Functional connectome of the five-factor model of personality

Intrinsic functional connectivity mediates the effect of personality traits on depressive symptoms

BACKGROUND

Personality traits have been proposed as risk factors for depressive symptoms. However, the neural mechanism behind these relationships is unclear. This study examined the possible mediating effect of resting-state functional connectivity networks on these relationships.

METHODS

Data from 153 healthy Germans were obtained from the MPI-Leipzig Mind-Brain-Body: Neuroanatomy & Connectivity Protocol database. Network-based statistics were used to identify significant functional connectivity networks that were positively and negatively associated with the personality traits of neuroticism, conscientiousness, and extraversion, with and without demographical covariates. Mediation analyses were performed for each personality trait and depressive symptoms with the significant positive and negative network strengths of the respective personality traits as mediators.

RESULTS

Neuroticism, conscientiousness, and extraversion were significantly correlated with depressive symptoms. Network-based statistics identified patterns of functional connectivity that were significantly associated with neuroticism and conscientiousness. After controlling for demographical covariates, significant conscientiousness-associated and extraversion-associated networks emerged. Mediation analysis concluded that only the neuroticism-positive network mediated the effect of neuroticism on depressive symptoms. When age and sex were controlled, the extraversion-positive network completely mediated the effect of extraversion on depressive symptoms.

CONCLUSIONS

These findings revealed that patterns of intrinsic functional networks predict personality traits and suggest that the relationship between personality traits and depressive symptoms may in part be due to their common patterns of intrinsic functional networks.

Resting-state functional network connectivity underlying conscientiousness in school-aged children

Conscientiousness is a personality trait that matures from early childhood to late adolescence, yet little is known about its underlying brain mechanisms during this period. To investigate this, our study examined the resting-state functional network connectivity (rsFNC) of 69 school-aged children (mean age = 10.12 years, range = 9-12) using a whole-brain region-of-interest (ROI) based analysis, based on functional magnetic resonance imaging (fMRI). The results indicated a positive association between conscientiousness and the rsFNC between the fronto-parietal network (FPN) and two brain networks: the somatosensory motor-hand network (SMHN) and the auditory network (AN). However, conscientiousness was negatively associated with the rsFNC between FPN and two other networks: the salience network (SN); the default mode network (DMN). Moreover, our results suggest that the FPN may play a hub role in the neural performance of children's conscientiousness. Intrinsic brain networks, particularly those involved in higher-order cognitive functions, impact children's conscientiousness. Therefore, FPN plays an important role in the development of children's personality, providing insight into the neural mechanisms underlying children's personality.

Neural Representation of Collective Self-esteem in Resting-state Functional Connectivity and its Validation in Task-dependent Modality

INTRODUCTION

Collective self-esteem (CSE) is an important personality variable, defined as self-worth derived from membership in social groups. A study explored the neural basis of CSE using a task-based functional magnetic resonance imaging (fMRI) paradigm; however, task-independent neural basis of CSE remains to be explored, and whether the CSE neural basis of resting-state fMRI is consistent with that of task-based fMRI is unclear.

METHODS

We built support vector regression (SVR) models to predict CSE scores using topological metrics measured in the resting-state functional connectivity network (RSFC) as features. Then, to test the reliability of the SVR analysis, the activation pattern of the identified brain regions from SVR analysis was used as features to distinguish collective self-worth from other conditions by multivariate pattern classification in task-based fMRI dataset.

RESULTS

SVR analysis results showed that leverage centrality successfully decoded the individual differences in CSE. The ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate gyrus, precuneus, orbitofrontal cortex, posterior insula, postcentral gyrus, inferior parietal lobule, temporoparietal junction, and inferior frontal gyrus, which are involved in self-referential processing, affective processing, and social cognition networks, participated in this prediction. Multivariate pattern classification analysis found that the activation pattern of the identified regions from the SVR analysis successfully distinguished collective self-worth from relational self-worth, personal self-worth and semantic control.

CONCLUSION

Our findings revealed CSE neural basis in the whole-brain RSFC network, and established the concordance between leverage centrality and the activation pattern (evoked during collective self-worth task) of the identified regions in terms of representing CSE.

Evaluating methods for measuring background connectivity in slow event-related functional magnetic resonance imaging designs

INTRODUCTION

Resting-state functional magnetic resonance imaging (fMRI) is widely used for measuring functional interactions between brain regions, significantly contributing to our understanding of large-scale brain networks and brain-behavior relationships. Furthermore, idiosyncratic patterns of resting-state connections can be leveraged to identify individuals and predict individual differences in clinical symptoms, cognitive abilities, and other individual factors. Idiosyncratic connectivity patterns are thought to persist across task states, suggesting task-based fMRI can be similarly leveraged for individual differences analyses.

METHOD

Here, we tested the degree to which functional interactions occurring in the background of a task during slow event-related fMRI parallel or differ from those captured during resting-state fMRI. We compared two approaches for removing task-evoked activity from task-based fMRI: (1) applying a low-pass filter to remove task-related frequencies in the signal, or (2) extracting residuals from a general linear model (GLM) that accounts for task-evoked responses.

RESULT

We found that the organization of large-scale cortical networks and individual's idiosyncratic connectivity patterns are preserved during task-based fMRI. In contrast, individual differences in connection strength can vary more substantially between rest and task. Compared to low-pass filtering, background connectivity obtained from GLM residuals produced idiosyncratic connectivity patterns and individual differences in connection strength that more resembled rest. However, all background connectivity measures were highly similar when derived from the low-pass-filtered signal or GLM residuals, indicating that both methods are suitable for measuring background connectivity.

CONCLUSION

Together, our results highlight new avenues for the analysis of task-based fMRI datasets and the utility of each background connectivity method.

Integration of structural brain networks is related to openness to experience: A diffusion MRI study with CSD-based tractography

Openness to experience is one of the big five traits of personality which recently has been the subject of several studies in neuroscience due to its importance in understanding various cognitive functions. However, the neural basis of openness to experience is still unclear. Previous studies have found largely heterogeneous results, suggesting that various brain regions may be involved in openness to experience. Here we suggested that performing structural connectome analysis may shed light on the neural underpinnings of openness to experience as it provides a more comprehensive look at the brain regions that are involved in this trait. Hence, we investigated the involvement of brain network structural features in openness to experience which has not yet been explored to date. The magnetic resonance imaging (MRI) data along with the openness to experience trait score from the self-reported NEO Five-Factor Inventory of 100 healthy subjects were evaluated from Human Connectome Project (HCP). CSD-based whole-brain probabilistic tractography was performed using diffusion-weighted images as well as segmented T1-weighted images to create an adjacency matrix for each subject. Using graph theoretical analysis, we computed global efficiency (GE) and clustering coefficient (CC) which are measures of two important aspects of network organization in the brain: functional integration and functional segregation respectively. Results revealed a significant negative correlation between GE and openness to experience which means that the higher capacity of the brain in combining information from different regions may be related to lower openness to experience.

Functional connectivity of the central autonomic and default mode networks represent neural correlates and predictors of individual personality

There is solid evidence for the prominent involvement of the central autonomic and default mode systems in shaping personality. However, whether functional connectivity of these systems can represent neural correlates and predictors of individual variation in personality traits is largely unknown. Resting-state functional magnetic resonance imaging data of 215 healthy young adults were used to construct the sympathetic (SN), parasympathetic (PN), and default mode (DMN) networks, with intra- and internetwork functional connectivity measured. Personality factors were assessed using the five-factor model. We examined the associations between personality factors and functional network connectivity, followed by performance of personality prediction based on functional connectivity using connectome-based predictive modeling (CPM), a recently developed machine learning approach. All personality factors (neuroticism, extraversion, conscientiousness, and agreeableness) other than openness were significantly correlated with intra- and internetwork functional connectivity of the SN, PN, and DMN. Moreover, the CPM models successfully predicted conscientiousness and agreeableness at the individual level using functional network connectivity. Our findings may expand existing knowledge regarding the neural substrates underlying personality.

The thresholding problem and variability in the EEG graph network parameters

Graph thresholding is a frequently used practice of eliminating the weak connections in brain functional connectivity graphs. The main aim of the procedure is to delete the spurious connections in the data. However, the choice of the threshold is arbitrary, and the effect of the threshold choice is not fully understood. Here we present the description of the changes in the global measures of a functional connectivity graph depending on the different proportional thresholds based on the 146 resting-state EEG recordings. The dynamics is presented in five different synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensors and source spaces. The analysis shows significant changes in the graph's global connectivity measures as a function of the chosen threshold which may influence the outcome of the study. The choice of the threshold could lead to different study conclusions; thus it is necessary to improve the reasoning behind the choice of the different analytic options and consider the adoption of different analytic approaches. We also proposed some ways of improving the procedure of thresholding in functional connectivity research.

Personality traits modulate the neural responses to handwriting processing

Handwriting is a vital skill for everyday human activities. It has a wealth of information about writers' characteristics and can hint toward underlying neurological conditions, such as Parkinson's disease, autism, dyslexia, and attention-deficit/hyperactivity disorder (ADHD). Many previous studies have reported a link between personality and individual differences in handwriting, but the evidence for the relationship tends to be anecdotal in nature. Using functional magnetic resonance imaging (fMRI), we examined whether the association between personality traits and handwriting was instantiated at the neural level. Results showed that the personality trait of conscientiousness modulated brain activation in the left premotor cortex and right inferior/middle frontal gyrus, which may reflect the impact of personality on orthography-to-grapheme transformation and executive control involved in handwriting. Such correlations were not observed in symbol-drawing or word-reading tasks, suggesting the specificity of the link between conscientiousness and handwriting in these regions. Moreover, using a connectome-based predictive modeling approach, we found that individuals' conscientiousness scores could be predicted based on handwriting-related functional brain networks, suggesting that the influence of personality on handwriting may occur within a broader network. Our findings provide neural evidence for the link between personality and handwriting processing, extending our understanding of the nature of individual differences in handwriting.

Brain default mode network mediates the association between negative perfectionism and exercise dependence

BACKGROUND AND AIMS

Perfectionism is correlated with the occurrence of exercise dependence. We aim to reveal the role of functional connectivity (FC) between gray matter (GM) and white matter (WM) networks in the association between perfectionism and exercise dependence.

METHODS

In this cross-sectional study, one hundred ten participants with exercise dependence underwent behavioral evaluation and resting-state functional magnetic resonance imaging. Perfectionism and exercise dependence were quantified using the Frost Multidimensional Perfectionism Scale (FMPS) and Exercise Dependence Scale (EDS). We used a K-means clustering algorithm to identify functional GM and WM networks and obtained the FCs of the GM-GM, GM-WM, and WM-WM networks. Partial correlation and mediation analyses were performed to explore the relationships among FCs, FMPS, and EDS.

RESULTS

We identified ten stable GM networks and nine WM networks. Of these, FCs existed between the corona radiata network (WM1) and default mode network (DMN, GM8), WM1 network and WM DMN (WM4), WM1 network and midbrain WM network (WM7), and WM4 network and inferior longitudinal fasciculus network (WM9). The WM1-GM8 and WM1-WM4 FCs were positively correlated with the EDS and negative FMPS. The mediating effects of the WM1-GM8 and WM1-WM4 FCs were established in the association between the negative dimensional FMPS and EDS.

DISCUSSION AND CONCLUSIONS

The WM1 network anatomically linked the subregions within the GM8 and WM4 networks, and WM1-GM8 and WM1-WM4 FCs mediated the association between negative dimensional FMPS and EDS. These findings indicated that DMN function might be involved in the increased risks of exercise dependence promoted by negative perfectionism.

On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances

In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, and then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees and kinds of flexibility may be adaptive (or maladaptive) in different contexts, specifically focusing on measures related to either more disjoint or cohesive dynamics. While disjointed flexibility may be useful for assessing neural entropy, cohesive flexibility may potentially serve as a proxy for self-organized criticality as a fundamental property enabling adaptive behavior in complex systems. Particular attention is given to recent studies in which flexibility methods have been used to investigate neurological and cognitive maturation, as well as the breakdown of conscious processing under varying levels of anesthesia. We further discuss how these findings and methods might be contextualized within the Free Energy Principle with respect to the fundamentals of brain organization and biological functioning more generally, and describe potential methodological advances from this paradigm. Finally, with relevance to computational psychiatry, we propose a research program for obtaining a better understanding of ways that dynamic networks may relate to different forms of psychological flexibility, which may be the single most important factor for ensuring human flourishing.

Comparative connectomics of the primate social brain

Social interaction is thought to provide a selection pressure for human intelligence, yet little is known about its neurobiological basis and evolution throughout the primate lineage. Recent advances in neuroimaging have enabled whole brain investigation of brain structure, function, and connectivity in humans and non-human primates (NHPs), leading to a nascent field of comparative connectomics. However, linking social behavior to brain organization across the primates remains challenging. Here, we review the current understanding of the macroscale neural mechanisms of social behaviors from the viewpoint of system neuroscience. We first demonstrate an association between the number of cortical neurons and the size of social groups across primates, suggesting a link between neural information-processing capacity and social capabilities. Moreover, by capitalizing on recent advances in species-harmonized functional MRI, we demonstrate that portions of the mirror neuron system and default-mode networks, which are thought to be important for representation of the other's actions and sense of self, respectively, exhibit similarities in functional organization in macaque monkeys and humans, suggesting possible homologies. With respect to these two networks, we describe recent developments in the neurobiology of social perception, joint attention, personality and social complexity. Together, the Human Connectome Project (HCP)-style comparative neuroimaging, hyperscanning, behavioral, and other multi-modal investigations are expected to yield important insights into the evolutionary foundations of human social behavior.

