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Licensed Unlicensed Requires Authentication Published by De Gruyter March 21, 2022

Normal development of the brain: a survey of joint structural–functional brain studies

  • Roxana Namiranian ORCID logo , Sahar Rahimi Malakshan , Hamid Abrishami Moghaddam EMAIL logo , Ali Khadem and Reza Jafari

Abstract

Joint structural–functional (S-F) developmental studies present a novel approach to address the complex neuroscience questions on how the human brain works and how it matures. Joint S-F biomarkers have the inherent potential to model effectively the brain’s maturation, fill the information gap in temporal brain atlases, and demonstrate how the brain’s performance matures during the lifespan. This review presents the current state of knowledge on heterochronous and heterogeneous development of S-F links during the maturation period. The S-F relationship has been investigated in early-matured unimodal and prolonged-matured transmodal regions of the brain using a variety of structural and functional biomarkers and data acquisition modalities. Joint S-F unimodal studies have employed auditory and visual stimuli, while the main focus of joint S-F transmodal studies has been resting-state and cognitive experiments. However, nonsignificant associations between some structural and functional biomarkers and their maturation show that designing and developing effective S-F biomarkers is still a challenge in the field. Maturational characteristics of brain asymmetries have been poorly investigated by the joint S-F studies, and the results were partially inconsistent with previous nonjoint ones. The inherent complexity of the brain performance can be modeled using multifactorial and nonlinear techniques as promising methods to simulate the impact of age on S-F relations considering their analysis challenges.


Corresponding author: Hamid Abrishami Moghaddam, Faculty of Electrical Engineering, K. N. Toosi University of Technology, PO Box 16315-1355, Tehran, Iran; and Inserm UMR 1105, Université de Picardie Jules Verne, 80054 Amiens, France, E-mail:

Funding source: Cognitive Sciences and Technologies Council

Award Identifier / Grant number: Unassigned

Acknowledgment

We thank Prof. Fabrice Wallois for his valuable suggestions on the initial idea of this paper. We would like also to thank Dr. Farveh Daneshvarfard for her comments on the initial phase of research, and Dr. Shirin Mavandadi for her comments on Figure 2. We would particularly like to thank Cognitive Sciences and Technologies Council (COGC), Iran in the framework of Neurobiom project number 96P97, for their support and guidance.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

Adibpour, P., Lebenberg, J., Kabdebon, C., Dehaene-Lambertz, G., and Dubois, J. (2020). Anatomo-functional correlates of auditory development in infancy. Dev. Cogn. Neurosci. 42: 100752, https://doi.org/10.1016/j.dcn.2019.100752.Search in Google Scholar PubMed PubMed Central

Baldoli, C., Scola, E., Della Rosa, P.A., Pontesilli, S., Longaretti, R., Poloniato, A., Scotti, R., Blasi, V., Cirillo, S., and Iadanza, A. (2015). Maturation of preterm newborn brains: a fMRI–DTI study of auditory processing of linguistic stimuli and white matter development. Brain Struct. Funct. 220: 3733–3751, https://doi.org/10.1007/s00429-014-0887-5.Search in Google Scholar PubMed

Barnes-Davis, M.E., Williamson, B.J., Merhar, S.L., Holland, S.K., and Kadis, D.S. (2020). Rewiring the extremely preterm brain: altered structural connectivity relates to language function. NeuroImage Clin. 25: 102194, https://doi.org/10.1016/j.nicl.2020.102194.Search in Google Scholar PubMed PubMed Central

Batista-García-Ramó, K. and Fernández-Verdecia, C.I. (2018). What we know about the brain structure–function relationship. Behav. Sci. 8: 39, https://doi.org/10.3390/bs8040039.Search in Google Scholar PubMed PubMed Central

Baum, G.L., Ciric, R., Roalf, D.R., Betzel, R.F., Moore, T.M., Shinohara, R.T., Kahn, A.E., Vandekar, S.N., Rupert, P.E., and Quarmley, M. (2017). Modular segregation of structural brain networks supports the development of executive function in youth. Curr. Biol. 27: 1561–1572, https://doi.org/10.1016/j.cub.2017.04.051.Search in Google Scholar PubMed PubMed Central

Baum, G.L., Cui, Z., Roalf, D.R., Ciric, R., Betzel, R.F., Larsen, B., Cieslak, M., Cook, P.A., Xia, C.H., and Moore, T.M. (2020). Development of structure–function coupling in human brain networks during youth. Proc. Natl. Acad. Sci. Unit. States Am. 117: 771–778, https://doi.org/10.1073/pnas.1912034117.Search in Google Scholar PubMed PubMed Central

Bazargani, N., Hillebrandt, H., Christoff, K., and Dumontheil, I. (2014). Developmental changes in effective connectivity associated with relational reasoning. Hum. Brain Mapp. 35: 3262–3276, https://doi.org/10.1002/hbm.22400.Search in Google Scholar PubMed PubMed Central

Beaulieu, C. (2002). The basis of anisotropic water diffusion in the nervous system–a technical review. NMR Biomed. 15: 435–455, https://doi.org/10.1002/nbm.782.Search in Google Scholar PubMed

Bechler, M.E., Swire, M., and ffrench‐Constant, C. (2018). Intrinsic and adaptive myelination—a sequential mechanism for smart wiring in the brain. Dev. Neurobiol. 78: 68–79, https://doi.org/10.1002/dneu.22518.Search in Google Scholar PubMed PubMed Central

Benders, M.J., Palmu, K., Menache, C., Borradori-Tolsa, C., Lazeyras, F., Sizonenko, S., Dubois, J., Vanhatalo, S., and Hüppi, P.S. (2015). Early brain activity relates to subsequent brain growth in premature infants. Cerebr. Cortex 25: 3014–3024, https://doi.org/10.1093/cercor/bhu097.Search in Google Scholar PubMed

