Susceptibility to everyday cognitive failure is reflected in functional network interactions in the resting brain

Bey, Katharina; Montag, Christian; Reuter, Martin; Weber, Bernd; Markett, Sebastian

doi:10.1016/j.neuroimage.2015.07.041

Cited by 15 publications

(13 citation statements)

References 64 publications

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“…The main findings revealed high detection accuracy for all of the Big-Five personality traits (N: 83.8%, E: 83.8%, O: 73%, A: 86.5%, and C: 83.8%), thus confirming the initial hypothesis that personality features can be automatically predicted by EEG during emotional stimulation. These findings appear to be consistent with the findings of prior studies of brain activation patterns in relation to emotional states that presented a specific display of physiological responses as indicative of equivalent personality features [22]. N is typically assessed with items emphasizing affective content; E, A, and C emphasize behavioral content; and O is represented primarily by cognitive content [63,64].…”

Section: Discussionsupporting

confidence: 87%

“…Resting-state connectivity EEG studies have been conducted within the framework of two concepts concerning the dynamic nature of brain networks. The first concept considers that brain networks are static across time [18][19][20][21][22]. As an example, in the study conducted by Toschi et al [18], the authors reported that conscientiousness (C) was linked to graph theoretical nodal properties of the regions included in the fronto-parietal and the default mode networks.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Moving forward, multiple connectivity studies have been recently conducted to understand the neural substrates of human personality (Adelstein et al, 2011;Aghajani et al, 2013;Beaty et al, 2016b;Bey, Montag, Reuter, Weber, & Markett, 2015;Bey et al, 2015;Dubois, Galdi, Han, Paul, & Adolphs, 2018;Gao, 2013;Kyeong, Kim, Park, & Hwang, 2014;Sebastian Markett, Montag, Melchers, Weber, & Reuter, 2016;Tompson et al, 2018). Interestingly, graph theoretical assessment derived from networks was applied to link topological brain features to the Big Five personality traits (Beaty et al, 2016b;Bey et al, 2015;Gao, 2013;Toschi et al, 2018). As an example, (Toschi et al 2018) shows that conscientiousness is linked to nodal properties (clustering coefficient, betweenness centrality and strength) of fronto-parietal and default mode network regions.…”

Section: Discussionmentioning

confidence: 99%

“…Our hypothesis is based on many recent studies suggesting that personality-related differences in functional connectivity are discernable during rest (Adelstein et al, 2011;Beaty et al, 2016;Bey, Montag, Reuter, Weber, & Markett, 2015;Gao, 2013;T. Li et al, 2017;Y.…”

Section: Discussionmentioning

confidence: 99%

Brain Network Dynamics Correlate with Personality Traits

et al. 2020

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Introduction: Identifying 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. Objective: In this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits. Method: Using 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. Results: Similar 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. Conclusion: These findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

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“…Moving forward, multiple connectivity studies have been recently conducted to understand the neural substrates of human personality (Adelstein et al, 2011;Aghajani et al, 2013;Beaty et al, 2016b;Bey et al, 2015Bey et al, , 2015Dubois et al, 2018;Gao, 2013;Kyeong et al, 2014;Markett et al, 2016Markett et al, , 2013Tompson et al, 2018). Interestingly, graph theoretical assessment derived from networks were applied to link topological brain features to the Big Five personality traits (Beaty et al, 2016b;Bey et al, 2015;Gao, 2013;Toschi et al, 2018). As an example, (Toschi et al 2018) shows that conscientiousness is linked to nodal properties (clustering coefficient, betweenness centrality and strength) of fronto-parietal and default mode network regions.…”

Section: Discussionmentioning

confidence: 99%

Brain network dynamics correlate with personality traits

Kabbara

Paban

Weill

et al. 2019

Preprint

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Identifying 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 personalityrelated differences in functional connectivity has been obtained through the stationary analysis, which do not capture the complex dynamical properties of brain networks. In this study, we aimed to evaluate the feasibility of dynamic network measures to predict personality traits. Using the EEGsource connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from EEG data acquired from 45 subjects during resting state. Then, several dynamic functional connectivity metrics were evaluated. Results showed a negative correlation between neuroticism and the dynamic variability of temporal lobe regions in terms of strength. In addition, a positive correlation was found between agreeableness and centrality variability of the posterior cingulate, which is known as a key hub in resting state networks. Results also revealed that extraversion is positively correlated with the dynamics of the superior parietal region. These findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

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Susceptibility to everyday cognitive failure is reflected in functional network interactions in the resting brain

Cited by 15 publications

References 64 publications

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

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

Brain Network Dynamics Correlate with Personality Traits

Brain network dynamics correlate with personality traits

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