Network Approaches to Understand Individual Differences in Brain Connectivity: Opportunities for Personality Neuroscience

Weak brain function and anxiety-related loop in harm-avoidance personality: A resting-state functional magnetic resonance imaging study

BACKGROUND

Personality is a unique and relatively stable psychological concept that defines individual human beings. It strongly influences long-term behavioral styles such as emotional expression. This study aims to elucidate the brain functional underpinning behind personality.

METHODS

A total of 97 young subjects were included. All subjects completed personality, emotion, and cognition scales, and resting-state functional magnetic resonance imaging scan. All subjects were divided into subtypes of harm avoidance (HA) and reward dependence (RD) by clustering analysis. Graph theory analysis and network-based analysis were used to explore the brain functional configurations of personalities.

RESULTS

HA subjects showed lower network metrics (P = 0.018) and node metrics (P < 0.009). A negative component network was observed in HA subjects (P < 0.001). Functional topology metrics were negatively correlated with the HA score. The amygdala-IPG functional connectivity mediated the positive correlation between personality HA and state anxiety.

CONCLUSION

Personality HA is associated with decreased functional configuration, which could influence emotion by downregulating amygdala-IPG coupling. These findings provide insight into how the brain shapes personality and related emotions.

Frontoparietal network topology as a neural marker of musical perceptual abilities

Why are some individuals more musical than others? Neither cognitive testing nor classical localizationist neuroscience alone can provide a complete answer. Here, we test how the interplay of brain network organization and cognitive function delivers graded perceptual abilities in a distinctively human capacity. We analyze multimodal magnetic resonance imaging, cognitive, and behavioral data from 200+ participants, focusing on a canonical working memory network encompassing prefrontal and posterior parietal regions. Using graph theory, we examine structural and functional frontoparietal network organization in relation to assessments of musical aptitude and experience. Results reveal a positive correlation between perceptual abilities and the integration efficiency of key frontoparietal regions. The linkage between functional networks and musical abilities is mediated by working memory processes, whereas structural networks influence these abilities through sensory integration. Our work lays the foundation for future investigations into the neurobiological roots of individual differences in musicality.

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.

Task-specific topology of brain networks supporting working memory and inhibition

Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.

Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals' brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

Static and dynamic fMRI-derived functional connectomes represent largely similar information

Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.

Hierarchical network structure as the source of hierarchical dynamics (power-law frequency spectra) in living and non-living systems: How state-trait continua (body plans, personalities) emerge from first principles in biophysics

Living systems are hierarchical control systems that display a small world network structure. In such structures, many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a 'power-law' cluster size distribution (a mereology). Just like their structure, the dynamics of living systems shows fractal-like qualities: the timeseries of inner message passing and overt behavior contain high frequencies or 'states' (treble) that are nested within lower frequencies or 'traits' (bass), producing a power-law frequency spectrum that is known as a 'state-trait continuum' in the behavioral sciences. Here, we argue that the power-law dynamics of living systems results from their power-law network structure: organisms 'vertically encode' the deep spatiotemporal structure of their (anticipated) environments, to the effect that many small clusters near the base of the hierarchy produce high frequency signal changes and fewer larger clusters at its top produce ultra-low frequencies. Such ultra-low frequencies exert a tonic regulatory pressure that produces morphological as well as behavioral traits (i.e., body plans and personalities). Nested-modular structure causes higher frequencies to be embedded within lower frequencies, producing a power-law state-trait continuum. At the heart of such dynamics lies the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.q., earthquakes, stock market fluctuations). Since hierarchical structure produces hierarchical dynamics, the development and collapse of hierarchical structure (e.g., during maturation and disease) should leave specific traces in system dynamics (shifts in lower frequencies, i.e. morphological and behavioral traits) that may serve as early warning signs to system failure. The applications of this idea range from (bio)physics and phylogenesis to ontogenesis and clinical medicine.