Bercury, K.K. and Macklin, W.B. (2015). Dynamics and mechanisms of CNS myelination. Dev. Cell 32: 447–458, https://doi.org/10.1016/j.devcel.2015.01.016.Search in Google Scholar PubMed PubMed Central

Betzel, R.F., Byrge, L., He, Y., Goñi, J., Zuo, X.N., and Sporns, O. (2014). Changes in structural and functional connectivity among resting-state networks across the human lifespan. Neuroimage 102: 345–357, https://doi.org/10.1016/j.neuroimage.2014.07.067.Search in Google Scholar PubMed

Birca, A., Vakorin, V.A., Porayette, P., Madathil, S., Chau, V., Seed, M., Doesburg, S.M., Blaser, S., Nita, D.A., and Sharma, R. (2016). Interplay of brain structure and function in neonatal congenital heart disease. Ann. Clin. Transl. Neurol. 3: 708–722, https://doi.org/10.1002/acn3.336.Search in Google Scholar PubMed PubMed Central

Bisiacchi, P. and Cainelli, E. (2021). Structural and functional brain asymmetries in the early phases of life: a scoping review. Brain Struct. Funct. 227: 479–496, doi:https://doi.org/10.1007/s00429-021-02256-1.Search in Google Scholar PubMed PubMed Central

Brown, T.T. and Jernigan, T.L. (2012). Brain development during the preschool years. Neuropsychol. Rev. 22: 313–333, https://doi.org/10.1007/s11065-012-9214-1.Search in Google Scholar PubMed PubMed Central

Bullmore, E. and Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10: 186–198, https://doi.org/10.1038/nrn2575.Search in Google Scholar PubMed

Burzynska, A.Z., Preuschhof, C., Bäckman, L., Nyberg, L., Li, S.C., Lindenberger, U., and Heekeren, H.R. (2010). Age-related differences in white matter microstructure: region-specific patterns of diffusivity. Neuroimage 49: 2104–2112, https://doi.org/10.1016/j.neuroimage.2009.09.041.Search in Google Scholar PubMed

Camos, V. and Barrouillet, P. (2018). Working memory in development. Routledge, London.10.4324/9781315660851Search in Google Scholar

Canolty, R.T. and Knight, R.T. (2010). The functional role of cross-frequency coupling. Trends Cognit. Sci. 14: 506–515, https://doi.org/10.1016/j.tics.2010.09.001.Search in Google Scholar PubMed PubMed Central

Cao, M., Huang, H., Peng, Y., Dong, Q., and He, Y. (2016). Toward developmental connectomics of the human brain. Front. Neuroanat. 10: 25, https://doi.org/10.3389/fnana.2016.00025.Search in Google Scholar PubMed PubMed Central

Cascio, C.J., Gerig, G., and Piven, J. (2007). Diffusion tensor imaging: application to the study of the developing brain. J. Am. Acad. Child Adolesc. Psychiatry 46: 213–223, https://doi.org/10.1097/01.chi.0000246064.93200.e8.Search in Google Scholar PubMed

Cavanna, A.E. and Trimble, M.R. (2006). The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129: 564–583, https://doi.org/10.1093/brain/awl004.Search in Google Scholar PubMed

Chomiak, T. and Hu, B. (2017). Mechanisms of hierarchical cortical maturation. Front. Cell. Neurosci. 11: 272, doi:https://doi.org/10.3389/fncel.2017.00272.Search in Google Scholar PubMed PubMed Central

Cone, B. and Whitaker, R. (2013). Dynamics of infant cortical auditory evoked potentials (CAEPs) for tone and speech tokens. Int. J. Pediatr. Otorhinolaryngol. 77: 1162–1173, https://doi.org/10.1016/j.ijporl.2013.04.030.Search in Google Scholar PubMed PubMed Central

Cowan, N. (2016). Working memory maturation: can we get at the essence of cognitive growth? Perspect. Psychol. Sci. 11: 239–264, https://doi.org/10.1177/1745691615621279.Search in Google Scholar PubMed PubMed Central

Czopka, T. and Lyons, D.A. (2013). Individual oligodendrocytes have only a few hours in which to generate new myelin sheaths in vivo. Dev. Cell 25: 599–609, https://doi.org/10.1016/j.devcel.2013.05.013.Search in Google Scholar PubMed PubMed Central

Daneshvarfard, F., Moghaddam, H.A., Kongolo, G., Wallois, F., and Mahmoudzadeh, M. (2020). Functional and structural correlates of the preterm infant’s brain: relating developmental changes of auditory evoked responses to structural maturation. Brain Struct. Funct.: 1–12, https://doi.org/10.1007/s00429-020-02117-3.Search in Google Scholar PubMed

Dell’Acqua, F. and Tournier, J. (2019). Modelling white matter with spherical deconvolution: how and why? NMR Biomed. 32: e3945.10.1002/nbm.3945Search in Google Scholar PubMed PubMed Central

Deng, F., Jiang, X., Zhu, D., Zhang, T., Li, K., Guo, L., and Liu, T. (2014). A functional model of cortical gyri and sulci. Brain Struct. Funct. 219: 1473–1491, https://doi.org/10.1007/s00429-013-0581-z.Search in Google Scholar PubMed PubMed Central

Dixon, M.L., De La Vega, A., Mills, C., Andrews-Hanna, J., Spreng, R.N., Cole, M.W., and Christoff, K. (2018). Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc. Natl. Acad. Sci. U.S.A. 115: E1598–E1607, https://doi.org/10.1073/pnas.1715766115.Search in Google Scholar PubMed PubMed Central

Dockstader, C., Gaetz, W., Rockel, C., and Mabbott, D.J. (2012). White matter maturation in visual and motor areas predicts the latency of visual activation in children. Hum. Brain Mapp. 33: 179–191, https://doi.org/10.1002/hbm.21203.Search in Google Scholar PubMed PubMed Central

