeling and myelination.\u003c/p\u003e\n\u003cp\u003eBecause of its distributed anatomical connections with limbic structures and neocortical regions, the temporal pole\u0026nbsp;has been associated with several high-level cognitive processes, such as visual processing for complex objects, face recognition and visual memory, autobiographical memory, semantic processing\u003csup\u003e71\u003c/sup\u003e. Moreover, it has been involved in several emotional (positive or negative) or affective circumstances, such as recalling emotionally intense autobiographical memories or watching an emotion-inducing movie\u003csup\u003e72\u003c/sup\u003e. Notably, on the bases of impaired recognition of facial and musical emotions found to be associated with atrophy of the right temporal pole\u003csup\u003e73\u003c/sup\u003e, the temporal pole is argued to be a conduit for integrating visceral information, sensory representations and memories for emotionally or socially-relevant concepts\u003csup\u003e72\u003c/sup\u003e. Such emotional processing and multimodal sensory integration may contribute to stable emotional and social behavior. Notably, stability in behavior is an important component of resilience. Furthermore, the temporal pole is involved in higher-level social cognition functions relating to the perception and comprehension of others\u0026rsquo; thoughts and actions\u003csup\u003e72\u003c/sup\u003e, and it is activated during theory of mind and empathy tasks\u003csup\u003e74\u003c/sup\u003e. Interestingly, temporal pole volume it positively correlates with scores in trait modesty\u003csup\u003e75\u003c/sup\u003e. Note that trait modesty has been associated with the social information processing and interpersonal perception that enables and motivates prosocial behavior and stable interpersonal relationships, and it is beneficial to adaptive psychological functioning\u003csup\u003e75\u003c/sup\u003e, all components of resilience.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsistently with previous studies in non-older groups\u003csup\u003e23,24\u003c/sup\u003e, and in full agreement with the present results, a recent study on older people reported that resilience capacities were positively related to the cortical thickness of the left temporal pole\u003csup\u003e26\u003c/sup\u003e. Specularly, a meta-analysis on brain volumes in subjects with PTSD showed reduced volume of temporal pole\u003csup\u003e76\u003c/sup\u003e. Considered in this context, it is remarkable that the morphometric features of the temporal pole may predict affective regulation and hence resilience, as occurring in the present Elastic Net model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFoot note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;*As Bandura wrote (2006), \u0026ldquo;\u003cem\u003eself-efficacy beliefs influence the challenges and goals people set for themselves and their commitment to them, how much effort they put forth in given endeavors, the outcomes they expect their efforts to produce, how long they persevere in the face of obstacles, their \u003cstrong\u003eresilience\u003c/strong\u003e to adversity, the quality of their emotional life and how much stress and depression they experience in coping with taxing environmental demands\u0026rdquo;.\u0026nbsp;\u003c/em\u003e(Bandura, A. \u003cem\u003eGuide for constructing self-efficacy scales\u003c/em\u003e. In \u003cem\u003eSelf-efficacy beliefs of adolescents,\u003c/em\u003e Vol. 5, pp. 307-337 (eds. Pajares F. \u0026amp; Urdan T.), Greenwich, CT: Information Age Publishing, 2006).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe profile of resilient people derived from the present model on one hand encompasses what has been inventively termed the \u0026lsquo;ordinary magic\u0026rsquo;\u003csup\u003e77\u003c/sup\u003e of strongly adaptive fundamental systems, such as positive personality characteristics, high cognitive abilities, psychological well-being linked to high self-efficacy and conscientiousness, low anxiety and depression, secure attachment, positive emotional experience, wider associative thinking, adaptive coping, broad and affective social support. On the other hand, the resilient profile includes brain structural correlates, indicating that resilient subjects are characterized by neural substrates reflecting efficient arousal modulation and emotional/cognitive regulation in a flexible interplay with the psychological and environmental factors. Interestingly, these multiple variables belonging to different domains are intertwined.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne of the main future challenges will be to gain a holistic model of resilience that encompasses key circuits in the brain, peripheral systems, psychological dimensions, and personality features.\u003c/p\u003e\n\u003cp\u003eThe present multidimensional predictive model can lead to a novel framework useful to study the assorted interconnections among psychological and neuronal variables able to confer resilience. Hopefully, the same predictors could be applied to research on the interplay between genetic predisposition and environmental factors in determining resilience or vulnerability to psychiatric disorders.