1932

Abstract

The exponential rise in the number of functional brain connectivity studies, particularly those examining intrinsic functional connectivity (iFC) at rest, and the promises of this work for unraveling the ontogeny of functional neural systems motivate this review. Shortly before this explosion in functional connectivity research, developmental neuroscientists had proposed theories based on neural systems models to explain behavioral changes, particularly in adolescence. The current review presents recent advances in imaging in brain connectivity research, which provides a unique tool for the study of neural systems. Understanding the potential of neuroimaging for refining neurodevelopmental models of brain function requires a description of various functional connectivity approaches. In this review, we describe task-based and resting-state functional magnetic resonance imaging (fMRI) analytic strategies, but we focus on iFC findings from resting-state data to describe general developmental trajectories of brain network organization. Finally, we use the example of drug addiction to frame a discussion of psychopathology that emerges in adolescence.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-clinpsy-032814-112753
2015-03-28
2024-04-20
Loading full text...

Full text loading...

/deliver/fulltext/clinpsy/11/1/annurev-clinpsy-032814-112753.html?itemId=/content/journals/10.1146/annurev-clinpsy-032814-112753&mimeType=html&fmt=ahah

Literature Cited

  1. Adkins D. 2013. When is puberty too early?. Durham, NC: Duke Univ. Health Syst http://www.dukemedicine.org/blog/when-puberty-too-early [Google Scholar]
  2. Anderson JS, Ferguson MA, Lopez-Larson M, Yurgelun-Todd D. 2011. Connectivity gradients between the default mode and attention control networks. Brain Connectivity 1:147–57 [Google Scholar]
  3. Angold A, Costello EJ. 2006. Puberty and depression. Child Adolesc. Psychiatr. Clin. N. Am. 15:919–37 [Google Scholar]
  4. Arnett JJ. 1999. Adolescent storm and stress, reconsidered. Am. Psychol. 54:317–26 [Google Scholar]
  5. Barnes A, Bullmore ET, Suckling J. 2009. Endogenous human brain dynamics recover slowly following cognitive effort. PLOS ONE 4:e6626 [Google Scholar]
  6. Beckmann CF, DeLuca M, Devlin JT, Smith SM. 2005. Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. B 360:1001–13 [Google Scholar]
  7. Bedard AC, Nichols S, Barbosa JA, Schachar R, Logan GD, Tannock R. 2002. The development of selective inhibitory control across the life span. Dev. Neuropsychol. 21:93–111 [Google Scholar]
  8. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34:537–41 [Google Scholar]
  9. Blanton RE, Levitt JG, Thompson PM, Narr KL, Capetillo-Cunliffe L. et al. 2001. Mapping cortical asymmetry and complexity patterns in normal children. Psychiatry Res. 107:29–43 [Google Scholar]
  10. Bourgeois JP, Rakic P. 1993. Changes of synaptic density in the primary visual cortex of the macaque monkey from fetal to adult stage. J. Neurosci. 13:2801–20 [Google Scholar]
  11. Brookes MJ, Woolrich M, Luckhoo H, Price D, Hale JR. et al. 2011. Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc. Natl. Acad. Sci. USA 108:16783–88 [Google Scholar]
  12. Buchanan CM, Eccles JS, Becker JB. 1992. Are adolescents the victims of raging hormones? Evidence for activational effects of hormones on moods and behavior at adolescence. Psychol. Bull. 111:62–107 [Google Scholar]
