Surface-based morphometry reveals the neuroanatomical basis of the five-factor model of personality

Behavioral adjustment moderates the effect of neuroticism on brain volume relative to intracranial volume

OBJECTIVE

The present study examined whether the effect of neuroticism on brain structure is moderated by behavioral adjustment.

BACKGROUND

Neuroticism is widely thought to be harmful to health. However, recent work using proinflammatory biomarkers showed that this effect depends on behavioral adjustment, the willingness and ability to adjust and cope with environmental contingencies, such as different opinions of others or unpredictable life situations. Here, we sought to extend this observation to "brain health" by testing total brain volume (TBV).

METHOD

Using a community sample of 125 Americans, we examined structural magnetic resonance imaging of the brain and quantified TBV. We tested whether the effect of neuroticism on TBV was moderated by behavioral adjustment, net of intracranial volume, age, sex, educational achievement, and race.

RESULTS

Behavioral adjustment significantly moderated the effect of neuroticism on TBV, such that neuroticism was associated with lower TBV only when behavioral adjustment was low. There was no such effect when behavioral adjustment was high.

CONCLUSION

The present findings suggest that neuroticism is not debilitating to those who constructively cope with stress. Implications are further discussed.

Abnormalities of brain structure and function in cervical spondylosis: a multi-modal voxel-based meta-analysis

Background

Previous neuroimaging studies have revealed structural and functional brain abnormalities in patients with cervical spondylosis (CS). However, the results are divergent and inconsistent. Therefore, the present study conducted a multi-modal meta-analysis to investigate the consistent structural and functional brain alterations in CS patients.

Methods

A comprehensive literature search was conducted in five databases to retrieve relevant resting-state functional magnetic resonance imaging (rs-fMRI), structural MRI and diffusion tensor imaging (DTI) studies that measured brain functional and structural differences between CS patients and healthy controls (HCs). Separate and multimodal meta-analyses were implemented, respectively, by employing Anisotropic Effect-size Signed Differential Mapping software.

Results

13 rs-fMRI studies that used regional homogeneity, amplitude of low-frequency fluctuations (ALFF) and fractional ALFF, seven voxel-based morphometry (VBM) studies and one DTI study were finally included in the present research. However, no studies on surface-based morphometry (SBM) analysis were included in this research. Due to the insufficient number of SBM and DTI studies, only rs-fMRI and VBM meta-analyses were conducted. The results of rs-fMRI meta-analysis showed that compared to HCs, CS patients demonstrated decreased regional spontaneous brain activities in the right lingual gyrus, right middle temporal gyrus (MTG), left inferior parietal gyrus and right postcentral gyrus (PoCG), while increased activities in the right medial superior frontal gyrus, bilateral middle frontal gyrus and right precuneus. VBM meta-analysis detected increased GMV in the right superior temporal gyrus (STG) and right paracentral lobule (PCL), while decreased GMV in the left supplementary motor area and left MTG in CS patients. The multi-modal meta-analysis revealed increased GMV together with decreased regional spontaneous brain activity in the left PoCG, right STG and PCL among CS patients.

Conclusion

This meta-analysis revealed that compared to HCs, CS patients had significant alterations in GMV and regional spontaneous brain activity. The altered brain regions mainly included the primary visual cortex, the default mode network and the sensorimotor area, which may be associated with CS patients' symptoms of sensory deficits, blurred vision, cognitive impairment and motor dysfunction. The findings may contribute to understanding the underlying pathophysiology of brain dysfunction and provide references for early diagnosis and treatment of CS.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, CRD42022370967.

Meta-analysis reveals a reduced surface area of the amygdala in individuals with attention deficit/hyperactivity disorder

Despite the reported lack of structural alterations in the amygdala of individuals with attention deficit/hyperactivity disorder (ADHD) in previous meta-analyses, subsequent observational studies produced conflicting results. Through incorporating the updated data from observational studies on structural features of the amygdala in ADHD, the primary goal of this study was to examine the anatomical differences in amygdala between subjects with ADHD and their neurotypical controls. Using the appropriate keyword strings, we searched the PubMed, Embase, and Web of Science databases for English articles from inception to February 2022. Eligibility criteria included observational studies comparing the structure of the amygdala between ADHD subjects and their comparators using magnetic resonance imaging (MRI). Subgroup analyses were conducted focusing on the amygdala side, as well as the use of different scanners and approach to segmentation. The effects of other continuous variables, such as age, intelligence quotient, and male percentage, on amygdala size were also investigated. Of the 5703 participants in 16 eligible studies, 2928 were diagnosed with ADHD. Compared with neurotypical controls, subjects with ADHD had a smaller amygdala surface area (particularly in the left hemisphere) but without a significant difference in volume between the two groups. Subgroup analysis of MRI scanners and different approaches to segmentation showed no statistically significant difference. There was no significant correlation between continuous variables and amygdala size. Our results showed consistent surface morphological alterations of the amygdala, in particular on the left side, in subjects with ADHD. However, the preliminary findings based on the limited data available for analysis warrant future studies for verification.

The neurobiology of openness as a personality trait

Openness is a multifaceted behavioral disposition that encompasses personal, interpersonal, and cultural dimensions. It has been suggested that the interindividual variability in openness as a personality trait is influenced by various environmental and genetic factors, as well as differences in brain functional and structural connectivity patterns along with their various associated cognitive processes. Alterations in degree of openness have been linked to several aspects of health and disease, being impacted by both physical and mental health, substance use, and neurologic conditions. This review aims to explore the current state of knowledge describing the neurobiological basis of openness and how individual differences in openness can manifest in brain health and disease.

Alterations of gut microbiota are associated with brain structural changes in the spectrum of Alzheimer's disease: the SILCODE study in Hainan cohort

Background

The correlation between gut microbiota and Alzheimer's disease (AD) is increasingly being recognized by clinicians. However, knowledge about the gut-brain-cognition interaction remains largely unknown.

Methods

One hundred and twenty-seven participants, including 35 normal controls (NCs), 62 with subjective cognitive decline (SCD), and 30 with cognitive impairment (CI), were included in this study. The participants underwent neuropsychological assessments and fecal microbiota analysis through 16S ribosomal RNA (rRNA) Illumina Miseq sequencing technique. Structural MRI data were analyzed for cortical anatomical features, including thickness, sulcus depth, fractal dimension, and Toro's gyrification index using the SBM method. The association of altered gut microbiota among the three groups with structural MRI metrics and cognitive function was evaluated. Furthermore, co-expression network analysis was conducted to investigate the gut-brain-cognition interactions.

