Brain Structure and Function

, Volume 223, Issue 6, pp 2699–2719 | Cite as

Predicting personality from network-based resting-state functional connectivity

  • Alessandra D. NostroEmail author
  • Veronika I. Müller
  • Deepthi P. Varikuti
  • Rachel N. Pläschke
  • Felix Hoffstaedter
  • Robert Langner
  • Kaustubh R. Patil
  • Simon B. Eickhoff
Original Article


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.


Functional networks Gender differences Hormonal influence Machine learning NEO-FFI Resting-state functional connectivity 



This study was supported by the Deutsche Forschungsgemeinschaft (DFG, EI 816/4-1, LA 3071/3-1), the National Institute of Mental Health (R01-MH074457), the Helmholtz Portfolio Theme “Supercomputing and Modelling for the Human Brain”, and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 7202070 (HBP SGA1).

Compliance with ethical standards

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

429_2018_1651_MOESM1_ESM.docx (5.9 mb)
Supplementary material 1 (DOCX 6044 KB)


  1. Adelstein JS, Shehzad Z, Mennes M et al (2011) Personality is reflected in the brain’s intrinsic functional architecture. PLoS One 6:e27633. PubMedPubMedCentralCrossRefGoogle Scholar
  2. Alawieh A, Sabra Z, Sabra M et al (2015) A rich-club organization in brain ischemia protein interaction network. Sci Rep. PubMedPubMedCentralCrossRefGoogle Scholar
  3. Allen TA, DeYoung CG (2016) Personality neuroscience and the five factor modelGoogle Scholar
  4. Allen EA, Erhardt EB, Damaraju E et al (2011) A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci 5:2. PubMedPubMedCentralCrossRefGoogle Scholar
  5. Arélin K, Mueller K, Barth C et al (2015) Progesterone mediates brain functional connectivity changes during the menstrual cycle—a pilot resting state MRI study. Front Neurosci 9:1–11. CrossRefGoogle Scholar
  6. Asahi S, Okamoto Y, Okada G et al (2004) Negative correlation between right prefrontal activity during response inhibition and impulsiveness: a fMRI study. Eur Arch Psychiatry Clin Neurosci 254:245–251. PubMedCrossRefGoogle Scholar
  7. Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26:839–851. PubMedCrossRefGoogle Scholar
  8. Bachrach Y, Kosinski M, Graepel T et al (2012) Personality and patterns of Facebook usage. Proc 3rd Annu ACM Web Sci Conf-WebSci’. 12 24–32.
  9. Baumgartner U, Buchholz HG, Bellosevich A et al (2006) High opiate receptor binding potential in the human lateral pain system. Neuroimage 30:692–699. PubMedCrossRefGoogle Scholar
  10. Beaty RE, Kaufman SB, Benedek M et al (2016) Personality and complex brain networks: the role of openness to experience in default network efficiency. Hum Brain Mapp 37:773–779. PubMedCrossRefGoogle Scholar
  11. Berridge KC, Robinson TE (1998) What is the role of dopamine in reward: Hedonic impact, reward learning, or incentive salience? Brain Res Rev 28:309–369. PubMedCrossRefGoogle Scholar
  12. Binder JR, Desai RH, Graves WW, Conant LL (2009) Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb Cortex 19:2767–2796. PubMedPubMedCentralCrossRefGoogle Scholar
  13. Bjørnebekk A, Fjell AM, Walhovd KB et al (2013) Neuronal correlates of the five factor model (FFM) of human personality: multimodal imaging in a large healthy sample. Neuroimage 65:194–208. PubMedCrossRefGoogle Scholar
  14. Bouchard TJ, McGue M (2003) Genetic and environmental influences on human psychological differences. J Neurobiol 54:4–45PubMedCrossRefGoogle Scholar
  15. Bromberg-Martin ES, Matsumoto M, Hikosaka O (2010) Dopamine in motivational control: rewarding, aversive, and alerting. Neuron 68:815–834PubMedPubMedCentralCrossRefGoogle Scholar
