A key objective of the emerging field of personality neuroscience is to link the great variety of the enduring dispositions of human behaviour with reliable markers of brain function. This can be achieved by analysing big data-sets with methods that model whole-brain connectivity patterns. To meet these expectations, we exploited a large repository of personality and neuroimaging measures made publicly available via the Human Connectome Project. Using connectomic analyses based on graph theory, we computed global and local indices of functional connectivity (e.g., nodal strength, efficiency, clustering, betweenness centrality) and related these metrics to the five-factor model (FFM) personality traits (i.e., neuroticism, extraversion, openness, agreeableness, and conscientiousness). The maximal information coefficient was used to assess for linear and nonlinear statistical dependencies across the graph “nodes”, which were defined as distinct large-scale brain circuits identified via independent component analysis. Multivariate regression models and “train/test” approaches were used to examine the associations between FFM traits and connectomic indices as well as to assess the generalizability of the main findings, while accounting for age and sex variability. Conscientiousness was the sole FFM trait linked to measures of higher functional connectivity in the fronto-parietal and default mode networks. This offers a mechanistic explanation of the behavioural observation that conscientious people are reliable and efficient in goal-setting or planning. Our study provides new inputs to understanding the neurological basis of personality and contributes to the development of more realistic models of the brain dynamics that mediate personality differences.

Need for Cognition (NFC) and Openness to Ideas are intellectual investment traits that are characterized by a tendency to seek out, engage in and enjoy effortful cognitive activity. Little, however, is known about the extent to which they are influenced by genetic and environmental factors. With the present contribution, we aim at furthering our knowledge on the mechanisms underlying intellectual investment traits by following-up on a recent investigation of the role of dopaminergic gene variation in intellectual investment. Employing a standard approach that relied on null-hypothesis significance testing, we found that, first, two dopaminergic genetic variants interacted in modulating individual differences in NFC, but not in Openness to Ideas; that, second, negative life events played a role in the modulation of Openness to Ideas, but not of NFC; and that, third, negative life events as assessed using another measure were only marginally related to Openness to Ideas while positive life events were associated with both Openness to Ideas and NFC, with the latter effect being also dependent on DRD4 exon III genotype. However, employing a Bayesian approach, the assumption of a genetic effect on investment traits was overall not supported, while the assumption of a role of positive life events in the modulation of investment traits could be confirmed, with a tentative increment in the prediction of NFC by adding an interaction of positive life events and DRD4 variation to the main effect of positive life events. Our findings underscore the importance to use different approaches in the field of personality neuroscience. To gain deeper insight into the basis of personality traits does not only require to consider genetic as well as environmental influences and their interplay, but also requires more differentiated statistical analyses that can at least in part tackle the often inconsistent findings in this field.

A converging literature has revealed the existence of a set of largely consistent, hierarchically organized personality traits, that is broader traits are able to be differentiated into more fine-grained traits, in both humans and chimpanzees. Despite recent work suggesting a neural basis to personality in chimpanzees, little is known with regard to the involvement of limbic structures (i.e., amygdala and hippocampus), which are thought to play important roles in emotion. Using saved maximum likelihood estimated exploratory factor scores (two to five factors) in the context of a series of path analyses, the current study examined associations among personality dimensions across various levels of the personality hierarchy and individual variability of amygdala and hippocampal grey matter (GM) volume in a sample of captive chimpanzees (N=191). Whereas results revealed no association between personality dimensions and amygdala volume, a more nuanced series of associations emerged between hippocampal GM volume and personality dimensions at various levels of the hierarchy. Hippocampal GM volume associated most notably with Alpha (a dimension reflecting a tendency to behave in an undercontrolled and agonistic way) at the most basic two-factor level of the hierarchy; associated positively with Disinhibition at the next level of the hierarchy (“Big Three”); and finally, associated positively with Impulsivity at the most fine-grained level (“five-factor model”) of the hierarchy. Findings underscore the importance of the hippocampus in the neurobiological foundation of personality, with support for its regulatory role of emotion. Further, results suggest the importance of the distinction between structure and function, particularly with regard to the amygdala.

