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.

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 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 Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data

2020 ◽  
Author(s):  
Arun S. Mahadevan ◽  
Ursula A. Tooley ◽  
Maxwell A. Bertolero ◽  
Allyson P. Mackey ◽  
Danielle S. Bassett
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Partial Correlation ◽  
Motion Artifact ◽  
Resting State Fmri ◽  
Alternative Methods ◽  
Fmri Data ◽  
Retest Reliability ◽  
Human Connectome Project ◽  
Test Retest Reliability

AbstractFunctional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of six different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

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 Dynamic mode decomposition of resting-state and task fMRI

2018 ◽  
Author(s):  
Jeremy Casorso ◽  
Xiaolu Kong ◽  
Wang Chi ◽  
Dimitri Van De Ville ◽  
B.T. Thomas Yeo ◽  
...  
Keyword(s):  
Time Series ◽  
Resting State ◽  
Motor Task ◽  
Component Analysis ◽  
Temporal Organization ◽  
Fmri Data ◽  
Dynamic Component ◽  
Human Connectome Project ◽  
Fmri Time Series

AbstractComponent analysis is a powerful tool to identify dominant patterns of interactions in multivariate datasets. In the context of fMRI data, methods such as principal component analysis or independent component analysis have been used to identify the brain networks shaping functional connectivity (FC). Importantly, these approaches are static in the sense that they ignore the temporal information contained in fMRI time series. Therefore, the corresponding components provide a static characterization of FC. Building upon recent findings suggesting that FC dynamics encode richer information about brain functional organization, we use a dynamic extension of component analysis to identify dynamic modes (DMs) of fMRI time series. We demonstrate the feasibility and relevance of this approach using resting-state and motor-task fMRI data of 730 healthy subjects of the Human Connectome Project (HCP). In resting-state, dominant DMs have strong resemblance with classical resting-state networks, with an additional temporal characterization of the networks in terms of oscillatory periods and damping times. In motor-task conditions, dominant DMs reveal interactions between several brain areas, including but not limited to the posterior parietal cortex and primary motor areas, that are not found with classical activation maps. Finally, we identify two canonical components linking the temporal properties of the resting-state DMs with 158 behavioral and demographic HCP measures. Altogether, these findings illustrate the benefits of the proposed dynamic component analysis framework, making it a promising tool to characterize the spatio-temporal organization of brain activity.

scholarly journals Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques

2021 ◽  
Vol 15 ◽  
Author(s):  
Ramon Casanova ◽  
Robert G. Lyday ◽  
Mohsen Bahrami ◽  
Jonathan H. Burdette ◽  
Sean L. Simpson ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Resting State ◽  
Brain Networks ◽  
Memory Task ◽  
Fmri Data ◽  
High Dimensional ◽  
Functional Brain ◽  
Learning Techniques ◽  
Human Connectome Project ◽  
Functional Brain Networks

Background: fMRI data is inherently high-dimensional and difficult to visualize. A recent trend has been to find spaces of lower dimensionality where functional brain networks can be projected onto manifolds as individual data points, leading to new ways to analyze and interpret the data. Here, we investigate the potential of two powerful non-linear manifold learning techniques for functional brain networks representation: (1) T-stochastic neighbor embedding (t-SNE) and (2) Uniform Manifold Approximation Projection (UMAP) a recent breakthrough in manifold learning.Methods: fMRI data from the Human Connectome Project (HCP) and an independent study of aging were used to generate functional brain networks. We used fMRI data collected during resting state data and during a working memory task. The relative performance of t-SNE and UMAP were investigated by projecting the networks from each study onto 2D manifolds. The levels of discrimination between different tasks and the preservation of the topology were evaluated using different metrics.Results: Both methods effectively discriminated the resting state from the memory task in the embedding space. UMAP discriminated with a higher classification accuracy. However, t-SNE appeared to better preserve the topology of the high-dimensional space. When networks from the HCP and aging studies were combined, the resting state and memory networks in general aligned correctly.Discussion: Our results suggest that UMAP, a more recent development in manifold learning, is an excellent tool to visualize functional brain networks. Despite dramatic differences in data collection and protocols, networks from different studies aligned correctly in the embedding space.

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