  16. Butrus N, Witenberg RT (2013) Some personality predictors of tolerance to human diversity: the roles of openness, agreeableness, and empathy. Aust Psychol 48:290–298. CrossRefGoogle Scholar
  17. Bzdok D, Schilbach L, Vogeley K et al (2012) Parsing the neural correlates of moral cognition: ALE meta-analysis on morality, theory of mind, and empathy. Brain Struct Funct 217:783–796. PubMedPubMedCentralCrossRefGoogle Scholar
  18. Ciric R, Wolf DH, Power JD et al (2017) Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage 154:174–187. PubMedCrossRefPubMedCentralGoogle Scholar
  19. Cohen J (1988) Statistical power analysis for the behavioral sciences. Stat Power Anal Behav Sci 2nd:567Google Scholar
  20. Colclough GL, Smith SM, Nichols TE et al (2017) The heritability of multi-modal connectivity in human brain activity. Elife 6:e20178. PubMedPubMedCentralCrossRefGoogle Scholar
  21. Costa PT, McCrae RR (1987) Neuroticism, somatic complaints, and disease: is the bark worse than the bite? J Pers 55:299–316. PubMedCrossRefGoogle Scholar
  22. Costa PT, McCrae RR (1992) Professional manual: revised NEO personality inventory (NEO-PI-R) and NEO five-factor inventory (NEO-FFI)Google Scholar
  23. Cremers HR, Demenescu LR, Aleman A et al (2010) Neuroticism modulates amygdala-prefrontal connectivity in response to negative emotional facial expressions. Neuroimage 49:963–970. PubMedCrossRefGoogle Scholar
  24. Cui Z, Su M, Li L et al (2017) Individualized prediction of reading comprehension ability using gray matter volume individualized prediction of reading comprehension ability using gray matter volume.
  25. De Vico Fallani F, Richiardi J, Chavez M, Achard S (2014) Graph analysis of functional brain networks: practical issues in translational neuroscience. Philos Trans R Soc B Biol Sci 369:20130521–20130521. CrossRefGoogle Scholar
  26. Denkova E, Dolcos S, Dolcos F (2012) Reliving emotional personal memories: affective biases linked to personality and sex-related differences. Emotion 12:515–528. PubMedCrossRefGoogle Scholar
  27. Depue RA, Collins PF (1999) Neurobiology of the structure of personality: dopamine, facilitation of incentive motivation, and extraversion. Behav Brain Sci 22:491–517PubMedGoogle Scholar
  28. DeSoto MC, Salinas M (2015) Neuroticism and cortisol: the importance of checking for sex differences. Psychoneuroendocrinology 62:174–179. PubMedCrossRefGoogle Scholar
  29. DeYoung CG (2010) Personality neuroscience and the biology of traits. Soc Personal Psychol Compass 4:1165–1180. CrossRefGoogle Scholar
  30. DeYoung CG (2013) The neuromodulator of exploration: a unifying theory of the role of dopamine in personality. Front Hum Neurosci. PubMedPubMedCentralCrossRefGoogle Scholar
  31. DeYoung C (2014) Openness/intellect: a dimension of personality reflecting cognitive exploration. APA Handb Personal Soc Psychol Personal Process Individ Differ 4:369–399. CrossRefGoogle Scholar
  32. DeYoung CG (2015) Cybernetic big five theory. J Res Pers 56:33–58. CrossRefGoogle Scholar
  33. DeYoung CG, Gray JR (2009) Personality neuroscience: explaining individual differences in affect, behaviour and cognitionGoogle Scholar
  34. DeYoung CG, Peterson JB, Higgins DM (2005) Sources of openness/intellect: cognitive and neuropsychological correlates of the fifth factor of personality. J Pers 73:825–858. PubMedCrossRefGoogle Scholar
  35. DeYoung CG, Quilty LC, Peterson JB (2007) Between facets and domains: 10 aspects of the big five. J Pers Soc Psychol 93:880–896. PubMedCrossRefGoogle Scholar
  36. DeYoung CG, Hirsh JB, Shane MS, Papademetris X (2010) Testing predictions from personality neuroscience: brain structure and the big five. 21:820–828.