Adherence to cognitive and physical exercise engagement: a challenge to successful dementia risk reduction and prevention efforts

With human life expectancy and proportion of older adults increasing, global use of evidence-supported preventative methods to minimize risk of brain-related disabilities such as Alzheimer's disease and other dementias-as well as interventions to slow rate of disease progression-is important. Sustained engagement in cognitive and physical exercise programs may prevent or delay dementia onset as well as maximize health and function of those with dementia. Despite awareness of the importance of cognitive and physical exercise to brain health, exercise program adherence by older adults is extremely challenging. In this Perspective article, we summarize what is known about contributors to exercise program adherence and strategies to promote it. We discuss our viewpoint on knowledge gaps regarding exercise adherence and research that needs to be conducted. We conclude by proposing a multi-dimensional exercise adherence assessment framework that includes portable neurophysiologic technologies to inform initial design and updating of individualized exercise programs that optimize sustained exercise program engagement and, ultimately, maximize brain health in older adults with and without mild cognitive impairment and dementia.

Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain

Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean |r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean |r| = 0.939) and with mean edge weight (mean |r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome.

Graph-matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score

Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome ProjectN = 997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.

Static and dynamic functional connectomes represent largely similar information

Functional connectivity (FC) of blood-oxygen-level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate) and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.

Variational dimensions of cingulate cortex functional connectivity and implications in neuropsychiatric disorders

Significant variations in brain functional connectivity exist in the healthy population, rendering the identification and characterization of their abnormalities in neuropsychiatric disorders difficult. Here, we proposed a new principal component analysis (PCA) approach to study variations in functional connectivity, focusing on major hubs of the salience network and default mode network, namely the anterior and posterior cingulate cortices. We analyzed the intersubject variability of human functional magnetic resonance imaging connectivity obtained from healthy, autistic, and schizophrenic subjects. Utilizing data from 1000 Functional Connectomes Project, COBRE, and ABIDE 1 database, we characterized the normal variations of the cingulate cortices with respect to top PCA dimensions. We showed that functional connectivity variations of the 2 cingulate cortices are constrained, in a parallel manner, by competing or cooperating interactions with different sensorimotor, associative, and limbic networks. In schizophrenic and autistic subjects, diffuse and subtle network changes along the same dimensions were found, which suggest significant behavioral implications of the variational dimensions. Furthermore, we showed that individual dynamic functional connectivity tends to fluctuate along the principal components of connectivity variations across individuals. Our results demonstrate the strength of this new approach in addressing the intrinsic variations of network connectivity in human brain and identifying their subtle changes in neuropsychiatric disorders.

Personality Neuroscience: An Emerging Field with Bright Prospects

Personality neuroscience is the study of persistent psychological individual differences, typically in the general population, using neuroscientific methods. It has the potential to shed light on the neurobiological mechanisms underlying individual differences and their manifestation in ongoing behavior and experience. The field was inaugurated many decades ago, yet has only really gained momentum in the last two, as suitable technologies have become widely available. Personality neuroscience employs a broad range of methods, including molecular genetics, pharmacological assays or manipulations, electroencephalography, and various neuroimaging modalities, such as magnetic resonance imaging and positron emission tomography. Although exciting progress is being made in this young field, much remains unknown. In this brief review, we discuss discoveries that have been made, methodological challenges and advances, and important questions that remain to be answered. We also discuss best practices for personality neuroscience research and promising future directions for the field.

Social cognitive network neuroscience

Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.

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

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

Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method

Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better identification of individuals and prediction of behavior. Therefore, we propose a multi-task learning based sparse convex alternating structure optimization (MTL-sCASO) method to decompose RSFC into individual-specific connectivity and individual-shared connectivity. We used synthetic data to validate the efficacy of the MTL-sCASO method. In addition, we verified that individual-specific connectivity achieves higher identification rates than the Pearson correlation (PC) method, and the individual-specific components observed in 886 individuals from the Human Connectome Project (HCP) examined in two sessions over two consecutive days might serve as individual fingerprints. Individual-specific connectivity has low inter-subject similarity (-0.005±0.023), while individual-shared connectivity has high inter-subject similarity (0.822±0.061). We also determined the anatomical locations (region or subsystem) related to individual attributes and common features. We find that individual-specific connectivity exhibits low degree centrality in the sensorimotor processing system but high degree centrality in the control system. Importantly, the individual-specific connectivity estimated by the MTL-sCASO method accurately predicts behavioral scores (improved by 9.4% compared to the PC method) in the cognitive dimension. The decomposition of individual-specific and individual-shared components from RSFC provides a new approach for tracing individual traits and group analysis using functional brain networks.