  13. Calhoun VD, Adali T. 2012. Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Rev. Biomed. Eng. 5:60–73 [Google Scholar]
  14. Casey B, Jones RM, Somerville LH. 2011. Braking and accelerating of the adolescent brain. J. Res. Adolesc. 21:21–33 [Google Scholar]
  15. Chambers RA, Taylor JR, Potenza MN. 2003. Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability. Am. J. Psychiatry 160:1041–52 [Google Scholar]
  16. Craddock RC, James GA, Holtzheimer PE 3rd, Hu XP, Mayberg HS. 2012. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33:1914–28 [Google Scholar]
  17. Dahl RE. 2004. Adolescent brain development: a period of vulnerabilities and opportunities. Keynote address. Ann. N. Y. Acad. Sci. 1021:1–22 [Google Scholar]
  18. Das P, Kemp AH, Liddell BJ, Brown KJ, Olivieri G. et al. 2005. Pathways for fear perception: modulation of amygdala activity by thalamo-cortical systems. NeuroImage 26:141–48 [Google Scholar]
  19. David O, Guillemain I, Saillet S, Reyt S, Deransart C. et al. 2008. Identifying neural drivers with functional MRI: an electrophysiological validation. PLOS Biol. 6:2683–97 [Google Scholar]
  20. de Pasquale F, Della Penna S, Snyder AZ, Lewis C, Mantini D. et al. 2010. Temporal dynamics of spontaneous MEG activity in brain networks. Proc. Natl. Acad. Sci. USA 107:6040–45 [Google Scholar]
  21. Dosenbach NU, Nardos B, Cohen AL, Fair DA, Power JD. et al. 2010. Prediction of individual brain maturity using fMRI. Science 329:1358–61 [Google Scholar]
  22. Ernst M, Fudge JL. 2009. A developmental neurobiological model of motivated behavior: anatomy, connectivity and ontogeny of the triadic nodes. Neurosci. Biobehav. Rev. 33:367–82 [Google Scholar]
  23. Ernst M, Hale E, O'Connell K. 2014. Response to commentaries regarding the Triadic Systems Model perspective. Brain Cogn. 89:122–26 [Google Scholar]
  24. Ernst M, Mueller SC. 2008. The adolescent brain: insights from functional neuroimaging research. Dev. Neurobiol. 68:729–43 [Google Scholar]
  25. Ernst M, Pine DS, Hardin M. 2006. Triadic model of the neurobiology of motivated behavior in adolescence. Psychol. Med. 36:299–312 [Google Scholar]
  26. Fair DA, Bathula D, Mills KL, Dias TG, Blythe MS. et al. 2010. Maturing thalamocortical functional connectivity across development. Front. Syst. Neurosci. 4:10 [Google Scholar]
  27. Fair DA, Cohen AL, Dosenbach NU, Church JA, Miezin FM. et al. 2008. The maturing architecture of the brain's default network. Proc. Natl. Acad. Sci. USA 105:4028–32 [Google Scholar]
  28. Fair DA, Cohen AL, Power JD, Dosenbach NU, Church JA. et al. 2009. Functional brain networks develop from a “local to distributed” organization. PLOS Comput. Biol. 5:e1000381 [Google Scholar]
  29. Fair DA, Dosenbach NU, Church JA, Cohen AL, Brahmbhatt S. et al. 2007. Development of distinct control networks through segregation and integration. Proc. Natl. Acad. Sci. USA 104:13507–12 [Google Scholar]
  30. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. USA 102:9673–78 [Google Scholar]
  31. Friston KJ. 2011. Functional and effective connectivity: a review. Brain Connect. 1:13–36 [Google Scholar]
  32. Friston KJ, Buechel C, Fink GR, Morris J, Rolls E, Dolan RJ. 1997. Psychophysiological and modulatory interactions in neuroimaging. NeuroImage 6:218–29 [Google Scholar]
  33. Friston KJ, Harrison L, Penny W. 2003. Dynamic causal modelling. NeuroImage 19:1273–302 [Google Scholar]
  34. Friston KJ, Kahan J, Biswal B, Razi A. 2014. A DCM for resting state FMRI. NeuroImage 94:396–407 [Google Scholar]