  37. Doyle OM, Mehta MA, Brammer MJ (2015) The role of machine learning in neuroimaging for drug discovery and development. Psychopharmacology 232:4179–4189. PubMedCrossRefGoogle Scholar
  38. Dreher J-C, Schmidt PJ, Kohn P et al (2007) Menstrual cycle phase modulates reward-related neural function in women. Proc Natl Acad Sci USA 104:2465–2470. PubMedPubMedCentralCrossRefGoogle Scholar
  39. Eickhoff SB, Grefkes C (2011) Approaches for the integrated analysis of structure, function and connectivity of the human brain. Clin EEG Neurosci 42:107–121. PubMedCrossRefGoogle Scholar
  40. Eickhoff SB, Stephan KE, Mohlberg H et al (2005) A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 25:1325–1335. PubMedCrossRefGoogle Scholar
  41. Eickhoff SB, Paus T, Caspers S et al (2007) Assignment of functional activations to probabilistic cytoarchitectonic areas revisited. Neuroimage 36:511–521. PubMedCrossRefGoogle Scholar
  42. Eysenck HJ (1967) Biological basis of personality. Nature 199:1031–1034. CrossRefGoogle Scholar
  43. Filippi M, Valsasina P, Misci P et al (2013) The organization of intrinsic brain activity differs between genders: a resting-state fMRI study in a large cohort of young healthy subjects. Hum Brain Mapp 34:1330–1343. PubMedCrossRefGoogle Scholar
  44. Fleischhauer M, Enge S, Brocke B et al (2010) Same or different? Clarifying the relationship of need for cognition to personality and intelligence. Personal Soc Psychol Bull 36:82–96. CrossRefGoogle Scholar
  45. Fox PT, Lancaster JL, Laird AR, Eickhoff SB (2014) Meta-analysis in human neuroimaging: computational modeling of large-scale databases peter. Annu Rev Neurosci 37:409–434. PubMedPubMedCentralCrossRefGoogle Scholar
  46. Gao Q, Xu Q, Duan X et al (2013) Extraversion and neuroticism relate to topological properties of resting-state brain networks. Front Hum Neurosci 7:257. PubMedPubMedCentralCrossRefGoogle Scholar
  47. Gazzola V, Aziz-Zadeh L, Keysers C (2006) Empathy and the somatotopic auditory mirror system in humans. Curr Biol 16:1824–1829. PubMedCrossRefGoogle Scholar
  48. Ge T, Holmes AJ, Buckner RL et al (2017) Heritability analysis with repeat measurements and its application to resting-state functional connectivity. Proc Natl Acad Sci 114:5521–5526. PubMedPubMedCentralCrossRefGoogle Scholar
  49. Glasser MF, Sotiropoulos SN, Wilson JA et al (2013) The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80:105–124. PubMedPubMedCentralCrossRefGoogle Scholar
  50. Golbeck J (2011) Predicting personality with social media. Proc 2011 Annu Conf Ext Abstr Hum factors Comput Syst CHI EA. 11 253–262.
  51. Golbeck J, Robles C, Edmondson M, Turner K (2011) Predicting personality from twitter. Proc—2011 IEEE Int Conf Privacy., Secur Risk Trust IEEE Int Conf Soc Comput PASSAT/SocialCom 149–156.