Over the past decade, advances in the interdisciplinary field of network science have provided a framework for understanding the intrinsic structure and function of human brain networks. A particularly fruitful area of this work has focused on patterns of functional connectivity derived from noninvasive neuroimaging techniques such as functional magnetic resonance imaging. An important subset of these efforts has bridged the computational approaches of network science with the rich empirical data and biological hypotheses of neuroscience, and this research has begun to identify features of brain networks that explain individual differences in social, emotional, and cognitive functioning. The most common approach estimates connections assuming a single configuration of edges that is stable across the experimental session. In the literature, this is referred to as a static network approach, and researchers measure static brain networks while a subject is either at rest or performing a cognitively demanding task. Research on social and emotional functioning has primarily focused on linking static brain networks with individual differences, but recent advances have extended this work to examine temporal fluctuations in dynamic brain networks. Mounting evidence suggests that both the strength and flexibility of time-evolving brain networks influence individual differences in executive function, attention, working memory, and learning. In this review, we first examine the current evidence for brain networks involved in cognitive functioning. Then we review some preliminary evidence linking static network properties to individual differences in social and emotional functioning. We then discuss the applicability of emerging dynamic network methods for examining individual differences in social and emotional functioning. We close with an outline of important frontiers at the intersection between network science and neuroscience that will enhance our understanding of the neurobiological underpinnings of social behavior.

Personality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 884 young healthy adults in the Human Connectome Project database. We attempted to predict personality traits from the “Big Five,” as assessed with the Neuroticism/Extraversion/Openness Five-Factor Inventory test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness, and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two intersubject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 hr of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; three denoising strategies; two alignment schemes; three models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=.24, R2=.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=.26, R2=.044). Other factors (Extraversion, Neuroticism, Agreeableness, and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors (“α” and “β”) from a principal components analysis of the Neuroticism/Extraversion/Openness Five-Factor Inventory factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=.27, R2=.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

Oxytocin is a neuropeptide known to influence social and cognitive processing across several mammalian species. There currently exists a mixed and controversial pattern of evidence that oxytocin pathway genes confer individual differences in social cognition and personality in humans. Inconsistencies across studies may in part be explained by the presence of intermediary, epigenetic, variables that exist between genotype and phenotype. This study was designed to investigate the association between epigenetic modification of the Oxytocin Receptor Gene (OXTR), via DNA methylation, and Big-5 personality traits. Genetic data were collected via saliva samples and analyzed to quantify DNA methylation within the promoter region of OXTR. The results indicate that Openness to Experience is associated with OXTR DNA methylation, while controlling for the remaining Big-5 personality dimensions (Neuroticism, Extraversion, Agreeableness, and Conscientiousness) and sex and age. This finding provides additional support for models associating oxytocin with individual differences in personality and identity in humans.

Clarifying individual differences that predict resilience or vulnerability to emotional distress is crucial for identifying etiological factors contributing to affective disturbances, and to promoting emotional well-being. Despite recent progress identifying specific brain regions and personality traits, it remains unclear whether there are common factors underlying the structural aspects of the brain and the personality traits that, in turn, protect against symptoms of emotional distress. In the present study, an integrative structural equation model was developed to examine the associations among (1) a latent construct of Control, representing the volumes of a system of prefrontal cortical (PFC) regions including middle, inferior, and orbital frontal cortices; (2) a latent construct of Resilience personality traits including cognitive reappraisal, positive affectivity, and optimism; and (3) Anxiety and Depression symptoms, in a sample of 85 healthy young adults. Results showed that the latent construct of PFC volumes positively predicted the latent construct of Resilience, which in turn negatively predicted Anxiety. Mediation analysis confirmed that greater latent PFC volume is indirectly associated with lower Anxiety symptoms through greater latent trait Resilience. The model did not show a significant mediation for Depression. These results support the idea that there are common volumetric and personality factors that help protect against symptoms of emotional distress. These findings provide strong evidence that such brain-personality-symptom approaches can provide novel insights with valuable implications for understanding the interaction of these factors in healthy and clinically diagnosed individuals.