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA sample of 69 healthy right-handed subjects (28 males: mean age \u0026plusmn;SD: 38.21 \u0026plusmn;11.87 years (y), 41 females: 41.88 \u0026plusmn;12.69 y) reporting no history of psychiatric or neurological diseases, participated in this study, which was part of a large research investigating the relations between brain and psychological dimensions\u003csup\u003e78\u0026ndash;80\u003c/sup\u003e. Data were filtered to keep only the participants who could contribute data for all variables included in the final analyses. Educational level ranged from an eighth grade to a post-graduate degree (mean education years \u0026plusmn; SD: 15.75 \u0026plusmn; 2.99 y).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll participants underwent MRI scanning and completed questionnaires. Inclusion and exclusion criteria are described in detail in Supplementary Materials.\u003c/p\u003e\n\u003cp\u003eThe investigation was carried out in accordance with the latest version of the Declaration of Helsinki. Both behavioral and MRI protocols were approved by the local ethic committee of the Santa Lucia Foundation IRCCS. Written informed consent of all participants was obtained before the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePsychological assessment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants\u0026rsquo; psychological profile was assessed by means of the Resilience Scale-10 (RS-10), Attachment Style Questionnaire (ASQ), Beck\u0026rsquo;s Depression Inventory Scale (BDI), Coping Orientation to Problems and Experiences (COPE), Emotion Regulation Questionnaire (ERQ), General Self-Efficacy Scale (GSES), Hamilton Anxiety Rating Scale (HAM-A), Hamilton Depression Rating Scale (HAM-D), Holmes and Rahe Stressful Event Scale (HR-SS), Interpersonal Reactivity Index (IRI), Positive and Negative Affect Schedule (PANAS), State-Trait Anger Expression Inventory (STAXI), State-Trait Anxiety Inventory-Form Y (STAI-Y), Toronto Alexithymia Scale (TAS-20), Raven\u0026apos;s Progressive Matrices (RPM).\u003c/p\u003e\n\u003cp\u003eParticipants\u0026rsquo; personality traits were assessed by means of the Big Five Questionnaire-2 (BFQ-2) and Temperament and Character Inventory (TCI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eItalian versions of psychological and personality scales were used. Tests and questionnaires are described in Supplementary Materials, in which descriptive statistics for socio-demographic and psychological variables are also reported (Table S1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Resilience Scale (RS-10)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResilience was measured by using the Italian version of RS-10\u003csup\u003e81,82\u003c/sup\u003e, which is a 10 item-version of the psychometrically sound Resilience Scale (RS)\u003csup\u003e29\u003c/sup\u003e.\u0026nbsp;A previous study\u003csup\u003e83\u003c/sup\u003e demonstrated the equivalence between the unifactorial 10-item version (RS-10) and the original version of RS (encompassing 25 items and measuring five essential characteristics of resilience). The unifactorial structure of RS-10 facilitated the definition of the resilience measure, which was our dependent variable.\u003c/p\u003e\n\u003cp\u003eThe 10 items of the test are rated on a 7-point Likert scale (from 1 = \u003cem\u003estrongly disagree\u003c/em\u003e to 7 = \u003cem\u003estrongly agree\u003c/em\u003e). The RS-10 gives total scores ranging from 10 to 70, with higher scores reflecting greater levels of resilience.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI Acquisition and Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants underwent a neuroimaging protocol including standard clinical sequences (FLAIR, DP-T2-weighted) and a volumetric whole-brain 3D high-resolution T1-weighted sequence, performed with a 3T Allegra M in Supplementary Materials. Volumetric whole-brain T1-weighted images were obtained in the sagittal plane using a Modified Driven Equilibrium Fourier Transform (MDEFT) sequence (Echo Time/Repetition Time-TE/TR- = 2.4/7.92 ms, flip angle 15, voxel size 1 x 1 x 1 mm\u003csup\u003e3\u003c/sup\u003e). All planar sequence acquisitions were obtained in the plane of the AC-PC line. The FreeSurfer imaging analysis suite (v5.1.3, http://surfer.nmr.mgh.harvard.edu/, accessed on 26 January 2020) was used for reconstructing volumes and cortical thickness of brain regions\u003csup\u003e84,85\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCerebellum parcellation was performed through a freely available\u0026nbsp;patch-based multi-atlas segmentation tool called CERES (CEREbellum Segmentation) able to automatically parcellate the cerebellar lobules. CERES\u003csup\u003e86\u003c/sup\u003e is part of broader software pipeline for volumetric brain analysis, namely volBrain (https://www.volbrain.net/).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNeuroimaging data acquisition and processing is detailed in Supplementary Materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData preparation and regression analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore regression modeling, a Spearman correlation filter was used (Table S2) to select independent variables significantly correlated to the dependent variable\u003csup\u003e87\u003c/sup\u003e.The 279 variables taken into account for correlations were: socio-demographic variables (n=2); psychological (n=34) and personality (n=37) variables from the 16 tests detailed in Supplementary Materials; cortical volume variables (n=66); sub-cortical volume variables (n=26); cortical thickness variables (n=66), cerebellar volume and thickness variables (n=48). Sex was used as between-subjects factor in non-parametric statistical analyses.