  52. Goldberg LR, Rosolack TK (1994) The big five factor structure as an integrative framework: an empirical comparison with Eysenck’s P-E-N model. In: Halverson Jr. CF, Kohnstamm GA, Martin RP (eds) The developing structure of temperament and personality from infancy to adulthood. Lawrence Erlbaum, New York, pp 7–35Google Scholar
  53. Gray J, Mcnaughton N (2000) The neuropsychology of anxiety: an enquiry into the functions of the septo-hippocampal system, second. Oxford Psychol Ser Second Edi:433. CrossRefGoogle Scholar
  54. Graziano WG, Habashi MM, Sheese BE, Tobin RM (2007) Agreeableness, empathy, and helping: A person × situation perspective. J Pers Soc Psychol 93:583–599. PubMedCrossRefGoogle Scholar
  55. Grosbras MH, Beaton S, Eickhoff SB (2012) Brain regions involved in human movement perception: a quantitative voxel-based meta-analysis. Hum Brain Mapp 33:431–454. PubMedCrossRefGoogle Scholar
  56. Haas BW, Brook M, Remillard L et al (2015) I know how you feel: The warm-altruistic personality profile and the empathic brain. PLoS One 10:1–15. CrossRefGoogle Scholar
  57. Harrison PJ, Tunbridge EM (2008) Catechol-O-methyltransferase (COMT): a gene contributing to sex differences in brain function, and to sexual dimorphism in the predisposition to psychiatric disorders. Neuropsychopharmacology 33:3037–3045. PubMedCrossRefGoogle Scholar
  58. Hassabis D, Spreng RN, Rusu AA et al (2014) Imagine all the people: How the brain creates and uses personality models to predict behavior. Cereb Cortex 24:1979–1987. PubMedCrossRefGoogle Scholar
  59. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Elements 1:337–387. CrossRefGoogle Scholar
  60. Hayes DJ, Northoff G (2012) Common brain activations for painful and non-painful aversive stimuli. BMC Neurosci 13:60. PubMedPubMedCentralCrossRefGoogle Scholar
  61. Hjelmervik H, Hausmann M, Osnes B et al (2014) Resting states are resting traits—an fMRI study of sex differences and menstrual cycle effects in resting state cognitive control networks. PLoS One 9:32–36. CrossRefGoogle Scholar
  62. Holmes CJ, Hoge R, Collins L et al (1998) Enhancement of MR images using registration for signal averaging. J Comput Assist Tomogr 22:324–333. PubMedCrossRefGoogle Scholar
  63. Hooker CI, Verosky SC, Miyakawa A et al (2008) The influence of personality on neural mechanisms of observational fear and reward learning. Neuropsychologia 46:2709–2724. PubMedPubMedCentralCrossRefGoogle Scholar
  64. Hu X, Erb M, Ackermann H et al (2011) Voxel-based morphometry studies of personality: issue of statistical model specification—effect of nuisance covariates. Neuroimage 54:1994–2005. PubMedCrossRefGoogle Scholar
  65. Iacoboni M (2009) Imitation, empathy, and mirror neurons. Annu Rev Psychol 60:653–670. PubMedCrossRefGoogle Scholar
  66. Iannetti GD, Mouraux A (2010) From the neuromatrix to the pain matrix (and back). Exp Brain Res 205:1–12PubMedCrossRefGoogle Scholar
  67. IBM Corp. Released (2011) IBM SPSS statistics for windows, Version 20.0Google Scholar
  68. Jang KL, Livesley WJ, Vernon P (1996) Heritability of the big five personality dimensions and their facets: a twin study. J Pers 64:577–591. PubMedCrossRefGoogle Scholar
  69. Jensen-Campbell LA, Graziano WG (2001) Agreeableness as a moderator of interpersonal conflict. J Pers 69:323–361. PubMedCrossRefGoogle Scholar
  70. Jokela M, Kivimäki M, Elovainio M, Keltikangas-Järvinen L (2009) Personality and having children: a two-way relationship. J Pers Soc Psychol 96:218–230. PubMedCrossRefGoogle Scholar
  71. Jorm AF (1987) Sex differences in neuroticism: a quantitative synthesis of published research. Aust N Z J Psychiatry 21:501–506. PubMedCrossRefGoogle Scholar
  72. Kennis M, Rademaker AR, Geuze E (2013) Neural correlates of personality: an integrative review. Neurosci Biobehav Rev 37:73–95. PubMedCrossRefGoogle Scholar
  73. Keysers C, Gazzola V (2007) Integrating simulation and theory of mind: from self to social cognition. Trends Cogn Sci 11:194–196PubMedCrossRefGoogle Scholar
  74. Kim H, Shimojo S, O’Doherty JP (2006) Is avoiding an aversive outcome rewarding? Neural substrates of avoidance learning in the human brain. PLoS Biol 4:1453–1461. CrossRefGoogle Scholar
  75. Koelsch S, Skouras S, Jentschke S (2013) Neural correlates of emotional personality: a structural and functional magnetic resonance imaging study. PLoS One. CrossRefPubMedPubMedCentralGoogle Scholar
  76. Kogler L, Müller VI, Chang A et al (2015) Psychosocial versus physiological stress—meta-analyses on deactivations and activations of the neural correlates of stress reactions. Neuroimage 119:235–251. PubMedPubMedCentralCrossRefGoogle Scholar
  77. Konishi Y, Tanii H, Otowa T et al (2014) Gender-specific association between the COMT Val158Met polymorphism and openness to experience in panic disorder patients. Neuropsychobiology 69:165–174. PubMedCrossRefGoogle Scholar
  78. Ktena SI, Arslan S, Parisot S, Rueckert D (2017) Exploring heritability of functional brain networks with inexact graph matching. Proc Int Symp Biomed Imaging 354–357Google Scholar
  79. Kumari V (2004) Personality predicts brain responses to cognitive demands. J Neurosci 24:10636–10641. PubMedCrossRefGoogle Scholar
  80. Kunisato Y, Okamoto Y, Okada G et al (2011) Personality traits and the amplitude of spontaneous low-frequency oscillations during resting state. Neurosci Lett 492:109–113. PubMedCrossRefGoogle Scholar
  81. Lahey BB (2009) Public health significance of neuroticism. Am Psychol 64:241–256. PubMedPubMedCentralCrossRefGoogle Scholar