Personality network neuroscience: Promises and challenges on the way toward a unifying framework of individual variability

We propose that the application of network theory to established psychological personality conceptions has great potential to advance a biologically plausible model of human personality. Stable behavioral tendencies are conceived as personality “traits.” Such traits demonstrate considerable variability between individuals, and extreme expressions represent risk factors for psychological disorders. Although the psychometric assessment of personality has more than hundred years tradition, it is not yet clear whether traits indeed represent “biophysical entities” with specific and dissociable neural substrates. For instance, it is an open question whether there exists a correspondence between the multilayer structure of psychometrically derived personality factors and the organizational properties of traitlike brain systems. After a short introduction into fundamental personality conceptions, this article will point out how network neuroscience can enhance our understanding about human personality. We will examine the importance of intrinsic (task-independent) brain connectivity networks and show means to link brain features to stable behavioral tendencies. Questions and challenges arising from each discipline itself and their combination are discussed and potential solutions are developed. We close by outlining future trends and by discussing how further developments of network neuroscience can be applied to personality research.

Brain Knows Who Is on the Same Wavelength: Resting-State Connectivity Can Predict Compatibility of a Female-Male Relationship

Prediction of the initial compatibility of heterosexual individuals based on self-reported traits and preferences has not been successful, even with significantly developed information technology. To overcome the limitations of self-reported measures and predict compatibility, we used functional connectivity profiles from resting-state functional magnetic resonance imaging (fMRI) data that carry rich individual-specific information sufficient to predict psychological constructs and activation patterns during social cognitive tasks. Several days after collecting data from resting-state fMRIs, participants undertook a speed-dating experiment in which they had a 3-min speed date with every other opposite-sex participant. Our machine learning algorithm successfully predicted whether pairs in the experiment were compatible or not using (dis)similarity of functional connectivity profiles obtained before the experiment. The similarity and dissimilarity of functional connectivity between individuals and these multivariate relationships contributed to the prediction, hence suggesting the importance of complementarity (observed as dissimilarity) as well as the similarity between an individual and a potential partner during the initial attraction phase. The result indicates that the salience network, limbic areas, and cerebellum are especially important for the feeling of compatibility. This research emphasizes the utility of neural information to predict complex phenomena in a social environment that behavioral measures alone cannot predict.

Common neurobiological correlates of resilience and personality traits within the triple resting-state brain networks assessed by 7-Tesla ultra-high field MRI

Despite numerous studies investigating resilience and personality trials, a paucity of information regarding their neurobiological commonalities at the level of the large resting-state networks (rsNWs) remains. Here we address this topic using the advantages of ultra-high-field (UHF) 7T-MRI, characterized by higher signal-to-noise ratio and increased sensitivity. The association between resilience, personality traits and three fMRI measures (fractional amplitude of low-frequency fluctuations (fALFF), degree centrality (DC) and regional homogeneity (ReHo)) determined for three core rsNWs (default mode (DMN), salience (SN), and central executive network (CEN)) were examined in 32 healthy volunteers. The investigation revealed a significant role of SN in both resilience and personality traits and a tight association of the DMN with resilience. DC in CEN emerged as a significant moderator for the correlations of resilience with the personality traits of neuroticism and extraversion. Our results indicate that the common neurobiological basis of resilience and the Big Five personality traits may be reflected at the level of the core rsNWs.

Mechanisms of the Effects of Parental Emotional Warmth on Extraversion in Children and Adolescents

The purpose of this study is to probe into the influence mechanism of parental emotional warmth (PEW) on extraversion for children and adolescents, as well as the moderating and mediating role of brain functional activity. Thirty-two children and adolescents underwent functional magnetic resonance imaging (fMRI) scans and completed Egna Minnen av Barndoms Uppfostran (EMBU) and Eysenck Personality Questionnaire (EPQ). Small-worldness (SW) of brain networks, fractional amplitude of low-frequency fluctuations (fALFF), and region-of-interest to region-of-interest (ROI-ROI) functional connectivity were calculated to study intrinsic neuronal activity. We found that PEW had a positive direct effect on extraversion, and all participants in the current study showed an efficient small-world structure. The positive association between PEW and extraversion was mediated by SW. Furthermore, the fALFF and extraversion were significantly and negatively correlated in the right precuneus and dorsolateral superior frontal gyrus. The mediating effect of SW was moderated by the functional connectivity between the right precuneus and the right dorsolateral superior frontal gyrus. The indirect effect was significant with lower level of the functional connectivity between the right precuneus and the right dorsolateral superior frontal gyrus. These findings indicate that SW of brain networks may be a key factor that accounts for the positive association between PEW and extraversion in children and adolescents and the level of the functional connectivity between the right precuneus and the right dorsolateral superior frontal gyrus could moderate the relationship.