Dubois, J., Adibpour, P., Poupon, C., Hertz-Pannier, L., and Dehaene-Lambertz, G. (2016). MRI and M/EEG studies of the white matter development in human fetuses and infants: review and opinion. Brain Plast. 2: 49–69, https://doi.org/10.3233/bpl-160031.Search in Google Scholar PubMed PubMed Central

Dubois, J., Dehaene-Lambertz, G., Kulikova, S., Poupon, C., Hüppi, P.S., and Hertz-Pannier, L. (2014). The early development of brain white matter: a review of imaging studies in fetuses, newborns and infants. Neuroscience 276: 48–71, https://doi.org/10.1016/j.neuroscience.2013.12.044.Search in Google Scholar PubMed

Dubois, J., Dehaene-lambertz, G., Soare, C., Cointepas, Y., Bihan, D. Le, and Hertz-pannier, L. (2008). Microstructural correlates of infant functional development: example of the visual pathways. J. Neurosci. 28: 1943–1948, https://doi.org/10.1523/jneurosci.5145-07.2008.Search in Google Scholar PubMed PubMed Central

Dumontheil, I. (2014). Development of abstract thinking during childhood and adolescence: the role of rostrolateral prefrontal cortex. Dev. Cogn. Neurosci. 10: 57–76, https://doi.org/10.1016/j.dcn.2014.07.009.Search in Google Scholar PubMed PubMed Central

Dumontheil, I., Houlton, R., Christoff, K., and Blakemore, S. (2010). Development of relational reasoning during adolescence. Dev. Sci. 13: F15–F24, https://doi.org/10.1111/j.1467-7687.2010.01014.x.Search in Google Scholar PubMed

Fair, D.A., Cohen, A.L., Dosenbach, N.U.F., Church, J.A., Miezin, F.M., Barch, D.M., Raichle, M.E., Petersen, S.E., and Schlaggar, B.L. (2008). The maturing architecture of the brain’s default network. Proc. Natl. Acad. Sci. U.S.A. 105: 4028–4032, https://doi.org/10.1073/pnas.0800376105.Search in Google Scholar PubMed PubMed Central

Fair, D.A., Cohen, A.L., Power, J.D., Dosenbach, N.U.F., Church, J.A., Miezin, F.M., Schlaggar, B.L., and Petersen, S.E. (2009). Functional brain networks develop from a “local to distributed” organization. PLoS Comput. Biol. 5: e1000381, doi:https://doi.org/10.1371/journal.pcbi.1000381.Search in Google Scholar PubMed PubMed Central

Fan, J., Fang, L., Wu, J., Guo, Y., and Dai, Q. (2020). From brain science to artificial intelligence. Engineering 6: 248–252, https://doi.org/10.1016/j.eng.2019.11.012.Search in Google Scholar

Feldman, D.E. and Brecht, M. (2005). Map plasticity in somatosensory cortex. Science 310: 810–815, https://doi.org/10.1126/science.1115807.Search in Google Scholar PubMed

Ferrari, M. and Quaresima, V. (2012). A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. Neuroimage 63: 921–935, https://doi.org/10.1016/j.neuroimage.2012.03.049.Search in Google Scholar PubMed

Ferrer, E., Whitaker, K.J., Steele, J.S., Green, C.T., Wendelken, C., and Bunge, S.A. (2013). White matter maturation supports the development of reasoning ability through its influence on processing speed. Dev. Sci. 16: 941–951, https://doi.org/10.1111/desc.12088.Search in Google Scholar PubMed PubMed Central

Filippi, M., Spinelli, E.G., Cividini, C., and Agosta, F. (2019). Resting state dynamic functional connectivity in neurodegenerative conditions: a review of magnetic resonance imaging findings. Front. Neurosci. 13: 657, https://doi.org/10.3389/fnins.2019.00657.Search in Google Scholar PubMed PubMed Central

Finn, A.S., Sheridan, M.A., Kam, C.L.H., Hinshaw, S., and D’Esposito, M. (2010). Longitudinal evidence for functional specialization of the neural circuit supporting working memory in the human brain. J. Neurosci. 30: 11062–11067, https://doi.org/10.1523/jneurosci.6266-09.2010.Search in Google Scholar

Finn, E.S., Shen, X., Scheinost, D., Rosenberg, M.D., Huang, J., Chun, M.M., Papademetris, X., and Constable, R.T. (2015). Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18: 1664, https://doi.org/10.1038/nn.4135.Search in Google Scholar PubMed PubMed Central

Fjell, A.M., Sneve, M.H., Grydeland, H., Storsve, A.B., Amlien, I.K., Yendiki, A., and Walhovd, K.B. (2017). Relationship between structural and functional connectivity change across the adult lifespan: a longitudinal investigation. Hum. Brain Mapp. 38: 561–573, https://doi.org/10.1002/hbm.23403.Search in Google Scholar PubMed PubMed Central

Fornito, A., Harrison, B.J., Zalesky, A., and Simons, J.S. (2012). Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. Proc. Natl. Acad. Sci. U.S.A. 109: 12788–12793, https://doi.org/10.1073/pnas.1204185109.Search in Google Scholar PubMed PubMed Central

Fox, S.E., Levitt, P., and Nelson, C.A.III (2010). How the timing and quality of early experiences influence the development of brain architecture. Child Dev. 81: 28–40, https://doi.org/10.1111/j.1467-8624.2009.01380.x.Search in Google Scholar PubMed PubMed Central

Gaetz, W., Roberts, T.P.L., Singh, K.D., and Muthukumaraswamy, S.D. (2012). Functional and structural correlates of the aging brain : relating visual cortex (V1) gamma band responses to age-related structural change. Hum. Brain Mapp. 33: 2035–2046, https://doi.org/10.1002/hbm.21339.Search in Google Scholar PubMed PubMed Central