Borderline personality disorder classification based on brain network measures during emotion regulation

Borderline Personality Disorder (BPD) is characterized by an increased emotional sensitivity and dysfunctional capacity to regulate emotions. While amygdala and prefrontal cortex interactions are regarded as the critical neural mechanisms underlying these problems, the empirical evidence hereof is inconsistent. In the current study, we aimed to systematically test different properties of brain connectivity and evaluate the predictive power to detect borderline personality disorder. Patients with borderline personality disorder (n = 51), cluster C personality disorder (n = 26) and non-patient controls (n = 44), performed an fMRI emotion regulation task. Brain network analyses focused on two properties of task-related connectivity: phasic refers to task-event dependent changes in connectivity, while tonic was defined as task-stable background connectivity. Three different network measures were estimated (strength, local efficiency, and participation coefficient) and entered as separate models in a nested cross-validated linear support vector machine classification analysis. Borderline personality disorder vs. non-patient controls classification showed a balanced accuracy of 55%, which was not significant under a permutation null-model, p = 0.23. Exploratory analyses did indicate that the tonic strength model was the highest performing model (balanced accuracy 62%), and the amygdala was one of the most important features. Despite being one of the largest data-sets in the field of BPD fMRI research, the sample size may have been limited for this type of classification analysis. The results and analytic procedures do provide starting points for future research, focusing on network measures of tonic connectivity, and potentially focusing on subgroups of BPD.

The dynamic modular fingerprints of the human brain at rest

The human brain is a dynamic modular network that can be decomposed into a set of modules, and its activity changes continually over time. At rest, several brain networks, known as Resting-State Networks (RSNs), emerge and cross-communicate even at sub-second temporal scale. Here, we seek to decipher the fast reshaping in spontaneous brain modularity and its relationships with RSNs. We use Electro/Magneto-Encephalography (EEG/MEG) to track the dynamics of modular brain networks, in three independent datasets (N = 568) of healthy subjects at rest. We show the presence of strikingly consistent RSNs, and a splitting phenomenon of some of these networks, especially the default mode network, visual, temporal and dorsal attentional networks. We also demonstrate that between-subjects variability in mental imagery is associated with the temporal characteristics of specific modules, particularly the visual network. Taken together, our findings show that large-scale electrophysiological networks have modularity-dependent dynamic fingerprints at rest.

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.

Brain network topology predicts participant adherence to mental training programs

Adherence determines the success and benefits of mental training (e.g., meditation) programs. It is unclear why some participants engage more actively in programs for mental training than others. Understanding neurobiological factors that predict adherence is necessary for understanding elements of learning and to inform better designs for new learning regimens. Clustering patterns in brain networks have been suggested to predict learning performance, but it is unclear whether these patterns contribute to motivational aspects of learning such as adherence. This study tests whether configurations of brain connections in resting-state fMRI scans can be used to predict adherence to two programs: meditation and creative writing. Results indicate that greater system segregation and clustering predict the number of practice sessions and class participation in both programs at a wide range of network thresholds (corrected p value < 0.05). At a local level, regions in subcortical circuitry such as striatum and accumbens predicted adherence in all subjects. Furthermore, there were also some important distinctions between groups: Adherence to meditation was predicted by connectivity within local network of the anterior insula and default mode network; and in the writing program, adherence was predicted by network neighborhood of frontal and temporal regions. Four machine learning methods were applied to test the robustness of the brain metric for classifying individual capacity for adherence and yielded reasonable accuracy. Overall, these findings underscore the fact that adherence and the ability to perform prescribed exercises is associated with organizational patterns of brain connectivity.