Default Mode Network Functional Connectivity Negatively Associated with Trait Openness to Experience

Evaluating associations between the five-factor personality domains and resting-state functional connectivity networks (e.g., default mode network, DMN) highlights distributed neurobiological systems linked to behaviorally relevant phenotypes. Establishing these associations can highlight a potential underlying role for these neural pathways in related clinical illness and treatment response. Here we examined associations between within- and between-network resting-state functional connectivity with functional magnetic resonance imaging (fMRI) and the five-factor personality domains: Openness to experience (Openness), Extraversion, Neuroticism, Agreeableness and Conscientiousness. We included data from 470 resting-state scan sessions and personality assessments in 295 healthy participants. Within- and between-network functional connectivity from 32 a priori defined regions was computed across seven resting-state networks. The association between functional connectivity and personality traits was assessed using generalized least squares. Within-network DMN functional connectivity was significantly negatively associated with trait Openness (regression coefficient= -0.0010; [95% CI] = [-0.0017, -0.0003]; pFWER = 0.033), seemingly driven by association with the Fantasy subfacet. Trait Extraversion was significantly negatively associated with functional connectivity between the visual and dorsal attention networks and positively associated with functional connectivity between the frontoparietal and language networks. Our findings provide evidence that resting-state DMN is associated with trait Openness and gives insight into personality neuroscience.

Gray matter structures associated with neuroticism: A meta-analysis of whole-brain voxel-based morphometry studies

Neuroticism is major higher-order personality trait and has been robustly associated with mental and physical health outcomes. Although a growing body of studies have identified neurostructural markers of neuroticism, the results remained highly inconsistent. To characterize robust associations between neuroticism and variations in gray matter (GM) structures, the present meta-analysis investigated the concurrence across voxel-based morphometry (VBM) studies using the anisotropic effect size signed differential mapping (AES-SDM). A total of 13 studies comprising 2,278 healthy subjects (1,275 females, 29.20 ± 14.17 years old) were included. Our analysis revealed that neuroticism was consistently associated with the GM structure of a cluster spanning the bilateral dorsal anterior cingulate cortex and extending to the adjacent medial prefrontal cortex (dACC/mPFC). Meta-regression analyses indicated that the neuroticism-GM associations were not confounded by age and gender. Overall, our study is the first whole-brain meta-analysis exploring the brain structural correlates of neuroticism, and the findings may have implications for the intervention of high-neuroticism individuals, who are at risk of mental disorders, by targeting the dACC/mPFC.

Reliability modelling of resting-state functional connectivity

Resting-state functional magnetic resonance imaging (rs-fMRI) has an inherently low signal-to-noise ratio largely due to thermal and physiological noise that attenuates the functional connectivity (FC) estimates. Such attenuation limits the reliability of FC and may bias its association with other traits. Low reliability also limits heritability estimates. Classical test theory can be used to obtain a true correlation estimate free of random measurement error from parallel tests, such as split-half sessions of a rs-fMRI scan. We applied a measurement model to split-half FC estimates from the resting-state fMRI data of 1003 participants from the Human Connectome Project (HCP) to examine the benefit of reliability modelling of FC in association with traits from various domains. We evaluated the efficiency of the measurement model on extracting a stable and reliable component of FC and its association with several traits for various sample sizes and scan durations. In addition, we aimed to replicate our previous findings of increased heritability estimates when using a measurement model in a longitudinal adolescent twin cohort. The split-half measurement model improved test-retest reliability of FC on average with +0.33 points (from +0.49 to +0.82), improved strength of associations between FC and various traits on average 1.2-fold (range 1.09-1.35), and increased heritability estimates on average with +20% points (from 39% to 59%) for the full HCP dataset. On average, about half of the variance in split-session FC estimates was attributed to the stable and reliable component of FC. Shorter scan durations showed greater benefit of reliability modelling (up to 1.6-fold improvement), with an additional gain for smaller sample sizes (up to 1.8-fold improvement). Reliability modelling of FC based on a split-half using a measurement model can benefit genetic and behavioral studies by extracting a stable and reliable component of FC that is free from random measurement error and improves genetic and behavioral associations.