Gao, W., Alcauter, S., Elton, A., Hernandez-Castillo, C.R., Smith, J.K., Ramirez, J., and Lin, W. (2015a). Functional network development during the first year: relative sequence and socioeconomic correlations. Cerebr. Cortex 25: 2919–2928, https://doi.org/10.1093/cercor/bhu088.Search in Google Scholar PubMed PubMed Central

Gao, W., Alcauter, S., Smith, J.K., Gilmore, J.H., and Lin, W. (2015b). Development of human brain cortical network architecture during infancy. Brain Struct. Funct. 220: 1173–1186, https://doi.org/10.1007/s00429-014-0710-3.Search in Google Scholar

Giedd, J.N., Lalonde, F.M., Celano, M.J., White, S.L., Wallace, G.L., Lee, N.R., and Lenroot, R.K. (2009). Anatomical brain magnetic resonance imaging of typically developing children and adolescents. J. Am. Acad. Child Adolesc. Psychiatry 48: 465.10.1097/CHI.0b013e31819f2715Search in Google Scholar

Gilmore, J.H., Knickmeyer, R.C., and Gao, W. (2018). Imaging structural and functional brain development in early childhood. Nat. Rev. Neurosci. 19: 123, https://doi.org/10.1038/nrn.2018.1.Search in Google Scholar

Gogtay, N., Giedd, J.N., Lusk, L., Hayashi, K.M., Greenstein, D., Vaituzis, A.C., Iii, T.F.N., Herman, D.H., Clasen, L.S., Toga, A.W., et al.. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proc. Natl. Acad. Sci. U.S.A. 101: 8174–8179, https://doi.org/10.1073/pnas.0402680101.Search in Google Scholar

Gómez, C.M., Barriga-Paulino, C.I., Rodríguez-Martínez, E.I., Rojas-Benjumea, M.Á., Arjona, A., and Gómez-González, J. (2018). The neurophysiology of working memory development: from childhood to adolescence and young adulthood. Rev. Neurosci. 29: 261–282, https://doi.org/10.1515/revneuro-2017-0073.Search in Google Scholar

Graziani, L.J., Katz, L., Cracco, R.Q., Cracco, J.B., and Weitzman, E.D. (1974). The maturation and interrelationship of EEG patterns and auditory evoked responses in premature infants. Electroencephalogr. Clin. Neurophysiol. 36: 367–375, https://doi.org/10.1016/0013-4694(74)90186-2.Search in Google Scholar

Güntürkün, O., Ströckens, F., and Ocklenburg, S. (2020). Brain lateralization: a comparative perspective. Physiol. Rev. 100: 1019–1063, https://doi.org/10.1152/physrev.00006.2019.Search in Google Scholar PubMed

Haartsen, R., Jones, E.J.H., and Johnson, M.H. (2016). Human brain development over the early years. Curr. Opin. Behav. Sci. 10: 149–154, https://doi.org/10.1016/j.cobeha.2016.05.015.Search in Google Scholar

Hagmann, P., Sporns, O., Madan, N., Cammoun, L., Pienaar, R., Wedeen, V.J., Meuli, R., and Thiran, J. (2010). White matter maturation reshapes structural connectivity in the late developing human brain. Proc. Natl. Acad. Sci. U.S.A. 107: 19067–19072, https://doi.org/10.1073/pnas.1009073107.Search in Google Scholar PubMed PubMed Central

Harikumar, A., Evans, D.W., Dougherty, C.C., Carpenter, K.L.H., and Michael, A.M. (2021). A review of the default mode network in autism spectrum disorders and attention deficit hyperactivity disorder. Brain Connect. 11: 253–263, https://doi.org/10.1089/brain.2020.0865.Search in Google Scholar PubMed PubMed Central

Hashimoto, T., Nguyen, Q.L., Rotaru, D., Keenan, T., Arion, D., Beneyto, M., Gonzalez-Burgos, G., and Lewis, D.A. (2009). Protracted developmental trajectories of GABAA receptor α1 and α2 subunit expression in primate prefrontal cortex. Biol. Psychiatr. 65: 1015–1023, https://doi.org/10.1016/j.biopsych.2009.01.004.Search in Google Scholar PubMed PubMed Central

Herschkowitz, N. (1988). Brain development in the fetus, neonate and infant. Neonatology 54: 1–19, https://doi.org/10.1159/000242818.Search in Google Scholar PubMed

Honey, C.J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J.-P., Meuli, R., and Hagmann, P. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl. Acad. Sci. U.S.A. 106: 2035–2040, https://doi.org/10.1073/pnas.0811168106.Search in Google Scholar PubMed PubMed Central

Hong, S., Dissing-Olesen, L., and Stevens, B. (2016). New insights on the role of microglia in synaptic pruning in health and disease. Curr. Opin. Neurobiol. 36: 128–134, https://doi.org/10.1016/j.conb.2015.12.004.Search in Google Scholar PubMed PubMed Central

Huntenburg, J.M., Bazin, P.-L., and Margulies, D.S. (2018). Large-scale gradients in human cortical organization. Trends Cognit. Sci. 22: 21–31, https://doi.org/10.1016/j.tics.2017.11.002.Search in Google Scholar PubMed

Immordino-Yang, M.H., Christodoulou, J.A., and Singh, V. (2012). Rest is not idleness: implications of the brain’s default mode for human development and education. Perspect. Psychol. Sci. 7: 352–364, https://doi.org/10.1177/1745691612447308.Search in Google Scholar PubMed

Ismail, F.Y., Fatemi, A., and Johnston, M.V. (2017). Cerebral plasticity: windows of opportunity in the developing brain. Eur. J. Paediatr. Neurol. 21: 23–48, https://doi.org/10.1016/j.ejpn.2016.07.007.Search in Google Scholar PubMed