  82. Lane DM (2013) Introduction to statistics. Introd to Stat 454–458.
  83. Langner R, Eickhoff SB (2013) Sustaining attention to simple tasks: a meta-analytic review of the neural mechanisms of vigilant attention. Psychol Bull 139:870–900. PubMedCrossRefGoogle Scholar
  84. Lee M, Smyser C, Shimony J (2012) {Resting-State} {fMRI:} a review of methods and clinical applications. Am J Neuroradiol. CrossRefPubMedGoogle Scholar
  85. Lei X, Yang T, Wu T (2015) Functional neuroimaging of extraversion-introversion. Neurosci Bull 31:663–675. PubMedPubMedCentralCrossRefGoogle Scholar
  86. Li J, Liu J (2010) Extraversion predicts individual differences in face recognition. pp 295–298Google Scholar
  87. Li N, Ma N, Liu Y et al (2013) Resting-state functional connectivity predicts impulsivity in economic decision-making. J Neurosci 33:4886–4895. PubMedCrossRefGoogle Scholar
  88. Liu X, Hairston J, Schrier M, Fan J (2011) Common and distinct networks underlying reward valence and processing stages: a meta-analysis of functional neuroimaging studies. Neurosci Biobehav Rev 35:1219–1236. PubMedCrossRefGoogle Scholar
  89. Liu W-Y, Weber B, Reuter M et al (2013) The big five of personality and structural imaging revisited: a VBM–DARTEL study. Neuroreport 24:375–380. PubMedCrossRefGoogle Scholar
  90. MacLean MH, Arnell KM (2010) Personality predicts temporal attention costs in the attentional blink paradigm. Psychon Bull Rev 17:556–562. PubMedCrossRefGoogle Scholar
  91. Madsen MK, Mc Mahon B, Andersen SB et al (2015) Threat-related amygdala functional connectivity is associated with 5-HTTLPR genotype and neuroticism. Soc Cogn Affect Neurosci 11:140–149. PubMedPubMedCentralCrossRefGoogle Scholar
  92. Magyar M, Gonda X, Pap D et al (2017) Decreased openness to experience is associated with migraine-type headaches in subjects with lifetime depression. Front Neurol. PubMedPubMedCentralCrossRefGoogle Scholar
  93. Marcus B, Machilek F, Schütz A (2006) Personality in cyberspace: personal Web sites as media for personality expressions and impressions. J Pers Soc Psychol 90:1014–1031. PubMedCrossRefGoogle Scholar
  94. Martin L, Clair J, Davis P et al (2006) Enhanced recognition of facial expressions of disgust in opiate users receiving maintenance treatment. Addiction 101:1598–1605. PubMedCrossRefGoogle Scholar
  95. McCrae RR, Costa PT (2004) A contemplated revision of the NEO Five-Factor Inventory. Pers Individ Dif 36:587–596. CrossRefGoogle Scholar
  96. Mohan G, Mulla ZR (2013) Openness to experience and work outcomes: exploring the moderating effects of conscientiousness and job complexityGoogle Scholar
  97. Molnar-Szakacs I, Arzy S (2009) Searching for an integrated self-representation. Commun Integr Biol 2:365–367PubMedPubMedCentralCrossRefGoogle Scholar
  98. Molnar-Szakacs I, Uddin LQ (2013) Self-processing and the default mode network: interactions with the mirror neuron system. Front Hum Neurosci. PubMedPubMedCentralCrossRefGoogle Scholar
  99. Nicholson N, Fenton-O’Creevy M, Soane E, Willman P (2002) Risk propensity and personality. London Edu/Docs/Risk 1–33. CrossRefGoogle Scholar
  100. Nostro AD, Müller VI, Reid AT, Eickhoff SB (2016) Correlations between personality and brain structure: a crucial role of gender. Cereb Cortex 1–15.