Results

The abundance of Lachnospiraceae, Lachnospiracea_incertae_sedis, Fusicatenibacter, and Anaerobutyricum decreased with cognitive ability. Rikenellaceae, Odoribacteraceae, and Alistipes were specifically enriched in the CI group. Mediterraneibacter abundance was correlated with changes in brain gray matter and cerebrospinal fluid volume (p = 0.0214, p = 0.0162) and significantly with changes in cortical structures in brain regions, such as the internal olfactory area and the parahippocampal gyrus. The three colonies enriched in the CI group were positively correlated with cognitive function and significantly associated with changes in cortical structure related to cognitive function, such as the precuneus and syrinx gyrus.

Conclusion

This study provided evidence that there was an inner relationship among the altered gut microbiota, brain atrophy, and cognitive decline. Targeting the gut microbiota may be a novel therapeutic strategy for early AD.

Premorbid Personality Traits as Risk Factors for Behavioral Addictions: A Systematic Review of a Vulnerability Hypothesis

The debate on personality structure and behavioral addictions is an outstanding issue. According to some authors, behavioral addictions could arise from a premorbid personality, while for others, it could result from a pathological use of technological tools. The current study aims to investigate whether, in the latest literature, personality traits have been identified as predictors of behavioral addictions. A literature search was conducted under the PRISMA methodology, considering the most relevant studies of the five-factor model from the past 10 years. Overall, most studies on addiction, personality traits, and personality genetics proved that behavioral addiction may be an epiphenomenon of a pre-existing personality structure, and that it more easily occurs in vulnerable subjects with emotional instability, negative affects, and unsatisfactory relationships with themselves, others, and events. Such neurotic personality structure was common to any addictive behavior, and was the main risk factor for both substance and behavioral addictions. Therefore, in clinical and educational contexts, it becomes crucial to primarily focus on the vulnerability factors, at-risk personality traits, and protective and moderating traits such as extroversion, agreeableness, conscientiousness, and openness to experience; meanwhile, treatment of behavioral addictions is frequently focused on overt pathological behaviors.

Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models

Background

The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques.

Objective

To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia.

Materials and methods

We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia.

Results

In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases.

Conclusion

The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

An ecological animal model of subthreshold depression in adolescence: behavioral and resting state 18F-FDG PET imaging characterization

The different depressive disorders that exist can take root at adolescence. For instance, some functional and structural changes in several brain regions have been observed from adolescence in subjects that display either high vulnerability to depressive symptoms or subthreshold depression. For instance, adolescents with depressive disorder have been shown to exhibit hyperactivity in hippocampus, amygdala and prefrontal cortex as well as volume reductions in hippocampus and amygdala (prefrontal cortex showing more variable results). However, no animal model of adolescent subthreshold depression has been developed so far. Our objective was to design an animal model of adolescent subthreshold depression and to characterize the neural changes associated to this phenotype. For this purpose, we used adolescent Swiss mice that were evaluated on 4 tests assessing cognitive abilities (Morris water maze), anhedonia (sucrose preference), anxiety (open-field) and stress-coping strategies (forced swim test) at postnatal day (PND) 28-35. In order to identify neural alterations associated to behavioral profiles, we assessed brain resting state metabolic activity in vivo using ¹⁸F-FDG PET imaging at PND 37. We selected three profiles of mice distinguished in a composite Z-score computed from performances in the behavioral tests: High, Intermediate and Low Depressive Risk (HDR, IDR and LDR). Compared to both IDR and LDR, HDR mice were characterized by passive stress-coping behaviors, low cognition and high anhedonia and anxiety and were associated with significant changes of ¹⁸F-FDG uptakes in several cortical and subcortical areas including prelimbic cortex, infralimbic cortex, nucleus accumbens, amygdala, periaqueductal gray and superior colliculus, all displaying higher metabolic activity, while only the thalamus was associated with lower metabolic activity (compared to IDR). LDR displayed an opposing behavioral phenotype and were associated with significant changes of ¹⁸F-FDG uptakes in the dorsal striatum and thalamus that both exhibited markedly lower metabolic activity in LDR. In conclusion, our study revealed changes in metabolic activities that can represent neural signatures for behavioral profiles predicting subthreshold depression at adolescence in a mouse model.

Empirical facts from search for replicable associations between cortical thickness and psychometric variables in healthy adults

The study of associations between inter-individual differences in brain structure and behaviour has a long history in psychology and neuroscience. Many associations between psychometric data, particularly intelligence and personality measures and local variations of brain structure have been reported. While the impact of such reported associations often goes beyond scientific communities, resonating in the public mind, their replicability is rarely evidenced. Previously, we have shown that associations between psychometric measures and estimates of grey matter volume (GMV) result in rarely replicated findings across large samples of healthy adults. However, the question remains if these observations are at least partly linked to the multidetermined nature of the variations in GMV, particularly within samples with wide age-range. Therefore, here we extended those evaluations and empirically investigated the replicability of associations of a broad range of psychometric variables and cortical thickness in a large cohort of healthy young adults. In line with our observations with GMV, our current analyses revealed low likelihood of significant associations and their rare replication across independent samples. We here discuss the implications of these findings within the context of accumulating evidence of the general poor replicability of structural-brain-behaviour associations, and more broadly of the replication crisis.

Early alterations in cortical morphology after neoadjuvant chemotherapy in breast cancer patients: A longitudinal magnetic resonance imaging study

There is growing evidence that chemotherapy may have a significant impact on the brains of breast cancer patients, causing changes in cortical morphology. However, early morphological alterations induced by chemotherapy in breast cancer patients are unclear. To investigate the patterns of those alterations, we compared female breast cancer patients (n = 45) longitudinally before (time point 0, TP0) and after (time point 1, TP1) the first cycle of neoadjuvant chemotherapy, using voxel‐based morphometry (VBM) and surface‐based morphometry (SBM). VBM and SBM alteration data underwent correlation analysis. We also compared cognition‐related neuropsychological tests in the breast cancer patients between TP0 and TP1. Reductions in gray matter volume, cortical thickness, sulcal depth, and gyrification index were found in most brain areas, while increments were found to be mainly concentrated in and around the hippocampus. Reductions of fractal dimension mainly occurred in the limbic and occipital lobes, while increments mainly occurred in the anterior and posterior central gyrus. Significant correlations were found between altered VBM and altered SBM mainly in the bilateral superior frontal gyrus. We found no significant differences in the cognition‐related neuropsychological tests before and after chemotherapy. The altered brain regions are in line with those associated with impaired cognitive domains in previous studies. We conclude that breast cancer patients showed widespread morphological alterations soon after neoadjuvant chemotherapy, despite an absence of cognitive impairments. The affected brain regions may indicate major targets of early brain damage after chemotherapy.

Relationship between impulsivity, uncontrolled eating and body mass index: a hierarchical model

BACKGROUND

Impulsivity increases the risk for obesity and weight gain. However, the precise role of impulsivity in the aetiology of overeating behavior and obesity is currently unknown. Here we examined the relationships between personality-related measures of impulsivity, Uncontrolled Eating, body mass index (BMI), and longitudinal weight changes. In addition, we analyzed the associations between general impulsivity domains and cortical thickness to elucidate brain vulnerability factors related to weight gain.