The Multilayer Network Approach in the Study of Personality Neuroscience

It has long been understood that a multitude of biological systems, from genetics, to brain networks, to psychological factors, all play a role in personality. Understanding how these systems interact with each other to form both relatively stable patterns of behaviour, cognition and emotion, but also vast individual differences and psychiatric disorders, however, requires new methodological insight. This article explores a way in which to integrate multiple levels of personality simultaneously, with particular focus on its neural and psychological constituents. It does so first by reviewing the current methodology of studies used to relate the two levels, where psychological traits, often defined with a latent variable model are used as higher-level concepts to identify the neural correlates of personality (NCPs). This is known as a top-down approach, which though useful in revealing correlations, is not able to include the fine-grained interactions that occur at both levels. As an alternative, we discuss the use of a novel complex system approach known as a multilayer network, a technique that has recently proved successful in revealing veracious interactions between networks at more than one level. The benefits of the multilayer approach to the study of personality neuroscience follow from its well-founded theoretical basis in network science. Its predictive and descriptive power may surpass that of statistical top-down and latent variable models alone, potentially allowing the discernment of more complete descriptions of individual differences, and psychiatric and neurological changes that accompany disease. Though in its infancy, and subject to a number of methodological unknowns, we argue that the multilayer network approach may contribute to an understanding of personality as a complex system comprised of interrelated psychological and neural features.

Structural equation modeling of the associations between amygdala activation, personality, and internalizing, externalizing symptoms of psychopathology

There is an expanding literature on the theoretical and empirical connections between personality and psychopathology, and their shared neurobiological correlates. Recent cybernetic theories of personality and psychopathology, as well as affective neuroscience theory, provide grounding for understanding neurobiological-personality-psychopathology (NPP) associations. With the emergence of large sample datasets (e.g., Human Connectome Project) advanced quantitative modeling can be used to rigorously test dynamic statistical representations of NPP connections. Also, research suggests that these connections are influenced by sex, and large samples provide the opportunity to examine how NPP associations might be moderated by sex. The current study used a large sample from the Duke Neurogenetics Study (DNS) to examine how amygdala activation to facial expressions was linked with self-report of personality traits and clinical interviews of internalizing and externalizing symptoms of psychopathology. Structural equation modeling results revealed direct associations of amygdala activation with personality trait expression, as well as indirect associations (though personality) with symptoms of psychopathology. Moreover, the NPP links were moderated by sex. The current results are in line with research that identifies a broader role played by the amygdala in personality and provide potential insights for continued research in personality neuroscience and recent theories on the neurobiology of personality.

Distinct patterns of thought mediate the link between brain functional connectomes and well-being

Ongoing thought patterns constitute important aspects of both healthy and abnormal human cognition. However, the neural mechanisms behind these daily experiences and their contribution to well-being remain a matter of debate. Here, using resting-state fMRI and retrospective thought sampling in a large neurotypical cohort (n = 211), we identified two distinct patterns of thought, broadly describing the participants' current concerns and future plans, that significantly explained variability in the individual functional connectomes. Consistent with the view that ongoing thoughts are an emergent property of multiple neural systems, network-based analysis highlighted the central importance of both unimodal and transmodal cortices in the generation of these experiences. Importantly, while state-dependent current concerns predicted better psychological health, mediating the effect of functional connectomes, trait-level future plans were related to better social health, yet with no mediatory influence. Collectively, we show that ongoing thoughts can influence the link between brain physiology and well-being.

Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing

Personality is the characteristic set of an individual’s behavioral and emotional patterns that evolve from biological and environmental factors. The recognition of personality profiles is crucial in making human–computer interaction (HCI) applications realistic, more focused, and user friendly. The ability to recognize personality using neuroscientific data underpins the neurobiological basis of personality. This paper aims to automatically recognize personality, combining scalp electroencephalogram (EEG) and machine learning techniques. As the resting state EEG has not so far been proven efficient for predicting personality, we used EEG recordings elicited during emotion processing. This study was based on data from the AMIGOS dataset reflecting the response of 37 healthy participants. Brain networks and graph theoretical parameters were extracted from cleaned EEG signals, while each trait score was dichotomized into low- and high-level using the k-means algorithm. A feature selection algorithm was used afterwards to reduce the feature-set size to the best 10 features to describe each trait separately. Support vector machines (SVM) were finally employed to classify each instance. Our method achieved a classification accuracy of 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, 83.8% for neuroticism, and 73% for openness.