Jeurissen, B., Descoteaux, M., Mori, S., and Leemans, A. (2019). Diffusion MRI fiber tractography of the brain. NMR Biomed. 32: e3785, https://doi.org/10.1002/nbm.3785.Search in Google Scholar PubMed

Jiang, H., Zhu, R., Tian, S., Wang, H., Chen, Z., Wang, X., Shao, J., Qin, J., Shi, J., and Liu, H. (2020). Structural–functional decoupling predicts suicide attempts in bipolar disorder patients with a current major depressive episode. Neuropsychopharmacology 45: 1735–1742, https://doi.org/10.1038/s41386-020-0753-5.Search in Google Scholar PubMed PubMed Central

Johnson, M.H. (2001). Functional brain development in humans. Nat. Rev. Neurosci. 2: 475–483, https://doi.org/10.1038/35081509.Search in Google Scholar PubMed

Jones, D.K. (2010). Challenges and limitations of quantifying brain connectivity in vivo with diffusion MRI. Imag. Med. 2: 341, https://doi.org/10.2217/iim.10.21.Search in Google Scholar

Jones, E.G. (2009). Synchrony in the interconnected circuitry of the thalamus and cerebral cortex. Ann. N. Y. Acad. Sci. 1157: 10–23, https://doi.org/10.1111/j.1749-6632.2009.04534.x.Search in Google Scholar PubMed

Kaufmann, T., Alnæs, D., Brandt, C.L., Bettella, F., Djurovic, S., Andreassen, O.A., and Westlye, L.T. (2018). Stability of the brain functional Connectome fingerprint in individuals with schizophrenia. JAMA Psychiatr. 75: 749–751, https://doi.org/10.1001/jamapsychiatry.2018.0844.Search in Google Scholar PubMed PubMed Central

Kilb, W. (2012). Development of the GABAergic system from birth to adolescence. Neuroscience 18: 613–630, https://doi.org/10.1177/1073858411422114.Search in Google Scholar PubMed

Kim, D.-J. and Min, B.-K. (2020). Rich-club in the brain’s macrostructure: insights from graph theoretical analysis. Comput. Struct. Biotechnol. J. 18: 1761–1773, https://doi.org/10.1016/j.csbj.2020.06.039.Search in Google Scholar PubMed PubMed Central

Klingberg, T., Forssberg, H., and Westerberg, H. (2002). Increased brain activity in frontal and parietal cortex underlies the development of visuospatial working memory capacity during childhood. J. Cognit. Neurosci. 14: 1–10, https://doi.org/10.1162/089892902317205276.Search in Google Scholar PubMed

Kolb, B. and Gibb, R. (2011). Brain plasticity and behaviour in the developing brain. J. Can. Acad. Child Adolesc. Psychiatry 20: 265.Search in Google Scholar

Kolb, B., Harker, A., and Gibb, R. (2017). Principles of plasticity in the developing brain. Dev. Med. Child Neurol. 59: 1218–1223, https://doi.org/10.1111/dmcn.13546.Search in Google Scholar PubMed

Kostović, I. and Judaš, M. (2010). The development of the subplate and thalamocortical connections in the human foetal brain. Acta Paediatr. 99: 1119–1127, https://doi.org/10.1111/j.1651-2227.2010.01811.x.Search in Google Scholar PubMed

Kostović, I. and Judaš, M. (2015). Embryonic and fetal development of the human cerebral cortex. Brain Mapp. 2: 167–175.10.1016/B978-0-12-397025-1.00193-7Search in Google Scholar

Kuhlman, S.J., Olivas, N.D., Tring, E., Ikrar, T., Xu, X., and Trachtenberg, J.T. (2013). A disinhibitory microcircuit initiates critical-period plasticity in the visual cortex. Nature 501: 543–546, https://doi.org/10.1038/nature12485.Search in Google Scholar PubMed PubMed Central

Lamblin, M.D., Andre, M., Challamel, M.J., Curzi-Dascalova, L., d’Allest, A.M., De, E.G., Moussalli-Salefranque, F., Navelet, Y., Plouin, P., and Radvanyi-Bouvet, M.F. (1999). Electroencephalography of the premature and term newborn. Maturational aspects and glossary. Neurophysiol. Clin. Clin. Neurophysiol. 29: 123–219, https://doi.org/10.1016/s0987-7053(99)80051-3.Search in Google Scholar

Larsen, B., Olafsson, V., Calabro, F., Laymon, C., Tervo-Clemmens, B., Campbell, E., Minhas, D., Montez, D., Price, J., and Luna, B. (2020). Maturation of the human striatal dopamine system revealed by PET and quantitative MRI. Nat. Commun. 11: 1–10, https://doi.org/10.1038/s41467-020-14693-3.Search in Google Scholar PubMed PubMed Central

Lebel, C., Treit, S., and Beaulieu, C. (2019). A review of diffusion MRI of typical white matter development from early childhood to young adulthood. NMR Biomed. 32: e3778, https://doi.org/10.1002/nbm.3778.Search in Google Scholar PubMed

Li, G., Han, D., Wang, C., Hu, W., Calhoun, V.D., and Wang, Y.P. (2020). Application of deep canonically correlated sparse autoencoder for the classification of schizophrenia. Comput. Methods Progr. Biomed. 183: 105073, doi:https://doi.org/10.1016/j.cmpb.2019.105073.Search in Google Scholar PubMed

Lippé, S., Kovacevic, N., and Mcintosh, A.R. (2009). Differential maturation of brain signal complexity in the human auditory and visual system. Front. Hum. Neurosci. 3: 48, https://doi.org/10.3389/neuro.09.048.2009.Search in Google Scholar PubMed PubMed Central

