Personality is reflected in the brain's intrinsic functional architecture

Positive Associations between the Personality Trait of Openness and Verbal Learning and Memory in Individuals with Parkinson's Disease: A Pilot Study

OBJECTIVE

Individuals with idiopathic Parkinson's disease (PD) often experience difficulties with verbal learning and memory, even in the absence of dementia. Higher levels of the personality trait of openness predict better learning and memory in other older adult populations, but openness's contributions in PD are unknown. Lower openness and alterations in openness's neural substrates in PD suggest that openness may have strong associations with memory in PD.

METHOD

We used the Big Five Inventory-2 (BFI-2) personality self-rating questionnaire and the Rey Auditory Verbal Learning Test (RAVLT) in a cross-sectional sample of 33 persons with PD (PwPD; 17 men), 26 healthy older adults (OA; 14 men), and 37 healthy younger adults (YA; 19 men). Correlation analysis examined relations between openness (BFI-2 open-mindedness) and verbal learning and memory (RAVLT performances). Correlation and regression analysis controlled for psychosocial and cognitive factors and examined possible moderators and mediators.

RESULTS

Significant, positive correlations between openness and RAVLT scores occurred in PwPD but not in OA or YA. Among PwPD, openness independently predicted most RAVLT scores in regression models. Its associations were not explained by PD duration, disease severity, disease stage, or sex. PwPD low in openness performed worse than OA. Among OA, older age predicted significantly more positive association between openness and memory.

CONCLUSIONS

Openness is positively associated with verbal memory in PwPD, as well as in healthy older adults (depending on age), with implications for the relevance of personality factors in cognition.

Can personality traits be predicted from resting-state EEG oscillations? A replication study

Personality neuroscience seeks to uncover the neurobiological underpinnings of personality. Identifying links between measures of brain activity and personality traits is important in this respect. Using an entirely inductive approach, Jach et al. (2020) attempted to predict personality trait scores from resting-state spectral electroencephalography (EEG) using multivariate pattern analysis (MVPA) and found meaningful results for Agreeableness. The exploratory nature of this work and concerns about replicability in general require a rigorous replication, which was the aim of the current study. We applied the same analytic approach to a large data set (N = 772) to evaluate the robustness of the previous results. Similar to Jach et al. (2020), 8 min of resting-state EEG before and after unrelated tasks with both eyes open and closed were analyzed using support vector regressions (SVR). A 10-fold cross-validation was used to evaluate the prediction accuracy between the spectral power of 59 EEG electrodes within 30 frequency bins ranging from 1 to 30 Hz and Big Five personality trait scores. We were not able to replicate the findings for Agreeableness. We extended the analysis by parameterizing the total EEG signal into its periodic and aperiodic signal components. However, neither component was meaningfully associated with the Big Five personality traits. Our results do not support the initial results and indicate that personality traits may at least not be substantially predictable from resting-state spectral power. Future identification of robust and replicable brain-personality associations will likely require alternative analysis methods and rigorous preregistration of all analysis steps.

The degenerate coding of psychometric profiles through functional connectivity archetypes

Introduction

Degeneracy in the brain-behavior code refers to the brain's ability to utilize different neural configurations to support similar functions, reflecting its adaptability and robustness. This study aims to explore degeneracy by investigating the non-linear associations between psychometric profiles and resting-state functional connectivity (RSFC).

Methods

The study analyzed RSFC data from 500 subjects to uncover the underlying neural configurations associated with various psychometric outcomes. Self-organized maps (SOM), a type of unsupervised machine learning algorithm, were employed to cluster the RSFC data. And identify distinct archetypal connectivity profiles characterized by unique within- and between-network connectivity patterns.

Results

The clustering analysis using SOM revealed several distinct archetypal connectivity profiles within the RSFC data. Each archetype exhibited unique connectivity patterns that correlated with various cognitive, physical, and socioemotional outcomes. Notably, the interaction between different SOM dimensions was significantly associated with specific psychometric profiles.

Discussion

This study underscores the complexity of brain-behavior interactions and the brain's capacity for degeneracy, where different neural configurations can lead to similar behavioral outcomes. These findings highlight the existence of multiple brain architectures capable of producing similar behavioral outcomes, illustrating the concept of neural degeneracy, and advance our understanding of neural degeneracy and its implications for cognitive and emotional health.

Neuroticism and openness exhibit an anti-correlation pattern to dissociable default mode network: using resting connectivity and structural equation modeling analysis

The default mode network (DMN) can be subdivided into ventral and dorsal subsystems, which serve affective cognition and mental sense construction, respectively. An internally dissociated pattern of anti-correlations was observed between these two subsystems. Although numerous studies on neuroticism and openness have demonstrated the neurological functions of the DMN, little is known about whether different subsystems and hubs regions within the network are engaged in different functions in response to the two traits. We recruited 223 healthy volunteers in this study and collected their resting-state functional magnetic resonance imaging (fMRI) and NEO Five-Factor Inventory scores. We used independent component analysis (ICA) to obtain the DMN, before further decomposing it into the ventral and dorsal subsystems. Then, the network coherence of hubs regions within subsystems was extracted to construct two structural equation models (SEM) to explore the relationship between neuroticism and openness traits and DMN. We observed that the ventral DMN could significantly predict positive openness and negative neuroticism. The dorsal DMN was diametrically opposed. Additionally, the medial prefrontal cortex (mPFC) and middle temporal gyrus (MTG), both of which are core hubs of the subnetworks within the DMN, are significantly positively correlated with neuroticism and openness. These findings may point to a biological basis that neuroticism and openness are engaged in opposite mechanisms and support the hypothesis about the functional dissociation of the DMN.

Harsh, unpredictable childhood environments are associated with inferior frontal gyrus connectivity and binge eating tendencies in late adolescents

Harsh, unpredictable childhood environments (HUCE) are associated with obesity older in life, but knowledge of how HUCE affect binge eating tendencies is lacking. Five hundred and one late adolescents aged 16-22 were recruited to finish resting state functional magnetic resonance imaging scan, behavioral measures including retrospective recall of childhood environmental harshness and unpredictability, binge eating tendencies and demographics. Three hundred and seventy-six of participants further completed the computerized visual probe task designed to evaluate attentional engagement towards high and low calorie food. As right inferior frontal gyrus (IFG) was the key nodes that related to both early life adversity and binge eating tendencies, it was treated as the interest region in the dynamic functional connectivity analyses. Results found that HUCE are associated with significant but modest decreases in connectivity of right inferior frontal gyrus (IFG)- bilateral medial frontal gyrus, right IFG - bilateral inferior parietal lobule (IPL), and right IFG - left superior frontal gyrus connectivity, as well as attentional engagement to high-calorie food and binge eating tendencies. A machine-learning method named linear support vector regression (SVR) and leave one out cross-validation (LOOCV) procedure used to examine the robustness of the brain-behavior relationship further confirm the findings. Mediation analyses suggested that right IFG - left IPL connectivity mediates the association of HUCE and binge eating tendencies. Findings suggest right IFG - left IPL connectivity may serve as a crucial neurobiological underpinning of HUCE to regulate binge eating behaviors. As such, these results contribute to a novel perspective and hypotheses in elucidating developmental neuro-mechanisms related to binge eating.

