scholarly journals Resting-state functional brain connectivity best predicts the personality dimension of openness to experience

2017 ◽  
Author(s):  
Julien Dubois ◽  
Paola Galdi ◽  
Yanting Han ◽  
Lynn K. Paul ◽  
Ralph Adolphs
Keyword(s):  
Personality Traits ◽  
Resting State ◽  
Fluid Intelligence ◽  
Small Sample ◽  
Openness To Experience ◽  
Personality Factors ◽  
Personality Factor ◽  
Personality Dimension ◽  
Human Connectome Project ◽  
Neuroimaging Data

AbstractPersonality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging data from 884 young healthy adults in the Human Connectome Project (HCP) database. We attempted to predict personality traits from the “Big Five”, as assessed with the NEO-FFI test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two inter-subject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 h of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; 3 denoising strategies; 2 alignment schemes; 3 models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=0.24, R2=0.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=0.26, R2=0.044). Other factors (Extraversion, Neuroticism, Agreeableness and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors (“α” and “β”) from a principal components analysis of the NEO-FFI factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=0.27, R2=0.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

scholarly journals Resting-State Functional Brain Connectivity Best Predicts the Personality Dimension of Openness to Experience

2018 ◽  
Vol 1 ◽  
Author(s):  
Julien Dubois ◽  
Paola Galdi ◽  
Yanting Han ◽  
Lynn K. Paul ◽  
Ralph Adolphs
Keyword(s):  
Personality Traits ◽  
Resting State ◽  
Small Sample ◽  
Openness To Experience ◽  
Fmri Data ◽  
Personality Factors ◽  
Personality Factor ◽  
Personality Dimension ◽  
Human Connectome Project ◽  
Neuroimaging Data

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

Investment Traits and Intelligence in Adulthood

2013 ◽  
Vol 34 (2) ◽  
pp. 82-89 ◽  
Author(s):  
Sophie von Stumm
Keyword(s):  
Individual Differences ◽  
Personality Traits ◽  
Fluid Intelligence ◽  
Need For Cognition ◽  
Openness To Experience ◽  
Intelligence Tests ◽  
Crystallized Intelligence ◽  
New Knowledge ◽  
The Relationship ◽  
Over Time

Intelligence-as-knowledge in adulthood is influenced by individual differences in intelligence-as-process (i.e., fluid intelligence) and in personality traits that determine when, where, and how people invest their intelligence over time. Here, the relationship between two investment traits (i.e., Openness to Experience and Need for Cognition), intelligence-as-process and intelligence-as-knowledge, as assessed by a battery of crystallized intelligence tests and a new knowledge measure, was examined. The results showed that (1) both investment traits were positively associated with intelligence-as-knowledge; (2) this effect was stronger for Openness to Experience than for Need for Cognition; and (3) associations between investment and intelligence-as-knowledge reduced when adjusting for intelligence-as-process but remained mostly significant.

scholarly journals Self-Expressive Creativity in the Adolescent Digital Domain: Personality, Self-Esteem, and Emotions

2019 ◽  
Vol 16 (22) ◽  
pp. 4527 ◽  
Author(s):  
Pérez-Fuentes ◽  
Molero Jurado ◽  
Gázquez Linares ◽  
Oropesa Ruiz ◽  
Simón Márquez ◽  
...  
Keyword(s):  
Emotional Intelligence ◽  
High School Students ◽  
Personality Traits ◽  
Self Esteem ◽  
Positive Influence ◽  
Openness To Experience ◽  
Positive Development ◽  
Personality Factors ◽  
School Students ◽  
Negative Effect

Background: Although self-expressive creativity is related to cyberbullying, it can also reinforce strengths that contribute to positive adolescent development. Our study concentrated on the relationships between personality traits and self-expressive creativity in the digital domain in an adolescent population. For this, we analyzed the effect of self-esteem and emotional intelligence as assets for positive development related to personality traits and self-expressive creativity. Methods: The study population included a total of 742 adolescents that were high-school students in the province of Almería, Spain. The following instruments were used: Big Five Inventory (BFI) to evaluate the five broad personality factors, Rosenberg Self-Esteem Scale (RSE), Expression, Management, and Emotion Recognition Evaluation Scale (TMMS-24), and the Creative Behavior Questionnaire: Digital (CBQD). Results: The cluster analysis revealed the existence of two profiles of adolescents based on their personality traits. The analysis showed that the group with the highest levels of extraversion and openness to experience and lowest levels of neuroticism were those who showed the highest scores in self-esteem, clarity, and emotional repair, as well as in self-expressive creativity. Higher scores in neuroticism and lower scores in extraversion and openness to experience showed a direct negative effect on self-expressive creativity and indirect effect through self-esteem and emotional attention, which acted as mediators in series. Conclusions: To counteract certain characteristics that increase adolescents’ vulnerability to social network bullying, a plan must be developed for adequate positive use of the Internet from a creative model that enables digital self-expression for acquiring identity and self-efficacy through the positive influence of peers, which promotes feelings of empowerment and self-affirmation through constructive tasks that reinforce self-esteem and emotional intelligence.