Litovsky, R. (2015). Development of the auditory system. Handb. Clin. Neurol. 129: 55–72, Elsevier, https://doi.org/10.1016/b978-0-444-62630-1.00003-2.Search in Google Scholar

Liu, C., Mang, S.C., and Moseley, M.E. (2010). In vivo generalized diffusion tensor imaging (GDTI) using higher‐order tensors (HOT). Magn. Reson. Med. 63: 243–252, https://doi.org/10.1002/mrm.22192.Search in Google Scholar PubMed PubMed Central

Lundgaard, I., Luzhynskaya, A., Stockley, J.H., Wang, Z., Evans, K.A., Swire, M., Volbracht, K., Gautier, H.O.B., Franklin, R.J.M., and Ffrench-Constant, C. (2013). Neuregulin and BDNF induce a switch to NMDA receptor-dependent myelination by oligodendrocytes. PLoS Biol. 11: e1001743, https://doi.org/10.1371/journal.pbio.1001743.Search in Google Scholar PubMed PubMed Central

Luo, N., Sui, J., Abrol, A., Lin, D., Chen, J., Vergara, V.M., Fu, Z., Du, Y., Damaraju, E., Xu, Y., et al.. (2020). Age-related structural and functional variations in 5,967 individuals across the adult lifespan. Hum. Brain Mapp. 41: 1725–1737, https://doi.org/10.1002/hbm.24905.Search in Google Scholar PubMed PubMed Central

Margulies, D.S., Ghosh, S.S., Goulas, A., Falkiewicz, M., Huntenburg, J.M., Langs, G., Bezgin, G., Eickhoff, S.B., Castellanos, F.X., and Petrides, M. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl. Acad. Sci. U.S.A. 113: 12574–12579, https://doi.org/10.1073/pnas.1608282113.Search in Google Scholar PubMed PubMed Central

Mateos-Aparicio, P. and Rodríguez-Moreno, A. (2019). The impact of studying brain plasticity. Front. Cell. Neurosci. 13: 66, https://doi.org/10.3389/fncel.2019.00066.Search in Google Scholar PubMed PubMed Central

McDougall, S., Riad, W.V., Silva-Gotay, A., Tavares, E.R., Harpalani, D., Li, G.-L., and Richardson, H.N. (2018). Myelination of axons corresponds with faster transmission speed in the prefrontal cortex of developing male rats. Eneuro 5: ENEURO.0203-18.2018, doi:https://doi.org/10.1523/ENEURO.0203-18.2018.Search in Google Scholar PubMed PubMed Central

McGee, A.W., Yang, Y., Fischer, Q.S., Daw, N.W., and Strittmatter, S.M. (2005). Experience-driven plasticity of visual cortex limited by myelin and Nogo receptor. Science 309: 2222–2226, https://doi.org/10.1126/science.1114362.Search in Google Scholar PubMed PubMed Central

Mesulam, M.M. (1998). From sensation to perception. Brain 121: 1013–1052, https://doi.org/10.1093/brain/121.6.1013.Search in Google Scholar PubMed

Millán, A.P., Torres, J.J., and Marro, J. (2019). How memory conforms to brain development. Front. Comput. Neurosci. 13: 22, https://doi.org/10.3389/fncom.2019.00022.Search in Google Scholar PubMed PubMed Central

Miller, D.J., Duka, T., Stimpson, C.D., Schapiro, S.J., Baze, W.B., McArthur, M.J., Fobbs, A.J., Sousa, A.M.M., Šestan, N., and Wildman, D.E. (2012). Prolonged myelination in human neocortical evolution. Proc. Natl. Acad. Sci. U.S.A. 109: 16480–16485, https://doi.org/10.1073/pnas.1117943109.Search in Google Scholar PubMed PubMed Central

Mount, C.W. and Monje, M. (2017). Wrapped to adapt: experience-dependent myelination. Neuron 95: 743–756, https://doi.org/10.1016/j.neuron.2017.07.009.Search in Google Scholar PubMed PubMed Central

Nave, K.-A. (2010). Myelination and support of axonal integrity by glia. Nature 468: 244–252, https://doi.org/10.1038/nature09614.Search in Google Scholar PubMed

Niessing, J., Ebisch, B., Schmidt, K.E., Niessing, M., Singer, W., and Galuske, R.A.W. (2005). Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science 309: 948–951, https://doi.org/10.1126/science.1110948.Search in Google Scholar PubMed

Nucifora, P.G.P., Verma, R., Lee, S.-K., and Melhem, E.R. (2007). Diffusion-tensor MR imaging and tractography: exploring brain microstructure and connectivity. Radiology 245: 367–384, https://doi.org/10.1148/radiol.2452060445.Search in Google Scholar PubMed

Oldham, S. and Fornito, A. (2019). The development of brain network hubs. Dev. Cogn. Neurosci. 36: 100607, https://doi.org/10.1016/j.dcn.2018.12.005.Search in Google Scholar

Onoda, K., Ishihara, M., and Yamaguchi, S. (2012). Decreased functional connectivity by aging is associated with cognitive decline. J. Cognit. Neurosci. 24: 2186–2198, https://doi.org/10.1162/jocn_a_00269.Search in Google Scholar

Østby, Y., Tamnes, C.K., Fjell, A.M., and Walhovd, K.B. (2011). Morphometry and connectivity of the fronto-parietal verbal working memory network in development. Neuropsychologia 49: 3854–3862, https://doi.org/10.1016/j.neuropsychologia.2011.10.001.Search in Google Scholar

Overbye, K., Huster, R.J., Walhovd, K.B., Fjell, A.M., and Tamnes, C.K. (2018). Development of the P300 from childhood to adulthood: a multimodal EEG and MRI study. Brain Struct. Funct. 223: 4337–4349, https://doi.org/10.1007/s00429-018-1755-5.Search in Google Scholar

