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Mapping Domain- and Age-Specific Functional Brain Activity for Children’s Cognitive and Affective Development

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Abstract

The human brain undergoes rapid development during childhood, with significant improvement in a wide spectrum of cognitive and affective functions. Mapping domain- and age-specific brain activity patterns has important implications for characterizing the development of children’s cognitive and affective functions. The current mainstay of brain templates is primarily derived from structural magnetic resonance imaging (MRI), and thus is not ideal for mapping children’s cognitive and affective brain development. By integrating task-dependent functional MRI data from a large sample of 250 children (aged 7 to 12) across multiple domains and the latest easy-to-use and transparent preprocessing workflow, we here created a set of age-specific brain functional activity maps across four domains: attention, executive function, emotion, and risky decision-making. Moreover, we developed a toolbox named Developmental Brain Functional Activity maps across multiple domains that enables researchers to visualize and download domain- and age-specific brain activity maps for various needs. This toolbox and maps have been released on the Neuroimaging Informatics Tools and Resources Clearinghouse website (http://www.nitrc.org/projects/dbfa). Our study provides domain- and age-specific brain activity maps for future developmental neuroimaging studies in both healthy and clinical populations.

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References

  1. Johnson MH. Functional brain development in humans. Nat Rev Neurosci 2001, 2: 475–483.

    Article  CAS  PubMed  Google Scholar 

  2. Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci 1999, 2: 861–863.

    Article  CAS  PubMed  Google Scholar 

  3. Shaw P, Greenstein D, Lerch J, Clasen L, Lenroot R, Gogtay N, et al. Intellectual ability and cortical development in children and adolescents. Nature 2006, 440: 676–679.

    Article  CAS  PubMed  Google Scholar 

  4. Golarai G, Ghahremani DG, Whitfield-Gabrieli S, Reiss A, Eberhardt JL, Gabrieli JDE, et al. Differential development of high-level visual cortex correlates with category-specific recognition memory. Nat Neurosci 2007, 10: 512–522.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Baum GL, Ciric R, Roalf DR, Betzel RF, Moore TM, Shinohara RT, et al. Modular segregation of structural brain networks supports the development of executive function in youth. Curr Biol 2017, 27(1561–1572): e8.

    Google Scholar 

  6. Fair DA, Cohen AL, Power JD, Dosenbach NUF, Church JA, Miezin FM, et al. Functional brain networks develop from a “local to distributed” organization. PLoS Comput Biol 2009, 5: e1000381.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Dosenbach NUF, Nardos B, Cohen AL, Fair DA, Power JD, Church JA, et al. Prediction of individual brain maturity using fMRI. Science 2010, 329: 1358–1361.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci U S A 2004, 101: 8174–8179.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Baum GL, Cui Z, Roalf DR, Ciric R, Betzel RF, Larsen B, et al. Development of structure–function coupling in human brain networks during youth. Proc Natl Acad Sci U S A 2020, 117: 771–778.

  10. Foulkes L, Blakemore S-J. Studying individual differences in human adolescent brain development. Nat Neurosci 2018, 21: 315–323.

    Article  CAS  PubMed  Google Scholar 

  11. Lenroot RK, Giedd JN. Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neurosci Biobehav Rev 2006, 30: 718–729.

    Article  PubMed  Google Scholar 

  12. Hazlett HC, Gu H, Munsell BC, Kim SH, Styner M, Wolff JJ, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature 2017, 542: 348–351.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Paus T, Keshavan M, Giedd JN. Why do many psychiatric disorders emerge during adolescence?. Nat Rev Neurosci 2008, 9: 947–957.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Xia Y, Lv D, Liang Y, Zhang H, Pei K, Shao R, et al. Abnormal brain structure and function in first-episode childhood-and adolescence-onset schizophrenia: Association with clinical symptoms. Neurosci Bull 2019, 35: 522–526.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Dobbing J, Sands J. Quantitative growth and development of human brain. Arch Dis Child 1973, 48: 757–767.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Giedd JN, Snell JW, Lange N, Rajapakse JC, Casey BJ, Kozuch PL, et al. Quantitative magnetic resonance imaging of human brain development: ages 4–18. Cereb Cortex 1996, 6: 551–559.

    Article  CAS  PubMed  Google Scholar 

  17. Qin S, Young CB, Supekar K, Uddin LQ, Menon V. Immature integration and segregation of emotion-related brain circuitry in young children. Proc Natl Acad Sci U S A 2012, 109: 7941–7946.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zuo X-N, He Y, Betzel RF, Colcombe S, Sporns O, Milham MP. Human connectomics across the life span. Trends Cogn Sci 2017, 21: 32–45.

    Article  PubMed  Google Scholar 

  19. Wang Y, Xu Q, Zuo C, Zhao L, Hao L. Longitudinal changes of cerebellar thickness in autism spectrum disorder. Neurosci Lett 2020:134949.