Predicting conversational satisfaction of face-to-face conversation through interpersonal similarity in resting-state functional connectivity

When conversing with an unacquainted person, if it goes well, we can obtain much satisfaction (referred to as conversational satisfaction). Can we predict how satisfied dyads will be with face-to-face conversation? To this end, we employed interpersonal similarity in whole-brain resting-state functional connectivity (RSFC), measured using functional magnetic resonance imaging before dyadic conversation. We investigated whether conversational satisfaction could be predicted from interpersonal similarity in RSFC using multivariate pattern analysis. Consequently, prediction was successful, suggesting that interpersonal similarity in RSFC is an effective neural biomarker predicting how much face-to-face conversation goes well. Furthermore, regression coefficients from predictive models suggest that both interpersonal similarity and dissimilarity contribute to good interpersonal relationships in terms of brain activity. The present study provides the potential of an interpersonal similarity approach using RSFC for understanding the foundations of human relationships and new neuroscientific insight into whether success in human interactions is predetermined.

The relationship of personality, alexithymia, anxiety symptoms, and odor awareness: a mediation analysis

OBJECTIVE

Personality, emotions, and olfaction exhibit partial anatomical overlap in the limbic system structure, establishing potential mechanisms between personality, affective disorders, and olfactory-related aspects. Thus, this study aims to investigate the associations among the Big Five personality traits, alexithymia, anxiety symptoms, and odor awareness.

METHODS

A total of 863 college participants were recruited for this study. All participants completed the Chinese Big Five Personality Inventory-15, the Odor Awareness Scale (OAS), the Toronto Alexithymia Scale-20, and the Generalized Anxiety Disorder Screener-7. Structural equation modeling was employed to examine the hypothesized mediated model.

RESULTS

The findings revealed the majority of significant intercorrelations among the dimensions of the Big Five personality traits, alexithymia, anxiety symptoms, and OAS (|r| = 0.072-0.567, p < 0.05). Alexithymia and anxiety symptoms exhibited a serial mediation effect between neuroticism and OAS (95%CI[0.001, 0.014]), conscientiousness and OAS (95%CI[-0.008, -0.001]), and extraversion and OAS (95%CI[-0.006, -0.001]). Anxiety symptoms mediated the relationship between agreeableness and OAS (95%CI[-0.023, -0.001]) and between openness and OAS (95%CI [0.004, 0.024]).

CONCLUSION

The mediating roles of alexithymia and anxiety symptoms between the Big Five personality traits and odor awareness support the idea of a certain level of association among personality, emotions, and olfaction, with the underlying role of the limbic system structure. This enhances our understanding of personality, emotions, and olfaction and provides insights for future intervention measures for affective disorders and olfactory dysfunctions.

A neural mechanism of cognitive reserve: The case of bilingualism

Cognitive Reserve (CR) refers to the preservation of cognitive function in the face of age- or disease-related neuroanatomical decline. While bilingualism has been shown to contribute to CR, the extent to which, and what particular aspect of, second language experience contributes to CR are debated, and the underlying neural mechanism(s) unknown. Intrinsic functional connectivity reflects experience-dependent neuroplasticity that occurs across timescales ranging from minutes to decades, and may be a neural mechanism underlying CR. To test this hypothesis, we used voxel-based morphometry and resting-state functional connectivity analyses of MRI data to compare structural and functional brain integrity between monolingual and bilingual older adults, matched on cognitive performance, and across levels of second language proficiency measured as a continuous variable. Bilingualism, and degree of second language proficiency specifically, were associated with lower gray matter integrity in a hub of the default mode network - a region that is particularly vulnerable to decline in aging and dementia - but preserved intrinsic functional network organization. Bilingualism moderated the association between neuroanatomical differences and cognitive decline, such that lower gray matter integrity was associated with lower executive function in monolinguals, but not bilinguals. Intrinsic functional network integrity predicted executive function when controlling for group differences in gray matter integrity and language status. Our findings confirm that lifelong bilingualism is a CR factor, as bilingual older adults performed just as well as their monolingual peers on tasks of executive function, despite showing signs of more advanced neuroanatomical aging, and that this is a consequence of preserved intrinsic functional network organization.

A shared novelty-seeking basis for creativity and curiosity

Curiosity and creativity are central pillars of human growth and invention. Although they have been studied extensively in isolation, the relationship between them has not yet been established. We propose that both curiosity and creativity emanate from the same mechanism of novelty seeking. We first present a synthesis showing that curiosity and creativity are affected similarly by a number of key cognitive faculties such as memory, cognitive control, attention, and reward. We then review empirical evidence from neuroscience research, indicating that the same brain regions are involved in both curiosity and creativity, focusing on the interplay between three major brain networks: the default mode network, the salience network, and the executive control network. After substantiating the link between curiosity and creativity, we propose a novelty-seeking model (NSM) that underlies them and suggests that the manifestation of the NSM is governed by one's state of mind.

A topography-based predictive framework for naturalistic viewing fMRI

Functional magnetic resonance imaging (fMRI) during naturalistic viewing (NV) provides exciting opportunities for studying brain functions in more ecologically valid settings. Understanding individual differences in brain functions during NV and their behavioural relevance has recently become an important goal. However, methods specifically designed for this purpose remain limited. Here, we propose a topography-based predictive framework (TOPF) to fill this methodological gap. TOPF identifies individual-specific evoked activity topographies in a data-driven manner and examines their behavioural relevance using a machine learning-based predictive framework. We validate TOPF on both NV and task-based fMRI data from multiple conditions. Our results show that TOPF effectively and stably captures individual differences in evoked brain activity and successfully predicts phenotypes across cognition, emotion and personality on unseen subjects from their activity topographies. Moreover, TOPF compares favourably with functional connectivity-based approaches in prediction performance, with the identified predictive brain regions being neurobiologically interpretable. Crucially, we highlight the importance of examining individual evoked brain activity topographies in advancing our understanding of the brain-behaviour relationship. We believe that the TOPF approach provides a simple but powerful tool for understanding brain-behaviour relationships on an individual level with a strong potential for clinical applications.