  101. Oktar N, Oktar Y (2015) Machine learning and neuroimaging. J Neurol Sci [Turkish] 32:1–4Google Scholar
  102. Orrù G, Pettersson-Yeo W, Marquand AF et al (2012) Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 36:1140–1152. PubMedCrossRefGoogle Scholar
  103. Oswald LM, Zandi P, Nestadt G et al (2006) Relationship between cortisol responses to stress and personality. 1583–1591.
  104. Ozer DJ, Benet-Martínez V (2006) Personality and the prediction of consequential outcomes. Annu Rev Psychol 57:401–421. PubMedCrossRefGoogle Scholar
  105. Pang Y, Cui Q, Wang Y et al (2016) Extraversion and neuroticism related to the resting-state effective connectivity of amygdala. Sci Rep. CrossRefPubMedPubMedCentralGoogle Scholar
  106. Passamonti L, Fera F, Magariello A et al (2006) Monoamine oxidase—a genetic variations influence brain activity associated with inhibitory control: new insight into the neural correlates of impulsivity. Biol Psychiatry 59:334–340. PubMedCrossRefGoogle Scholar
  107. Passamonti L, Terracciano A, Riccelli R et al (2015) Increased functional connectivity within mesocortical networks in open people. Neuroimage 104:301–309. PubMedCrossRefGoogle Scholar
  108. Pasternak GW, Pan Y-X (2013) Mu opioids and their receptors: evolution of a concept. Pharmacol Rev 65:1257–1317. PubMedPubMedCentralCrossRefGoogle Scholar
  109. Pearman A (2009) Predictors of subjective memory in young adults. 101–107.
  110. Peciña S, Smith KS, Berridge KC (2006) Hedonic hot spots in the brain. Neurosci 12:500–511. CrossRefGoogle Scholar
  111. Plitt M, Barnes KA, Wallace GL et al (2015) Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism. Proc Natl Acad Sci 112:E6699-6706. CrossRefGoogle Scholar
  112. Power R, Pluess M (2015) Heritability estimates of the Big Five personality traits based on common genetic variants. Transl Psychiatry 5:e604. PubMedPubMedCentralCrossRefGoogle Scholar
  113. Power JD, Cohen AL, Nelson SM et al (2011) Functional network organization of the human brain. Neuron 72:665–678. PubMedPubMedCentralCrossRefGoogle Scholar
  114. Qin P, Northoff G (2011) How is our self related to midline regions and the default-mode network? Neuroimage 57:1221–1233PubMedCrossRefGoogle Scholar
  115. Quercia D, Kosinski M, Stillwell D, Crowcroft J. Our twitter profiles, our selves: predicting personality with twitterGoogle Scholar
  116. Roberts BW, Jackson JJ, Fayard JV et al (2009) Conscientiousness. Handb Individ Differ Soc Behav 369–381Google Scholar
  117. Rosenberg MD, Finn ES, Scheinost D et al (2016) A neuromarker of sustained attention from whole-brain functional connectivity. Nat Neurosci 19:165–171. PubMedCrossRefGoogle Scholar
  118. Rottschy C, Langner R, Dogan I et al (2012) Modelling neural correlates of working memory: a coordinate-based meta-analysis. Neuroimage 60:830–846. PubMedCrossRefGoogle Scholar
  119. Rusting CL, Larsen RJ (1997) Extraversion, neuroticism, and susceptibility to positive and negative affect: A test of two theoretical models. Pers Individ Dif 22:607–612. CrossRefGoogle Scholar
  120. Ryan JP, Sheu LK, Gianaros PJ (2011) Resting state functional connectivity within the cingulate cortex jointly predicts agreeableness and stressor-evoked cardiovascular reactivity. Neuroimage 55:363–370. PubMedCrossRefGoogle Scholar
  121. Sabatinelli D, Fortune EE, Li Q et al (2011) Emotional perception: Meta-analyses of face and natural scene processing. Neuroimage 54:2524–2533. PubMedCrossRefGoogle Scholar