\u003c/p\u003e\n\u003cp\u003eSelected predictors, along with socio-demographics as covariates (namely, age, education and sex), were used in a regression analysis to predict the resilience score. In order to manage the presence of several predictors, the regression analysis was performed with the Elastic Net method. The Elastic Net modulates regression coefficients to penalize complex models implementing implicit feature selection through a regularization approach that combines ridge and LASSO regression\u003csup\u003e88,89\u003c/sup\u003e. The model was trained using repeated k-fold cross-validation, with 5 folds repeated 5 times, to avoid overfitting and ensure results reliability. Repeated k-fold cross-validation was preferred to single-run k-fold cross-validation because it ensures a more accurate estimate of results by reporting the mean result across all folds from all runs. Repeated cross-validation reduces the error in the estimate of mean model performance. On each run of this cross-validation procedure, data were centered and scaled. The model was evaluated with the R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eand the Root Mean Squared Error (RMSE) metrics. Feature importance was investigated by evaluating the model\u0026apos;s regression coefficients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank all the participants in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by the Italian Ministry of Health, Ricerca Corrente 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors conceptualized and designed the study. EP, FP and GS gathered and analyzed neuroimaging data. EP, DL and DC gathered and analyzed psychological and personality data. CF, DD and AT performed correlation analyses and regression modeling. All authors contributed to the interpretation of data and were involved in writing and critically revising the manuscript, which all authors approved for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflict of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eADDITIONAL INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSouthwick, S. 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(Springer New York, New York, NY, 2013).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Psychological constructs, personality traits, brain volumes, cortical thickness, Resilience Scale-10, elastic net. ","lastPublishedDoi":"10.21203/rs.3.rs-4485591/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4485591/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eResearch in neuroscience has shifted the focal point from a pathological orientation (responses to recover from trauma or stress) to an emphasis on the role of resilience in health (protective factors to maintain health levels despite eventual adversities). Even if multiple single factors impact resilience capacities, an integrative predictive model including psychological constructs, personality traits and brain structural features may offer a deeper knowledge on trait resilience. We examined the associations between Resilience Scale-10 scores with numerous psychological dimensions, personality traits, and neuro-morphological features (brain volumes and thickness) in healthy adults of both sexes. Furthermore, we investigated the predictors potentially associated to resilience by regression modeling.\u003c/p\u003e\n\u003cp\u003eResilience values were predicted: positively by some personality characteristics (\u003ca href=\"https://en.wikipedia.org/wiki/Conscientiousness\"\u003eConscientiousness, Openness, \u003c/a\u003eResourcefulness, Enlightened second nature), psychological dimensions (Self-efficacy, Positive affect, Confidence), and brain morphological aspects (volumes of amygdala and hippocampus, and cortical thickness of temporal pole); and negatively by other personality traits (Fear of uncertainty) and psychological dimensions (Anxiety, Depression, Need for Approval).\u003c/p\u003e\n\u003cp\u003eThe identification of the multiple psychological and personality features and neuro-morphological aspects associated to resilience represents a critical step to understand the factors that predispose individuals to be resilient and eventually to develop novel approaches for resilience promotion.\u003c/p\u003e","manuscriptTitle":"The psychological and neuro-morphological predictors of resilience in healthy adults: The whole is more than the sum of its parts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 02:27:18","doi":"10.21203/rs.3.rs-4485591/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df855566-c147-43f7-844c-c2d0b0fd227a","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33072632,"name":"Biological sciences/Neuroscience/Emotion"},{"id":33072633,"name":"Biological sciences/Neuroscience/Social neuroscience"}],"tags":[],"updatedAt":"2025-01-30T05:08:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 02:27:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4485591","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4485591","identity":"rs-4485591","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}