Coping styles vary with species' sociality and life history: A systematic review and meta-regression analysis

Despite a long history of animal studies investigating coping styles, the causal connections between behavior and stress physiology remain unclear. Consistency across taxa in effect sizes would support the idea of a direct causal link maintained by either functional or developmental dependencies. Alternatively, lack of consistency would suggest coping styles are evolutionarily labile. Here, we investigated correlations between personality traits and baseline and stress-induced glucocorticoid levels using a systematic review and meta-analysis. Most personality traits did not consistently vary with either baseline or stress-induced glucocorticoids. Only aggression and sociability showed a consistent negative correlation with baseline glucocorticoids. We found that life history variation affected the relationship between stress-induced glucocorticoid levels and personality traits, especially anxiety and aggression. The relationship between anxiety and baseline glucocorticoids depended on species' sociality with solitary species showing more positive effect sizes. Thus, integration between behavioral and physiological traits depends on species' sociality and life history and suggests high evolutionary lability of coping styles.

The para-hippocampal-medial frontal gyrus functional connectivity mediates the relationship between dispositional optimism and procrastination

Procrastination is a prevalent phenomenon throughout the world, which can lead to worse consequences across life domains, such as academic performance, mental health, and even public policy. Despite the evidence for the association between dispositional optimism and procrastination, the neural mechanisms underlying this link remain unexplored. To address this issue, we employed voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods to explore the underlying links between dispositional optimism and procrastination in a large sample (N=408). The self-report results showed that dispositional optimism was negatively associated with procrastination (r= -.30, p<.001). The VBM analysis indicated that dispositional optimism was positively correlated with gray matter volumes (GMV) in the right para-hippocampal (rPHC), and negatively correlated with GMV in the left cerebellum. Moreover, the functional connectivity analysis with the rPHC as a seed region revealed that rPHC-rMFC (right medial frontal gyrus) functional connectivity was negatively associated with dispositional optimism. Furthermore, the mediation analysis showed that the rPHC-rMFC connectivity partially mediated the relationship between dispositional optimism and procrastination. These results suggested that the rPHC-rMFC connectivity engaged in less task aversiveness by episodic prospection may underlie the association between dispositional optimism and procrastination, which provides a new perspective to understand the relationship between dispositional optimism and procrastination. DATA AVAILABILITY STATEMENT: The data that support the findings of this study are available from the corresponding author upon reasonable request.

Neurocircuitry of Personality Traits and Intent in Decision-Making

Even though most personality features are moderately stable throughout life, changes can be observed, which influence one's behavioral patterns. A variety of subjective assessments can be performed to track these changes; however, the subjective characteristic of these assessments may lead to questions about intentions and values. The use of neuroimaging techniques may aid the investigation of personality traits through a more objective lens, overcoming the barriers imposed by confounders. Here, neurocircuits associated with changes in personality domains were investigated to address this issue. Cortical systems involved in traits such as extraversion and neuroticism were found to share multiple components, as did traits of agreeableness and conscientiousness, with these four features revolving around the activation and structural integrity of the medial prefrontal cortex (mPFC). The attribute of openness appears scattered throughout cortical and subcortical regions, being discussed here as a possible reflection of intent, at the same time modulating and being governed by other traits. Insights on how systems operate on personality may increase comprehension on factors acting on the evolution, development, and consolidation of personality traits through life, as in neurocognitive disorders.

Omnipresence of the sensorimotor-association axis topography in the human connectome

Low-dimensional representations are increasingly used to study meaningful organizational principles within the human brain. Most notably, the sensorimotor-association axis consistently explains the most variance in the human connectome as its so-called principal gradient, suggesting that it represents a fundamental organizational principle. While recent work indicates these low dimensional representations are relatively robust, they are limited by modeling only certain aspects of the functional connectivity structure. To date, the majority of studies have restricted these approaches to the strongest connections in the brain, treating weaker or negative connections as noise despite evidence of meaningful structure among them. The present work examines connectivity gradients of the human connectome across a full range of connectivity strengths and explores the implications for outcomes of individual differences, identifying potential dependencies on thresholds and opportunities to improve prediction tasks. Interestingly, the sensorimotor-association axis emerged as the principal gradient of the human connectome across the entire range of connectivity levels. Moreover, the principal gradient of connections at intermediate strengths encoded individual differences, better followed individual-specific anatomical features, and was also more predictive of intelligence. Taken together, our results add to evidence of the sensorimotor-association axis as a fundamental principle of the brain's functional organization, since it is evident even in the connectivity structure of more lenient connectivity thresholds. These more loosely coupled connections further appear to contain valuable and potentially important information that could be used to improve our understanding of individual differences, diagnosis, and the prediction of treatment outcomes.

Uncovering individual differences in fine-scale dynamics of functional connectivity

Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain-behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.

The establishment of the general microexpression recognition ability and its relevant brain activity

Microexpressions are very transitory expressions lasting about 1/25∼1/2 s, which can reveal people's true emotions they try to hide or suppress. The PREMERT (pseudorandom ecological microexpression recognition test) could test the individual's microexpression recognition ability with six microexpression Ms (the mean of accuracy rates of a microexpression type under six expression backgrounds), and six microexpression SDs (the standard deviation of accuracy rates of this microexpression type under six expression backgrounds), but it and other studies did not explore the general microexpression recognition ability (the GMERA) or could not test the GMERA effectively. Therefore, the current study put forward and established the GMERA with the behavioral data of the PREMERT. The spontaneous brain activity in the resting state is a stable index to measure individual cognitive characteristics. Therefore, the current study explored the relevant resting-state brain activity of the GMERA indicators to prove that GMERA is an individual cognitive characteristic from brain mechanisms with the neuroimaging data of the PREMERT. The results showed that (1) there was a three-layer hierarchical structure in human microexpression recognition ability: The GMERA (the highest layer); recognition of a type of microexpression under different expression backgrounds (the second layer); and recognition of a certain microexpression under a certain expression background (the third layer). A common factor GMERA was extracted from the six microexpression types recognition in PREMERT. Four indicators of the GMERA were calculated from six microexpression Ms and six microexpression SDs, such as GMERAL (level of GMERA), GMERAF (fluctuation of GMERA), GMERAB (background effect of GMERA), and GMERABF (fluctuation of GMERAB), which had good parallel-forms reliability, calibration validity, and ecological validity. The GMERA provided a concise and comprehensive overview of the individual's microexpression recognition ability. The PREMERT was proved as a good test to measure the GMERA. (2) ALFFs (the amplitude of low-frequency fluctuations) in both eyes-closed and eyes-opened resting-states and ALFFs-difference could predict the four indicators of the GMERA. The relevant resting-state brain areas were some areas of the expression recognition network, the microexpression consciousness and attention network, and the motor network for the change from expression backgrounds to microexpression. (3) The relevant brain areas of the GMERA and different types of microexpression recognition belonged to the three cognitive processes, but the relevant brain areas of the GMERA were the "higher-order" areas to be more concise and critical than those of different types of microexpression recognition.