  122. Salimi-Khorshidi G, Douaud G, Beckmann CF et al (2014) Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage 90:449–468. PubMedPubMedCentralCrossRefGoogle Scholar
  123. Samartsidis P, Montagna S, Nichols TE, Johnson TD (2017) The coordinate-based meta-analysis of neuroimaging data. Stat Sci Volume 32:580–599CrossRefGoogle Scholar
  124. Sampaio A, Soares JM, Coutinho J et al (2014) The big five default brain: functional evidence. Brain Struct Funct 219:1913–1922. PubMedCrossRefGoogle Scholar
  125. Sapolsky RM (1994) Glucocorticoids, stress and exacerbation of excitotoxic neuron death. Semin Neurosci 6:323–331CrossRefGoogle Scholar
  126. Satterthwaite TD, Elliott MA, Gerraty RT et al (2013) An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage 64:240–256. PubMedCrossRefGoogle Scholar
  127. Schilbach L, Derntl B, Aleman A et al (2016) Differential patterns of dysconnectivity in mirror neuron and mentalizing networks in schizophrenia. Schizophr Bull sbw015. CrossRefGoogle Scholar
  128. Selleck RA, Baldo BA (2017) Feeding-modulatory effects of mu-opioids in the medial prefrontal cortex: a review of recent findings and comparison to opioid actions in the nucleus accumbens. Psychopharmacology 234:1439–1449PubMedPubMedCentralCrossRefGoogle Scholar
  129. Servaas MN, Geerligs L, Renken RJ et al (2015) Connectomics and neuroticism: an altered functional network organization. Neuropsychopharmacology 40:296–304. PubMedCrossRefGoogle Scholar
  130. Shanmugan S, Epperson CN (2014) Estrogen and the prefrontal cortex: Towards a new understanding of estrogen’s effects on executive functions in the menopause transition. Hum Brain Mapp 35:847–865PubMedCrossRefGoogle Scholar
  131. Spreng RN, Mar RA, Kim ASN (2008) The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: a quantitative meta-analysis. J Cogn Neurosci 21:489–510. CrossRefGoogle Scholar
  132. Sprouse-Blum AS, Smith G, Sugai D, Parsa FD (2010) Understanding endorphins and their importance in pain management. Hawaii Med J 69:70–71PubMedPubMedCentralGoogle Scholar
  133. Studer-Luethi B, Jaeggi SM, Buschkuehl M, Perrig WJ (2012) Influence of neuroticism and conscientiousness on working memory training outcome. Pers Individ Dif 53:44–49. CrossRefGoogle Scholar
  134. Sutin AR, Terracciano A, Kitner-Triolo MH et al (2011) Personality traits prospectively predict verbal fluency in a lifespan sample. Psychol Aging 26:994–999. PubMedPubMedCentralCrossRefGoogle Scholar
  135. Tejeda HA, Hanks AN, Scott L et al (2015) Prefrontal cortical kappa opioid receptors attenuate responses to amygdala inputs. Neuropsychopharmacology 40:2856–2864. PubMedPubMedCentralCrossRefGoogle Scholar
  136. Tipping M (2001) Sparse Bayesian learning and the relevance vector mach. J Mach Learn Res 1:211–244. CrossRefGoogle Scholar
  137. Tzschoppe J, Nees F, Banaschewski T et al (2014) Aversive learning in adolescents: modulation by amygdala-prefrontal and amygdala-hippocampal connectivity and neuroticism. Neuropsychopharmacology 39:875–884. PubMedCrossRefGoogle Scholar
  138. Van Essen DC, Smith SM, Barch DM et al (2013) The WU-Minn human connectome project: an overview. Neuroimage 80:62–79. PubMedPubMedCentralCrossRefGoogle Scholar
  139. van den Heuvel MP, van Soelen ILC, Stam CJ et al (2013) Genetic control of functional brain network efficiency in children. Eur Neuropsychopharmacol 23:19–23. PubMedCrossRefGoogle Scholar
  140. Varikuti DP, Hoffstaedter F, Genon S et al (2016) Resting-state test retest reliability of a priori defined canonical networks over different preprocessing steps. Brain Struct Funct 1–22.
