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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Vydáno v:Brain Structure and Function Ročník 223; číslo 6; s. 2699 - 2719
Hlavní autoři: Nostro, Alessandra D., Müller, Veronika I., Varikuti, Deepthi P., Pläschke, Rachel N., Hoffstaedter, Felix, Langner, Robert, Patil, Kaustubh R., Eickhoff, Simon B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2018
Springer Nature B.V
Témata:
Adult
Biomedical and Life Sciences
Biomedicine
Brain - diagnostic imaging
Brain - physiology
Brain Mapping
Cell Biology
Connectome
Female
Females
Gender
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Male
Males
Memory
Meta-Analysis as Topic
Nerve Net - diagnostic imaging
Nerve Net - physiology
Nervous system
Neural networks
Neurology
Neurosciences
Neurosis
Original Article
Oxygen - blood
Personality
Personality - physiology
Personality traits
Rest
Sex differences
Social organization
Young Adult
Resting-state functional connectivity
Gender differences
Hormonal influence
Machine learning
NEO-FFI
Functional networks
ISSN:1863-2653, 1863-2661, 1863-2661, 0340-2061
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Shrnutí: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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ISSN:1863-2653
1863-2661
1863-2661
0340-2061
DOI:10.1007/s00429-018-1651-z