Predicting personality from network-based resting-state functional connectivity
Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed t...
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| Published in: | Brain Structure and Function Vol. 223; no. 6; pp. 2699 - 2719 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.07.2018
Springer Nature B.V |
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| ISSN: | 1863-2653, 1863-2661, 1863-2661, 0340-2061 |
| Online Access: | Get full text |
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| Abstract | Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC–personality relations should not be considered independently of gender. |
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| AbstractList | Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC-personality relations should not be considered independently of gender. Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC-personality relations should not be considered independently of gender.Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC-personality relations should not be considered independently of gender. |
| Author | Hoffstaedter, Felix Patil, Kaustubh R. Varikuti, Deepthi P. Pläschke, Rachel N. Eickhoff, Simon B. Müller, Veronika I. Nostro, Alessandra D. Langner, Robert |
| AuthorAffiliation | 1 Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225 Düsseldorf, Germany 3 Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425 Jülich, Germany 2 Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225 Düsseldorf, Germany |
| AuthorAffiliation_xml | – name: 2 Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225 Düsseldorf, Germany – name: 3 Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425 Jülich, Germany – name: 1 Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225 Düsseldorf, Germany |
| Author_xml | – sequence: 1 givenname: Alessandra D. orcidid: 0000-0003-4702-3499 surname: Nostro fullname: Nostro, Alessandra D. email: nostro@uni-duesseldorf.de organization: Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich – sequence: 2 givenname: Veronika I. surname: Müller fullname: Müller, Veronika I. organization: Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich – sequence: 3 givenname: Deepthi P. surname: Varikuti fullname: Varikuti, Deepthi P. organization: Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich – sequence: 4 givenname: Rachel N. surname: Pläschke fullname: Pläschke, Rachel N. organization: Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich – sequence: 5 givenname: Felix surname: Hoffstaedter fullname: Hoffstaedter, Felix organization: Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich – sequence: 6 givenname: Robert surname: Langner fullname: Langner, Robert organization: Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich – sequence: 7 givenname: Kaustubh R. surname: Patil fullname: Patil, Kaustubh R. organization: Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich – sequence: 8 givenname: Simon B. surname: Eickhoff fullname: Eickhoff, Simon B. organization: Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29572625$$D View this record in MEDLINE/PubMed |
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| ISSN | 1863-2653 1863-2661 |
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| Issue | 6 |
| Keywords | Resting-state functional connectivity Gender differences Hormonal influence Machine learning NEO-FFI Functional networks |
| Language | English |
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| PublicationPlace | Berlin/Heidelberg |
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| PublicationTitle | Brain Structure and Function |
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| PublicationYear | 2018 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
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