METHODS

Students (N = 2318) in their first year of university-a risky period for weight gain-completed questionnaire measures of impulsivity and eating behavior at the beginning of the school year. We also collected their weight at the end of the term (N = 1177). Impulsivity was divided into three factors: stress reactivity, reward sensitivity and lack of self-control. Using structural equation models, we tested a hierarchical relationship, in which impulsivity traits were associated with Uncontrolled Eating, which in turn predicted BMI and weight change. Seventy-one participants underwent T1-weighted MRI to investigate the correlation between impulsivity and cortical thickness.

RESULTS

Impulsivity traits showed positive correlations with Uncontrolled Eating. Higher scores in Uncontrolled Eating were in turn associated with higher BMI. None of the impulsivity-related measurements nor Uncontrolled Eating were correlated with longitudinal weight gain. Higher stress sensitivity was associated with increased cortical thickness in the superior temporal gyrus. Lack of self-control was positively associated with increased thickness in the superior medial frontal gyrus. Finally, higher reward sensitivity was associated with lower thickness in the inferior frontal gyrus.

CONCLUSION

The present study provides a comprehensive characterization of the relationships between different facets of impulsivity and obesity. We show that differences in impulsivity domains might be associated with BMI via Uncontrolled Eating. Our results might inform future clinical strategies aimed at fostering self-control abilities to prevent and/or treat unhealthy weight gain.

Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain

We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.

Using Keeper Questionnaires to Capture Zoo-Housed Tiger (Panthera tigris) Personality: Considerations for Animal Management

Individual personalities affect animal experiences of zoo environments, impact on an animal’s coping ability and have potential implications for welfare. Keeper assessments have been identified as a quick and reliable way of capturing data on personality in a range of species and have practical application in improving animal welfare on an individual level. Despite widespread recognition of the importance of animal personality within a zoo environment, there is a paucity of research into tiger personality and the potential impact of this on tiger experiences within zoos. This research investigated the personality of 34 tigers (19 Amur and 15 Sumatran) across 14 facilities in the UK using keeper ratings and identified changes keepers made in animal husbandry to support tiger welfare. Reliability across keepers (n = 49) was established for nine adjectives and a principal component analysis identified three personality components: ‘anxious’, ‘quiet’ and ‘sociable’. When subspecies were combined, there was no relationship between tiger scores on the personality components and age or sex of tigers (p > 0.05). Subspecies of tiger was not related to scores on the ‘quiet’ or ‘sociable’ components (p > 0.05). Sumatran tigers scored more highly than Amur tigers on the ‘anxious’ component (mean ± SD, Sumatran: 3.0 ± 1.7, Amur: 1.8 ± 0.6, p < 0.05). Analysis within subspecies found that male Amur tigers were more sociable than females (mean ± SD, males: 5.5 ± 0.707; females: 4.15 ± 0.55). Amur tiger age was also negatively correlated with scores on the sociable personality component (R = −0.742, p < 0.05). No significant differences were seen in Sumatran tigers. Keepers reported a number of changes to husbandry routines based on their perceptions of their tigers’ personality/needs. However, there was no significant relationship between these changes and tiger personality scores (p > 0.05). Despite significant evolutionary differences between Amur and Sumatran tigers, there are no subspecies specific guidelines for zoo tigers. This research has highlighted the potential for these two subspecies to display personality differences and we advocate further research into this area. Specifically, we highlight a need to validate the relationship between tiger personality, management protocols and behavioural and physiological metrics of welfare. This will enable a fuller understanding of the impact of personality on zoo tiger experiences and will enable identification of evidence-based best practice guidelines.

Brain Plasticity Reflects Specialized Cognitive Development Induced by Musical Training

Learning a musical instrument requires a long period of training and might induce structural and functional changes in the brain. Previous studies have shown brain plasticity resulting from training with a musical instrument. However, these studies did not distinguish the effects on brain plasticity of specific musical instruments as they examined the brain of musicians who had learned a single musical instrument/genre and did not control for confounding factors, such as common or interactive effects involved in music training. To address this research gap, the present work investigated musicians who had experience with both a piano and a wind instrument, for example, flute, trumpet, clarinet etc. By examining the difference between the 2 musical instruments in the same subject, we avoided the effects common to all musical instruments and the confounding factors. Therefore, we identified several high-tier brain areas displaying a brain plasticity specific to each musical instrument. Our findings show that learning a musical instrument might result in the development of high cognitive functions reflecting the skills/abilities unique to the instrument played.

Neuroanatomical and functional substrates of the greed personality trait

Greedy individuals often exhibit more impulsive decision-making and short-sighted behaviors. It has been assumed that altered reward circuitry and prospection network is associated with greed personality trait (GPT). In this study, we first explored the morphological characteristics (i.e., gray matter volume; GMV) of GPT combined with univariate and multivariate pattern analysis (MVPA) approaches. Second, we adopted a revised version of inter-temporal choice task and independently manipulated the amount and delay time of future rewards. Using brain-imaging design, reward- and prospection-related brain activations were assessed and their associations with GPT were further examined. The MVPA results showed that GPT was associated with the GMVs in the right lateral frontal pole cortex, left ventromedial prefrontal cortex, right lateral occipital cortex, and right occipital pole. Additionally, we observed that the amount-relevant brain activations (responding to reward circuitry) in the lateral orbitofrontal cortex were negatively associated with individual's variability in GPT scores, whereas the delay time-relevant brain activations (responding to prospection network system) in the dorsolateral prefrontal cortex, dorsomedial prefrontal cortex, superior parietal lobule, and anterior cingulate cortex were positively associated with individual's variability in GPT scores. These findings not only provide novel insights into the neuroanatomical substrates underlying the human dispositional greed, but also suggest the critical roles of reward and prospection processing on the greed.

Interactions between Personality, Depression, Anxiety and Cognition to Understand Early Stage of Alzheimer's Disease

The multifaceted nature of Alzheimer's disease (AD) and Mild cognitive impairment (MCI) can lead to wide inter-individual differences in disease manifestation in terms of brain pathology and cognition. The lack of understanding of phenotypic diversity in AD arises from a difficulty in understanding the integration of different levels of network organization (i.e. genes, neurons, synapses, anatomical regions, functions) and in inclusion of other information such as neuropsychiatric characteristics, personal history, information regarding general health or subjective cognitive complaints in a coherent model. Non-cognitive factors, such as personality traits and behavioral and psychiatric symptoms, can be informative markers of early disease stage. It is known that personality can affect cognition and behavioral symptoms. The aim of the paper is to review the different types of interactions existing between personality, depression/anxiety, and cognition and cognitive disorders at behavioral and brain/genetic levels.