Linking Personality Traits to Individual Differences in Affective Spaces

Different individuals respond differently to emotional stimuli in their environment. Therefore, to understand how emotions are represented mentally will ultimately require investigations into individual-level information. Here we tasked participants with freely arranging emotionally charged images on a computer screen according to their subjective emotional similarity (yielding a unique affective space for each participant) and subsequently sought external validity of the layout of the individuals’ affective spaces through the five-factor personality model (Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness) assessed via the NEO Five-Factor Inventory. Applying agglomerative hierarchical clustering to the group-level affective space revealed a set of underlying affective clusters whose within-cluster dissimilarity, per individual, was then correlated with individuals’ personality scores. These cluster-based analyses predominantly revealed that the dispersion of the negative cluster showed a positive relationship with Neuroticism and a negative relationship with Conscientiousness, a finding that would be predicted by prior work. Such results demonstrate the non-spurious structure of individualized emotion information revealed by data-driven analyses of a behavioral task (and validated by incorporating psychological measures of personality) and corroborate prior knowledge of the interaction between affect and personality. Future investigations can similarly combine hypothesis- and data-driven methods to extend such findings, potentially yielding new perspectives on underlying cognitive processes, disease susceptibility, or even diagnostic/prognostic markers for mental disorders involving emotion dysregulation.

Associations between personality and whole-brain functional connectivity at rest: Evidence across the adult lifespan

INTRODUCTION

Personality is associated with cognitive, emotional, and social functioning, and can play a role in age-related cognitive decline and dementia risk; however, little is known about the brain dynamics underlying personality characteristics, and whether they are moderated by age.

METHODS

We investigated the associations between personality and resting-state functional MRI data from 365 individuals across the adult lifespan (20-80 years). Participants completed the 50-item International Personality Item Pool and a resting-state imaging protocol on a 3T MRI scanner. Within-network connectivity values were computed based on predefined networks. Regression analyzes were conducted in order to investigate personality-connectivity associations, as well as moderation by age. All models controlled for potential confounders (such as age, sex, education, IQ, and the other personality traits).

RESULTS

We found that openness was positively associated with connectivity in the default-mode network, neuroticism was negatively associated with both the ventral and dorsal attention networks, and agreeableness was negatively associated with the dorsal attention network. In addition, age moderated the association between conscientiousness and the frontoparietal network, indicating that this association become stronger in older age.

CONCLUSIONS

Our findings demonstrate that personality is associated with brain connectivity, which may contribute to identifying personality profiles that play a role in protection against or risk for age-related brain changes and dementia.

Time-resolved connectome of the five-factor model of personality

The human brain is characterized by highly dynamic patterns of functional connectivity. However, it is unknown whether this time-variant 'connectome' is related to the individual differences in the behavioural and cognitive traits described in the five-factor model of personality. To answer this question, inter-network time-variant connectivity was computed in n = 818 healthy people via a dynamical conditional correlation model. Next, network dynamicity was quantified throughout an ad-hoc measure (T-index) and the generalizability of the multi-variate associations between personality traits and network dynamicity was assessed using a train/test split approach. Conscientiousness, reflecting enhanced cognitive and emotional control, was the sole trait linked to stationary connectivity across several circuits such as the default mode and prefronto-parietal network. The stationarity in the 'communication' across large-scale networks offers a mechanistic description of the capacity of conscientious people to 'protect' non-immediate goals against interference over-time. This study informs future research aiming at developing more realistic models of the brain dynamics mediating personality differences.

Tensor network factorizations: Relationships between brain structural connectomes and traits

Advanced brain imaging techniques make it possible to measure individuals' structural connectomes in large cohort studies non-invasively. Given the availability of large scale data sets, it is extremely interesting and important to build a set of advanced tools for structural connectome extraction and statistical analysis that emphasize both interpretability and predictive power. In this paper, we developed and integrated a set of toolboxes, including an advanced structural connectome extraction pipeline and a novel tensor network principal components analysis (TN-PCA) method, to study relationships between structural connectomes and various human traits such as alcohol and drug use, cognition and motion abilities. The structural connectome extraction pipeline produces a set of connectome features for each subject that can be organized as a tensor network, and TN-PCA maps the high-dimensional tensor network data to a lower-dimensional Euclidean space. Combined with classical hypothesis testing, canonical correlation analysis and linear discriminant analysis techniques, we analyzed over 1100 scans of 1076 subjects from the Human Connectome Project (HCP) and the Sherbrooke test-retest data set, as well as 175 human traits measuring different domains including cognition, substance use, motor, sensory and emotion. The test-retest data validated the developed algorithms. With the HCP data, we found that structural connectomes are associated with a wide range of traits, e.g., fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain structural connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity. We also demonstrated that our extracted structural connectomes and analysis method can give superior prediction accuracies compared with alternative connectome constructions and other tensor and network regression methods.

