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The neural basis underlying procrastination: a large-scale study of brain networks

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  • ReceivedSep 4, 2018
  • AcceptedSep 28, 2018
  • PublishedDec 6, 2018

Abstract

Procrastination is a type of irrational behavior during which an individual voluntarily delays the execution of a task despite perceiving such behavior as harmful. Procrastination adversely affects people’s emotions, academic performances, and social achievements; it reduces their subjective well-being, and worse, their physical and mental health. The brain systems that regulate procrastination are still largely undetermined; previous studies have only explored the relationships between procrastination and regional brain activities and cerebral functional connections. Because procrastination involves complex psychological motivations that are dependent on elements such as task value representation, cognitive control, and the approach-avoidance conflict, some limitations to exploring the neural basis of procrastination using univariate and regional brain activities may exist. Therefore, this study used graph-theoretic analysis to investigate the connectivity patterns of large-scale brain networks underlying procrastination. We first defined the 264 regions of interest (ROIs) introduced by Power et al. (2011) as nodes of the network, and then reconstructed the ten intrinsic large-scale brain networks using graph-theoretic analysis. The results showed that the constructed brain network had better network topology attributes, such as a higher local and global efficiency. The strength of functional connectivity within each of these ten brain networks and that between them was then calculated using Pearson’s correlation. Finally, the association between procrastination and the functional network connectivity of brain networks was examined. We found that procrastination was negatively associated with the functional connectivity within the cingulo-opercular network (CON), whereas it was positively associated with the functional connectivity within the subcortical network (SCN). We also found a positive correlation between procrastination and the functional connectivity between the salience network (SAN) and the SCN. These results suggest that the functional network connectivity within the CON and the SCN, as well as that between the SAN and the SCN may be the neural basis underlying procrastination, which highlights the critical role of cognitive control and impulsive value representation in procrastination.


Funded by

国家自然科学基金(31571128)

中央高校基本科研业务经费创新团队项目(SWU1509392)


Interest statement

同等贡献


References

[1] Steel P. The nature of procrastination: a meta-analytic and theoretical review of quintessential self-regulatory failure. Psychol Bull, 2007, 133: 65-94 CrossRef PubMed Google Scholar

[2] Harriott J, Ferrari J R. Prevalence of procrastination among samples of adults. Psychol Rep, 1996, 78: 611-616 CrossRef Google Scholar

[3] Solomon L J, Rothblum E D. Academic procrastination: frequency and cognitive-behavioral correlates.. J Couns Psychol, 1984, 31: 503-509 CrossRef Google Scholar

[4] Sirois F M, Melia-Gordon M L, Pychyl T A. “I’ll look after my health, later”: an investigation of procrastination and health. Personal Individ Differ, 2003, 35: 1167-1184 CrossRef Google Scholar

[5] Stead R, Shanahan M J, Neufeld R W J. “I’ll go to therapy, eventually”: procrastination, stress and mental health. Personal Individ Differ, 2010, 49: 175-180 CrossRef Google Scholar

[6] Ferrari J R. Procrastination and impulsiveness: two sides of a coin? In: McCown W G, Johnson J L, Shure M B. The Impulsive Client: Theory, Research, and Treatment. Washington, DC: American Psychological Association, 1993. 265–276. Google Scholar

[7] Kachgal M M, Hansen L S, Nutter K J. Academic procrastination prevention/intervention: strategies and recommendations. J Devel Educ, 2001, 25: 1–14. Google Scholar

[8] Ackerman D S, Gross B L. My instructor made me do it: task characteristics of procrastination. J Mark Educ, 2005, 27: 5-13 CrossRef Google Scholar

[9] Anderson E M. The relationships among task characteristics, self-regulation and procrastination. Doctoral Dissertation. Chicago: Loyola University of Chicago, 2001. Google Scholar

[10] Frederick S, Loewenstein G, O’donoghue T. Time discounting and time preference: a critical review. J Econ Lit, 2002, 40: 351-401 CrossRef Google Scholar