  20. Satterthwaite TD, Connolly JJ, Ruparel K, Calkins ME, Jackson C, Elliott MA, et al. The philadelphia neurodevelopmental cohort: A publicly available resource for the study of normal and abnormal brain development in youth. Neuroimage 2016, 124: 1115–1119.

    Article  PubMed  Google Scholar 

  21. Lisdahl KM, Sher KJ, Conway KP, Gonzalez R, Feldstein Ewing SW, Nixon SJ, et al. Adolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods. Dev Cogn Neurosci 2018, 32: 80–96.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Schumann G, Loth E, Banaschewski T, Barbot A, Barker G, Büchel C, et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol Psychiatry 2010, 15: 1128–1139.

    Article  CAS  PubMed  Google Scholar 

  23. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc London Ser B Biol Sci 2001, 356: 1293–1322.

    Article  CAS  Google Scholar 

  24. Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 2011, 54: 313–327.

    Article  PubMed  Google Scholar 

  25. Zhao T, Liao X, Fonov VS, Wang Q, Men W, Wang Y, et al. Unbiased age-specific structural brain atlases for Chinese pediatric population. Neuroimage 2019, 189: 55–70.

    Article  PubMed  Google Scholar 

  26. Sanchez CE, Richards JE, Almli CR. Age-Specific MRI Templates for Pediatric Neuroimaging. Dev Neuropsychol 2012, 37: 379–399.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Richards JE, Sanchez C, Phillips-Meek M, Xie W. A database of age-appropriate average MRI templates. Neuroimage 2016, 124: 1254–1259.

    Article  PubMed  Google Scholar 

  28. Xie W, Richards JE, Lei D, Zhu H, Lee K, Gong Q. The construction of MRI brain/head templates for Chinese children from 7 to 16 years of age. Dev Cogn Neurosci 2015, 15: 94–105..

    Article  PubMed  PubMed Central  Google Scholar 

  29. Yoon U, Fonov VS, Perusse D, Evans AC, Group BDC. The effect of template choice on morphometric analysis of pediatric brain data. Neuroimage 2009, 45: 769–777.

    Article  PubMed  Google Scholar 

  30. Casey BJ, Giedd JN, Thomas KM. Structural and functional brain development and its relation to cognitive development. Biol Psychol 2000, 54: 241–257.

    Article  CAS  PubMed  Google Scholar 

  31. Shaw P, Kabani NJ, Lerch JP, Eckstrand K, Lenroot R, Gogtay N, et al. Neurodevelopmental trajectories of the human cerebral cortex. J Neurosci 2008, 28: 3586–3594.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Rueda MR, Fan J, McCandliss BD, Halparin JD, Gruber DB, Lercari LP, et al. Development of attentional networks in childhood. Neuropsychologia 2004, 42: 1029–1040.

    Article  PubMed  Google Scholar 

  33. Konrad K, Neufang S, Thiel CM, Specht K, Hanisch C, Fan J, et al. Development of attentional networks: an fMRI study with children and adults. Neuroimage 2005, 28: 429–439.

    Article  PubMed  Google Scholar 

  34. Van Leijenhorst L, Moor BG, de Macks ZAO, Rombouts SARB, Westenberg PM, Crone EA. Adolescent risky decision-making: neurocognitive development of reward and control regions. Neuroimage 2010, 51: 345–355.

    Article  PubMed  Google Scholar 

  35. Ellis CT, Turk-Browne NB. Infant fMRI: a model system for cognitive neuroscience. Trends Cogn Sci 2018, 22: 375–387.

    Article  PubMed  PubMed Central  Google Scholar 

  36. De Luca CR, Leventer RJ. Developmental trajectories of executive functions across the lifespan. Psychology Press, 2010, 57–90.

  37. Scherf KS, Behrmann M, Humphreys K, Luna B. Visual category-selectivity for faces, places and objects emerges along different developmental trajectories. Dev Sci 2007, 10: F15-30.

    Article  PubMed  Google Scholar 

  38. Zhou Q, Hofer C, Eisenberg N, Reiser M, Spinrad TL, Fabes RA. The developmental trajectories of attention focusing, attentional and behavioral persistence, and externalizing problems during school-age years. Dev Psychol 2007, 43: 369.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Meng FC, Xu XJ, Song TJ, Shou XJ, Wang XL, Han SP, et al. Development of an autism subtyping questionnaire based on social behaviors. Neurosci Bull 2018, 34: 789–800.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Klenberg L, Korkman M, Lahti-Nuuttila P. Differential development of attention and executive functions in 3-to 12-year-old Finnish children. Dev Neuropsychol 2001, 20: 407–428.

    Article  CAS  PubMed  Google Scholar 

  41. Geier C, Luna B. The maturation of incentive processing and cognitive control. Pharmacol Biochem Behav 2009, 93: 212–221.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Esteban O, Markiewicz CJ, Blair RW, Moodie CA, Isik AI, Erramuzpe A, et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 2019, 16: 111.

    Article  CAS  PubMed  Google Scholar 

  43. Fan J, McCandliss BD, Sommer T, Raz A, Posner MI. Testing the efficiency and independence of attentional networks. J Cogn Neurosci 2002, 14: 340–347.

    Article  PubMed  Google Scholar 

  44. Hariri AR, Mattay VS, Tessitore A, Kolachana B, Fera F, Goldman D, et al. Serotonin transporter genetic variation and the response of the human amygdala. Science 2002, 297: 400–403.

    Article  CAS  PubMed  Google Scholar 

  45. Lejuez CW, Read JP, Kahler CW, Richards JB, Ramsey SE, Stuart GL, et al. Evaluation of a behavioral measure of risk taking: the Balloon Analogue Risk Task (BART). J Exp Psychol Appl 2002, 8: 75.

    Article  CAS  PubMed  Google Scholar 

  46. Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform 2011, 5: 13.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Johnson MH. Interactive Specialization: A domain-general framework for human functional brain development?. Dev Cogn Neurosci 2011, 1: 7–21.

    Article  PubMed  Google Scholar 

  48. Johnson MH. Functional Brain Development in Infants: Elements of an Interactive Specialization Framework. Child Dev 2000, 71: 75–81.

    Article  CAS  PubMed  Google Scholar 

  49. Fan J, Mccandliss B, Fossella J, Flombaum J, Posner M. The activation of attentional networks. Neuroimage 2005, 26: 471–479.

    Article  PubMed  Google Scholar 

  50. Xuan B, Mackie M-A, Spagna A, Wu T, Tian Y, Hof PR, et al. The activation of interactive attentional networks. Neuroimage 2016, 129: 308–319.

    Article  PubMed  Google Scholar 

  51. Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 2002, 3: 201.

    Article  CAS  PubMed  Google Scholar 

  52. Raz A, Buhle J. Typologies of attentional networks. Nat Rev Neurosci 2006, 7.

  53. Hao L, Sang N, Du X, Qiu J, Wei D, Chen X. Examining brain structures associated with attention networks in a large sample of young adults: a voxel-based morphometry study. Sci Bull 2015, 60: 1824–1832.

    Article  Google Scholar 

  54. Paus T. Location and function of the human frontal eye-field: a selective review. Neuropsychologia 1996, 34: 475–483.

    Article  CAS  PubMed  Google Scholar 

  55. Casco C, Tressoldi PE, Dellantonio A. Visual selective attention and reading efficiency are related in children. Cortex 1998, 34: 531–546.

    Article  CAS  PubMed  Google Scholar 

  56. Lam CM, Beale IL. Relations among sustained attention, reading performance, and teachers’ ratings of behavior problems. Remedial Spec Educ 1991, 12: 40–47.

    Article  Google Scholar 

  57. Purvis KL, Tannock R. Language abilities in children with attention deficit hyperactivity disorder, reading disabilities, and normal controls. J Abnorm Child Psychol 1997, 25: 133–144.

    Article  CAS  PubMed  Google Scholar 

  58. Rabiner D, Coie JD, Group CPPR. Early attention problems and children’s reading achievement: A longitudinal investigation. J Am Acad Child Adolesc Psychiatry 2000, 39: 859–867.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Facoetti A, Turatto M, Lorusso ML, Mascetti GG. Orienting of visual attention in dyslexia: evidence for asymmetric hemispheric control of attention. Exp Brain Res 2001, 138: 46–53.

    Article  CAS  PubMed  Google Scholar 

  60. Harter MR, Anllo-Vento L, Wood FB. Event-related potentials, spatial orienting, and reading disabilities. Psychophysiology 1989, 26: 404–421.

    Article  CAS  PubMed  Google Scholar 

  61. Hillen R, Günther T, Kohlen C, Eckers C, van Ermingen-Marbach M, Sass K, et al. Identifying brain systems for gaze orienting during reading: fMRI investigation of the Landolt paradigm. Front Hum Neurosci 2013, 7: 384.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Anderson P. Assessment and development of executive function (EF) during childhood. Child Neuropsychol 2002, 8: 71–82.

    Article  PubMed  Google Scholar 

  63. Chugani HT. A critical period of brain development: studies of cerebral glucose utilization with PET. Prev Med (Baltim) 1998, 27: 184–188.

    Article  CAS  Google Scholar 

  64. Kail R. Sources of age differences in speed of processing. Child Dev 1986:969–987.

  65. Owen AM, McMillan KM, Laird AR, Bullmore E. N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp 2005, 25:46–59.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Blokland GAM, McMahon KL, Hoffman J, Zhu G, Meredith M, Martin NG, et al. Quantifying the heritability of task-related brain activation and performance during the N-back working memory task: a twin fMRI study. Biol Psychol 2008, 79: 70–79.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Thomas KM, Drevets WC, Whalen PJ, Eccard CH, Dahl RE, Ryan ND, et al. Amygdala response to facial expressions in children and adults. Biol Psychiatry 2001, 49: 309–316.

    Article  CAS  PubMed  Google Scholar 

  68. Campbell R, Elgar K, Kuntsi J, Akers R, Terstegge J, Coleman M, et al. The classification of ‘fear’from faces is associated with face recognition skill in women. Neuropsychologia 2002, 40: 575–584.

    Article  PubMed  Google Scholar 

  69. Zahn-Waxler C, Shirtcliff EA, Marceau K. Disorders of childhood and adolescence: Gender and psychopathology. Annu Rev Clin Psychol 2008, 4: 275–303.

    Article  PubMed  Google Scholar 

  70. Rao H, Korczykowski M, Pluta J, Hoang A, Detre JA. Neural correlates of voluntary and involuntary risk taking in the human brain: an fMRI Study of the Balloon Analog Risk Task (BART). Neuroimage 2008, 42: 902–910.

    Article  PubMed  Google Scholar 

  71. Calhoun VD, Wager TD, Krishnan A, Rosch KS, Seymour KE, Nebel MB, et al. The impact of T1 versus EPI spatial normalization templates for fMRI data analyses. Hum Brain Mapp 2017, 38: 5331–5342.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Gargouri F, Kallel F, Delphine S, Ben Hamida A, Lehéricy S, Valabregue R. The influence of preprocessing steps on graph theory measures derived from resting state fMRI. Front Comput Neurosci 2018, 12: 8.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Gavrilescu M, Stuart GW, Rossell S, Henshall K, McKay C, Sergejew AA, et al. Functional connectivity estimation in fMRI data: influence of preprocessing and time course selection. Hum Brain Mapp 2008, 29: 1040–1052.

    Article  PubMed  Google Scholar 

  74. Ge Y, Pan Y, Dou W. Analysis of BOLD fMRI signal preprocessing pipeline on different datasets while reducing false positive rates. BIBE 2018, Int Conf Biol Inf Biomed Eng, VDE, 2018, 1–8.

  75. Esteban O, Ciric R, Finc K, Blair RW, Markiewicz CJ, Moodie CA, et al. Analysis of task-based functional MRI data preprocessed with fMRIPrep. BioRxiv 2019: 694364.

  76. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey replication. Arch Gen Psychiatry 2005, 62: 593.

    Article  PubMed  Google Scholar 

  77. Kessler RC, Wang PS. The descriptive epidemiology of commonly occurring mental disorders in the united states. Annu Rev Public Health 2008, 29: 115–129.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank the National Center for Protein Sciences at Peking University for assistance with MRI data acquisition. This work was supported by the National Natural Science Foundation of China (31522028, 71834002, 31530031, 81571056, 31521063, and 61775139), the Youth Science and Technology Innovation Program, Beijing Brain Initiative of Beijing Municipal Science and Technology Commission (Z181100001518003), the Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (CNLZD1503 and CNLZD1703), and the Fundamental Research Funds for the Central Universities. We thank Professor Xi-Nian Zuo for comments and helpful discussions of this manuscript.

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  1. Lei Hao, Lei Li and Menglu Chen have contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China

    Lei Hao, Menglu Chen, Jiahua Xu, Min Jiang, Yanpei Wang, Yong He, Sha Tao, Qi Dong & Shaozheng Qin

  2. College of Teacher Education, Southwest University, Chongqing, 400715, China

    Lei Hao

  3. Key Laboratory of Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China

    Lei Hao, Menglu Chen, Jiahua Xu, Min Jiang, Yanpei Wang, Yong He & Shaozheng Qin

  4. School of Information Engineering, Huzhou University, Huzhou, 313000, China

    Lei Li & Linhua Jiang

  5. Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China

    Xu Chen & Jiang Qiu

  6. Faculty of Psychology, Southwest University, Chongqing, 400715, China

    Xu Chen & Jiang Qiu

  7. Beijing HuiLongGuan Hospital, Peking University, Beijing, 100096, China

    Shuping Tan

  8. Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China

    Jia-Hong Gao

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  1. Lei Hao
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Hao, L., Li, L., Chen, M. et al. Mapping Domain- and Age-Specific Functional Brain Activity for Children’s Cognitive and Affective Development. Neurosci. Bull. 37, 763–776 (2021). https://doi.org/10.1007/s12264-021-00650-7

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