  35. Gathercole SE, Pickering SJ, Ambridge B, Wearing H. 2004. The structure of working memory from 4 to 15 years of age. Dev. Psychol. 40:177–90 [Google Scholar]
  36. Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H. et al. 1999. Brain development during childhood and adolescence: a longitudinal MRI study. Nat. Neurosci. 2:861–63 [Google Scholar]
  37. Gogtay N, Thompson PM. 2010. Mapping gray matter development: implications for typical development and vulnerability to psychopathology. Brain Cogn. 72:6–15 [Google Scholar]
  38. Hardin MG, Ernst M. 2009. Functional brain imaging of development-related risk and vulnerability for substance use in adolescents. J. Addict. Med. 3:47–54 [Google Scholar]
  39. Hoff GEA, Van Den Heuvel M, Benders MJNL, Kersbergen KJ, de Vries LS. 2013. On development of functional brain connectivity in the young brain. Front. Hum. Neurosci. 7:650 [Google Scholar]
  40. Hwang K, Hallquist MN, Luna B. 2013. The development of hub architecture in the human functional brain network. Cereb. Cortex 23:2380–93 [Google Scholar]
  41. Jo HJ, Saad ZS, Simmons WK, Milbury LA, Cox RW. 2010. Mapping sources of correlation in resting state fMRI, with artifact detection and removal. NeuroImage 52:571–82 [Google Scholar]
  42. Kahan J, Foltynie T. 2013. Understanding DCM: ten simple rules for the clinician. NeuroImage 83:542–49 [Google Scholar]
  43. Kann L, Kinchen S, Shanklin SL, Flint KH, Kawkins J. et al. 2014. Youth risk behavior surveillance—United States, 2013. MMWR Surveill. Summ. 63:No. SS-3
  44. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. 2005. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry 62:593–602 [Google Scholar]
  45. Khundrakpam BS, Reid A, Brauer J, Carbonell F, Lewis J. et al. 2013. Developmental changes in organization of structural brain networks. Cereb. Cortex 23:2072–85 [Google Scholar]
  46. Knutson B, Adams CM, Fong GW, Hommer D. 2001. Anticipation of increasing monetary reward selectively recruits nucleus accumbens. J. Neurosci. 21:RC159 [Google Scholar]
  47. Lenroot RK, Gogtay N, Greenstein DK, Wells EM, Wallace GL. et al. 2007. Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage 36:1065–73 [Google Scholar]
  48. Liao W, Mantini D, Zhang Z, Pan Z, Ding J. et al. 2010. Evaluating the effective connectivity of resting state networks using conditional Granger causality. Biol. Cybern. 102:57–69 [Google Scholar]
  49. 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. 35:1219–36 [Google Scholar]
  50. Lohmann G, Erfurth K, Muller K, Turner R. 2012. Critical comments on dynamic causal modelling. NeuroImage 59:2322–29 [Google Scholar]
  51. Mädler B, Drabycz SA, Kolind SH, Whittall KP, MacKay AL. 2008. Is diffusion anisotropy an accurate monitor of myelination? Correlation of multicomponent T2 relaxation and diffusion tensor anisotropy in human brain. Magn. Reson. Imaging 26:874–88 [Google Scholar]
  52. McKeown MJ, Hansen LK, Sejnowsk TJ. 2003. Independent component analysis of functional MRI: What is signal and what is noise?. Curr. Opin. Neurobiol. 13:620–29 [Google Scholar]
  53. Meunier D, Lambiotte R, Bullmore ET. 2010. Modular and hierarchically modular organization of brain networks. Front. Neurosci. 4:200 [Google Scholar]
  54. Morris RG. 1999. D.O. Hebb. The Organization of Behavior Wiley New York: 1949. Brain Res. Bull. 50437 [Google Scholar]
  55. Munoz DP, Istvan PJ. 1998. Lateral inhibitory interactions in the intermediate layers of the monkey superior colliculus. J. Neurophysiol. 79:1193–209 [Google Scholar]
  56. Murphy K, Birn RM, Bandettini PA. 2013. Resting-state fMRI confounds and cleanup. NeuroImage 80:349–59 [Google Scholar]
  57. Natl. Inst. Drug Abuse (NIAD) 2013. Monitoring the Future Survey: Overview of Findings 2013. Bethesda, MD: NIDA http://www.drugabuse.gov/monitoring-future-survey-overview-findings-2013
  58. O'Muircheartaigh J, Dean DC 3rd, Dirks H, Waskiewicz N, Lehman K. et al. 2013. Interactions between white matter asymmetry and language during neurodevelopment. J. Neurosci. 33:16170–77 [Google Scholar]
  59. Paus T. 2005. Mapping brain maturation and cognitive development during adolescence. Trends Cogn. Sci. 9:60–68 [Google Scholar]
  60. Penny WD, Stephan KE, Daunizeau J, Rosa MJ, Friston KJ. et al. 2010. Comparing families of dynamic causal models. PLOS Comput. Biol. 6:e1000709 [Google Scholar]
  61. Penny WD, Stephan KE, Mechelli A, Friston KJ. 2004. Comparing dynamic causal models. NeuroImage 22:1157–72 [Google Scholar]
  62. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59:2142–54 [Google Scholar]
  63. Ramsey JD, Hanson SJ, Hanson C, Halchenko YO, Poldrack RA, Glymour C. 2010. Six problems for causal inference from fMRI. NeuroImage 49:1545–58 [Google Scholar]
  64. Rehme AK, Eickhoff SB, Grefkes C. 2013. State-dependent differences between functional and effective connectivity of the human cortical motor system. NeuroImage 67:237–46 [Google Scholar]
  65. Richards JM, Plate RC, Ernst M. 2013. A systematic review of fMRI reward paradigms used in studies of adolescents versus adults: the impact of task design and implications for understanding neurodevelopment. Neurosci. Biobehav. Rev. 37:976–91 [Google Scholar]
  66. Roebroeck A, Formisano E, Goebel R. 2005. Mapping directed influence over the brain using Granger causality and fMRI. NeuroImage 25:230–42 [Google Scholar]
  67. Roy AK, Fudge JL, Kelly C, Perry JS, Daniele T. et al. 2013. Intrinsic functional connectivity of amygdala-based networks in adolescent generalized anxiety disorder. J. Am. Acad. Child Adolesc. Psychiatry 52:290–99.e2 [Google Scholar]
  68. Rubia K. 2013. Functional brain imaging across development. Eur. Child Adolesc. Psychiatry 22:719–31 [Google Scholar]
  69. Rubinov M, Sporns O. 2010. Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52:1059–69 [Google Scholar]
  70. Saad ZS, Gotts SJ, Murphy K, Chen G, Jo HJ. et al. 2012. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect. 2:25–32 [Google Scholar]
  71. Satterthwaite TD, Wolf DH, Ruparel K, Erus G, Elliott MA. 2013. Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth. NeuroImage 83:45–57 [Google Scholar]
  72. Schultz DH, Balderston NL, Helmstetter FJ. 2012. Resting-state connectivity of the amygdala is altered following Pavlovian fear conditioning. Front. Hum. Neurosci. 6:242 [Google Scholar]
  73. Sepulcre J, Sabuncu MR, Johnson KA. 2012. Network assemblies in the functional brain. Curr. Opin. Neurol. 25:384–91 [Google Scholar]
  74. Smith AB, Halari R, Giampetro V, Brammer M, Rubia K. 2011a. Developmental effects of reward on sustained attention networks. NeuroImage 56:1693–704 [Google Scholar]
  75. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM. et al. 2009. Correspondence of the brain's functional architecture during activation and rest. Proc. Natl. Acad. Sci. USA 106:13040–45 [Google Scholar]