Intra-cortical myelin mediates personality differences

OBJECTIVE

Differences in myelination in the cortical mantle are important neurobiological mediators of variability in cognitive, emotional, and behavioral functioning. Past studies have found that personality traits reflecting such variability are linked to neuroanatomical and functional changes in prefrontal and temporo-parietal cortices. Whether these effects are partially mediated by the differences in intra-cortical myelin remains to be established.

METHOD

To test this hypothesis, we employed vertex-wise intra-cortical myelin maps in n = 1,003 people from the Human Connectome Project. Multivariate regression analyses were used to test for the relationship between intra-cortical myelin and each of the five-factor model's personality traits, while accounting for age, sex, intelligence quotient, total intracranial volume, and the remaining personality traits.

RESULTS

Neuroticism negatively related to frontal-pole myelin and positively to occipital cortex myelin. Extraversion positively related to superior parietal myelin. Openness negatively related to anterior cingulate myelin, while Agreeableness positively related to orbitofrontal myelin. Conscientiousness positively related to frontal-pole myelin and negatively to myelin content in the dorsal anterior cingulate cortex.

CONCLUSIONS

Intra-cortical myelin levels in brain regions with prolonged myelination are positively associated with personality traits linked to favorable outcome measures. These findings improve our understanding of the neurobiological underpinnings of variability in common behavioral dispositions.

Variability and Reproducibility of Directed and Undirected Functional MRI Connectomes in the Human Brain

A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing the inter- and intra-subject variability of connectivity matrices, as well as graph-theoretical measures, in a large (n = 1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected, as opposed to directed, methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra-subject variabilities in both directed and undirected connectomic measures.

Connectome-based individualized prediction of temperament trait scores

Temperament consists of multi-dimensional traits that affect various domains of human life. Evidence has shown functional connectome-based predictive models are powerful predictors of cognitive abilities. Putatively, individuals' innate temperament traits may be predictable by unique patterns of brain functional connectivity (FC) as well. However, quantitative prediction for multiple temperament traits at the individual level has not yet been studied. Therefore, we were motivated to realize the individualized prediction of four temperament traits (novelty seeking [NS], harm avoidance [HA], reward dependence [RD] and persistence [PS]) using whole-brain FC. Specifically, a multivariate prediction framework integrating feature selection and sparse regression was applied to resting-state fMRI data from 360 college students, resulting in 4 connectome-based predictive models that enabled prediction of temperament scores for unseen subjects in cross-validation. More importantly, predictive models for HA and NS could be successfully generalized to two relevant personality traits for unseen individuals, i.e., neuroticism and extraversion, in an independent dataset. In four temperament trait predictions, brain connectivities that show top contributing power commonly concentrated on the hippocampus, prefrontal cortex, basal ganglia, amygdala, and cingulate gyrus. Finally, across independent datasets and multiple traits, we show person's temperament traits can be reliably predicted using functional connectivity strength within frontal-subcortical circuits, indicating that human social and behavioral performance can be characterized by specific brain connectivity profile.

Network Neuroscience and Personality

Personality and individual differences originate from the brain. Despite major advances in the affective and cognitive neurosciences, however, it is still not well understood how personality and single personality traits are represented within the brain. Most research on brain-personality correlates has focused either on morphological aspects of the brain such as increases or decreases in local gray matter volume, or has investigated how personality traits can account for individual differences in activation differences in various tasks. Here, we propose that personality neuroscience can be advanced by adding a network perspective on brain structure and function, an endeavor that we label personality network neuroscience. With the rise of resting-state functional magnetic resonance imaging (MRI), the establishment of connectomics as a theoretical framework for structural and functional connectivity modeling, and recent advancements in the application of mathematical graph theory to brain connectivity data, several new tools and techniques are readily available to be applied in personality neuroscience. The present contribution introduces these concepts, reviews recent progress in their application to the study of individual differences, and explores their potential to advance our understanding of the neural implementation of personality. Trait theorists have long argued that personality traits are biophysical entities that are not mere abstractions of and metaphors for human behavior. Traits are thought to actually exist in the brain, presumably in the form of conceptual nervous systems. A conceptual nervous system refers to the attempt to describe parts of the central nervous system in functional terms with relevance to psychology and behavior. We contend that personality network neuroscience can characterize these conceptual nervous systems on a functional and anatomical level and has the potential do link dispositional neural correlates to actual behavior.