This article gives a short overview on the life and achievements of Jaak Panksepp. Jaak Panksepp dedicated his life to the study of mammalian emotions. By means of electrical stimulation of the brain and psychopharmacological challenges he carved out seven primary emotional systems being highly conserved across different species of mammals including homo sapiens. The primary emotional systems are called SEEKING, CARE, LUST, PLAY (positive emotions), and FEAR, RAGE, SADNESS (negative emotions). While his early career was characterized by the direct study of these primary emotions in mammals, in his late career he invested more and more time in applying his knowledge to different fields of psychology including personality neuroscience and psychiatry.

The Virtual Personalities Model is a motive-based neural network model that provides both a psychological model and a computational implementation that explicates the dynamics and often large within-person variability in behavior that arises over time. At the same time the same model can produce—across many virtual personalities—between-subject variability in behavior that when factor analyzed yields familiar personality structure (e.g., the Big Five). First, we describe our personality model and its implementation as a neural network model. Second, we focus on detailing the neurobiological underpinnings of this model. Third, we examine the learning mechanisms, and their biological substrates, as ways that the model gets “wired up,” discussing Pavlovian and Instrumental conditioning, Pavlovian to Instrumental transfer, and habits. Finally, we describe the dynamics of how initial differences in propensities (e.g., dopamine functioning), wiring differences due to experience, and other factors could operate together to develop and change personality over time, and how this might be empirically examined. Thus, our goal is to contribute to the rising chorus of voices seeking a more precise neurobiologically based science of the complex dynamics underlying personality.

Theories in personality neuroscience must aim to be consistent with several levels of explanation. If we view personality traits as constructs located only at the psychological level, we must still make their explanations compatible with observations and theories at lower levels, particularly with what we know at the neural level. If we view personality traits as constructs located only at the neural level, we will still need to predict their emergent effects at the psychological level. Personality theory at present treats traits as psychological-level constructs, with even the recent neurally oriented Cybernetic Big Five Theory specified in terms of a “conceptual nervous system” and not requiring complete or immediate translation into neural mechanisms. Here, we argue for the existence of phylogenetically old, neural-level traits that are substantially conserved across many vertebrate species. We first ask what known mechanisms control trait-like properties of neural systems: Focusing on hormones, the GABAA receptor, and amine neurotransmitter systems. We derive from what we know about these sources of neuronal modulation some metatheoretical principles to guide the future development of those aspects of personality theory, starting with neural-level trait constructs and drawing implications for higher-level trait psychology observations. Current descriptive approaches such as the Big Five are an essential precursor to personality neuroscience, but may not map one-to-one to the mechanisms and constructs of a neuroscience-based approach to traits.

The present work gives a short overview of central aspects of Jaak Panksepp’s Affective Neuroscience Theory (AN theory) and its relevance for modern personality neuroscience. In contrast to the widely used Big Five approach to studying and understanding human personality, AN theory provides researchers with a distinct roadmap to the biological basis of personality, including molecular and neuroanatomical candidates, to understand individual differences in human behavior. Such molecular and neuroanatomical brain candidates have been derived by means of electrical brain stimulation and pharmacological challenges, while investigating primary emotional systems anchored in the subcortical mammalian brain. Research results derived from the study of emotions in mammals are also of relevance for humans because ancient layers of our minds—those layers where primary emotions originate—have been homologously conserved across species. From an evolutionary perspective, this makes sense because primal emotions represent “built-in tools for survival” for all mammals. In this context, Montag and Panksepp recently illustrated a potential ancient neurobiological effect by carving out robust associations between individual differences in primary emotions (assessed via self-report) and the Big Five in a cross-cultural study with data from the United States, Germany, and China. These associations together with some ideas derived from MacLean’s Triune Brain concept highlighted (a) that primary emotions likely represent the phylogenetically oldest parts of human personality and (b) that primary emotions influence human personality in a bottom-up fashion given their localization in ancient subcortical brain regions. A comment on the work by Montag and Panksepp asked for insights on putative links between primary emotions and facets of the Big Five. Therefore, we provide some first insights into such associations from recent Germany data. In addition, the present work provides a new short version of the Affective Neuroscience Personality Scales to assess individual differences in primary emotions.