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.

Design and implementation of an EEG-based recognition mechanism for the openness trait of the Big Five

The differentiation between the openness and other dimensions of the Big Five personality model indicates that it is necessary to design a specific paradigm as a supplement to the Big Five recognition. The present study examined the relationship between one's openness trait of the Big Five model and the task-related power change of upper alpha band (10–12 Hz). We found that individuals from the high openness group displayed a stronger alpha synchronization over a frontal area in symbolic reasoning task, while the reverse applied in the deductive reasoning task. The results indicated that these two kinds of reasoning tasks could be used as supplement of the Big Five recognition. Besides, we divided one's openness score into three levels and proposed a hybrid-SNN (Spiking Neural Networks)-ANN (Analog Neural Networks) architecture based on EEGNet to recognize one's openness level, named Spike-EEGNet. The recognition accuracy of the two tasks was 90.6 and 92.2%. This result was highly significant for the validation of using a model with hybrid-SNN-ANN architecture for EEG-based openness trait recognition.

Personality similarity predicts synchronous neural responses in fMRI and EEG data

Successful communication and cooperation among different members of society depends, in part, on a consistent understanding of the physical and social world. What drives this alignment in perspectives? We present evidence from two neuroimaging studies using functional magnetic resonance imaging (fMRI; N = 66 with 2145 dyadic comparisons) and electroencephalography (EEG; N = 225 with 25,200 dyadic comparisons) to show that: (1) the extent to which people’s neural responses are synchronized when viewing naturalistic stimuli is related to their personality profiles, and (2) that this effect is stronger than that of similarity in gender, ethnicity and political affiliation. The localization of the fMRI results in combination with the additional eye tracking analyses suggest that the relationship between personality similarity and neural synchrony likely reflects alignment in the interpretation of stimuli and not alignment in overt visual attention. Together, the findings suggest that similarity in psychological dispositions aligns people’s reality via shared interpretations of the external world.

"Nothing to see here": No structural brain differences as a function of the Big Five personality traits from a systematic review and meta-analysis

Personality reflects social, affective, and cognitive predispositions that emerge from genetic and environmental influences. Contemporary personality theories conceptualize a Big Five Model of personality based on the traits of neuroticism, extraversion, agreeableness, conscientiousness, and openness to experience. Starting around the turn of the millennium, neuroimaging studies began to investigate functional and structural brain features associated with these traits. Here, we present the first study to systematically evaluate the entire published literature of the association between the Big Five traits and three different measures of brain structure. Qualitative results were highly heterogeneous, and a quantitative meta-analysis did not produce any replicable results. The present study provides a comprehensive evaluation of the literature and its limitations, including sample heterogeneity, Big Five personality instruments, structural image data acquisition, processing, and analytic strategies, and the heterogeneous nature of personality and brain structures. We propose to rethink the biological basis of personality traits and identify ways in which the field of personality neuroscience can be strengthened in its methodological rigor and replicability.

Affect and Cognition in Managerial Decision Making: A Systematic Literature Review of Neuroscience Evidence

How do affect and cognition interact in managerial decision making? Over the last decades, scholars have investigated how managers make decisions. However, what remains largely unknown is the interplay of affective states and cognition during the decision-making process. We offer a systematization of the contributions produced on the role of affect and cognition in managerial decision making by considering the recent cross-fertilization of management studies with the neuroscience domain. We implement a Systematic Literature Review of 23 selected contributions dealing with the role of affect and cognition in managerial decisions that adopted neuroscience techniques/points of view. Collected papers have been analyzed by considering the so-called reflexive (X-) and reflective (C-) systems in social cognitive neuroscience and the type of decisions investigated in the literature. Results obtained help to support an emerging "unified" mind processing theory for which the two systems of our mind are not in conflict and for which affective states have a driving role toward cognition. A research agenda for future studies is provided to scholars who are interested in advancing the investigation of affect and cognition in managerial decision making, also through neuroscience techniques - with the consideration that these works should be at the service of the behavioral strategy field.

The cognitive basis of psychosocial impact in COVID-19 pandemic. Does it encircle the default mode network of the brain? A pragmatic proposal

Epigenetics, hypothalamic-pituitary axes, environmental and metabolic influences, and transgenerational plasticity govern social behavior. Cognitive research considers the brain's default mode network (DMN) as a central hub that integrates various cognitive and social processing domains responsible for emotion perception, empathy, theory of mind, and morality. Hence, DMN is regarded as the "social brain." Upsurge in social turmoil, social anxiety, panic, depression, post-traumatic stress, hoarding, herd behavior, substance and behavioral addictions, sexual abuse, and violence in the time of the COVID-19 pandemic are intricately related to personality traits resulting in disruptive social cognition and social behavior, conceptualized as the result of unsettling and disruption of the functional nexus of the DMN. Considering overt and conspicuous display of neuroticism during the current pandemic, its impact upon modulation of the DMN functional nexus and the DMN itself, and the potential to presage cognitive impairment in the future, the authors caution that an increase in the global burden of dementia may be one of the long-term ramifications of COVID-19. Social behavior, a functional derivative of the DMN, can strikingly affect the functional nexus of DMN and the DMN itself, in a centripetal way via the phenomenon called "Experience-Dependent Plasticity," with long-term consequences. In this review, we intend to 1) decipher the association between social cognition and social behavior with the DMN, in time of COVID-19; and to 2) discuss the prospective aftermath of disrupted social behavior during the pandemic on modulation/alteration of functional connectomes of DMN or the DMN itself in the time ahead.

Association between Neuroticism and Premenstrual Affective/Psychological Symptomatology

Neuroticism and premenstrual conditions share pleiotropic loci and are strongly associated. It is presently not known which DSM-5 symptoms of premenstrual syndrome/premenstrual mood disorder are associated with neuroticism. We enrolled 45 study participants to provide prospective daily ratings of affective ("depression", "anxiety, "anger", "mood swings") and psychological ("low interest", "feeling overwhelmed", and "difficulty concentrating") symptoms across two-three menstrual cycles (128 total cycles). Generalized additive modeling (gam function in R) was implemented to model the relationships between neuroticism and the premenstrual increase in symptomatology. Significance level was adjusted using the False Discovery Rate method and models were adjusted for current age and age of menarche. Results of the association analysis revealed that "low interest" (p ≤ 0.05) and "difficulty concentrating" (p ≤ 0.001) were significantly associated with neuroticism. None of the remaining symptoms reached statistical significance. The late luteal phase of the menstrual cycle is characterized by complex symptomatology, reflecting a physiological milieu of numerous biological processes. By identifying co-expression between neuroticism and specific premenstrual symptomatology, the present study improves our understanding of the premenstrual conditions and provides a platform for individualized treatment developments.