  76. Smith SM, Miller KL, Salimi-Khorshidi G, Webster M, Beckmann CF. et al. 2011b. Network modelling methods for fMRI. NeuroImage 54:875–91 [Google Scholar]
  77. Snook L, Paulson LA, Roy D, Phillips L, Beaulieu C. 2005. Diffusion tensor imaging of neurodevelopment in children and young adults. NeuroImage 26:1164–73 [Google Scholar]
  78. Snyder AN, Bockbrader MA, Hoffa AM, Dzemidzic MA, Talavage TM. et al. 2011. Psychometrically matched tasks evaluating differential fMRI activation during form and motion processing. Neuropsychology 25:622–33 [Google Scholar]
  79. Sporns O. 2013. Structure and function of complex brain networks. Dialogues Clin. Neurosci. 15:247–62 [Google Scholar]
  80. Sridharan D, Levitin DJ, Menon V. 2008. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc. Natl. Acad. Sci. USA 105:12569–74 [Google Scholar]
  81. Steinberg L, Albert D, Cauffman E, Banich M, Graham S, Wollard J. 2008. Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: evidence for a dual systems model. Dev. Psychol. 44:1764–78 [Google Scholar]
  82. Stephan KE, Penny WD, Moran RJ, den Ouden HE, Daunizeau J, Friston KJ. 2010. Ten simple rules for dynamic causal modeling. NeuroImage 49:3099–109 [Google Scholar]
  83. Stevens MC, Pearlson GD, Calhoun VD. 2009. Changes in the interaction of resting-state neural networks from adolescence to adulthood. Hum. Brain Mapp. 30:2356–66 [Google Scholar]
  84. Strogatz SH. 2001. Exploring complex networks. Nature 410:268–76 [Google Scholar]
  85. Supekar K, Musen M, Menon V. 2009. Development of large-scale functional brain networks in children. PLOS Biol. 7:e1000157 [Google Scholar]
  86. Thomason ME, Dassanayake MT, Shen S, Katkuri Y, Alexis M. et al. 2013. Cross-hemispheric functional connectivity in the human fetal brain. Sci. Transl. Med. 5:173ra24 [Google Scholar]
  87. Torrisi S, Moody TD, Vizueta N, Thomason ME, Monti MM. et al. 2013a. Differences in resting corticolimbic functional connectivity in bipolar I euthymia. Bipolar Disord. 15:156–66 [Google Scholar]
  88. Torrisi SJ, Lieberman MD, Bookheimer SY, Altshuler LL. 2013b. Advancing understanding of affect labeling with dynamic causal modeling. NeuroImage 82:481–88 [Google Scholar]
  89. Uddin LQ, Supekar KS, Ryali S, Menon V. 2011. Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. J. Neurosci. 31:18578–89 [Google Scholar]
  90. van den Heuvel MP, Hulshoff Pol HE. 2010. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20:519–34 [Google Scholar]
  91. van der Marel K, Klomp A, Meerhoff GF, Schipper P, Lucassen PJ. et al. 2014. Long-term oral methylphenidate treatment in adolescent and adult rats: differential effects on brain morphology and function. Neuropsychopharmacology 39:263–73 [Google Scholar]
  92. Webb JT, Ferguson MA, Nielsen JA, Anderson JS. 2013. BOLD Granger causality reflects vascular anatomy. PLOS ONE 8:e84279 [Google Scholar]
  93. Williams BR, Ponesse JS, Schachar RJ, Logan GD, Tannock R. 1999. Development of inhibitory control across the life span. Dev. Psychol. 35:205–13 [Google Scholar]
  94. Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. 2011. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8:665–70 [Google Scholar]
  95. Zhang D, Raichle ME. 2010. Disease and the brain's dark energy. Nat. Rev. Neurol. 6:15–28 [Google Scholar]
/content/journals/10.1146/annurev-clinpsy-032814-112753
Loading
/content/journals/10.1146/annurev-clinpsy-032814-112753
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error