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

scholarly journals The impact of personality traits on attitudes toward learning communication skills

2020 ◽  
Vol 30 (1) ◽  
pp. e37326
Author(s):  
Camila Ament Giuliani dos Santos Franco ◽  
Renato Soleiman Franco ◽  
Dario Cecilio-Fernandes ◽  
Milton Severo ◽  
Maria Amélia Ferreira
Keyword(s):  
Personality Traits ◽  
Communication Skills ◽  
Attitude Scale ◽  
Openness To Experience ◽  
Personality Factors ◽  
Undergraduate Medical Students ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Attitudes Toward Learning ◽  
The Impact

Aims: The aim of this study was to investigate the association between personality traits and attitudes toward learning communication skills in undergraduate medical students. The relation between students’ attitudes and personality trait could help us identify those who those who will need more support to develop communication skills, based on their personality traits.Methods: The data was collected data from an intentional and cross-sectional sample composed of 204 students from three Brazilian universities. The students answered questionnaires containing the Communication Skills Attitude Scale (CSAS-BR) and the Big Five Mini-Markers (BFMM) for personality. Data were analyzed using frequency calculations, principal components analysis, and the multiple linear regression model.Results: Seven among 26 items of the original Communication Skills Attitude Scale (CSAS) presented factor loads lower than |0.30| and must be excluded in the CSAS -BR that showed one domain including positive and negative attitudes. The value of Cronbach’s alpha of the 19-item scale was 0.894. The BFMM showed similar dimensional results with five domains with Cronbach’s alpha values of 0.804 for Extroversion, 0.753 for agreeableness, 0.755 for conscientiousness, 0.780 for neuroticism and 0.668 for openness. There were positive and statically significant linear associations with the CSAS-BR and agreeableness (β: 0.230, p<0.001), extraversion (β: 0.150, p=0.030), and openness to experience (β: 0.190, p=0.010). These personality factors drive social interactions and interpersonal relations, which involve the tendency to be friendly, flexible, and cooperative; to show a willing disposition; and the ability to actively engage with others. Conclusions: Based on the methods applied in this study, the results demonstrated a relation between agreeableness, extraversion and openness to experience with attitudes on communication skills in students from three Brazilian universities. Our results suggest that the evaluation of personality traits can contribute to the recognition of students for whom the establishment of special teaching strategies can improve communication skills.

scholarly journals Phenotype Discovery from Population Brain Imaging

2020 ◽  
Author(s):  
Weikang Gong ◽  
Christian F. Beckmann ◽  
Stephen M. Smith
Keyword(s):  
Fluid Intelligence ◽  
Disease Diagnosis ◽  
Population Variability ◽  
Non Invasive ◽  
Human Connectome Project ◽  
Analysis Strategies ◽  
Neuroimaging Data ◽  
Using Data ◽  
First Time ◽  
The Brain

Neuroimaging allows for the non-invasive study of the brain in rich detail. Data-driven discovery of patterns of population variability in the brain has the potential to be extremely valuable for early disease diagnosis and understanding the brain. The resulting patterns can be used as imaging-derived phenotypes (IDPs), and may complement existing expert-curated IDPs. However, population datasets, comprising many different structural and functional imaging modalities from thousands of subjects, provide a computational challenge not previously addressed. Here, for the first time, a multimodal independent component analysis approach is presented that is scalable for data fusion of voxel-level neuroimaging data in the full UK Biobank (UKB) dataset, that will soon reach 100,000 imaged subjects. This new computational approach can estimate modes of population variability that enhance the ability to predict thousands of phenotypic and behavioural variables using data from UKB and the Human Connectome Project. A high-dimensional decomposition achieved improved predictive power compared with widely-used analysis strategies, single-modality decompositions and existing IDPs. In UKB data (14,503 subjects with 47 different data modalities), many interpretable associations with non-imaging phenotypes were identified, including multimodal spatial maps related to fluid intelligence, handedness and disease, in some cases where IDP-based approaches failed.

scholarly journals Robust prediction of individual personality from brain functional connectome

2020 ◽  
Vol 15 (3) ◽  
pp. 359-369 ◽  
Author(s):  
Huanhuan Cai ◽  
Jiajia Zhu ◽  
Yongqiang Yu
Keyword(s):  
Personality Traits ◽  
Large Scale ◽  
Five Factor Model ◽  
Brain Regions ◽  
Individual Variability ◽  
Personality Factors ◽  
Imaging Features ◽  
Imaging Data ◽  
Functional Connectome ◽  
Human Connectome Project

Abstract 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.

Using Personality Traits to Understand the Influence of Personality on Computer Programming

2016 ◽  
Vol 18 (1) ◽  
pp. 28-48 ◽  
Author(s):  
Zahra Karimi ◽  
Ahmad Baraani-Dastjerdi ◽  
Naser Ghasem-Aghaee ◽  
Stefan Wagner
Keyword(s):  
Personality Traits ◽  
Computer Programming ◽  
Openness To Experience ◽  
Personality Factors ◽  
Software Projects ◽  
Programming Students ◽  
Negative Effect ◽  
Software Psychology ◽  
Personality Facets ◽  
Positive Effect

Computer programming is complex and all personality factors might influence it. Personality factors are comprehensive but broad and, therefore, lower level traits may help understanding the influence of personality on computer programming. The objective of this paper is to extend the empirical knowledge in software psychology by using narrow personality traits as well as broad personality traits to explain the influence of personality. The authors surveyed 68 programming students developing software projects to investigate the influence of personality on performance in computer programming. They measured five broad personality factors, 17 personality facets, prior experience, attitude and self-assessed survey performance. They also used the grade students achieved in the software projects as an indicator of software quality. It was found that prior programming experience, attitude towards programming, academic performance, Openness to Experience, Conscientiousness, Extraversion and Agreeableness have a positive effect on performance in computer programming. However, one facet of Openness to Experience and facets of Neuroticism revealed negative effect. The authors found an indication that different aspects of personality factors have different influences on computer programming. Personality facets show larger effect than personality and help explaining the influence of personality. More studies are needed to strengthen the findings and clarify the situation.

scholarly journals Harnessing Human Connectome Project Data for Replication Studies: A Demonstration for Resting State-Task Network Correspondence

2017 ◽  
Author(s):  
Lisa D. Nickerson
Keyword(s):  
Open Access ◽  
Resting State ◽  
Major Barrier ◽  
Replication Studies ◽  
Human Connectome Project ◽  
Neuroimaging Data ◽  
Neuroimaging Study ◽  
Methodological Problems ◽  
Release Data ◽  
Project Data

AbstractAfter a series of reports uncovered various methodological problems with functional magnetic resonance imaging (fMRI) research, considerable attention has been given to principles and practices to improve reproducibility of neuroimaging findings, including promotion of openness, transparency, and data sharing. However, much less attention has been given to use of open access neuroimaging datasets to conduct replication studies. A major barrier to reproducing neuroimaging studies is their high cost, in money and labor, and utilizing such datasets is an obvious solution for breaking down this barrier. The Human Connectome Project (HCP) is an open access dataset consisting of extensive behavioral and neuroimaging data from over 1,100 individuals and there are numerous ongoing HCP-harmonized studies of lifespan and disease that will ultimately release data through HCP infrastructure. To bring attention to the HCP and related projects as an important resource for conducting replication studies, I used the HCP to conduct a replication of a highly cited neuroimaging study that showed correspondence between resting state and task brain networks.

scholarly journals Temporal complexity of fMRI is reproducible and correlates with higher order cognition

2019 ◽  
Author(s):  
Amir Omidvarnia ◽  
Andrew Zalesky ◽  
Sina Mansour ◽  
Dimitri Van De Ville ◽  
Graeme D. Jackson ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Resting State ◽  
Brain Function ◽  
Fluid Intelligence ◽  
Global Network ◽  
Normal Brain ◽  
Temporal Complexity ◽  
Multi Scale ◽  
Human Connectome Project

AbstractIt has been hypothesized that resting state networks (RSNs) likely display unique temporal complexity fingerprints, quantified by their multi-scale entropy patterns [1]. This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of [1] was that resting state functional magnetic resonance imaging (rsfMRI) data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 1000 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-minute rsfMRI recordings and parcellated into 379 brain regions. We quantified multi-scale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multi-scale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values ofrandmincluding the values used in [1]. Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the sub-cortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition timeTR) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameterris equal to 0.5. A strong relationship was observed between temporal complexity of RSNs and fluid intelligence (people’s capacity to reason and think flexibly) through step-wise regression analysis suggesting that complex dynamics of the human brain is an important attribute of high-level brain function. Finally, the results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales, likely due to the regulation of neural synchrony at local and global network levels.

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