Resting-State Functional Connectivity Associated With Extraversion and Agreeableness in Adolescence

Although adolescence is a period in which developmental changes occur in brain connectivity, personality formation, and peer interaction, few studies have examined the neural correlates of personality dimensions related to social behavior within adolescent samples. The current study aims to investigate whether adolescents’ brain functional connectivity is associated with extraversion and agreeableness, personality dimensions linked to peer acceptance, social network size, and friendship quality. Considering sex-variant neural maturation in adolescence, we also examined sex-specific associations between personality and functional connectivity. Using resting-state functional magnetic resonance imaging (fMRI) data from a community sample of 70 adolescents aged 12–15, we examined associations between self-reported extraversion and agreeableness and seed-to-whole brain connectivity with the amygdala as a seed region of interest. Then, using 415 brain regions that correspond to 8 major brain networks and subcortex, we explored neural connectivity within brain networks and across the whole-brain. We conducted group-level multiple regression analyses with the regressors of extraversion, agreeableness, and their interactions with sex. Results demonstrated that amygdala connectivity with the postcentral gyrus, middle temporal gyrus, and the temporal pole is positively associated with extraversion in girls and negatively associated with extraversion in boys. Agreeableness was positively associated with amygdala connectivity with the middle occipital cortex and superior parietal cortex, in the same direction for boys and girls. Results of the whole-brain connectivity analysis revealed that the connectivity of the postcentral gyrus, located in the dorsal attention network, with regions in default mode network (DMN), salience/ventral attention network, and control network (CON) was associated with extraversion, with most connections showing positive associations in girls and negative associations in boys. For agreeableness, results of the within-network connectivity analysis showed that connections within the limbic network were positively associated with agreeableness in boys while negatively associated with or not associated with agreeableness in girls. Results suggest that intrinsic functional connectivity may contribute to adolescents’ individual differences in extraversion and agreeableness and highlights sex-specific neural connectivity patterns associated with the two personality dimensions. This study deepens our understanding of the neurobiological correlates of adolescent personality that may lead to different developmental trajectories of social experience.

The Moderating Role of Sleep Quality on the Association between Neuroticism and Frontal Executive Function in Older Adults

OBJECTIVE/BACKGROUND

Personality traits are regarded as risk factors for cognitive impairment in older adults, while sleep disturbance and physical inactivity are also considered as modifiable risk factors. Therefore, it could be beneficial to investigate the effects of those modifiable risk factors on the relationship between personality traits and cognitive functions, to prepare appropriate strategies for mitigating cognitive impairment.

PARTICIPANTS

A total of 155 cognitively unimpaired older adults were included.

METHODS

All participants underwent cognitive function tests using the Seoul Neuropsychological Screening Battery and examinations for personality traits using the Big Five Inventory. Individual physical activity and sleep quality were assessed using the International Physical Activity Questionnaire and Pittsburgh Sleep Quality Index, respectively. A hierarchical linear multiple regression analysis was performed to demonstrate the direct association between personality traits and cognitive functions, and the multiple moderator analysis was used to analyze the moderating effects of lifestyle factors on this association.

RESULTS

Among the five personality traits, only neuroticism was negatively associated with the frontal executive and visuospatial functions after controlling age, sex, and years of education. Interestingly, the negative relationship between neuroticism and frontal executive function was alleviated in older adults with higher sleep quality.

CONCLUSIONS

Our findings demonstrated that higher sleep quality has significant moderating effects on the negative association between neuroticism and frontal executive functions in older adults, which suggests intervention for improving sleep quality such as cognitive behavioral therapy can be considered in older adults who have personality traits associated with a high risk of cognitive impairment.

Neurometabolite correlates with personality and stress in healthy emerging adults: A focus on sex differences

Personality traits have been linked with both brain structure and function. However, the exact relationship between personality traits and other behavioural measures with neurometabolites, measured with proton magnetic resonance spectroscopy, is not clear. Here we investigated the association between behavioural measures (i.e., personality traits, resilience, perceived stress, self-esteem, hopelessness, psychological distress) and metabolite ratios (i.e., of choline-containing compounds [Cho], creatine and phosphocreatine [Cr], and N-acetyl-aspartate [NAA]) in the posterior cingulate cortex (pCC) and the dorsal anterior cingulate cortex (dACC) and surrounding white matter (WM) regions in healthy emerging adults (N = 57, 26 women, mean age=23.40 years, SD=2.50). The pCC and the dACC were selected for their known involvement as important brain network hubs and their association to five factor personality dimensions and other psychological measures. Spectral analysis as well as statistics for demographic, clinical, and imaging data were performed. Correlation and multiple regression analyses were used to test the relationship between metabolite ratios and behavioural scores in the entire sample as well as in female and male participants separately. The entire sample showed significant (p<0.05) negative correlates of stress with the NAA/Cr ratio in the pCC, and of extraversion with WM metabolite ratios. In regards of sex differences, a significantly higher NAA/Cho ratio in the pCC (p<0.05), the dACC (p<0.01), and in the left and right posterior WM matter (p<0.05), and a lower Cho/Cr ratio in the dACC (p<0.01) was detected in women. Moreover, the two sexes differed in regards of metabolite correlates of openness, conscientiousness, extraversion, agreeableness, neuroticism, stress, hopelessness, and self-esteem, and in multiple regression model predictions. Our results point to a role of the ACC in conscientiousness through its involvement in higher-order cognitive control as part of the salience network and internally directed thoughts as part of the default mode network (DMN). Furthermore, the two sexes differ in terms of metabolite correlates of openness and conscientiousness in the pCC, suggesting mental process involvement through the DMN, and of agreeableness in the dACC, possibly through involvement in social cognitive processes, particularly in women. Additionally, our results suggest that the ACC is linked to the so-called Alpha-factor of personality. Our findings on stress correlates contribute to the existing literature of the involvement of the ACC as part of the limbic system. In addition, our results suggest a possible role of the pCC in stress-regulatory processes through a possible co-involvement of stress, hopelessness, and self-esteem in the pCC in men, where higher self-esteem may help to cope with stress.