Towards an Individual Differences Perspective in Mindfulness Training Research: Theoretical and Empirical Considerations

A growing body of research indicates that mindfulness training can have beneficial effects on critical aspects of psychological well-being, cognitive function, and brain health. Although these benefits have been generalized to the population level, individual variability in observed effects of mindfulness training has not been systematically investigated. Research on other similar forms of psychological intervention demonstrates that individual differences are prominent in terms of intervention responsiveness and outcomes. Furthermore, individual characteristics such as personality traits have been shown to play a crucial role in influencing the effects of intervention. In light of these lines of evidence, we review representative work on individual differences in mindfulness training and advocate for an individual difference perspective in mindfulness training research. We discuss relevant empirical evidence of individual differences potentially influencing behavioral outcomes of mindfulness training, focusing on both cognitive function and psychological well-being. Finally, theoretical considerations and potentially fruitful research strategies and directions for studying individual differences in mindfulness training are discussed, including those involving cognitive neuroscience methods.

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

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

Functional brain network reconfiguration during learning in a dynamic environment

When learning about dynamic and uncertain environments, people should update their beliefs most strongly when new evidence is most informative, such as when the environment undergoes a surprising change or existing beliefs are highly uncertain. Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that adapt their learning dynamics more appropriately in response to these factors. The key feature of this whole-brain pattern of functional connectivity is stronger connectivity, or functional integration, between the fronto-parietal and other functional systems. Our results provide new insights regarding the association between dynamic adjustments in learning and dynamic, large-scale changes in functional connectivity across the brain.

Brain Functional Networks Study of Subacute Stroke Patients With Upper Limb Dysfunction After Comprehensive Rehabilitation Including BCI Training

Brain computer interface (BCI)-based training is promising for the treatment of stroke patients with upper limb (UL) paralysis. However, most stroke patients receive comprehensive treatment that not only includes BCI, but also routine training. The purpose of this study was to investigate the topological alterations in brain functional networks following comprehensive treatment, including BCI training, in the subacute stage of stroke. Twenty-five hospitalized subacute stroke patients with moderate to severe UL paralysis were assigned to one of two groups: 4-week comprehensive treatment, including routine and BCI training (BCI group, BG, n = 14) and 4-week routine training without BCI support (control group, CG, n = 11). Functional UL assessments were performed before and after training, including, Fugl-Meyer Assessment-UL (FMA-UL), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT). Neuroimaging assessment of functional connectivity (FC) in the BG was performed by resting state functional magnetic resonance imaging. After training, as compared with baseline, all clinical assessments (FMA-UL, ARAT, and WMFT) improved significantly (p < 0.05) in both groups. Meanwhile, better functional improvements were observed in FMA-UL (p < 0.05), ARAT (p < 0.05), and WMFT (p < 0.05) in the BG. Meanwhile, FC of the BG increased across the whole brain, including the temporal, parietal, and occipital lobes and subcortical regions. More importantly, increased inter-hemispheric FC between the somatosensory association cortex and putamen was strongly positively associated with UL motor function after training. Our findings demonstrate that comprehensive rehabilitation, including BCI training, can enhance UL motor function better than routine training for subacute stroke patients. The reorganization of brain functional networks topology in subacute stroke patients allows for increased coordination between the multi-sensory and motor-related cortex and the extrapyramidal system. Future long-term, longitudinal, controlled neuroimaging studies are needed to assess the effectiveness of BCI training as an approach to promote brain plasticity during the subacute stage of stroke.

Intra-cortical myelin mediates personality differences

OBJECTIVE

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

Linking brain network reconfiguration and intelligence: Are we there yet?

Recent applications of dynamic network analyses to functional neuroimaging data have revealed relationships between a number of cognition conditions and the dynamic reconfiguration of brain networks. Here we critically review such applications of network neuroscience to intelligence. After providing an overview of network neuroscience, we center our discussion around the recently proposed Network Neuroscience Theory of Intelligence (Barbey, 2017). We evaluate and review existing empirical support for the theses made by this theory and argue that while studies strongly suggest their plausibility, evidence to date has largely been indirect. We propose avenues for future research to directly evaluate these theses by overcoming the methodological and analytical shortcomings of previous studies. In doing so, our goal is to stimulate future empirical investigations and present valuable ways forward in the network neuroscience of intelligence.

Psychological resilience correlates with EEG source-space brain network flexibility

We aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience. To tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-item Connor-Davidson Resilience Scale (CD-RISC). By using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha, and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time. The results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha, and beta bands. This study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks.