Functional network organizations of two contrasting temperament groups in dimensions of novelty seeking and harm avoidance

Temperament & Character account for brain functional connectivity at rest: A diathesis-stress model of functional dysregulation in psychosis

The human brain's resting-state functional connectivity (rsFC) provides stable trait-like measures of differences in the perceptual, cognitive, emotional, and social functioning of individuals. The rsFC of the prefrontal cortex is hypothesized to mediate a person's rational self-government, as is also measured by personality, so we tested whether its connectivity networks account for vulnerability to psychosis and related personality configurations. Young adults were recruited as outpatients or controls from the same communities around psychiatric clinics. Healthy controls (n = 30) and clinically stable outpatients with bipolar disorder (n = 35) or schizophrenia (n = 27) were diagnosed by structured interviews, and then were assessed with standardized protocols of the Human Connectome Project. Data-driven clustering identified five groups of patients with distinct patterns of rsFC regardless of diagnosis. These groups were distinguished by rsFC networks that regulate specific biopsychosocial aspects of psychosis: sensory hypersensitivity, negative emotional balance, impaired attentional control, avolition, and social mistrust. The rsFc group differences were validated by independent measures of white matter microstructure, personality, and clinical features not used to identify the subjects. We confirmed that each connectivity group was organized by differential collaborative interactions among six prefrontal and eight other automatically-coactivated networks. The temperament and character traits of the members of these groups strongly accounted for the differences in rsFC between groups, indicating that configurations of rsFC are internal representations of personality organization. These representations involve weakly self-regulated emotional drives of fear, irrational desire, and mistrust, which predispose to psychopathology. However, stable outpatients with different diagnoses (bipolar or schizophrenic psychoses) were highly similar in rsFC and personality. This supports a diathesis-stress model in which different complex adaptive systems regulate predisposition (which is similar in stable outpatients despite diagnosis) and stress-induced clinical dysfunction (which differs by diagnosis).

The neurobiology of obsessive-compulsive personality disorder: a systematic review

BACKGROUND

A number of recent investigations have focused on the neurobiology of obsessive-compulsive personality disorder (OCPD). However, there have been few reviews of this literature with no detailed model proposed. We therefore undertook a systematic review of these investigations, aiming to map the available evidence and investigate whether it is possible to formulate a detailed model of the neurobiology of OCPD.

METHODS

OCPD can be considered from both categorical and dimensional perspectives. An electronic search was therefore conducted using terms that would address not only OCPD as a category, but also related constructs, such as perfectionism, that would capture research on neuropsychology, neuroimaging, neurochemistry, and neurogenetics.

RESULTS

A total of 1059 articles were retrieved, with 87 ultimately selected for abstract screening, resulting in a final selection of 49 articles focusing on neurobiological investigations relevant to OCPD. Impaired executive function and cognitive inflexibility are common neuropsychological traits in this condition, and suggest that obsessive-compulsive disorder (OCD) and OCPD may lie on a continuum. However, neuroimaging studies in OCPD indicate the involvement of specific neurocircuitry, including the precuneus and amygdala, and so suggest that OCD and OCPD may have important differences. Although OCPD has a heritable component, we found no well-powered genetic studies of OCPD.

CONCLUSION

Although knowledge in this area has advanced, there are insufficient data on which to base a comprehensive model of the neurobiology of OCPD. Given the clinical importance of OCPD, further work to understand the mechanisms that underpin this condition is warranted.

Inter-species cortical registration between macaques and humans using a functional network property under a spherical demons framework

Systematic evaluation of cortical differences between humans and macaques calls for inter-species registration of the cortex that matches homologous regions across species. For establishing homology across brains, structural landmarks and biological features have been used without paying sufficient attention to functional homology. The present study aimed to determine functional homology between the human and macaque cortices, defined in terms of functional network properties, by proposing an iterative functional network-based registration scheme using surface-based spherical demons. The functional connectivity matrix of resting-state functional magnetic resonance imaging (rs-fMRI) among cortical parcellations was iteratively calculated for humans and macaques. From the functional connectivity matrix, the functional network properties such as principal network components were derived to estimate a deformation field between the human and macaque cortices. The iterative registration procedure updates the parcellation map of macaques, corresponding to the human connectome project’s multimodal parcellation atlas, which was used to derive the macaque’s functional connectivity matrix. To test the plausibility of the functional network-based registration, we compared cortical registration using structural versus functional features in terms of cortical regional areal change. We also evaluated the interhemispheric asymmetry of regional area and its inter-subject variability in humans and macaques as an indirect validation of the proposed method. Higher inter-subject variability and interhemispheric asymmetry were found in functional homology than in structural homology, and the assessed asymmetry and variations were higher in humans than in macaques. The results emphasize the significance of functional network-based cortical registration across individuals within a species and across species.

Temperament, character and decision-making characteristics of patients with major depressive disorder following a suicide attempt

BACKGROUND

Multiple psychological factors of suicidal behaviour have been identified so far; however, little is known about state-dependent alterations and the interplay of the most prominent components in a suicidal crisis. Thus, the combined effect of particular personality characteristics and decision-making performance was observed within individuals who recently attempted suicide during a major depressive episode.

METHODS

Fifty-nine medication-free major depressed patients with a recent suicide attempt (within 72 h) and forty-five healthy control individuals were enrolled in this cross-sectional study. Temperament and character factors, impulsivity and decision-making performance were assessed. Statistical analyses aimed to explore between-group differences and the most powerful contributors to suicidal behaviour during a depressive episode.

RESULTS

Decision-making and personality differences (i.e. impulsivity, harm avoidance, self-directedness, cooperativeness and transcendence) were observed between the patient and the control group. Among these variables, decision-making, harm avoidance and self-directedness were shown to have the strongest impact on a recent suicide attempt of individuals with a diagnosis of major depressive disorder according to the results of the binary logistic regression analysis. The model was significant, adequately fitted the data and correctly classified 79.8% of the cases.

CONCLUSIONS

The relevance of deficient decision-making, high harm avoidance and low self-directedness was modelled in the case of major depressed participants with a recent suicide attempt; meaning that these individuals can be described with the myopia for future consequences, a pessimistic, anxious temperament; and a character component resulting in the experience of aimlessness and helplessness. Further studies that use a within-subject design should identify and confirm additional characteristics specific to the suicidal mind.

Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics

The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed‐effect group‐level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior. MEM and its energy landscape properties showed high subject specificity comparable to that of functional connectivity. Canonical correlation analysis identified canonical variables for MEM highly associated with cognitive scores. Inter‐individual variations of cognitive scores were also reflected in energy landscape properties such as energies, occupation times, and basin sizes at local minima. We conclude that BMEM provides an efficient method to characterize dynamic properties of individuals using energy landscape analysis of individual brain states.

Novelty seeking is associated with increased body weight and orbitofrontal grey matter volume reduction

Novelty seeking (NS) has previously been identified as a personality trait that is associated with elevated body mass index (BMI) and obesity. Of note, both obesity and reduced impulse control - a core feature of NS - have previously been associated with grey matter volume (GMV) reductions in the orbitofrontal cortex (OFC). Yet, it remains unknown, if body weight-related grey matter decline in the OFC might be explained by higher levels of NS. To address this question, we studied associations between NS, BMI and brain structure in 355 healthy subjects. Brain images were pre-processed using voxel-based morphometry (VBM). BMI was calculated from self-reported height and weight. The Tridimensional Personality Questionnaire (TPQ) was used to assess NS. NS and BMI were associated positively (r = .137, p = .01) with NS being a significant predictor of BMI (B = 0.172; SE B = 0.05; ß = 0.184; p = 0.001). Significant associations between BMI and GMV specifically in the OFC (x = -44, y = 56, z = -2, t(350) = 4.34, k = 5, p_FWE = 0.011) did not uphold when correcting for NS in the model. In turn, a significant negative association between NS and OFC GMV was found independent of BMI (x = -2, y = 48, z = -10, t(349) = 4.42, k = 88, p_FWE = 0.008). Body mass-related grey matter decrease outside the OFC could not be attributed to NS. Our results suggest that body-weight-related orbitofrontal grey matter reduction can at least partly be linked to higher levels of NS. Given the pivotal role of the OFC in overweight as well as cognitive domains such as impulse inhibition, executive control and reward processing, its association with NS seems to provide a tenable neurobiological correlate for future research.

Re-visiting Riemannian geometry of symmetric positive definite matrices for the analysis of functional connectivity

Common representations of functional networks of resting state fMRI time series, including covariance, precision, and cross-correlation matrices, belong to the family of symmetric positive definite (SPD) matrices forming a special mathematical structure called Riemannian manifold. Due to its geometric properties, the analysis and operation of functional connectivity matrices may well be performed on the Riemannian manifold of the SPD space. Analysis of functional networks on the SPD space takes account of all the pairwise interactions (edges) as a whole, which differs from the conventional rationale of considering edges as independent from each other. Despite its geometric characteristics, only a few studies have been conducted for functional network analysis on the SPD manifold and inference methods specialized for connectivity analysis on the SPD manifold are rarely found. The current study aims to show the significance of connectivity analysis on the SPD space and introduce inference algorithms on the SPD manifold, such as regression analysis of functional networks in association with behaviors, principal geodesic analysis, clustering, state transition analysis of dynamic functional networks and statistical tests for network equality on the SPD manifold. We applied the proposed methods to both simulated data and experimental resting state fMRI data from the human connectome project and argue the importance of analyzing functional networks under the SPD geometry. All the algorithms for numerical operations and inferences on the SPD manifold are implemented as a MATLAB library, called SPDtoolbox, for public use to expediate functional network analysis on the right geometry.

Gait pattern analysis and clinical subgroup identification: a retrospective observational study

To identify basic gait features and abnormal gait patterns that are common to different neurological or musculoskeletal conditions, such as cerebral stroke, Parkinsonian disorders, radiculopathy, and musculoskeletal pain.In this retrospective study, temporal-spatial, kinematic, and kinetic gait parameters were analyzed in 424 patients with hemiplegia after stroke, 205 patients with Parkinsonian disorders, 216 patients with radiculopathy, 167 patients with musculoskeletal pain, and 316 normal controls (total, 1328 subjects). We assessed differences according to the condition and used a community detection algorithm to identify subgroups within each condition. Additionally, we developed a prediction model for subgroup classification according to gait speed and maximal hip extension in the stance phase.The main findings can be summarized as follows. First, there was an asymmetric decrease of the knee/ankle flexion angles in hemiplegia and a marked reduction of the hip/knee range of motion with increased moment in Parkinsonian disorders. Second, three abnormal gait patterns, including fast gait speed with adequate maximal hip extension, fast gait speed with inadequate maximal hip extension, and slow gait speed, were found throughout the conditions examined. Third, our simple prediction model based on gait speed and maximal hip extension angle was characterized by a high degree of accuracy in predicting subgroups within a condition.Our findings suggest the existence of specific gait patterns within and across conditions. Our novel subgrouping algorithm can be employed in routine clinical settings to classify abnormal gait patterns in various neurological disorders and guide the therapeutic approach and monitoring.

Neural basis of romantic partners' decisions about participation in leisure activity

Leisure activity is one of key ingredients for individual happiness and life satisfaction. Enjoying leisure activity with one’s partner can increase marital satisfaction. This study aimed to identify the neural basis of making decisions on participation in a leisure activity with one’s romantic partner as well as the relationship between leisure activity and satisfaction with life. Thirty-seven soon-to-be married heterosexual couples were participated in functional MRI while deciding participation in specific leisure activities in the individual, partner, with-friend, and with-partner conditions. We constructed analysis of variance models and investigated couple characteristics such as personality similarity, leisure activity matching rate, and spatial similarity in the bilateral frontoparietal network. The results showed decreased activity in the bilateral hippocampus during the task in the with-partner condition. Individual leisure activity was correlated with quality of life in males, whereas participation in leisure activity might require more cognitive loading on the dorsolateral prefrontal cortex in females. The leisure activity matching rate was correlated with courtship period, personality similarity, and spatial similarity of the right frontoparietal network during the task. These findings suggest that although there are different activation pattern in making decisions on leisure activity between romantic couples, spatial similarity of the partner’s social brain networks may be a marker that predicts how well the couple enjoys leisure activity together. In addition, our couples’ data analysis provides a scientific basis for the saying that romantic couples become more similar the longer they are together.

Opposing patterns of neuronal variability in the sensorimotor network mediate cyclothymic and depressive temperaments

Affective temperaments have been described since the early 20th century and may play a central role in psychiatric illnesses, such as bipolar disorder (BD). However, the neuronal basis of temperament is still unclear. We investigated the relationship of temperament with neuronal variability in the resting state signal-measured by fractional standard deviation (fSD) of Blood-Oxygen-Level Dependent signal-of the different large-scale networks, that is, sensorimotor network (SMN), along with default-mode, salience and central executive networks, in standard frequency band (SFB) and its sub-frequencies slow4 and slow5, in a large sample of healthy subject (HC, n = 109), as well as in the various temperamental subgroups (i.e., cyclothymic, hyperthymic, depressive, and irritable). A replication study on an independent dataset of 121 HC was then performed. SMN fSD positively correlated with cyclothymic z-score and was significantly increased in the cyclothymic temperament compared to the depressive temperament subgroups, in both SFB and slow4. We replicated our findings in the independent dataset. A relationship between cyclothymic temperament and neuronal variability, an index of intrinsic neuronal activity, in the SMN was found. Cyclothymic and depressive temperaments were associated with opposite changes in the SMN variability, resembling changes previously described in manic and depressive phases of BD. These findings shed a novel light on the neural basis of affective temperament and also carry important implications for the understanding of a potential dimensional continuum between affective temperaments and BD, on both psychological and neuronal levels.

Testing Trait-State Isomorphism in a New Domain: An Exploratory Manipulation of Openness to Experience

The trait-state isomorphism hypothesis holds that personality traits and states (i.e., trait-related behavior) are characterized by similar outcomes (Fleeson, 2001). Openness is associated with creative thinking, personal growth, and positive affect. Engaging in behavior associated with openness has also been found to covary with feelings of authenticity. In the present experiment, participants (N = 210) completed a pre-test assessment, five daily exercises designed to either be inert (control condition) or engage the behaviors and cognitions associated with openness (experimental condition), a post-test assessment, and a 2 week follow up assessment. Results supported the isomorphism hypothesis for positive affect but not creative thinking ability or personal growth. Furthermore, open behavior was only associated with authenticity for individuals high on trait openness.

Connectome-based individualized prediction of temperament trait scores

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

Network Neuroscience and Personality

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

Temperament and Character Inventory in Bipolar Disorder versus Healthy Controls and Modulatory Effects of 3 Key Functional Gene Variants

BACKGROUND

Bipolar disorder (BD) has been associated with temperamental and personality traits, although the relationship is still to be fully elucidated. Several studies investigated the genetic basis of temperament and character, identifying catechol-O-methyltransferase (COMT), brain derived neurotrophic factor (BDNF), and serotonin transporter (5-HTT) gene variants as strong candidates.

METHODS

In the GECO-BIP study, 125 BD patients and 173 HC were recruited. Subjects underwent to a detailed assessment and the temperament and character inventory 125 items (TCI) was administrated. Three functional genetic variants within key candidate genes (COMT rs4680, BDNF rs6265, and the serotonin-transporter-linked polymorphic region (5-HTTLPR)) were genotyped. Univariate and multivariate analyses were performed.

RESULTS

Compared to HC, BD patients showed higher scores in novelty seeking (NS; p = 0.001), harm avoidance (HA; p < 0.001), and self transcendence (St; p < 0.001), and lower scores in self directness (p < 0.001) and cooperativeness (p < 0.001) TCI dimensions. Concerning the genetic analyses, COMT rs4680 was associated with NS in the total sample (p = 0.007) and in the male subsample (p = 0.022). When performing the analysis in the HC and BD samples, the association was confirmed only in HC (p = 0.012), and in the HC male subgroup in particular (p = 0.004). BDNF rs6265 was associated with St in the BD group (p = 0.017).

CONCLUSION

COMT rs4680 may modulate NS in males in the general population. This effect was not detected in BD patients, probably because BD alters the neurobiological basis of some TCI dimensions. BDNF rs6265 seems to modulate St TCI dimension only in BD patients, possibly modulating the previously reported association between rs6265 and BD treatment response. Further studies are needed to confirm our findings.

Novel subgroups of attention-deficit/hyperactivity disorder identified by topological data analysis and their functional network modular organizations

Attention-deficit/hyperactivity disorder (ADHD) is a clinically heterogeneous condition and identification of clinically meaningful subgroups would open up a new window for personalized medicine. Thus, we aimed to identify new clinical phenotypes in children and adolescents with ADHD and to investigate whether neuroimaging findings validate the identified phenotypes. Neuroimaging and clinical data from 67 children with ADHD and 62 typically developing controls (TDCs) from the ADHD-200 database were selected. Clinical measures of ADHD symptoms and intelligence quotient (IQ) were used as input features into a topological data analysis (TDA) to identify ADHD subgroups within our sample. As external validators, graph theoretical measures obtained from the functional connectome were compared to address the biological meaningfulness of the identified subtypes. The TDA identified two unique subgroups of ADHD, labelled as mild symptom ADHD (mADHD) and severe symptom ADHD (sADHD). The output topology shape was repeatedly observed in the independent validation dataset. The graph theoretical analysis showed a decrease in the degree centrality and PageRank in the bilateral posterior cingulate cortex in the sADHD group compared with the TDC group. The mADHD group showed similar patterns of intra- and inter-module connectivity to the sADHD group. Relative to the TDC group, the inter-module connectivity between the default mode network and executive control network were significantly increased in the sADHD group but not in the mADHD group. Taken together, our results show that the data-driven TDA is potentially useful in identifying objective and biologically relevant disease phenotypes in children and adolescents with ADHD.

Individuality manifests in the dynamic reconfiguration of large-scale brain networks during movie viewing

Individuality, the uniqueness that distinguishes one person from another, may manifest as diverse rearrangements of functional connectivity during heterogeneous cognitive demands; yet, the neurobiological substrates of individuality, reflected in inter-individual variations of large-scale functional connectivity, have not been fully evidenced. Accordingly, we explored inter-individual variations of functional connectivity dynamics, subnetwork patterns and modular architecture while subjects watched identical video clips designed to induce different arousal levels. How inter-individual variations are manifested in the functional brain networks was examined with respect to four contrasting divisions: edges within the anterior versus posterior part of the brain, edges with versus without corresponding anatomically-defined structural pathways, inter- versus intra-module connections, and rich club edge types. Inter-subject variation in dynamic functional connectivity occurred to a greater degree within edges localized to anterior rather than posterior brain regions, without adhering to structural connectivity, between modules as opposed to within modules, and in weak-tie local edges rather than strong-tie rich-club edges. Arousal level significantly modulates inter-subject variability in functional connectivity, edge patterns, and modularity, and particularly enhances the synchrony of rich-club edges. These results imply that individuality resides in the dynamic reconfiguration of large-scale brain networks in response to a stream of cognitive demands.

New evidence of heterogeneity in social anxiety disorder: defining two qualitatively different personality profiles taking into account clinical, environmental and genetic factors

PURPOSE

To study qualitatively different subgroups of social anxiety disorder (SAD) based on harm avoidance (HA) and novelty seeking (NS) dimensions.

METHOD

One hundred and forty-two university students with SAD (SCID-DSM-IV) were included in the study. The temperament dimensions HA and NS from the Cloninger's Temperament and Character Inventory were subjected to cluster analysis to identify meaningful subgroups. The identified subgroups were compared for sociodemographics, SAD severity, substance use, history of suicide and self-harm attempts, early life events, and two serotonin transporter gene polymorphisms (5-HTTLPR and STin2.VNTR).

RESULTS

Two subgroups of SAD were identified by cluster analysis: a larger (61% of the sample) inhibited subgroup of subjects with "high-HA/low-NS", and a smaller (39%) atypical impulsive subgroup with high-moderate HA and NS. The two groups did not differ in social anxiety severity, but did differ in history of lifetime impulsive-related-problems. History of suicide attempts and self-harm were as twice as frequent in the impulsive subgroup. Significant differences were observed in the pattern of substance misuse. Whereas subjects in the inhibited subgroup showed a greater use of alcohol (P=0.002), subjects in the impulsive subgroup showed a greater use of substances with a high-sensation-seeking profile (P<0.001). The STin2.VNTR genotype frequency showed an inverse distribution between subgroups (P=0.005).

CONCLUSIONS

Our study provides further evidence for the presence of qualitatively different SAD subgroups and the propensity of a subset of people with SAD to exhibit impulsive, high-risk behaviors.