Human personality is reflected in patterns—or networks—of behavior, either in thought or action. Curiosity is an oft-treasured component of one’s personality, commonly associated with information-seeking proclivities with distinct neurophysiological correlates. The markers of curiosity can differ substantially across people, suggesting the possibility that personality also determines the architectural style of one’s curiosity. Yet progress in defining those styles, and marking their neurophysiological basis, has been hampered by fairly fundamental difficulties in defining curiosity itself. Here, we offer and exercise a definition of the practice of curiosity as knowledge network building, one particular pattern of thought behavior. To unpack this definition and motivate its utility, we begin with a short primer on network science and describe how the mathematical object of a network can be used to map items and relations that are characteristic of bodies of knowledge. Next, we turn to a discussion of how networks grow, how their growth can be modeled, and how the practice of curiosity can be formalized as a process of network growth. We pay particular attention to how individuals may differ in how they build their knowledge networks, and discuss how the sort, manner, and action of building can be modulated by experience. We discuss how this definition of the practice of curiosity motivates new experiments and theory development at the interdisciplinary intersection of network science, personality neuroscience, education, and curiosity studies. We close with a note on the potential of network science to inform studies of other domains of personality, and the patterns of thought– or action–behavior characteristic thereof.

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.

A complex web of social and moral norms governs many everyday human behaviors, acting as the glue for social harmony. The existence of moral norms helps elucidate the psychological motivations underlying a wide variety of seemingly puzzling behavior, including why humans help or trust total strangers. In this review, we examine four widespread moral norms: Fairness, altruism, trust, and cooperation, and consider how a single social instrument—reciprocity—underpins compliance to these norms. Using a game theoretic framework, we examine how both context and emotions moderate moral standards, and by extension, moral behavior. We additionally discuss how a mechanism of reciprocity facilitates the adherence to, and enforcement of, these moral norms through a core network of brain regions involved in processing reward. In contrast, violating this set of moral norms elicits neural activation in regions involved in resolving decision conflict and exerting cognitive control. Finally, we review how a reinforcement mechanism likely governs learning about morally normative behavior. Together, this review aims to explain how moral norms are deployed in ways that facilitate flexible moral choices.

Rumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

Metacognition is the capacity to evaluate and control one’s own cognitive processes. Metacognition operates over a range of cognitive domains, such as perception and memory, but the neurocognitive architecture supporting this ability remains controversial. Is metacognition enabled by a common, domain-general resource that is recruited to evaluate performance on a variety of tasks? Or is metacognition reliant on domain-specific modules? This article reviews recent literature on the domain-generality of human metacognition, drawing on evidence from individual differences and neuroimaging. A meta-analysis of behavioral studies found that perceptual metacognitive ability was correlated across different sensory modalities, but found no correlation between metacognition of perception and memory. However, evidence for domain-generality from behavioral data may suffer from a lack of power to identify correlations across model parameters indexing metacognitive efficiency. Neuroimaging data provide a complementary perspective on the domain-generality of metacognition, revealing co-existence of neural signatures that are common and distinct across tasks. We suggest that such an architecture may be appropriate for “tagging” generic feelings of confidence with domain-specific information, in turn forming the basis for priors about self-ability and modulation of higher-order behavioral control.

This paper reviews progress in the application of computational models to personality, developmental, and clinical neuroscience. We first describe the concept of a computational phenotype, a collection of parameters derived from computational models fit to behavioral and neural data. This approach represents individuals as points in a continuous parameter space, complementing traditional trait and symptom measures. One key advantage of this representation is that it is mechanistic: The parameters have interpretations in terms of cognitive processes, which can be translated into quantitative predictions about future behavior and brain activity. We illustrate with several examples how this approach has led to new scientific insights into individual differences, developmental trajectories, and psychopathology. We then survey some of the challenges that lay ahead.