Overbye, K., Walhovd, K.B., Paus, T., Fjell, A.M., Huster, R.J., and Tamnes, C.K. (2019). Error processing in the adolescent brain: age-related differences in electrophysiology, behavioral adaptation, and brain morphology. Dev. Cogn. Neurosci. 38: 100665, doi:https://doi.org/10.1016/j.dcn.2019.100665.Search in Google Scholar

Pang, E.W. (2011). Practical aspects of running developmental studies in the MEG. Brain Topogr. 24: 253–260, https://doi.org/10.1007/s10548-011-0175-0.Search in Google Scholar

Paterson, S.J., Heim, S., Friedman, J.T., Choudhury, N., and Benasich, A.A. (2006). Development of structure and function in the infant brain: implications for cognition, language and social behaviour. Neurosci. Biobehav. Rev. 30: 1087–1105, https://doi.org/10.1016/j.neubiorev.2006.05.001.Search in Google Scholar

Paus, T., Collins, D.L., Evans, A.C., Leonard, G., Pike, B., and Zijdenbos, A. (2001). Maturation of white matter in the human brain: a review of magnetic resonance studies. Brain Res. Bull. 54: 255–266, https://doi.org/10.1016/s0361-9230(00)00434-2.Search in Google Scholar

Perlman, S.B., Huppert, T.J., and Luna, B. (2016). Functional near-infrared spectroscopy evidence for development of prefrontal engagement in working memory in early through middle childhood. Cerebr. Cortex 26: 2790–2799, https://doi.org/10.1093/cercor/bhv139.Search in Google Scholar PubMed PubMed Central

Pujol, R., Lavigne-Rebillard, M., and Lenoir, M. (1998). Development of sensory and neural structures in the mammalian cochlea. In: Development of the auditory system. Springer, pp. 146–192, https://doi.org/10.1007/978-1-4612-2186-9_4.Search in Google Scholar

Rebello, K., Moura, L.M., Pinaya, W.H.L., Rohde, L.A., and Sato, J.R. (2018). Default mode network maturation and environmental adversities during childhood. Chronic Stress 2, 2470547018808295, https://doi.org/10.1177/2470547018808295.Search in Google Scholar PubMed PubMed Central

Roberts, T.P.L., Khan, S.Y., Blaskey, L., Dell, J., Levy, S.E., Zarnow, D.M., and Christopher Edgar, J. (2009). Developmental correlation of diffusion anisotropy with auditory-evoked response. Neuroreport 20: 1586–1591, https://doi.org/10.1097/wnr.0b013e3283306854.Search in Google Scholar

Rogers, C.E., Lean, R.E., Wheelock, M.D., and Smyser, C.D. (2018). Aberrant structural and functional connectivity and neurodevelopmental impairment in preterm children. J. Neurodev. Disord. 10: 1–13, https://doi.org/10.1186/s11689-018-9253-x.Search in Google Scholar PubMed PubMed Central

Romero‐Garcia, R., Atienza, M., and Cantero, J.L. (2014). Predictors of coupling between structural and functional cortical networks in normal aging. Hum. Brain Mapp. 35: 2724–2740.10.1002/hbm.22362Search in Google Scholar PubMed PubMed Central

Rozovsky, K., Ventureyra, E.C.G., and Miller, E. (2013). Fast-brain MRI in children is quick, without sedation, and radiation-free, but beware of limitations. J. Clin. Neurosci. 20: 400–405, https://doi.org/10.1016/j.jocn.2012.02.048.Search in Google Scholar PubMed

Rudie, J.D., Brown, J.A., Beck-Pancer, D., Hernandez, L.M., Dennis, E.L., Thompson, P.M., Bookheimer, S.Y., and Dapretto, M. (2013). Altered functional and structural brain network organization in autism. NeuroImage Clin. 2: 79–94, https://doi.org/10.1016/j.nicl.2012.11.006.Search in Google Scholar PubMed PubMed Central

Rudrauf, D. (2014). Structure-function relationships behind the phenomenon of cognitive resilience in neurology: insights for neuroscience and medicine. Adv. Neurosci. 2014: 462765, doi:https://doi.org/10.1155/2014/462765.Search in Google Scholar

Schmithorst, V.J., Wilke, M., Dardzinski, B.J., and Holland, S.K. (2002). Correlation of white matter diffusivity and anisotropy with age during childhood and adolescence: a cross-sectional diffusion-tensor MR imaging study. Radiology 222: 212–218, https://doi.org/10.1148/radiol.2221010626.Search in Google Scholar PubMed PubMed Central

Schwarzkopf, D.S., Robertson, D.J., Song, C., Barnes, G.R., and Rees, G. (2012). The frequency of visually induced gamma-band oscillations depends on the size of early human visual cortex. J. Neurosci. 32: 1507–1512, https://doi.org/10.1523/jneurosci.4771-11.2012.Search in Google Scholar PubMed PubMed Central

Schweinsburg, A.D., Nagel, B.J., and Tapert, S.F. (2005). fMRI reveals alteration of spatial working memory networks across adolescence. J. Int. Neuropsychol. Soc. 11: 631–644, https://doi.org/10.1017/S1355617705050757.Search in Google Scholar PubMed PubMed Central

Sen, B., Borle, N.C., Greiner, R., and Brown, M.R.G. (2018). A general prediction model for the detection of ADHD and Autism using structural and functional MRI. PLoS One 13: e0194856, https://doi.org/10.1371/journal.pone.0194856.Search in Google Scholar PubMed PubMed Central

Simms, N.K., Frausel, R.R., and Richland, L.E. (2018). Working memory predicts children’s analogical reasoning. J. Exp. Child Psychol. 166: 160–177, https://doi.org/10.1016/j.jecp.2017.08.005.Search in Google Scholar PubMed