[11] Dewitte S, Schouwenburg H C. Procrastination, temptations, and incentives: the struggle between the present and the future in procrastinators and the punctual. Eur J Pers, 2002, 16: 469-489 CrossRef Google Scholar

[12] Wu H, Gui D, Lin W, et al. The procrastinators want it now: behavioral and event-related potential evidence of the procrastination of intertemporal choices. Brain Cogn, 2016, 107: 16-23 CrossRef PubMed Google Scholar

[13] Ariely D, Wertenbroch K. Procrastination, deadlines, and performance: self-control by precommitment. Psychol Sci, 2002, 13: 219-224 CrossRef PubMed Google Scholar

[14] Rebetez M M L, Rochat L, van der Linden M. Cognitive, emotional, and motivational factors related to procrastination: a cluster analytic approach. Personal Individ Differences, 2015, 76: 1-6 CrossRef Google Scholar

[15] Gustavson D E, Miyake A, Hewitt J K, et al. Genetic relations among procrastination, impulsivity, and goal-management ability. Psychol Sci, 2014, 25: 1178-1188 CrossRef PubMed Google Scholar

[16] Loehlin J C, Martin N G. The genetic correlation between procrastination and impulsivity. Twin Res Hum Genet, 2014, 17: 512-515 CrossRef PubMed Google Scholar

[17] Liu P, Feng T. The overlapping brain region accounting for the relationship between procrastination and impulsivity: a voxel-based morphometry study. Neuroscience, 2017, 360: 9-17 CrossRef PubMed Google Scholar

[18] van Eerde W. A meta-analytically derived nomological network of procrastination. Personal Individ Differ, 2003, 35: 1401-1418 CrossRef Google Scholar

[19] Sun Y, Li S. Testing the effect of risk on intertemporal choice in the chinese cultural context. J Soc Psychol, 2001, 151: 517-522 CrossRef PubMed Google Scholar

[20] Ferrari J R, Emmons R A. Methods of procrastination and their relation to self-control and self-reinforcement: an exploratory study. J Soc Behav Personal, 1995, 10: 135–142. Google Scholar

[21] Rabin L A, Fogel J, Nutter-Upham K E. Academic procrastination in college students: the role of self-reported executive function. J Clin Exp Neuropsychol, 2011, 33: 344-357 CrossRef PubMed Google Scholar

[22] Hu Y, Liu P, Guo Y, et al. The neural substrates of procrastination: a voxel-based morphometry study. Brain Cogn, 2018, 121: 11-16 CrossRef PubMed Google Scholar

[23] Zhang W, Wang X, Feng T. Identifying the neural substrates of procrastination: a resting-state fMRI study. Sci Rep, 2016, 6: 33203 CrossRef PubMed ADS Google Scholar

[24] Wu Y, Li L, Yuan B, et al. Individual differences in resting-state functional connectivity predict procrastination. Personal Individ Differ, 2016, 95: 62-67 CrossRef Google Scholar

[25] Bressler S L, Menon V. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn Sci, 2010, 14: 277-290 CrossRef PubMed Google Scholar

[26] Larson-Prior L J, Power J D, Vincent J L, et al. Modulation of the brain’s functional network architecture in the transition from wake to sleep. Prog Brain Res, 2011, 193: 277–294. Google Scholar

[27] Sylvester C M, Corbetta M, Raichle M E, et al. Functional network dysfunction in anxiety and anxiety disorders. Trends Neurosci, 2012, 35: 527-535 CrossRef PubMed Google Scholar

[28] Varela F, Lachaux J P, Rodriguez E, et al. The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci, 2001, 2: 229-239 CrossRef PubMed Google Scholar

[29] Zuo X N, He Y, Betzel R F, et al. Human connectomics across the life span. Trends Cogn Sci, 2017, 21: 32-45 CrossRef PubMed Google Scholar

[30] Liao W, Zhang Z, Mantini D, et al. Relationship between large-scale functional and structural covariance networks in idiopathic generalized epilepsy. Brain Connect, 2013, 3: 240-254 CrossRef PubMed Google Scholar

[31] Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 2010, 52: 1059-1069 CrossRef PubMed Google Scholar

[32] Sporns O, Chialvo D R, Kaiser M, et al. Organization, development and function of complex brain networks. Trends Cogn Sci, 2004, 8: 418-425 CrossRef PubMed Google Scholar

[33] Dawe S, Gullo M J, Loxton N J. Reward drive and rash impulsiveness as dimensions of impulsivity: implications for substance misuse. Addict Behav, 2004, 29: 1389-1405 CrossRef PubMed Google Scholar

[34] Bari A, Robbins T W. Inhibition and impulsivity: behavioral and neural basis of response control. Prog Neurobiol, 2013, 108: 44-79 CrossRef PubMed Google Scholar

[35] Dalley J W, Everitt B J, Robbins T W. Impulsivity, compulsivity, and top-down cognitive control. Neuron, 2011, 69: 680-694 CrossRef PubMed Google Scholar

[36] Wang Q, Chen C, Cai Y, et al. Dissociated neural substrates underlying impulsive choice and impulsive action. Neuroimage, 2016, 134: 540-549 CrossRef PubMed Google Scholar

[37] Chen Z, Guo Y, Suo T, et al. Coupling and segregation of large-scale brain networks predict individual differences in delay discounting. Biol Psychol, 2018, 133: 63-71 CrossRef PubMed Google Scholar

[38] Bell P T, Shine J M. Subcortical contributions to large-scale network communication. Neurosci Biobehav Rev, 2016, 71: 313-322 CrossRef PubMed Google Scholar

[39] De La Fuente A, Xia S, Branch C, et al. A review of attention-deficit/hyperactivity disorder from the perspective of brain networks. Front Hum Neurosci, 2013, 7: 192 CrossRef PubMed Google Scholar

[40] Stanger C, Elton A, Ryan S R, et al. Neuroeconomics and adolescent substance abuse: individual differences in neural networks and delay discounting. J Am Acad Child Adolesc Psychiatry, 2013, 52: 747-755 CrossRef Google Scholar

[41] Worhunsky P D, Stevens M C, Carroll K M, et al. Functional brain networks associated with cognitive control, cocaine dependence, and treatment outcome. Psychol Addict Behav, 2013, 27: 477-488 CrossRef PubMed Google Scholar

[42] Power J D, Cohen A L, Nelson S M, et al. Functional network organization of the human brain. Neuron, 2011, 72: 665-678 CrossRef PubMed Google Scholar

[43] Cole M W, Pathak S, Schneider W. Identifying the brain’s most globally connected regions. NeuroImage, 2010, 49: 3132-3148 CrossRef PubMed Google Scholar

[44] Power J D, Schlaggar B L, Lessov-Schlaggar C N, et al. Evidence for hubs in human functional brain networks. Neuron, 2013, 79: 798-813 CrossRef PubMed Google Scholar

[45] Spreng R N, Sepulcre J, Turner G R, et al. Intrinsic architecture underlying the relations among the default, dorsal attention, and frontoparietal control networks of the human brain. J Cogn Neurosci, 2013, 25: 74-86 CrossRef PubMed Google Scholar

[46] Yan C G, Craddock R C, Zuo X N, et al. Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage, 2013, 80: 246-262 CrossRef PubMed Google Scholar

[47] Lay C H. At last, my research article on procrastination. J Res Personal, 1986, 20: 474-495 CrossRef Google Scholar

[48] Dewitte S, Lens W. Procrastinators lack a broad action perspective. Eur J Pers, 2010, 14: 121-140 CrossRef Google Scholar

[49] Spada M M, Hiou K, Nikcevic A V. Metacognitions, emotions, and procrastination. J Cogn Psychoth, 2006, 20: 319-326 CrossRef Google Scholar

[50] Song X W, Dong Z Y, Long X Y, et al. REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS ONE, 2011, 6: e25031 CrossRef PubMed ADS Google Scholar

[51] Bohlin L, Edler D, Lancichinetti A, et al. Community detection and visualization of networks with the map equation framework. In: Ding Y, Rousseau R, Wolfram D, Eds. Measuring Scholarly Impact. Cham: Springer. 2014. 3–34. Google Scholar

[52] Rosvall M, Bergstrom C T. Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci USA, 2008, 105: 1118-1123 CrossRef PubMed ADS arXiv Google Scholar

[53] Fortunato S. Community detection in graphs. Phys Rep, 2009, 486: 75-174 CrossRef ADS arXiv Google Scholar

[54] Fortunato S, Lancichinetti A. Community detection algorithms: a comparative analysis: invited presentation, extended abstract. In: Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools. Pisa, 2009. Google Scholar

[55] Mohr H, Wolfensteller U, Frimmel S, et al. Sparse regularization techniques provide novel insights into outcome integration processes. NeuroImage, 2015, 104: 163-176 CrossRef PubMed Google Scholar

[56] Murphy K, Birn R M, Handwerker D A, et al. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?. Neuroimage, 2009, 44: 893-905 CrossRef PubMed Google Scholar

[57] Maslov S, Sneppen K. Specificity and stability in topology of protein networks. Science, 2002, 296: 910-913 CrossRef PubMed ADS Google Scholar

[58] He Y, Chen Z J, Evans A C. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral Cortex, 2007, 17: 2407-2419 CrossRef PubMed Google Scholar

[59] Wang X F, Chen G. Complex networks: small-world, scale-free and beyond. IEEE Circ Syst Mag, 2003, 3: 6-20 CrossRef Google Scholar

[60] Whitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect, 2012, 2: 125-141 CrossRef PubMed Google Scholar

[61] Stern E R, Fitzgerald K D, Welsh R C, et al. Resting-state functional connectivity between fronto-parietal and default mode networks in obsessive-compulsive disorder. PLoS ONE, 2012, 7: e36356 CrossRef PubMed ADS Google Scholar

[62] Braun U, Plichta M M, Esslinger C, et al. Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. NeuroImage, 2012, 59: 1404-1412 CrossRef PubMed Google Scholar

[63] Wig G S, Laumann T O, Petersen S E. An approach for parcellating human cortical areas using resting-state correlations. Neuroimage, 2014, 93: 276-291 CrossRef PubMed Google Scholar

[64] Mohr H, Wolfensteller U, Betzel R F, et al. Integration and segregation of large-scale brain networks during short-term task automatization. Nat Commun, 2016, 7: 13217 CrossRef PubMed ADS Google Scholar

[65] Else-Quest N M, Hyde J S, Goldsmith H H, et al. Gender differences in temperament: a meta-analysis. Psychol Bull, 2006, 132: 33-72 CrossRef PubMed Google Scholar

[66] Dosenbach N U F, Fair D A, Cohen A L, et al. A dual-networks architecture of top-down control. Trends Cogn Sci, 2008, 12: 99-105 CrossRef PubMed Google Scholar

[67] Seeley W W, Menon V, Schatzberg A F, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci, 2007, 27: 2349-2356 CrossRef Google Scholar

[68] Buckner R L, Andrews-Hanna J R, Schacter D L. The brain’s default network: anatomy, function, and relevance to disease. Ann New York Acad Sci, 2008, 1124: 1-38 CrossRef PubMed ADS Google Scholar

[69] Xue G. The neural representations underlying human episodic memory. Trends Cogn Sci, 2018, 22: 544-561 CrossRef PubMed Google Scholar

[70] Leech R, Kamourieh S, Beckmann C F, et al. Fractionating the default mode network: distinct contributions of the ventral and dorsal posterior cingulate cortex to cognitive control. J Neurosci, 2011, 31: 3217-3224 CrossRef Google Scholar

[71] Cole M W, Reynolds J R, Power J D, et al. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat Neurosci, 2013, 16: 1348-1355 CrossRef PubMed Google Scholar

[72] Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 2009, 10: 186-198 CrossRef PubMed Google Scholar

[73] Dosenbach N U F, Visscher K M, Palmer E D, et al. A core system for the implementation of task sets. Neuron, 2006, 50: 799-812 CrossRef PubMed Google Scholar

[74] Johnston K, Levin H M, Koval M J, et al. Top-down control-signal dynamics in anterior cingulate and prefrontal cortex neurons following task switching. Neuron, 2007, 53: 453-462 CrossRef PubMed Google Scholar

[75] Ploran E J, Nelson S M, Velanova K, et al. Evidence accumulation and the moment of recognition: dissociating perceptual recognition processes using fMRI. J Neurosci, 2007, 27: 11912-11924 CrossRef Google Scholar

[76] Rushworth M F S, Buckley M J, Behrens T E J, et al. Functional organization of the medial frontal cortex. Curr Opin Neurobiol, 2007, 17: 220-227 CrossRef PubMed Google Scholar

[77] Rushworth M F S, Walton M E, Kennerley S W, et al. Action sets and decisions in the medial frontal cortex. Trends Cogn Sci, 2004, 8: 410-417 CrossRef PubMed Google Scholar

[78] Wang Q, Luo S, Monterosso J, et al. Distributed value representation in the medial prefrontal cortex during intertemporal choices. J Neurosci, 2014, 34: 7522-7530 CrossRef Google Scholar

[79] Buckholtz J W, Treadway M T, Cowan R L, et al. Dopaminergic network differences in human impulsivity. Science, 2010, 329: 532 CrossRef PubMed ADS Google Scholar

[80] Pessiglione M, Seymour B, Flandin G, et al. Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans. Nature, 2006, 442: 1042-1045 CrossRef PubMed ADS Google Scholar

[81] Davis C, Levitan R D, Kaplan A S, et al. Reward sensitivity and the D2 dopamine receptor gene: a case-control study of binge eating disorder. Prog Neuro-Psychoph Biol Psychiat, 2008, 32: 620-628 CrossRef PubMed Google Scholar

[82] Metcalfe J, Mischel W. A hot/cool-system analysis of delay of gratification: dynamics of willpower.. Psychol Rev, 1999, 106: 3-19 CrossRef Google Scholar

[83] McClure S M, Laibson D I, Loewenstein G, et al. Separate neural systems value immediate and delayed monetary rewards. Science, 2004, 306: 503-507 CrossRef PubMed ADS Google Scholar

[84] Menon V, Uddin L Q. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct, 2010, 214: 655-667 CrossRef PubMed Google Scholar

[85] Nelson L D, Bernat E M, Holroyd C B, et al. Loss and error information impact feedback-locked brain potentials in a gambling task. Int J Psychophysiol, 2008, 69: 208 CrossRef Google Scholar

[86] Medford N, Critchley H D. Conjoint activity of anterior insular and anterior cingulate cortex: awareness and response. Brain Struct Funct, 2010, 214: 535-549 CrossRef PubMed Google Scholar

[87] Marsh A A, Blair K S, Vythilingam M, et al. Response options and expectations of reward in decision-making: the differential roles of dorsal and rostral anterior cingulate cortex. Neuroimage, 2007, 35: 979-988 CrossRef PubMed Google Scholar

[88] Berkman E T, Falk E B, Lieberman M D. Interactive effects of three core goal pursuit processes on brain control systems: goal maintenance, performance monitoring, and response inhibition. PLoS ONE, 2012, 7: e40334 CrossRef PubMed ADS Google Scholar

[89] Figner B, Knoch D, Johnson E J, et al. Lateral prefrontal cortex and self-control in intertemporal choice. Nat Neurosci, 2010, 13: 538-539 CrossRef PubMed Google Scholar

[90] Hare T A, Camerer C F, Rangel A. Self-control in decision-making involves modulation of the vmpfc valuation system. Science, 2009, 324: 646-648 CrossRef PubMed ADS Google Scholar

  • Figure 1

    The normal distribution of the scores of Pure Procrastination Scale (PPS) (A) and the significant contrast between the genders (B) (color online). *, P<0.05

  • Figure 2

    Visualization of these re-constructed large-scale brain networks derived from Power atlas. A: Cingulo-Opercular Network (CON); B: Salience Network (SN); C: Subcortical Network (SCN)

  • Figure 3

    Correlations of functional connectivity between large-scale brain networks on the scores of Pure Procrastination. A: The positive correlation between the intra-connection of SCN and procrastination; B: the significant inverse correlation between the intra-connection of the CON and procrastination; C: the significant positive correlation between the connection of SAN-SCN and procrastination. r indicates the correlation coefficient of Pearson; P means the corrected P-value

  • Table 1   The summary of topological organizations of these re-constructed large-scale brain networks and corresponding topological metrics of random networks

    均值

    自由度

    t

    P

    P值(校正后)

    感觉运动网络

    全局效率

    0.37

    218

    126.96

    <0.001

    <0.001

    局部效率

    0.63

    218

    107.31

    <0.001

    <0.001

    平均节点度

    4.21

    218

    69.90

    <0.001

    <0.001

    节点数

    29

    扣带控制网络

    全局效率

    0.27

    218

    75.21

    <0.001

    <0.001

    局部效率

    0.50

    218

    38.10

    <0.001

    <0.001

    平均节点度

    1.93

    218

    64.12

    <0.001

    <0.001

    节点数

    14

    听觉网络

    全局效率

    0.27

    218

    92.02

    <0.001

    <0.001

    局部效率

    0.46

    218

    30.60

    <0.001

    <0.001

    平均节点度

    1.77

    218

    23.52

    <0.001

    <0.001

    节点数

    13

    默认网络

    全局效率

    0.46

    218

    290.05

    <0.001

    <0.001

    局部效率

    0.66

    218

    30.60

    <0.001

    <0.001

    平均节点度

    8.25

    218

    54.85

    <0.001

    <0.001

    节点数

    56

    视觉网络

    全局效率

    0.37

    218

    106.16

    <0.001

    <0.001

    局部效率

    0.65

    218

    112.87

    <0.001

    <0.001

    平均节点度

    4.52

    218

    30.02

    <0.001

    <0.001

    节点数

    31

    额顶控制网络

    全局效率

    0.37

    218

    140.49

    <0.001

    <0.001

    局部效率

    0.60

    218

    91.51

    <0.001

    <0.001

    平均节点度

    3.60

    218

    39.89

    <0.001

    <0.001

    节点数

    25

    突显网络

    全局效率

    0.32

    218

    96.39

    <0.001

    <0.001

    局部效率

    0.58

    218

    63.80

    <0.001

    <0.001

    平均节点度

    2.56

    218

    19.99

    <0.001

    <0.001

    节点数

    18

    皮层下网络

    全局效率

    0.24

    218

    96.04

    <0.001

    <0.001

    局部效率

    0.58

    218

    63.80

    <0.001

    <0.001

    平均节点度

    1.27

    218

    23.52

    <0.001

    <0.001

    节点数

    13

    腹侧注意网络

    全局效率

    0.19

    218

    95.56

    <0.001

    <0.001

    局部效率

    0.22

    218

    8.37

    <0.001

    <0.001

    平均节点度

    1.27

    218

    23.52

    <0.001

    <0.001

    节点数

    8

    背侧注意网络

    全局效率

    0.24

    218

    82.86

    <0.001

    <0.001

    局部效率

    0.53

    218

    31.63

    <0.001

    <0.001

    平均节点度

    1.55

    218

    25.69

    <0.001

    <0.001

    节点数

    11

     

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