  141. Varoquaux G, Thirion B (2014) How machine learning is shaping cognitive neuroimaging. Gigascience 3:28. PubMedPubMedCentralCrossRefGoogle Scholar
  142. Varoquaux G, Raamana P, Engemann D et al (2016) Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. arXiv:160605201 [statML] 1–14.
  143. Verweij KJH, Yang J, Lahti J et al (2012) Maintenance of genetic variation in human personality: testing evolutionary models by estimating heritability due to common causal variants and investigating the effect of distant inbreeding. Evolution 66:3238–3251. PubMedPubMedCentralCrossRefGoogle Scholar
  144. Viken RJ, Rose RJ, Kaprio J, Koskenvuo M (1994) A developmental genetic analysis of adult personality: extraversion and neuroticism from 18 to 59 years of age. J Pers Soc Psychol 66:722–730. PubMedCrossRefGoogle Scholar
  145. Wang Y, Fan Y, Bhatt P, Davatzikos C (2010) High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables. Neuroimage 50:1519–1535. PubMedPubMedCentralCrossRefGoogle Scholar
  146. Weis S, Hodgetts S, Hausmann M (2017) Sex differences and menstrual cycle effects in cognitive and sensory resting state networks. Brain CognGoogle Scholar
  147. Xia M, Wang J, He Y (2013) BrainNet viewer: a network visualization tool for human brain connectomics. PLoS One. CrossRefPubMedPubMedCentralGoogle Scholar
  148. Xu J, Moeller S, Auerbach EJ et al (2013) Evaluation of slice accelerations using multiband echo planar imaging at 3T. Neuroimage 83:991–1001. PubMedCrossRefGoogle Scholar
  149. Yadollahi P, Khalaginia Z, Vedadhir A et al (2014) The study of predicting role of personality traits in the perception of labor pain. Iran J Nurs Midwifery Res 19:S97–S102PubMedPubMedCentralGoogle Scholar
  150. Yang W, Cun L, Du X et al (2015) Gender differences in brain structure and resting-state functional connectivity related to narcissistic personality. Sci Rep 5:10924. PubMedPubMedCentralCrossRefGoogle Scholar
  151. Yarkoni T (2015) Neurobiological substrates of personality: a critical overview. APA Handb Personal Soc Psychol 4:61–83.
  152. Ziomkiewicz A, Wichary S, Bochenek D et al (2012) Temperament and ovarian reproductive hormones in women: evidence from a study during the entire menstrual cycle. Horm Behav 61:535–540. PubMedCrossRefGoogle Scholar
  153. Zobel A, Barkow K, Schulze-Rauschenbach S et al (2004) High neuroticism and depressive temperament are associated with dysfunctional regulation of the hypothalamic-pituitary-adrenocortical system in healthy volunteers. Acta Psychiatr Scand 109:392–399. PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Alessandra D. Nostro
    • 1
    • 2
    • 3
    Email author
  • Veronika I. Müller
    • 1
    • 2
    • 3
  • Deepthi P. Varikuti
    • 1
    • 2
    • 3
  • Rachel N. Pläschke
    • 1
    • 2
    • 3
  • Felix Hoffstaedter
    • 2
    • 3
  • Robert Langner
    • 1
    • 2
    • 3
  • Kaustubh R. Patil
    • 1
    • 3
  • Simon B. Eickhoff
    • 1
    • 2
    • 3
  1. 1.Institute of Systems Neuroscience, Medical FacultyHeinrich-Heine University DüsseldorfDüsseldorfGermany
  2. 2.Institute of Clinical Neuroscience and Medical PsychologyHeinrich-Heine University DüsseldorfDüsseldorfGermany
  3. 3.Institute of Neuroscience and Medicine (INM-1,7)Research Centre JülichJülichGermany

Personalised recommendations