Predicting behavior through dynamic modes in resting-state fMRI data

Dynamic properties of resting-state functional connectivity (FC) provide rich information on brain-behavior relationships. Dynamic mode decomposition (DMD) has been used as a method to characterize FC dynamics. However, it remains unclear whether dynamic modes (DMs), spatial-temporal coherent patterns computed by DMD, provide information about individual behavioral differences. This study established a methodological approach to predict individual differences in behavior using DMs. Furthermore, we investigated the contribution of DMs within each of seven specific frequency bands (0-0.1,...,0.6-0.7 Hz) for prediction. To validate our approach, we tested whether each of 59 behavioral measures could be predicted by performing multivariate pattern analysis on a Gram matrix, which was created using subject-specific DMs computed from resting-state functional magnetic resonance imaging (rs-fMRI) data of individuals. DMD successfully predicted behavior and outperformed temporal and spatial independent component analysis, which is the conventional data decomposition method for extracting spatial activity patterns. Most of the behavioral measures that were predicted with significant accuracy in a permutation test were related to cognition. We found that DMs within frequency bands <0.2 Hz primarily contributed to prediction and had spatial structures similar to several common resting-state networks. Our results indicate that DMD is efficient in extracting spatiotemporal features from rs-fMRI data.

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.

Determinants and modulators of human social decisions

Social decision making is a highly complex process that involves diverse cognitive mechanisms, and it is driven by the precise processing of information from both the environment and from the internal state. On the one hand, successful social decisions require close monitoring of others' behavior, in order to track their intentions; this can guide not only decisions involving other people, but also one's own choices and preferences. On the other hand, internal states such as own reward or changes in hormonal and neurotransmitter states shape social decisions and their underlying neural function. Here, we review the current literature on modulators and determinants of human social decisions.

Perturbation of resting-state network nodes preferentially propagates to structurally rather than functionally connected regions

Combining Transcranial Magnetic Stimulation (TMS) with electroencephalography (EEG) offers the opportunity to study signal propagation dynamics at high temporal resolution in the human brain. TMS pulse induces a local effect which propagates across cortical networks engaging distant cortical and subcortical sites. However, the degree of propagation supported by the structural compared to functional connectome remains unclear. Clarifying this issue would help tailor TMS interventions to maximize target engagement. The goal of this study was to establish the contribution of functional and structural connectivity in predicting TMSinduced signal propagation after perturbation of two distinct brain networks. For this purpose, 24 healthy individuals underwent two identical TMS-EEG visits where neuronavigated TMS pulses were delivered to nodes of the default mode network (DMN) and the dorsal attention network (DAN). The functional and structural connectivity derived from each individual stimulation spot were characterized via functional magnetic resonance imaging (fMRI) and Diffusion Weighted Imaging (DWI), and signal propagation across these two metrics was compared. Direct comparison between the signal extracted from brain regions either functionally or structurally connected to the stimulation sites, shows a stronger activation over cortical areas connected via white matter pathways, with a minor contribution of functional projections. This pattern was not observed when analyzing spontaneous resting state EEG activity. Overall, results suggest that structural links can predict network-level response to perturbation more accurately than functional connectivity. Additionally, DWI-based estimation of propagation patterns can be used to estimate off-target engagement of other networks and possibly guide target selection to maximize specificity.

Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes

Recent years have seen a surge of research on variability in functional brain connectivity within and between individuals, with encouraging progress toward understanding the consequences of this variability for cognition and behavior. At the same time, well-founded concerns over rigor and reproducibility in psychology and neuroscience have led many to question whether functional connectivity is sufficiently reliable, and call for methods to improve its reliability. The thesis of this opinion piece is that when studying variability in functional connectivity-both across individuals and within individuals over time-we should use behavior prediction as our benchmark rather than optimize reliability for its own sake. We discuss theoretical and empirical evidence to compel this perspective, both when the goal is to study stable, trait-level differences between people, as well as when the goal is to study state-related changes within individuals. We hope that this piece will be useful to the neuroimaging community as we continue efforts to characterize inter- and intra-subject variability in brain function and build predictive models with an eye toward eventual real-world applications.

Personality and behavioral changes after brain tumor resection: a lesion mapping study

BACKGROUND

Cognitive functioning is generally well preserved in patients with diffuse low-grade glioma (DLGG), even in the case of extended tumor and resection. To date, the question of personality changes in these patients has received little attention. Our aim was to investigate to what extent certain aspects of personality and behaviors could be affected by DLGG resection.

METHODS

We used self-reported personality questionnaires (NOEPI-R and TCI-R) and hetero-evaluation of executive behavioral changes in a large sample of 98 patients operated on for DLGG. To compare the patients' scores from the personality questionnaires, we recruited 47 healthy controls participants. To identify the putative neural networks associated with behavioral changes, a combination of voxel-wise and tract-wise lesion-symptom mapping was performed.

RESULTS

First, results revealed no difference between patients and controls for each subdimension of the NOEPI-R. Regarding the TCI-R, the character dimensions and three out of four temperament dimensions did not differ. Second, behavioral changes (Irritability, Hypoactivity, Anticipative disorders, and disinterest) were reported between 40 and 50% of cases. Third, some personality dimensions (as neuroticism) were strongly predictive of postoperative behavioral disorders (as hypoactivity). Lastly, specific behavioral changes were associated with selective damage to cortical (left inferior frontal gyrus, supplementary motor area, and right fusiform gyrus) and white matter (left inferior fronto-occipital and uncinate fasciculi, right cingulum) structures.

CONCLUSION

This study demonstrates that extensive lesions caused by DLGGs and their surgical resection have no or minor impact on patients' personality. However, specific personality dimensions are strongly predictive of behavioral disorders suggesting that the observed surgically related behavioral changes are modulated by the personality profile. Finally, the lesion mapping analyses indicate that damage to differential cortical or white matter structures leads to distinct patterns of behavioral abnormalities.

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.

Online betting intensity is linked with Extraversion and Conscientiousness

INTRODUCTION

Extraversion and Conscientiousness are well-studied personality traits associated with reward processing and goal prioritization, respectively, and bear on individual differences in financial risk-taking. Using unique large datasets, we investigated the link between these traits and male online gamblers' actual betting participation and intensity.

METHOD

We combined datasets containing online horse betting data (during 2015-2016) from the Finnish monopoly betting company, administrative registry data from Statistics Finland, and personality trait measures from the Finnish Defence Forces corresponding to Extraversion and Conscientiousness as defined in the five-factor model. We modelled associations between these traits and betting participation (n = 471,968) and intensity (n = 11,217) among male horse bettors (age = 36-53).

RESULTS

Controlling for demographics and IQ, individuals scoring high on Conscientiousness (or Extraversion) were less (or more) likely to bet and less (or more) intensive bettors-even when personality was measured 16-34 years before betting occurred. One SD personality score increase represented an annual decrease (Conscientiousness) or increase (Extraversion) of €570-754 in spending.

CONCLUSIONS

Extraversion and Conscientiousness are implicated in real-life financial behavior with tangible consequences for individuals. These effects are stronger than for many known demographic variables used in gambling studies and persist up to 34 years after personality has been measured.

Network-level macroscale structural connectivity predicts propagation of transcranial magnetic stimulation

Information processing in the brain is mediated by structural white matter pathways and is highly dependent on topological brain properties. Here we combined transcranial magnetic stimulation (TMS) with high-density electroencephalography (EEG) and Diffusion Weighted Imaging (DWI), specifically looking at macroscale connectivity to understand whether regional, network-level or whole-brain structural properties are more responsible for stimulus propagation. Neuronavigated TMS pulses were delivered over two individually defined nodes of the default mode (DMN) and dorsal attention (DAN) networks in a group of healthy subjects, with test-retest reliability assessed 1-month apart. TMS-evoked activity was predicted by the modularity and structural integrity of the stimulated network rather than the targeted region(s) or the whole-brain connectivity, suggesting network-level structural connectivity as more relevant than local and global brain properties in shaping TMS signal propagation. The importance of network structural connectome was unveiled only by evoked activity, but not resting-state data. Future clinicals interventions might enhance target engagement by adopting DWI-guided, network-focused TMS.

The manifestation of individual differences in sensitivity to punishment during resting state is modulated by eye state

Structural and functional neuroimaging studies have shown that brain areas associated with fear and anxiety (defensive system areas) are modulated by individual differences in sensitivity to punishment (SP). However, little is known about how SP is related to brain functional connectivity and the factors that modulate this relationship. In this study, we investigated whether a simple methodological manipulation, such as performing a resting state with eyes open or eyes closed, can modulate the manifestation of individual differences in SP. To this end, we performed an exploratory fMRI resting state study in which a group of participants (n = 88) performed a resting state with eyes closed and another group (n = 56) performed a resting state with eyes open. All participants completed the Sensitivity to Punishment and Sensitivity to Reward Questionnaire. Seed-based functional connectivity analyses were performed in the amygdala, hippocampus, and periaqueductal gray (PAG). Our results showed that the relationship between SP and left amygdala-precuneus and left hippocampus-precuneus functional connectivity was modulated by eye state. Moreover, in the eyes open group, SP was negatively related to the functional connectivity between the PAG and amygdala and between the PAG and left hippocampus, and it was positively related to the functional connectivity between the amygdala and hippocampus. Together, our results may suggest underlying differences in the connectivity between anxiety-related areas based on eye state, which in turn would affect the manifestation of individual differences in SP.

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.

Digital technology adoption scale in the blended learning context in higher education: Development, validation and testing of a specific tool

The main aim of the present study was to develop, validate and test an extended Technology Acceptance Model (TAM) that contributes to the overall understanding of students' intention to use digital tools in a blended learning context of higher education. The external bidimensional factor of familiarity with digital tools, which is not usually explained by the TAM, was included, and evaluated. Following a four-stage scale development technique, a seven-dimensional 25-item survey was developed, which includes two external correlated variables: familiarity with high-tech digital tools and familiarity with traditional digital tools, two mediator variables—computer anxiety, and perceived barriers, and three response variables, perceived usefulness, perceived ease of use and intention to use. The initial version of the survey was administered on 250 undergraduate students. Next, for another sample of 206 students, latent dimensions of the survey were tested using exploratory factor analysis. The structure of the survey was validated in two other subsequent stages with one sample of 262 responses of undergraduates and one of 310 responses of master's students from two different universities. All students who agreed to participate in research attended blended learning. The validity, reliability and invariance of the instrument were established by psychometric analyses. Collected data indicated that the survey has an adequate multifactorial structure that is reliable and invariant across degree levels. The scale is recommended for use in higher education studies targeting the promotion of blended learning and reduction of negative attitudes of learners toward digital instruments, supporting university professors to select their own efficient way to teach.

Resting-state functional brain connectivity in a predominantly African-American sample of older adults: exploring links among personality traits, cognitive performance, and the default mode network

The personality traits of neuroticism, openness, and conscientiousness are relevant factors for cognitive aging outcomes. The present study examined how these traits were associated with cognitive abilities and corresponding resting-state functional connectivity (RSFC) of the default mode network (DMN) in an older and predominantly minority sample. A sample of 58 cognitively unimpaired, largely African-American, older adults (M age = 68.28 ± 8.33) completed a standard RSFC magnetic resonance imaging sequence, a Big Five measure of personality, and delayed memory, Stroop, and verbal fluency tasks. Personality trait associations of within-network connectivity of the posterior cingulate cortex (PCC), a hub of the DMN, were examined using a seed-based approach. Trait scores were regressed on cognitive performance (delayed memory for neuroticism, Stroop for conscientiousness, and verbal fluency for openness). Greater openness predicted greater verbal fluency and greater RSFC between the PCC and eight clusters, including the medial prefrontal cortex, left middle frontal gyrus, and precuneus. Greater PCC–precuneus connectivity predicted greater verbal fluency. Neuroticism and conscientiousness did not significantly predict either cognitive performance or RSFC. Although requiring replication and elaboration, the results implicate openness as a contributing factor to cognitive aging via concomitant cognitive performance and connectivity within cortical hubs of the DMN and add to the sparse literature on these variables in a diverse group of older adults.

Personality reflection in the brain's intrinsic functional architecture remains elusive

In the last years, there has been a considerable increase of research into the neuroimaging correlates of inter-individual temperament and character variability-an endeavour for which the term 'personality neuroscience' was coined. Among other neuroimaging modalities and approaches, substantial work focuses on functional connectivity in resting state (rs-FC) functional magnetic resonance imaging data. In the current paper, we set out to independently query the questions asked in a highly cited study that reported a range of functional connectivity correlates of personality dimensions assessed by the widely used 'Big Five' Personality Inventory. Using a larger sample (84 subjects) and an equivalent data analysis pipeline, we obtained widely disagreeing results compared to the original study. Overall, the results were in line with the hypotheses of no relation between functional connectivity and personality, when more precise permutation-based multiple testing procedures were applied. The results demonstrate that as with other neuroimaging studies, great caution should be applied when interpreting the findings, among other reasons due to multiple testing problem involved at several levels in many neuroimaging studies. Of course, the current study results can not ultimately disprove the existence of some link between personality and brain's intrinsic functional architecture, but clearly shows that its form is very likely different and much more subtle and elusive than was previously reported.

Robust prediction of individual personality from brain functional connectome

Neuroimaging studies have linked inter-individual variability in the brain to individualized personality traits. However, only one or several aspects of personality have been effectively predicted based on brain imaging features. The objective of this study was to construct a reliable prediction model of personality in a large sample by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. High-quality resting-state functional magnetic resonance imaging data of 810 healthy young participants from the Human Connectome Project dataset were used to construct large-scale brain networks. Personality traits of the five-factor model (FFM) were assessed by the NEO Five Factor Inventory. We found that CPM successfully and reliably predicted all the FFM personality factors (agreeableness, openness, conscientiousness and neuroticism) other than extraversion in novel individuals. At the neural level, we found that the personality-associated functional networks mainly included brain regions within default mode, frontoparietal executive control, visual and cerebellar systems. Although different feature selection thresholds and parcellation strategies did not significantly influence the prediction results, some findings lost significance after controlling for confounds including age, gender, intelligence and head motion. Our finding of robust personality prediction from an individual's unique functional connectome may help advance the translation of 'brain connectivity fingerprinting' into real-world personality psychological settings.

Resting state functional connectivity underlying musical creativity

While the behavior of "being musically creative"- improvising, composing, songwriting, etc.-is undoubtedly a complex and highly variable one, recent neuroscientific investigation has offered significant insight into the neural underpinnings of many of the creative processes contributing to such behavior. A previous study from our research group (Bashwiner et al., 2016), which examined two aspects of brain structure as a function of creative musical experience, found significantly increased cortical surface area or subcortical volume in regions of the default-mode network, a motor planning network, and a "limbic" network. The present study sought to determine how these regions coordinate with one another and with other regions of the brain in a large number of participants (n ​= ​218) during a task-neutral period, i.e., during the "resting state." Deriving from the previous study's results a set of eleven regions of interest (ROIs), the present study analyzed the resting-state functional connectivity (RSFC) from each of these seed regions as a function of creative musical experience (assessed via our Musical Creativity Questionnaire). Of the eleven ROIs investigated, nine showed significant correlations with a total of 22 clusters throughout the brain, the most significant being located in bilateral cerebellum, right inferior frontal gyrus, midline thalamus (particularly the mediodorsal nucleus), and medial premotor regions. These results support prior reports (by ourselves and others) implicating regions of the default-mode, executive, and motor-planning networks in musical creativity, while additionally-and somewhat unanticipatedly-including a potentially much larger role for the salience network than has been previously reported in studies of musical creativity.

Individual Cortical Entropy Profile: Test-Retest Reliability, Predictive Power for Cognitive Ability, and Neuroanatomical Foundation

The entropy profiles of cortical activity have become novel perspectives to investigate individual differences in behavior. However, previous studies have neglected foundational aspects of individual entropy profiles, that is, the test-retest reliability, the predictive power for cognitive ability in out-of-sample data, and the underlying neuroanatomical basis. We explored these issues in a large young healthy adult dataset (Human Connectome Project, N = 998). We showed the whole cortical entropy profile from resting-state functional magnetic resonance imaging is a robust personalized measure, while subsystem profiles exhibited heterogeneous reliabilities. The limbic network exhibited lowest reliability. We tested the out-of-sample predictive power for general and specific cognitive abilities based on reliable cortical entropy profiles. The default mode and visual networks are most crucial when predicting general cognitive ability. We investigated the anatomical features underlying cross-region and cross-individual variations in cortical entropy profiles. Cortical thickness and structural connectivity explained spatial variations in the group-averaged entropy profile. Cortical folding and myelination in the attention and frontoparietal networks determined predominantly individual cortical entropy profile. This study lays foundations for brain-entropy-based studies on individual differences to understand cognitive ability and related pathologies. These findings broaden our understanding of the associations between neural structures, functional dynamics, and cognitive ability.

Neural basis of interindividual variability in social perception in typically developing children and adolescents using diffusion tensor imaging

Humans show great interindividual variability in the degree they engage in social relationship. The neural basis of this variability is still poorly understood, particularly in children. In this study, we aimed to investigate the neural basis of interindividual variability in the first step of social behavior, that is social perception, in typically developing children. For that purpose, we first used eye-tracking to objectively measure eye-gaze processing during passive visualization of social movie clips in 24 children and adolescents (10.5 ± 2.9 y). Secondly, we correlated eye-tracking data with measures of fractional anisotropy, an index of white matter microstructure, obtained using diffusion tensor imaging MRI. The results showed a large interindividual variability in the number of fixations to the eyes of characters during visualization of social scenes. In addition, whole-brain analysis showed a significant positive correlation between FA and number of fixations to the eyes,mainly in the temporal part of the superior longitudinal fasciculi bilaterally, adjacent to the posterior superior temporal cortex. Our results indicate the existence of a neural signature associated with the interindividual variability in social perception in children, contributing for better understanding the neural basis of typical and atypical development of a broader social expertise.

Toward a Neural Model of the Openness-Psychoticism Dimension: Functional Connectivity in the Default and Frontoparietal Control Networks

Psychosis proneness has been linked to heightened Openness to Experience and to cognitive deficits. Openness and psychotic disorders are associated with the default and frontoparietal networks, and the latter network is also robustly associated with intelligence. We tested the hypothesis that functional connectivity of the default and frontoparietal networks is a neural correlate of the openness-psychoticism dimension. Participants in the Human Connectome Project (N = 1003) completed measures of psychoticism, openness, and intelligence. Resting state functional magnetic resonance imaging was used to identify intrinsic connectivity networks. Structural equation modeling revealed relations among personality, intelligence, and network coherence. Psychoticism, openness, and especially their shared variance were related positively to default network coherence and negatively to frontoparietal coherence. These associations remained after controlling for intelligence. Intelligence was positively related to frontoparietal coherence. Research suggests that psychoticism and openness are linked in part through their association with connectivity in networks involving experiential simulation and cognitive control. We propose a model of psychosis risk that highlights roles of the default and frontoparietal networks. Findings echo research on functional connectivity in psychosis patients, suggesting shared mechanisms across the personality-psychopathology continuum.

Individualized perturbation of the human connectome reveals reproducible biomarkers of network dynamics relevant to cognition

Large-scale brain networks are often described using resting-state functional magnetic resonance imaging (fMRI). However, the blood oxygenation level-dependent (BOLD) signal provides an indirect measure of neuronal firing and reflects slow-evolving hemodynamic activity that fails to capture the faster timescale of normal physiological function. Here we used fMRI-guided transcranial magnetic stimulation (TMS) and simultaneous electroencephalography (EEG) to characterize individual brain dynamics within discrete brain networks at high temporal resolution. TMS was used to induce controlled perturbations to individually defined nodes of the default mode network (DMN) and the dorsal attention network (DAN). Source-level EEG propagation patterns were network-specific and highly reproducible across sessions 1 month apart. Additionally, individual differences in high-order cognitive abilities were significantly correlated with the specificity of TMS propagation patterns across DAN and DMN, but not with resting-state EEG dynamics. Findings illustrate the potential of TMS-EEG perturbation-based biomarkers to characterize network-level individual brain dynamics at high temporal resolution, and potentially provide further insight on their behavioral significance.