Predicting personality from network-based resting-state functional connectivity
Autor: | Felix Hoffstaedter, Deepthi P. Varikuti, Rachel Pläschke, Simon B. Eickhoff, Kaustubh R. Patil, Robert Langner, Veronika I. Müller, Alessandra D. Nostro |
---|---|
Rok vydání: | 2017 |
Předmět: |
Agreeableness
Adult Male Histology media_common.quotation_subject Rest 050105 experimental psychology Article Correlation 03 medical and health sciences Young Adult 0302 clinical medicine Meta-Analysis as Topic Openness to experience Connectome Image Processing Computer-Assisted Personality Humans 0501 psychology and cognitive sciences ddc:610 Big Five personality traits media_common Brain Mapping Extraversion and introversion Resting state fMRI General Neuroscience 05 social sciences Brain Neuroticism Magnetic Resonance Imaging Oxygen Female Anatomy Nerve Net Psychology 030217 neurology & neurosurgery Cognitive psychology |
Zdroj: | Brain structure & function 223(6), 2699–2719 (-). doi:10.1007/s00429-018-1651-z |
ISSN: | 1863-2661 |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: |