Smit, D.J.A., Boersma, M., Schnack, H.G., Micheloyannis, S., Boomsma, D.I., Hulshoff Pol, H.E., Stam, C.J., and de Geus, E.J.C. (2012). The brain matures with stronger functional connectivity and decreased randomness of its network. PLoS One 7: e36896, doi:https://doi.org/10.1371/journal.pone.0036896.Search in Google Scholar PubMed PubMed Central

Smyser, C.D., Inder, T.E., Shimony, J.S., Hill, J.E., Degnan, A.J., Snyder, A.Z., and Neil, J.J. (2010). Longitudinal analysis of neural network development in preterm infants. Cerebr. Cortex 20: 2852–2862, https://doi.org/10.1093/cercor/bhq035.Search in Google Scholar PubMed PubMed Central

Spreng, R.N., Mar, R.A., and Kim, A.S.N. (2009). The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: a quantitative meta-analysis. J. Cognit. Neurosci. 21: 489–510, https://doi.org/10.1162/jocn.2008.21029.Search in Google Scholar PubMed

Stark, D.E., Margulies, D.S., Shehzad, Z.E., Reiss, P., Kelly, A.M.C., Uddin, L.Q., Gee, D.G., Roy, A.K., Banich, M.T., and Castellanos, F.X. (2009). Regional variation in interhemispheric coordination of intrinsic hemodynamic fluctuations. J. Neurosci. 29: 13754–13764, https://doi.org/10.1523/JNEUROSCI.4544-08.2008.Search in Google Scholar PubMed PubMed Central

Stufflebeam, S.M., Witzel, T., Mikulski, S., Hämäläinen, M.S., Temereanca, S., Barton, J.J.S., Tuch, D.S., and Manoach, D.S. (2008). A non-invasive method to relate the timing of neural activity to white matter microstructural integrity. Neuroimage 42: 710–716, https://doi.org/10.1016/j.neuroimage.2008.04.264.Search in Google Scholar PubMed PubMed Central

Suárez, L.E., Markello, R.D., Betzel, R.F., and Misic, B. (2020). Linking structure and function in macroscale brain networks. Trends Cognit. Sci. 24: 302–315, https://doi.org/10.1016/j.tics.2020.01.008.Search in Google Scholar PubMed

Sui, J., Adali, T., Yu, Q., Chen, J., and Calhoun, V.D. (2012). A review of multivariate methods for multimodal fusion of brain imaging data. J. Neurosci. Methods 204: 68–81, https://doi.org/10.1016/j.jneumeth.2011.10.031.Search in Google Scholar PubMed PubMed Central

Supekar, K., Uddin, L.Q., Prater, K., Amin, H., Greicius, M.D., and Menon, V. (2010). Development of functional and structural connectivity within the default mode network in young children. Neuroimage 52: 290–301, https://doi.org/10.1016/j.neuroimage.2010.04.009.Search in Google Scholar PubMed PubMed Central

Takesian, A.E. and Hensch, T.K. (2013). Balancing plasticity/stability across brain development. Prog. Brain Res. 207: 3–34, https://doi.org/10.1016/b978-0-444-63327-9.00001-1.Search in Google Scholar

Taylor, M.J. and Baldeweg, T. (2002). Application of EEG, ERP and intracranial recordings to the investigation of cognitive functions in children. Dev. Sci. 5: 318–334, https://doi.org/10.1111/1467-7687.00372.Search in Google Scholar

Taylor, M.J. and Pang, E.W. (2014). MEG and cognitive developmental studies. In: Magnetoencephalography. Springer, pp. 557–577, https://doi.org/10.1007/978-3-642-33045-2_25.Search in Google Scholar

Telzer, E.H., McCormick, E.M., Peters, S., Cosme, D., Pfeifer, J.H., and van Duijvenvoorde, A.C.K. (2018). Methodological considerations for developmental longitudinal fMRI research. Dev. Cogn. Neurosci. 33: 149–160, https://doi.org/10.1016/j.dcn.2018.02.004.Search in Google Scholar

Terribilli, D., Schaufelberger, M.S., Duran, F.L.S., Zanetti, M.V., Curiati, P.K., Menezes, P.R., Scazufca, M., Amaro, E.Jr, Leite, C.C., and Busatto, G.F. (2011). Age-related gray matter volume changes in the brain during non-elderly adulthood. Neurobiol. Aging 32: 354–368, https://doi.org/10.1016/j.neurobiolaging.2009.02.008.Search in Google Scholar

Thompson, P.M., Sowell, E.R., Gogtay, N., Giedd, J.N., Vidal, C.N., Hayashi, K.M., Leow, A., Nicolson, R., Rapoport, J.L., and Toga, A.W. (2005). Structural MRI and brain development. Int. Rev. Neurobiol. 67: 285–323, https://doi.org/10.1016/s0074-7742(05)67009-2.Search in Google Scholar

Tierney, A.L. and Nelson, C.A.III (2009). Brain development and the role of experience in the early years. Zero Three 30: 9.Search in Google Scholar

Uddin, L.Q., Supekar, K., and Menon, V. (2010). Typical and atypical development of functional human brain networks: insights from resting-state FMRI. Front. Syst. Neurosci. 4: 21, https://doi.org/10.3389/fnsys.2010.00021.Search in Google Scholar PubMed PubMed Central

Van den Heuvel, M., Mandl, R., Luigjes, J., and Pol, H.H. (2008). Microstructural organization of the cingulum tract and the level of default mode functional connectivity. J. Neurosci. 28: 10844–10851, https://doi.org/10.1523/jneurosci.2964-08.2008.Search in Google Scholar

Van Den Heuvel, M.P., Kahn, R.S., Goñi, J., and Sporns, O. (2012). High-cost, high-capacity backbone for global brain communication. Proc. Natl. