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Shared neurogenetic architecture links adolescent neurodevelopmental deviations to adult psychopathological procrastination

Molecular Psychiatry (2026)Cite this article

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Abstract

While general procrastination is common, psychopathological procrastination, a debilitating phenotype often indicative of subclinical psychiatric conditions, remains poorly understood in terms of its neurobiological underpinning. Challenging its traditional conceptualization as a mere behavioral deficit, we investigated the neurogenetic architecture of psychopathological procrastination. Leveraging a prospective adolescent twin cohort (N = 71 twin pairs) with neuroanatomical imaging (baseline) and psychopathological procrastination phenotyping in young adulthood (8-year follow-up), we first established moderate heritability for psychopathological procrastination (h² = 0.47, 95% CI: 0.14 - 0.71). Employing normative modeling of brain morphology, we found that adolescent neurodevelopmental deviations, specifically within the nucleus accumbens (NAcc), predicted adult psychopathological procrastination. Crucially, these predictive adolescent NAcc deviations exhibited a strong shared genetic basis with adult psychopathological procrastination (rg = 0.89, 95% CI: 0.89 - 1.00). Beyond regional effects, psychopathological-procrastination-specific whole-brain deviation patterns were identified, which showed neurobiological enrichment with cortical functional gradient and key dopaminergic (DAT/D1) and serotonergic (5-HT receptors) neurotransmitter systems. Both cross-sectional and longitudinal transcriptomic integration of these neuroimaging signatures with human brain gene expression data pinpointed significant associations with molecular transport, neuroimmune responses, and neuroinflammation, further implicating dysregulation within serotonergic and dopaminergic pathways. Collectively, our findings delineate a multisystem neurogenetic architecture of psychopathological procrastination, providing supportive evidence that recontextualizes this debilitating phenotype from a simple behavioral issue to a condition with neurodevelopmental antecedents, potentially suggesting its conceptualization as a subclinical brain disorder.

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Fig. 1: Analytic flowchart of the present study.
Fig. 2: ACE models and brain morphological deviations from normative modeling methods.
Fig. 3: Prospective prediction of brain morphological deviations to psychopathological procrastination.
Fig. 4: Spatial similarity of representation similarity (RS) pattern to multiscale normative maps.
Fig. 5: Enrichment analysis to psychopathological-procrastination-specific gene set (PLS2).
Fig. 6: Theoretical schematic of neurogenetic dysregulation model for conceptualizing psychopathological procrastination as a “brain disorder”.

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Data availability

Data supporting reproducibility of this study are available in the main text and the supplementary materials. Data for neuroimaging normative modeling derives from ENIGMA Consortium Lifespan Working Group (https://enigma.ini.usc.edu/). Data for macroscale brain phenotype are curated by Neuromap (https://github.com/netneurolab/neuromaps). Data for supporting overrepresentation analysis (ORA) and neurobiological enrichment analysis are generated from Metascape (https://metascape.org/gp/index.html) and GAMBA (http://dutchconnectomelab.nl/GAMBA/) databases, respectively. Gene expression data are constructed from Allen Human Brain Atlas (AHBA) (https://human.brain-map.org/static/) and BrainSpan (Atlas of the Developing Human Brain) (https://www.brainspan.org/). The raw genetic neuroimaging data are available under restricted access for data legal restrictions. Specifically, these genetic neuroimaging data are collected by the Beijing Twin Data Collection Project, which has not yet completed. Thus, given biometric data that are undergoing collection in an ongoing project, the legal censorship to share them from data regulation authorities is not available at this time, until this project is fully completed. For scientific collaborations or research purposes, any researchers can obtain access by reaching out Dr. Yuan Zhou (zhouyuan@psych.ac.cn).

Code availability

No custom codes are central in analyzing data. Neuroimaging normative modeling has been conducted via online tool (CentileBrain, https://centilebrain.org). Spatial alignment to map whole-brain deviation pattern into brain phenotype is implemented by Neuromap (https://github.com/netneurolab/neuromap). Visualization to brain maps capitalizes on R packages “ggseg” (https://ggseg.github.io/ggseg/) and its derivatives, such as “ggsegSchaefer”(https://github.com/ggseg/ggsegSchaefer). Schematic diagram for the theoretical model is generated by Biorender (Trial version).

References

  1. Steel P. The nature of procrastination: a meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bull. 2007;133:65.

    Article  Google Scholar 

  2. Ozer BU, Demir A, Ferrari JR. Exploring academic procrastination among Turkish students: possible gender differences in prevalence and reasons. J Soc Psychol. 2009;149:241–57.

    Article  PubMed  Google Scholar 

  3. Afzal S, Jami H. Prevalence of academic procrastination and reasons for academic procrastination in university students. J Behavioural Sci. 2018;28:51–69.

    Google Scholar 

  4. Svartdal F, Pfuhl G, Nordby K, Foschi G, Klingsieck KB, Rozental A, et al. On the Measurement of Procrastination: Comparing Two Scales in Six European Countries. Front Psychol. 2016;7:1307.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kim H, Kim H, Lee W-K, Han S, Carlbring P, Rozental A. Assessing procrastination in Korean: A study of the translation and validation of the Pure Procrastination Scale and a reexamination of the Irrational Procrastination Scale in a student and community sample. Cogent Psychol. 2020;7:1809844.

    Article  Google Scholar 

  6. Rozental A, Forsell E, Svensson A, Forsström D, Andersson G, Carlbring P. Psychometric evaluation of the Swedish version of the pure procrastination scale, the irrational procrastination scale, and the susceptibility to temptation scale in a clinical population. BMC Psychol. 2014;2:54.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Nguyen B, Steel P, Ferrari JR. Procrastination’s impact in the workplace and the workplace’s impact on procrastination. Int J Selection Assess. 2013;21:388–99.

    Article  Google Scholar 

  8. Ferrari JR, Harriott JS, Zimmerman M. The social support networks of procrastinators: Friends or family in times of trouble? Personality Individ Differences. 1998;26:321–31.

    Article  Google Scholar 

  9. Hoshino Y, Kataoka S, Ansai T. Association of personality traits with dental visit procrastination by Japanese university students. Biopsychosoc Med. 2023;17:33.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Johansson F, Rozental A, Edlund K, Côté P, Sundberg T, Onell C, et al. Associations between procrastination and subsequent health outcomes among university students in Sweden. JAMA Netw Open. 2023;6:e2249346.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Sirois FM. Is procrastination a vulnerability factor for hypertension and cardiovascular disease? Testing an extension of the procrastination–health model. J Behav Med. 2015;38:578–89.

    Article  PubMed  Google Scholar 

  12. Sirois FM, Stride CB, Pychyl TA. Procrastination and health: a longitudinal test of the roles of stress and health behaviours. Br J Health Psychol. 2023;28:860–75.

    Article  PubMed  Google Scholar 

  13. Zhang R, Chen Z, Hu B, Zhou F, Feng T. The anxiety‐specific hippocampus–prefrontal cortex pathways links to procrastination through self‐control. Hum Brain Mapp. 2022;43:1738–48.

    Article  PubMed  Google Scholar 

  14. Zhang X, Chen K, Wang M, Chen C. The relationship between academic procrastination and internet addiction in college students: The multiple mediating effects of intrusive thinking and depression-anxiety-stress. Psychology. 2022;13:591–606.

    Article  Google Scholar 

  15. Rist F, Engberding M, Hoecker A, Wolf-Lettmann J, Fischbach E-M. Diagnostic criteria to differentiate pathological procrastinators from common delayers: a re-analysis. Front Psychol. 2023;14:1147401.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Chen Z, Liu X, Xu T, Li W, Zhang R, Wu Y, et al. Subclinical psychiatric symptomatology and a proposed diagnostic criterion separating psychopathological procrastinators from trait procrastinators. Personality Ment health. 2025;19:e70022.

    Article  Google Scholar 

  17. Niermann HC, Scheres A. The relation between procrastination and symptoms of attention‐deficit hyperactivity disorder (ADHD) in undergraduate students. Int J methods Psychiatr Res. 2014;23:411–21.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Uzun Ozer B, O’Callaghan J, Bokszczanin A, Ederer E, Essau C. Dynamic interplay of depression, perfectionism and self-regulation on procrastination. Br J Guidance Counselling. 2014;42:309–19.

    Article  Google Scholar 

  19. Hannah Lee J, Suh H. Perfectionism and alcohol-related problems: the role of procrastination. J Am Coll Health. 2024;72:11–9.

    Article  PubMed  Google Scholar 

  20. Yeh Y-C, Wang P-W, Huang M-F, Lin P-C, Chen C-S, Ko C-H. The procrastination of internet gaming disorder in young adults: the clinical severity. Psychiatry Res. 2017;254:258–62.

    Article  PubMed  Google Scholar 

  21. Hutchison TE, Penney AM, Crompton JE. Procrastination and anxiety: exploring the contributions of multiple anxiety-related disorders. Curr Issues Personality Psychol. 2018;6:122–9.

    Article  Google Scholar 

  22. Shalev I. Use of a self-regulation failure framework and the NIMH research domain criterion (RDoC) to understand the problem of procrastination. Front Psychiatry. 2018;9:213.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Rozental A, Carlbring P. Understanding and treating procrastination: a review of a common self-regulatory failure. Psychology. 2014;5:1488.

    Article  Google Scholar 

  24. Rozental A, Carlbring P. Internet-based cognitive behavior therapy for procrastination: Study protocol for a randomized controlled trial. JMIR Res Protoc. 2013;2:e2801.

    Article  Google Scholar 

  25. Insel TR. The NIMH research domain criteria (RDoC) project: precision medicine for psychiatry. Am J psychiatry. 2014;171:395–7.

    Article  PubMed  Google Scholar 

  26. Kraemer HC. Research domain criteria (RDoC) and the DSM—two methodological approaches to mental health diagnosis. JAMA psychiatry. 2015;72:1163–4.

    Article  PubMed  Google Scholar 

  27. Cuthbert BN. The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World psychiatry. 2014;13:28–35.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Cuthbert BN. The role of RDoC in future classification of mental disorders. Dialogues Clin Neurosci. 2020;22:81–5.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Woody ML, Gibb BE. Integrating NIMH research domain criteria (RDoC) into depression research. Curr Opin Psychol. 2015;4:6–12.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Glenn CR, Cha CB, Kleiman EM, Nock MK. Understanding suicide risk within the Research Domain Criteria (RDoC) framework: Insights, challenges, and future research considerations. Clin psychological Sci. 2017;5:568–92.

    Article  Google Scholar 

  31. Mittal VA, Wakschlag LS. Research domain criteria (RDoC) grows up: strengthening neurodevelopmental investigation within the RDoC framework. J Affect Disord. 2016;216:30.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Chen Z, Liu P, Zhang C, Feng T. Brain morphological dynamics of procrastination: The crucial role of the self-control, emotional, and episodic prospection network. Cereb Cortex. 2020;30:2834–53.

    Article  PubMed  Google Scholar 

  33. Chen Z, Feng T. Neural connectome features of procrastination: current progress and future direction. Brain Cognition. 2022;161:105882.

    Article  PubMed  Google Scholar 

  34. Chen Z, Liu P, Zhang C, Yu Z, Feng T. Neural markers of procrastination in white matter microstructures and networks. Psychophysiology. 2021;58:e13782.

    Article  PubMed  Google Scholar 

  35. Chen Z, Zhang R, Xie J, Liu P, Zhang C, Zhao J, et al. Hybrid brain model accurately predict human procrastination behavior. Cogn Neurodyn. 2022;16:1107–21.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Le Bouc R, Pessiglione M. A neuro-computational account of procrastination behavior. Nat Commun. 2022;13:5639.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Loehlin JC, Martin NG. The genetic correlation between procrastination and impulsivity. Twin Res Hum Genet. 2014;17:512–5.

    Article  PubMed  Google Scholar 

  38. Gustavson DE, Miyake A, Hewitt JK, Friedman NP. Understanding the cognitive and genetic underpinnings of procrastination: Evidence for shared genetic influences with goal management and executive function abilities. J Exp Psychology: Gen. 2015;144:1063.

    Article  Google Scholar 

  39. Gustavson DE, du Pont A, Hatoum AS, Rhee SH, Kremen WS, Hewitt JK, et al. Genetic and environmental associations between procrastination and internalizing/externalizing psychopathology. Clin Psychological Sci. 2017;5:798–815.

    Article  Google Scholar 

  40. Cuthbert BN. Research domain criteria (RDoC): progress and potential. Curr Directions Psychological Sci. 2022;31:107–14.

    Article  Google Scholar 

  41. Lilienfeld SO. The Research Domain Criteria (RDoC): an analysis of methodological and conceptual challenges. Behav Res Ther. 2014;62:129–39.

    Article  PubMed  Google Scholar 

  42. Segal A, Parkes L, Aquino K, Kia SM, Wolfers T, Franke B, et al. Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders. Nat Neurosci. 2023;26:1613–29.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Feczko E, Miranda-Dominguez O, Marr M, Graham AM, Nigg JT, Fair DA. The heterogeneity problem: approaches to identify psychiatric subtypes. Trends Cogn Sci. 2019;23:584–601.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Marquand AF, Kia SM, Zabihi M, Wolfers T, Buitelaar JK, Beckmann CF. Conceptualizing mental disorders as deviations from normative functioning. Mol Psychiatry. 2019;24:1415–24.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Marquand AF, Wolfers T, Mennes M, Buitelaar J, Beckmann CF. Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders. Biol Psychiatry: Cogn Neurosci Neuroimaging. 2016;1:433–47.

    PubMed  Google Scholar 

  46. Rutherford S, Barkema P, Tso IF, Sripada C, Beckmann CF, Ruhe HG, et al. Evidence for embracing normative modeling. Elife. 2023;12:e85082.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Rutherford S, Kia SM, Wolfers T, Fraza C, Zabihi M, Dinga R, et al. The normative modeling framework for computational psychiatry. Nat Protoc. 2022;17:1711–34.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Sun X, Sun J, Lu X, Dong Q, Zhang L, Wang W, et al. Mapping neurophysiological subtypes of major depressive disorder using normative models of the functional connectome. Biol Psychiatry. 2023;94:936–47.

    Article  PubMed  Google Scholar 

  49. Jiang A, Ma X, Li S, Wang L, Yang B, Wang S, et al. Age-atypical brain functional networks in autism spectrum disorder: a normative modeling approach. Psychol Med. 2024;54:2042–53.

    Article  PubMed  Google Scholar 

  50. Ge R, Yu Y, Qi YX, Fan Y-n, Chen S, Gao C, et al. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation. Lancet Digital Health. 2024;6:e211–e21.

    Article  PubMed  Google Scholar 

  51. Shankman SA, Gorka SM. Psychopathology research in the RDoC era: Unanswered questions and the importance of the psychophysiological unit of analysis. Int J Psychophysiol. 2015;98:330–7.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Markello RD, Hansen JY, Liu Z-Q, Bazinet V, Shafiei G, Suárez LE, et al. Neuromaps: structural and functional interpretation of brain maps. Nat Methods. 2022;19:1472–9.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Jin Y, Zheng D, Gu R, Fan Q, Dietz M, Wang C, et al. Substantial heritability underlies fairness norm adaptation capability and its neural basis. Adv Sci. 2024;12:2411070.

    Article  Google Scholar 

  54. Wang Y, Zheng D, Chen J, Rao L-L, Li S, Zhou Y. Born for fairness: evidence of genetic contribution to a neural basis of fairness intuition. Soc Cognit Affect Neurosci. 2019;14:539–48.

    Article  Google Scholar 

  55. Neale MC, Hunter MD, Pritikin JN, Zahery M, Brick TR, Kirkpatrick RM, et al. OpenMx 2.0: extended structural equation and statistical modeling. Psychometrika. 2016;81:535–49.

    Article  PubMed  Google Scholar 

  56. Posthuma D, Polderman TJ. What have we learned from recent twin studies about the etiology of neurodevelopmental disorders? Curr Opin Neurol. 2013;26:111–21.

    Article  PubMed  Google Scholar 

  57. Allen P, Baldwin H, Bartholomeusz CF, Chee MW, Chen X, Cooper RE, et al. Normative modeling of brain morphometry in clinical high risk for psychosis. JAMA psychiatry. 2024;81:77–88.

    Article  Google Scholar 

  58. Oh EJ, Min JJ, Kwon SS, Kim SB, Choi CW, Jung YH, et al. Breech presentation in twins as a risk factor for developmental dysplasia of the hip. J Pediatric Orthop. 2022;42:e55–e8.

    Article  Google Scholar 

  59. Mahjani B, Klei L, Buxbaum Grice AS, Larsson H, Hultman CM, Sandin S, et al. Direct additive genetics and maternal effect contribute to the risk of tourette disorder. J neurology, neurosurgery, psychiatry. 2023;94:638–42.

    Article  Google Scholar 

  60. Bruna, Campos, Lilian P, Cristina, Tania L, Kiehl, et al. Monozygotic twin sisters differ in voice quality: acoustic analysis of the diphthong [‘oj] produced by Brazilian native speakers. Soc Sci Humanities Open. 2024;10:101062.

    Google Scholar 

  61. Weinstein SM, Vandekar SN, Li B, Alexander‐Bloch AF, Raznahan A, Li M, et al. Network enrichment significance testing in brain–phenotype association studies. Hum Brain Mapp. 2024;45:e26714.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102:15545–50.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Markello RD, Arnatkeviciute A, Poline J-B, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. elife. 2021;10:e72129.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Hansen JY, Shafiei G, Markello RD, Smart K, Cox SM, Nørgaard M, et al. Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nat Neurosci. 2022;25:1569–81.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Markello RD, Misic B. Comparing spatial null models for brain maps. NeuroImage. 2021;236:118052.

    Article  PubMed  Google Scholar 

  66. Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489:391–9.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Arnatkevic̆iūtė A, Fulcher BD, Fornito A. A practical guide to linking brain-wide gene expression and neuroimaging data. Neuroimage. 2019;189:353–67.

    Article  PubMed  Google Scholar 

  68. Miller JA, Ding S-L, Sunkin SM, Smith KA, Ng L, Szafer A, et al. Transcriptional landscape of the prenatal human brain. Nature. 2014;508:199–206.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Wang Y, Wang Y, Wang H, Ma L, Eickhoff SB, Madsen KH, et al. Spatio-molecular profiles shape the human cerebellar hierarchy along the sensorimotor-association axis. Cell Rep. 2024;43:113770.

    Article  PubMed  Google Scholar 

  70. Hapfelmeier A, Hothorn T, Ulm K Random Forest variable importance with missing data. 2012.

  71. Kumar L, Futschik ME. Mfuzz: a software package for soft clustering of microarray data. Bioinformation. 2007;2:5.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Hung J-H, Yang T-H, Hu Z, Weng Z, DeLisi C. Gene set enrichment analysis: performance evaluation and usage guidelines. Brief Bioinf. 2012;13:281–91.

    Article  Google Scholar 

  73. Wei Y, de Lange SC, Pijnenburg R, Scholtens LH, Ardesch DJ, Watanabe K, et al. Statistical testing in transcriptomic-neuroimaging studies: a how-to and evaluation of methods assessing spatial and gene specificity. Hum Brain Mapp. 2022;43:885–901.

    Article  PubMed  Google Scholar 

  74. Cesarini D, Johannesson M, Magnusson PK, Wallace B. The behavioral genetics of behavioral anomalies. Manage Sci. 2012;58:21–34.

    Article  Google Scholar 

  75. Barkus E, Martin EA, KKy Wong, Chan RC. The importance of studying psychopathology in subclinical populations. PsyCh J. 2022;11:147–8.

    Article  Google Scholar 

  76. Zhang B, Liu S, Chen S, Yan F, Ke Y, Chen L, et al. Common and unique neural activities in subclinical depression and major depressive disorder indicate the development of brain impairments in different depressive stages. J Affect Disord. 2022;317:278–86.

    Article  PubMed  Google Scholar 

  77. Consortium B, Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, et al. Analysis of shared heritability in common disorders of the brain. Science. 2018;360:eaap8757.

    Article  Google Scholar 

  78. Kaufmann T, van der Meer D, Doan NT, Schwarz E, Lund MJ, Agartz I, et al. Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nat Neurosci. 2019;22:1617–23.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Marquand A, Rutherford S, Wolfers T Normative modeling for clinical neuroscience. Computational and Network Modeling of Neuroimaging Data: Elsevier; 2024. p. 309-29.

  80. Liu L, Jia D, He Z, Wen B, Zhang X, Han S. Individualized functional connectome abnormalities obtained using two normative model unveil neurophysiological subtypes of obsessive compulsive disorder. Prog Neuro-Psychopharmacol Biol Psychiatry. 2024;135:111122.

    Article  Google Scholar 

  81. Parkes L, Moore TM, Calkins ME, Cook PA, Cieslak M, Roalf DR, et al. Transdiagnostic dimensions of psychopathology explain individuals’ unique deviations from normative neurodevelopment in brain structure. Transl Psychiatry. 2021;11:232.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Zhang S, Liu P, Feng T. To do it now or later: The cognitive mechanisms and neural substrates underlying procrastination. Wiley Interdiscip Rev Cogn Sci. 2019;10:e1492.

    Article  PubMed  Google Scholar 

  83. Kroemer NB, Guevara A, Teodorescu IC, Wuttig F, Kobiella A, Smolka MN. Balancing reward and work: anticipatory brain activation in NAcc and VTA predict effort differentially. Neuroimage. 2014;102:510–9.

    Article  PubMed  Google Scholar 

  84. Li Z, Zhang C-y, Huang J, Wang Y, Yan C, Li K, et al. Improving motivation through real-time fMRI-based self-regulation of the nucleus accumbens. Neuropsychology. 2018;32:764.

    Article  PubMed  Google Scholar 

  85. Zhang PY, Ma WJ. Temporal discounting predicts procrastination in the real world. Sci Rep. 2024;14:14642.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Dohmatob E, Dumas G, Bzdok D. Dark control: The default mode network as a reinforcement learning agent. Hum Brain Mapp. 2020;41:3318–41.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Dobryakova E, Smith DV. Reward enhances connectivity between the ventral striatum and the default mode network. NeuroImage. 2022;258:119398.

    Article  PubMed  Google Scholar 

  88. 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.

    Article  PubMed  Google Scholar 

  89. Xu T, Sirois FM, Zhang L, Yu Z, Feng T. Neural basis responsible for self-control association with procrastination: Right MFC and bilateral OFC functional connectivity with left dlPFC. J Res Personality. 2021;91:104064.

    Article  Google Scholar 

  90. Gelernter J. Genetics of complex traits in psychiatry. Biol Psychiatry. 2015;77:36–42.

    Article  PubMed  Google Scholar 

  91. Korologou-Linden R, Leyden GM, Relton CL, Richmond RC, Richardson TG. Multi-omics analyses of cognitive traits and psychiatric disorders highlight brain-dependent mechanisms. Hum Mol Genet. 2023;32:885–96.

    Article  Google Scholar 

  92. Alemany S, Jansen PR, Muetzel RL, Marques N, El Marroun H, Jaddoe VW, et al. Common polygenic variations for psychiatric disorders and cognition in relation to brain morphology in the general pediatric population. J Am Acad Child Adolesc Psychiatry. 2019;58:600–7.

    Article  PubMed  Google Scholar 

  93. Liu S, Smit DJ, Abdellaoui A, van Wingen GA, Verweij KJ. Brain structure and function show distinct relations with genetic predispositions to mental health and cognition. Biol Psychiatry: Cogn Neurosci Neuroimaging. 2023;8:300–10.

    PubMed  Google Scholar 

  94. Jamil L, Ashouri A, Zamirinejad S, Mahaki B. Investigating the structural model of procrastination based on transdiagnostic factors. Med J Islamic Repub Iran. 2021;35:120.

    Google Scholar 

  95. Pearlstein JG, Johnson SL, Timpano KR, Stamatis CA, Robison M, Carver CS. Emotion‐related impulsivity across transdiagnostic dimensions of psychopathology. J personality. 2024;92:342–60.

    Article  PubMed  Google Scholar 

  96. Davies PT, Pearson JK, Cicchetti D, Martin MJ, Cummings EM. Emotional insecurity as a mediator of the moderating role of dopamine genes in the association between interparental conflict and youth externalizing problems. Dev psychopathology. 2019;31:1111–26.

    Article  Google Scholar 

  97. Cimino S, Cerniglia L, De Carli P, Simonelli A. DNA methylation and allelic polymorphism at the dopamine transporter promoter affect internalizing and externalizing symptoms in preschoolers. Child Psychiatry Hum Dev. 2021;52:281–90.

    Article  PubMed  Google Scholar 

  98. Gonçalves ECD, Lieberknecht V, Horewicz VV, Rabelo BD, Felipetti FA, Rodrigues ALS, et al. Dopaminergic receptors as neuroimmune mediators in experimental autoimmune encephalomyelitis. Mol Neurobiol. 2021;58:5971–85.

    Article  PubMed  Google Scholar 

  99. Hodo TW, De Aquino MTP, Shimamoto A, Shanker A. Critical neurotransmitters in the neuroimmune network. Front Immunol. 2020;11:1869.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Mukhara D, Oh U, Neigh GN. Chapter 15 - Neuroinflammation. In: Lanzenberger R, Kranz GS, Savic I, editors Handbook of Clinical Neurology. 175: Elsevier; 2020. p. 235-59.

  101. Rathbone ATL, Tharmaradinam S, Jiang S, Rathbone MP, Kumbhare DA. A review of the neuro-and systemic inflammatory responses in post concussion symptoms: introduction of the “post-inflammatory brain syndrome” PIBS. Brain, behavior, Immun. 2015;46:1–16.

    Article  Google Scholar 

  102. Miller AH. Beyond depression: the expanding role of inflammation in psychiatric disorders. World psychiatry. 2020;19:108.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Wu H, Denna TH, Storkersen JN, Gerriets VA. Beyond a neurotransmitter: The role of serotonin in inflammation and immunity. Pharmacol Res. 2019;140:100–14.

    Article  PubMed  Google Scholar 

  104. Zhang X, Kanter K, Chen J, Kim S, Wang Y, Adeyemi C, et al. Low catechol-O-methyltransferase and stress potentiate functional pain and depressive behavior, especially in female mice. Pain. 2020;161:446–58.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Vasunilashorn SM, Ngo LH, Jones RN, Inouye SK, Hall KT, Gallagher J, et al. The association between C-reactive protein and postoperative delirium differs by catechol-O-methyltransferase genotype. Am J Geriatric Psychiatry. 2019;27:1–8.

    Article  Google Scholar 

Download references

Acknowledgements

We do appreciate Dr. Xuerong Liu for assistance on proofreading and thank to Prof. Tingyong Feng for scientific comments on theoretical conceptualization. We are sincerely thankful to Dr. Runsen Chen and Dr. Diyang Qu for their comments and suggestions to improve writing quality.

Funding

National Natural Science Foundation of China (32300907, 72033006, CZY, ZY). Chongqing Natural Science Foundation (CSTB2025NSCQ-GPX0570, General Project, CZY). PLA Talent Program Foundation (2022160258, CZY). AMU-RD Scholar Foundation (202211001, CZY).

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Author notes

  1. These authors contributed equally: Yuanyuan Hu, Yancheng Tang and Wei Li.

Authors and Affiliations

  1. State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China

    Yuanyuan Hu, Dang Zheng, Yuening Jin, Qingchen Fan & Yuan Zhou

  2. Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China

    Yuanyuan Hu, Yuening Jin, Qingchen Fan & Yuan Zhou

  3. Experimental Research Center of Medical and Psychological Science, School of Psychology, Third Military Medical University, Chongqing, 400038, China

    Yancheng Tang, Wei Li & Zhiyi Chen

  4. Department of Early Childhood Education, China National Children’s Center, Beijing, 100035, China

    Dang Zheng

  5. Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, Netherlands

    Jan K. Buitelaar

  6. Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands

    Jan K. Buitelaar

  7. The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University Beijing, Beijing, China

    Yuan Zhou

  8. Faculty of Psychology, Southwest University, Chongqing, 400038, China

    Zhiyi Chen

  9. Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, 400038, China

    Zhiyi Chen

Authors
  1. Yuanyuan Hu
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  2. Yancheng Tang
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  3. Wei Li
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  4. Dang Zheng
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  7. Jan K. Buitelaar
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  8. Yuan Zhou
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  9. Zhiyi Chen
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Contributions

Conceptualization and Writing—original draft: HYY, YCT, CZY. Methodology: HYY, ZD, JYN. Investigation: HYY, FQC. Visualization: CZY, HYY, LW. Supervision: CZY, ZY, JKB. Writing—review & editing: JKB, LW, ZY, CZY.

Corresponding authors

Correspondence to Yuan Zhou or Zhiyi Chen.

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Competing interests

Authors declare that they have no competing interests.

Ethics approval and consent to participate

All methods were performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments (most recently, the 2013 revision). The study protocol and analytic pipeline were officially approved by the Institutional Review Board (IRB) of the Institute of Psychology (Chinese Academy of Sciences, China, #H20037) and the IRB of the Faculty of Psychology (Southwest University, China, #H24121). Informed consent was obtained from all the human participants prior to their involvement in the study. This study does not involve any other live vertebrates (animals).

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Hu, Y., Tang, Y., Li, W. et al. Shared neurogenetic architecture links adolescent neurodevelopmental deviations to adult psychopathological procrastination. Mol Psychiatry (2026). https://doi.org/10.1038/s41380-025-03423-0

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