Soft Tensor Regression

Statistical methods relating tensor predictors to scalar outcomes in a regression model generally vectorize the tensor predictor and estimate the coefficients of its entries employing some form of regularization, use summaries of the tensor covariate, or use a low dimensional approximation of the coefficient tensor. However, low rank approximations of the coefficient tensor can suffer if the true rank is not small. We propose a tensor regression framework which assumes a soft version of the parallel factors (PARAFAC) approximation. In contrast to classic PARAFAC where each entry of the coefficient tensor is the sum of products of row-specific contributions across the tensor modes, the soft tensor regression (Softer) framework allows the row-specific contributions to vary around an overall mean. We follow a Bayesian approach to inference, and show that softening the PARAFAC increases model flexibility, leads to improved estimation of coefficient tensors, more accurate identification of important predictor entries, and more precise predictions, even for a low approximation rank. From a theoretical perspective, we show that employing Softer leads to a weakly consistent posterior distribution of the coefficient tensor, irrespective of the true or approximation tensor rank, a result that is not true when employing the classic PARAFAC for tensor regression. In the context of our motivating application, we adapt Softer to symmetric and semi-symmetric tensor predictors and analyze the relationship between brain network characteristics and human traits.

Cortical surface area alterations shaped by genetic load for neuroticism

Neuroticism has been shown to act as an important risk factor for major depressive disorder (MDD). Genetic and neuroimaging research has independently revealed biological correlates of neurotic personality including cortical alterations in brain regions of high relevance for affective disorders. Here we investigated the influence of a polygenic score for neuroticism (PGS) on cortical brain structure in a joint discovery sample of n = 746 healthy controls (HC) and n = 268 MDD patients. Findings were validated in an independent replication sample (n = 341 HC and n = 263 MDD). Subgroup analyses stratified for case-control status and analyses of associations between neurotic phenotype and cortical measures were carried out. PGS for neuroticism was significantly associated with a decreased cortical surface area of the inferior parietal cortex, the precuneus, the rostral cingulate cortex and the inferior frontal gyrus in the discovery sample. Similar associations between PGS and surface area of the inferior parietal cortex and the precuneus were demonstrated in the replication sample. Subgroup analyses revealed negative associations in the latter regions between PGS and surface area in both HC and MDD subjects. Neurotic phenotype was negatively correlated with surface area in similar cortical regions including the inferior parietal cortex and the precuneus. No significant associations between PGS and cortical thickness were detected. The morphometric overlap of associations between both PGS and neurotic phenotype in similar cortical regions closely related to internally focused cognition points to the potential relevance of genetically shaped cortical alterations in the development of neuroticism.

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.

Neuroanatomical correlates of impulsive traits in children aged 9 to 10

Impulsivity refers to a set of traits that are generally negatively related to critical domains of adaptive functioning and are core features of numerous psychiatric disorders. The current study examined the gray and white matter correlates of five impulsive traits measured using an abbreviated version of the UPPS-P (Urgency, (lack of) Premeditation, (lack of) Perseverance, Sensation-Seeking, Positive Urgency) impulsivity scale in children aged 9 to 10 (N = 11,052) from the Adolescent Brain and Cognitive Development (ABCD) study. Linear mixed effect models and elastic net regression were used to examine features of regional gray matter and white matter tractography most associated with each UPPS-P scale; intraclass correlations were computed to examine the similarity of the neuroanatomical correlates among the scales. Positive Urgency showed the most robust association with neuroanatomy, with similar but less robust associations found for Negative Urgency. Perseverance showed little association with neuroanatomy. Premeditation and Sensation Seeking showed intermediate associations with neuroanatomy. Critical regions across measures include the dorsolateral prefrontal cortex, lateral temporal cortex, and orbitofrontal cortex; critical tracts included the superior longitudinal fasciculus and inferior fronto-occipital fasciculus. Negative Urgency and Positive Urgency showed the greatest neuroanatomical similarity. Some UPPS-P traits share neuroanatomical correlates, while others have distinct correlates or essentially no relation to neuroanatomy. Neuroanatomy tended to account for relatively little variance in UPPS-P traits (i.e., Model R2 < 1%) and effects were spread throughout the brain, highlighting the importance of well powered samples. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Little evidence for associations between the Big Five personality traits and variability in brain gray or white matter

Attempts to link the Big Five personality traits of Openness-to-Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism with variability in trait-like features of brain structure have produced inconsistent results. Small sample sizes and heterogeneous methodology have been suspected in driving these inconsistencies. Here, using data collected from 1,107 university students (636 women, mean age 19.69 ​± ​1.24 years), representing the largest sample to date of unrelated individuals, we tested for associations between the Big Five personality traits and measures of cortical thickness and surface area, subcortical volume, and white matter microstructural integrity. In addition to replication analyses based on a prior study, we conducted exploratory whole-brain analyses. Four supplementary analyses were also conducted to examine 1) possible associations with lower-order facets of personality; 2) modulatory effects of sex; 3) effect of controlling for non-target personality traits; and 4) parcellation scheme effects. Our analyses failed to identify significant associations between the Big Five personality traits and brain morphometry, except for a weak association between greater surface area of the superior temporal gyrus and lower conscientiousness scores. As the latter association is not supported by previous studies, it should be treated with caution. Our supplementary analyses mirrored these predominantly null findings, suggesting they were not substantively biased by our analytic choices. Collectively, these results indicate that if there are associations between the Big Five personality traits and brain structure, they are likely of very small effect size and will require very large samples for reliable detection.

Unsupervised stratification in neuroimaging through deep latent embeddings

There is growing evidence that the use of stringent and dichotomic diagnostic categories in many medical disciplines (particularly 'brain sciences' as neurology and psychiatry) is an oversimplification. Although clear diagnostic boundaries remain useful for patients, families, and their access to dedicated NHS and health care services, the traditional dichotomic categories are not helpful to describe the complexity and large heterogeneity of symptoms across many and overlapping clinical phenotypes. With the advent of 'big' multimodal neuroimaging databases, data-driven stratification of the wide spectrum of healthy human physiology or disease based on neuroimages is theoretically become possible. However, this conceptual framework is hampered by severe computational constraints. In this paper we present a novel, deep learning based encode-decode architecture which leverages several parameter efficiency techniques generate latent deep embedding which compress the information contained in a full 3D neuroimaging volume by a factor 1000 while still retaining anatomical detail and hence rendering the subsequent stratification problem tractable. We train our architecture on 1003 brain scan derived from the human connectome project and demonstrate the faithfulness of the obtained reconstructions. Further, we employ a data driven clustering technique driven by a grid search in hyperparameter space to identify six different strata within the 1003 healthy community dwelling individuals which turn out to correspond to highly significant group differences in both physiological and cognitive data. Indicating that the well-known relationships between such variables and brain structure can be probed in an unsupervised manner through our novel architecture and pipeline. This opens the door to a variety of previously inaccessible applications in the realm of data driven stratification of large cohorts based on neuroimaging data.Clinical Relevance -With our approach, each person can be described and classified within a multi-dimensional space of data, where they are uniquely classified according to their individual anatomy, physiology and disease-related anatomical and physiological alterations.

From Description to Explanation: Integrating Across Multiple Levels of Analysis to Inform Neuroscientific Accounts of Dimensional Personality Pathology

Dimensional approaches to psychiatric nosology are rapidly transforming the way researchers and clinicians conceptualize personality pathology, leading to a growing interest in describing how individuals differ from one another. Yet, in order to successfully prevent and treat personality pathology, it is also necessary to explain the sources of these individual differences. The emerging field of personality neuroscience is well-positioned to guide the transition from description to explanation within personality pathology research. However, establishing comprehensive, mechanistic accounts of personality pathology will require personality neuroscientists to move beyond atheoretical studies that link trait differences to neural correlates without considering the algorithmic processes that are carried out by those correlates. We highlight some of the dangers we see in overpopulating personality neuroscience with brain-trait associational studies and offer a series of recommendations for personality neuroscientists seeking to build explanatory theories of personality pathology.

Testing relationships between multimodal modes of brain structural variation and age, sex and polygenic scores for neuroticism in children and adolescents

Human brain development involves spatially and temporally heterogeneous changes, detectable across a wide range of magnetic resonance imaging (MRI) measures. Investigating the interplay between multimodal MRI and polygenic scores (PGS) for personality traits associated with mental disorders in youth may provide new knowledge about typical and atypical neurodevelopment. We derived independent components across cortical thickness, cortical surface area, and grey/white matter contrast (GWC) (n = 2596, 3-23 years), and tested for associations between these components and age, sex and-, in a subsample (n = 878), PGS for neuroticism. Age was negatively associated with a single-modality component reflecting higher global GWC, and additionally with components capturing common variance between global thickness and GWC, and several multimodal regional patterns. Sex differences were found for components primarily capturing global and regional surface area (boys > girls), but also regional cortical thickness. For PGS for neuroticism, we found weak and bidirectional associations with a component reflecting right prefrontal surface area. These results indicate that multimodal fusion is sensitive to age and sex differences in brain structure in youth, but only weakly to polygenic load for neuroticism.

Borderline Personality Traits Are Not Correlated With Brain Structure in Two Large Samples

BACKGROUND

Borderline personality disorder is associated with severe psychiatric presentations and has been linked to variability in brain structure. Dimensional models of borderline personality traits (BPTs) have become influential; however, associations between BPTs and brain structure remain poorly understood.

METHODS

We tested whether BPTs are associated with regional cortical thickness, cortical surface area, and subcortical volumes (n = 152 brain structure metrics) in data from the Duke Neurogenetics Study (n = 1299) and Human Connectome Project (n = 1099). Positive control analyses tested whether BPTs are associated with related behaviors (e.g., suicidal thoughts and behaviors, psychiatric diagnoses) and experiences (e.g., adverse childhood experiences).

RESULTS

While BPTs were robustly associated with all positive control measures, they were not significantly associated with any brain structure metrics in the Duke Neurogenetics Study or Human Connectome Project, or in a meta-analysis of both samples. The strongest findings from the meta-analysis showed a positive association between BPTs and volumes of the left ventral diencephalon and thalamus (p values < .005 uncorrected, p values > .1 false discovery rate-corrected). Contrasting high and low BPT decile groups (n = 552) revealed no false discovery rate-significant associations with brain structure.

CONCLUSIONS

We find replicable evidence that BPTs are not associated with brain structure despite being correlated with independent behavioral measures. Prior reports linking brain morphology to borderline personality disorder may be driven by factors other than traits (e.g., severe presentations, comorbid conditions, severe childhood adversity, or medication) or reflect false positives. The etiology or consequences of BPTs may not be attributable to brain structure measured via magnetic resonance imaging. Future studies of BPTs will require much larger sample sizes to detect these very small effects.

Quantification of anticipation of excitement with a three-axial model of emotion with EEG

OBJECTIVE

Multiple facets of human emotion underlie diverse and sparse neural mechanisms. Among the many existing models of emotion, the two-dimensional circumplex model of emotion is an important theory. The use of the circumplex model allows us to model variable aspects of emotion; however, such momentary expressions of one's internal mental state still lacks a notion of the third dimension of time. Here, we report an exploratory attempt to build a three-axis model of human emotion to model our sense of anticipatory excitement, 'Waku-Waku' (in Japanese), in which people predictively code upcoming emotional events.

APPROACH

Electroencephalography (EEG) data were recorded from 28 young adult participants while they mentalized upcoming emotional pictures. Three auditory tones were used as indicative cues, predicting the likelihood of the valence of an upcoming picture: positive, negative, or unknown. While seeing an image, the participants judged its emotional valence during the task and subsequently rated their subjective experiences on valence, arousal, expectation, and Waku-Waku immediately after the experiment. The collected EEG data were then analyzed to identify contributory neural signatures for each of the three axes.

MAIN RESULTS

A three-axis model was built to quantify Waku-Waku. As expected, this model revealed the considerable contribution of the third dimension over the classical two-dimensional model. Distinctive EEG components were identified. Furthermore, a novel brain-emotion interface was proposed and validated within the scope of limitations.

SIGNIFICANCE

The proposed notion may shed new light on the theories of emotion and support multiplex dimensions of emotion. With the introduction of the cognitive domain for a brain-computer interface, we propose a novel brain-emotion interface. Limitations of the study and potential applications of this interface are discussed.

Neuroanatomical Changes in Leber's Hereditary Optic Neuropathy: Clinical Application of 7T MRI Submillimeter Morphometry

Leber’s hereditary optic neuropathy (LHON) is one of the mitochondrial diseases that causes loss of central vision, progressive impairment and subsequent degeneration of retinal ganglion cells (RGCs). In recent years, diffusion tensor imaging (DTI) studies have revealed structural abnormalities in visual white matter tracts, such as the optic tract, and optic radiation. However, it is still unclear if the disease alters only some parts of the white matter architecture or whether the changes also affect other subcortical areas of the brain. This study aimed to improve our understanding of morphometric changes in subcortical brain areas and their associations with the clinical picture in LHON by the application of a submillimeter surface-based analysis approach to the ultra-high-field 7T magnetic resonance imaging data. To meet these goals, fifteen LHON patients and fifteen age-matched healthy subjects were examined. For all individuals, quantitative analysis of the morphometric results was performed. Furthermore, morphometric characteristics which differentiated the groups were correlated with variables covering selected aspects of the LHON clinical picture. Compared to healthy controls (HC), LHON carriers showed significantly lower volume of both palladiums (left p = 0.023; right p = 0.018), the right accumbens area (p = 0.007) and the optic chiasm (p = 0.014). Additionally, LHON patients have significantly higher volume of both lateral ventricles (left p = 0.034; right p = 0.02), both temporal horns of the lateral ventricles (left p = 0.016; right p = 0.034), 3rd ventricle (p = 0.012) and 4th ventricle (p = 0.002). Correlation between volumetric results and clinical data showed that volume of both right and left lateral ventricles significantly and positively correlated with the duration of the illness (left R = 0.841, p = 0.002; right R = 0.755, p = 0.001) and the age of the LHON participants (left R = 0.656, p = 0.007; right R = 0.691, p = 0.004). The abnormalities in volume of the LHON patients’ subcortical structures indicate that the disease can cause changes not only in the white matter areas constituting visual tracts, but also in the other subcortical brain structures. Furthermore, the correlation between those results and the illness duration suggests that the disease might have a neurodegenerative nature; however, to fully confirm this observation, longitudinal studies should be conducted.

Empathy-Related Brain Activity in Somatosensory Cortex Protects From Tactile Priming Effects: A Pilot Study

Empathy influences how we perceive, understand, and interact with our social environment. Previous studies suggested a network of different brain regions as a neural substrate for empathy, including, in particular, insula and anterior cingulate cortex (ACC). In addition, a contribution of the somatosensory cortices for this empathy related network has been suggested. This is remarkable, given that other recent studies have revealed a role for the somatosensory cortex in various social tasks. For example, in experiments using tactile priming, incidental haptic sensations are found to influence judgment recommendations. Here, we aimed to test if this engagement of the somatosensory cortices during tactile priming can be predicted by the participant’s empathy personality traits. We assessed participant’s empathy and personality traits by means of the Interpersonal Reactivity Index (IRI) and NEO-FFI and tested whether trait empathy is associated with the tactile priming effect in social judgments. Results revealed that empathy predicted the tactile priming effect negatively. This was accompanied by a reduced engagement of the somatosensory cortex, which has been shown to be associated with the priming effect. We conclude that empathy seems to protect people from tactile priming effects.

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.

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.

Predicting change trajectories of neuroticism from baseline brain structure using whole brain analyses and latent growth curve models in adolescents

Adolescence is a vulnerable time for personality development. Especially neuroticism with its link to the development of psychopathology is of interest concerning influential factors. The present study exploratorily investigates neuroanatomical signatures for developmental trajectories of neuroticism based on a voxel-wise whole-brain structural equation modelling framework. In 1,814 healthy adolescents of the IMAGEN sample, the NEO-FFI was acquired at three measurement occasions across five years. Based on a partial measurement invariance second-order latent growth curve model we conducted whole-brain analyses on structural MRI data at age 14 years, predicting change in neuroticism over time. We observed that a reduced volume in the pituitary gland was associated with the slope of neuroticism over time. However, no relations with prefrontal areas emerged. Both findings are discussed against the background of possible genetic and social influences that may account for this result.

Reduced betweenness centrality of a sensory-motor vestibular network in subclinical agoraphobia

Agoraphobic patients feel dizzy in crowded open spaces and respond to this symptom with excessive fear and avoidance. These clinical features show great similitude with the newly defined syndrome of persistent postural perceptual dizziness (PPPD). Patients with PPPD show decreased activity and connectivity in regions of the vestibular cortex. Due to the great overlap between these two conditions, we hypothesized that individuals with sub-clinical agoraphobia would show reduction in the connectivity features of these regions. We selected a group of healthy individuals from the Human Connectome Project that self-reported agoraphobia episodes, and compared it with a control group. We accurately matched the two groups for psychological measures and personality traits in order to study the neural correlates of vestibular symptoms independently of possible psychiatric vulnerabilities. We found that the agoraphobia group showed reduced betweenness centrality of a network encompassing key regions of the vestibular cortex. Dysfunctions of the vestibular cortex may explain the dizziness symptom for a disorder previously labelled as psychogenic.

Brain age prediction using deep learning uncovers associated sequence variants

Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual’s predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N=12378$$\end{document}N=12378, replication set: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N=4456$$\end{document}N=4456) yielded two sequence variants, rs1452628-T (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =-0.08$$\end{document}β=−0.08, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P=1.15\times{10}^{-9}$$\end{document}P=1.15×10−9) and rs2435204-G (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta =0.102$$\end{document}β=0.102, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P=9.73\times 1{0}^{-12}$$\end{document}P=9.73×10−12). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2).

Machine learning algorithms can be trained to estimate age from brain structural MRI. Here, the authors introduce a new deep-learning-based age prediction approach, and then carry out a GWAS of the difference between predicted and chronological age, revealing two associated variants.

Reduced cortical folding in multi-modal vestibular regions in persistent postural perceptual dizziness

Persistent postural perceptual dizziness (PPPD) is a common functional vestibular disorder that is triggered and sustained by a complex interaction between physiological and psychological factors affecting spatial orientation and postural control. Past functional neuroimaging research and one recent structural (i.e., voxel-based morphometry-VBM) study have identified alterations in vestibular, visuo-spatial, and limbic brain regions in patients with PPPD and anxiety-prone normal individuals. However, no-one thus far has employed surface based morphometry (SBM) to explore whether cortical morphology in patients with PPPD differs from that of healthy people. We calculated SBM measures from structural MR images in 15 patients with PPPD and compared them to those from 15 healthy controls matched for demographics, personality traits known to confer risk for PPPD as well as anxiety and depressive symptoms that are commonly comorbid with PPPD. We tested for associations between SBM measures and dizziness severity in patients with PPPD. Relative to controls, PPPD patients showed significantly decreased local gyrification index (LGI) in multi-modal vestibular regions bilaterally, specifically the posterior insular cortices, supra-marginal gyri, and posterior superior temporal gyri (p < 0.001). Within the PPPD group, dizziness severity positively correlated with LGI in visual areas and negatively with LGI in the right superior parietal cortex. These findings demonstrate abnormal cortical folding in vestibular cortices and correlations between dizziness severity and cortical folding in visual and somatosensory spatial association areas in PPPD patients, which provides new insights into the pathophysiological mechanisms underlying this disorder.

Neuroanatomical correlates of personality traits in temporal lobe epilepsy: Findings from the Epilepsy Connectome Project

Behavioral and personality disorders in temporal lobe epilepsy (TLE) have been a topic of interest and controversy for decades, with less attention paid to alterations in normal personality structure and traits. In this investigation, core personality traits (the Big 5) and their neurobiological correlates in TLE were explored using the Neuroticism Extraversion Openness-Five Factor Inventory (NEO-FFI) and structural magnetic resonance imaging (MRI) through the Epilepsy Connectome Project (ECP). NEO-FFI scores from 67 individuals with TLE (34.6 ± 9.5 years; 67% women) were compared to 31 healthy controls (32.8 ± 8.9 years; 41% women) to assess differences in the Big 5 traits (agreeableness, openness, conscientiousness, neuroticism, and extraversion). Individuals with TLE showed significantly higher neuroticism, with no significant differences on the other traits. Neural correlates of neuroticism were then determined in participants with TLE including cortical and subcortical volumes. Distributed reductions in cortical gray matter volumes were associated with increased neuroticism. Subcortically, hippocampal and amygdala volumes were negatively associated with neuroticism. These results offer insight into alterations in the Big 5 personality traits in TLE and their brain-related correlates.

Brain structure links trait conscientiousness to academic performance

In the long history of identifying factors to predict academic performance, conscientiousness, a so-called ‘big five’ personality trait describing self-regulation and goal-directed behavior, has emerged as a stable predictor for this purpose. However, the neuroanatomical substrates of trait conscientiousness and the underlying brain mechanism linking trait conscientiousness and academic performance are still largely unknown. Here, we examined these issues in 148 high school students within the same grade by estimating cortical gray matter volume (GMV) utilizing a voxel-based morphometry method based on structural magnetic resonance imaging. A whole-brain regression analysis showed that trait conscientiousness was positively associated with the GMV in the bilateral superior parietal lobe (SPL) and was negatively associated with the GMV in the right middle frontal gyrus (MFG). Furthermore, mediation analysis revealed that trait conscientiousness mediated the influences of the SPL and MFG volume on academic performance. Importantly, our results persisted even when we adjusted for general intelligence, family socioeconomic status and ‘big five’ personality traits other than conscientiousness. Altogether, our study suggests that the GMV in the frontoparietal network is a neurostructural marker of adolescents’ conscientiousness and reveals a potential brain-personality-achievement pathway for predicting academic performance in which gray matter structures affect academic performance through trait conscientiousness.

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.

Sex Hormones and Gender Role Relate to Gray Matter Volumes in Sexually Dimorphic Brain Areas

The present study investigates the relationship of circulating sex hormone levels and gender role to gray matter volumes in sexually dimorphic brain areas and explores, whether these relationships are modulated by biological sex (as assigned at birth based on sexual anatomy) or oral contraceptive (OC) use. It was hypothesized that testosterone and masculinity relate positively to gray matter volumes in areas that are typically larger in men, like the hippocampus or cerebellum, while estradiol/progesterone and femininity relate positively to gray matter volumes in the frontal cortex. To that end, high resolution structural MRI scans, sex hormone levels and gender role self-assessments were obtained in a large sample 89 men, 89 naturally cycling (NC) women, and 60 OC users. Men showed larger regional gray matter volumes than women in the cerebellum and bilateral clusters spanning the putamen and parts of the hippocampi/parahippocampi and fusiform gyri. In accordance with our hypotheses, a significant positive association of testosterone to hippocampal volumes was observed in women irrespective of OC use. Participant's self-reported femininity was significantly positively associated with gray matter volumes in the left middle frontal gyrus (MFG) in men. In addition several differences between OC-users and NC women were identified.

Brain gray matter correlates of extraversion: A systematic review and meta-analysis of voxel-based morphometry studies

Extraversion is a fundamental personality dimension closely related to an individual's life outcomes and mental health. Although an increasing number of studies have attempted to identify the neurostructural markers of extraversion, the results have been highly inconsistent. The current study aimed to achieve a comprehensive understanding of brain gray matter (GM) correlates of extraversion with a systematic review and meta-analysis approach. Our review showed relatively high interstudy heterogeneity among previous findings. Our meta-analysis of whole-brain voxel-based morphometry studies revealed that extraversion was stably associated with six core brain regions. Additionally, meta-regression analyses identified brain regions where the associations of extraversion with GM volume were modulated by gender and age. The relationships between extraversion and GM structures were discussed based on three extraversion-related functional systems. Furthermore, we explained the gender and age effects. Overall, our study is the first to reveal a comprehensive picture of brain GM correlates of extraversion, and the findings may be useful for the selection of targeted brain areas for extraversion interventions.

Cortical morphometry of the five-factor model of personality: findings from the Human Connectome Project full sample

This study is a replication of an existing large study (N = 507) on the surface-based morphometric correlates of five-factor model (FFM) personality traits. The same methods were used as the original study in another large sample drawn from the same population (N = 597) with results then being aggregated from both samples (N = 1104), providing the largest investigation into the neuroanatomical correlates of FFM personality traits to date. Clusters of association between brain morphometry and each FFM trait are reported. For neuroticism, agreeableness, openness and conscientiousness clusters of association were found in the dorsolateral prefrontal cortex for at least one morphometric index. Morphometry in various other regions was also associated with each personality trait. While some regions found in the original study were confirmed in the replication and full samples, others were not, highlighting the importance of replicating even high-quality, well-powered studies. Effect sizes were very similar in the replication and whole samples as those found in the original study. As a whole, the current results provide the strongest evidence to date on the neuroanatomical correlates of personality and highlights challenges in using this approach to understanding the neural correlates of personality.

Chronic pain is associated with a brain aging biomarker in community-dwelling older adults

Chronic pain is associated with brain atrophy with limited evidence on its impact in the older adult's brain. We aimed to determine the associations between chronic pain and a brain aging biomarker in persons aged 60 to 83 years old. Participants of the Neuromodulatory Examination of Pain and Mobility Across the Lifespan (NEPAL) study (N = 47) completed demographic, psychological, and pain assessments followed by a quantitative sensory testing battery and a T1-weighted magnetic resonance imaging. We estimated a brain-predicted age difference (brain-PAD) that has been previously reported to predict overall mortality risk (brain-PAD, calculated as brain-predicted age minus chronological age), using an established machine-learning model. Analyses of covariances and Pearson/Spearman correlations were used to determine associations of brain-PAD with pain, somatosensory function, and psychological function. Individuals with chronic pain (n = 33) had "older" brains for their age compared with those without (n = 14; F[1,41] = 4.9; P = 0.033). Greater average worst pain intensity was associated with an "older" brain (r = 0.464; P = 0.011). Among participants with chronic pain, those who reported having pain treatments during the past 3 months had "younger" brains compared with those who did not (F[1,27] = 12.3; P = 0.002). An "older" brain was significantly associated with decreased vibratory (r = 0.323; P = 0.033) and thermal (r = 0.345; P = 0.023) detection, deficient endogenous pain inhibition (F[1,25] = 4.6; P = 0.044), lower positive affect (r = -0.474; P = 0.005), a less agreeable (r = -0.439; P = 0.020), and less emotionally stable personality (r = -0.387; P = 0.042). Our findings suggest that chronic pain is associated with added "age-like" brain atrophy in relatively healthy, community-dwelling older individuals, and future studies are needed to determine the directionality of our findings. A brain aging biomarker may help identify people with chronic pain at a greater risk of functional decline and poorer health outcomes.

The Challenges of Diagnostic Imaging in the Era of Big Data

The diagnostic imaging field has undergone considerable growth both in terms of technological development and market expansion; with the following increasing production of a considerable amount of data that potentially fully poses diagnostic imaging in the Big data in the context of healthcare. Nevertheless, the mere production of a large amount of data does not automatically permit the real exploitation of their intrinsic value. Therefore, it is necessary to develop digital platforms and applications that favor the correct and advantageous management of diagnostic images such as Big data. This work aims to frame the role of diagnostic imaging in this new scenario, emphasizing the open challenges in exploiting such intense data generation for decision making with Big data analytics.

Differences regarding the five-factor personality model in patients with subjective cognitive decline and mild cognitive impairment

Personality and dementia are connected in different ways. A broad knowledge about personality and prodromal stages of dementia might be helpful to identify dementia as early as possible. Hence, personality differences between three cognitively impaired groups on the basis of patients’ self-assessments of personality traits and connections between personality and cognitive functioning were examined via a cross-sectional study. The sample consisted of cognitively impaired patients (N = 133), aged 50 and older, who came to a memory clinic due to cognitive complaints. The test procedure encompassed a cognitive screening, the Neuropsychological Test Battery Vienna (NTBV), and self-assessment questionnaires such as the Big Five Plus One Persönlichkeitsinventar (B5PO). While patients with subjective cognitive decline (SCD) did not differ from those with non-amnestic mild cognitive impairment (naMCI) concerning the different personality traits, patients with amnestic mild cognitive impairment (aMCI) showed significantly lower scores for extraversion (p < 0.05), openness (p < 0.001), and empathy (p < 0.001) than patients with SCD as well as patients with naMCI. Thus, cognitively impaired groups mainly differ concerning personality traits depending on whether they do show memory decline or not.

No evidence for morphometric associations of the amygdala and hippocampus with the five-factor model personality traits in relatively healthy young adults

Despite the important functional role of the amygdala and hippocampus in socioemotional functioning, there have been limited adequately powered studies testing how the structure of these regions relates to putatively relevant personality traits such as neuroticism. Additionally, recent advances in MRI analysis methods provide unprecedented accuracy in measuring these structures and enable segmentation into their substructures. Using the new FreeSurfer amygdala and hippocampus segmentation pipelines with the full Human Connectome Project sample (N = 1105), the current study investigated whether the morphometry of these structures is associated with the five-factor model (FFM) personality traits in a sample of relatively healthy young adults. Drawing from prior findings, the following hypotheses were tested: 1) amygdala and hippocampus gray matter volume would be associated with neuroticism, 2) CA2/3 and dentate gyrus would account for the relationship of the hippocampus with neuroticism, and 3) amygdala gray matter volume would be inversely associated with extraversion. Exploratory analyses were conducted investigating potential associations between all of the FFM traits and the structure of the hippocampus and amygdala and their subregions. Despite some previous positive findings of whole amygdala and hippocampus with personality traits and related psychopathology (e.g., depression), the current results indicated no relationships between the any of the brain regions and the FFM personality traits. Given the large sample and utilization of sophisticated analytic methodology, the current study suggests no association of amygdala and hippocampus morphometry with major domains of personality.

Personality Profiles Are Associated with Functional Brain Networks Related to Cognition and Emotion

Personality factors as defined by the "five-factor model" are some of the most investigated characteristics that underlie various types of complex behavior. These are, however, often investigated as isolated traits that are conceptually independent, yet empirically are typically strongly related to each other. We apply Independent Component Analysis to these personality factors as measured by the NEO-FFI in 471 healthy subjects from the Human Connectome Project to investigate independent personality profiles that incorporate all five original factors. Subsequently we examine how these profiles are related to patterns of resting-state brain activity in specific networks-of-interest related to cognition and emotion. We find that a personality profile of contrasting openness and agreeableness is associated with engagement of a subcortical-medial prefrontal network and the dorsolateral prefrontal cortex. Likewise, a profile of contrasting extraversion and conscientiousness is associated with activity in the precuneus. This study shows a novel approach to investigating personality and how it is related to patterns of activity in the resting brain.

The neurocognitive correlates of academic diligence in adolescent girls

Academic diligence is the ability to regulate behavior in the service of goals, and a predictor of educational attainment. Here we combined behavioral, structural MRI, functional MRI and connectivity data to investigate the neurocognitive correlates of diligence. We assessed whether individual differences in diligence are related to the interplay between frontal control and striatal reward systems, as predicted by the dual-systems hypothesis of adolescent development. We obtained behavioral measures of diligence from 40 adolescent girls (aged 14-15 years) using the Academic Diligence Task. We collected structural imaging data for each participant, as well as functional imaging data during an emotional go-no-go self-control task. As predicted by the dual-systems hypothesis, we found that inferior frontal activation and gyrification correlated with academic diligence. However, neither striatal activation nor structure, nor fronto-striatal connectivity, showed clear associations with diligence. Instead, we found prominent activation of temporal areas during the go-no-go task. This suggests that academic diligence is associated with an extended network of brain regions.

Dynamical brain connectivity estimation using GARCH models: An application to personality neuroscience

It has recently become evident that the functional connectome of the human brain is a dynamical entity whose time evolution carries important information underpinning physiological brain function as well as its disease-related aberrations. While simple sliding window approaches have had some success in estimating dynamical brain connectivity in a functional MRI (fMRI) context, these methods suffer from limitations related to the arbitrary choice of window length and limited time resolution. Recently, Generalized autoregressive conditional heteroscedastic (GARCH) models have been employed to generate dynamical covariance models which can be applied to fMRI. Here, we employ a GARCH-based method (dynamic conditional correlation - DCC) to estimate dynamical brain connectivity in the Human Connectome Project (HCP) dataset and study how the dynamic functional connectivity behaviors related to personality as described by the five-factor model. Openness, a trait related to curiosity and creativity, is the only trait associated with significant differences in the amount of time-variability (but not in absolute median connectivity) of several inter-network functional connections in the human brain. The DCC method offers a novel window to extract dynamical information which can aid in elucidating the neurophysiological underpinning of phenomena to which conventional static brain connectivity estimates are insensitive.

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.

Examining neural correlates of psychopathology using a lesion-based approach

Studies of individuals with focal brain damage have long been used to expand understanding of the neural basis of psychopathology. However, most previous studies were conducted using small sample sizes and relatively coarse methods for measuring psychopathology or mapping brain-behavior relationships. Here, we examined the factor structure and neural correlates of psychopathology in 232 individuals with focal brain damage, using their responses to the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF). Factor analysis and voxel-based lesion symptom mapping were used to examine the structure and neural correlates of psychopathology in this sample. Consistent with existing MMPI-2-RF literature, separate internalizing, externalizing, and psychotic symptom dimensions were found. In addition, a somatic dimension likely reflecting neurological symptoms was identified. Damage to the medial temporal lobe, including the hippocampus, was associated with scales related to both internalizing problems and psychoticism. Damage to the medial temporal lobe and orbitofrontal cortex was associated with both a general distrust of others and beliefs that one is being personally targeted by others. These findings provide evidence for the critical role of dysfunction in specific frontal and temporal regions in the development of psychopathology.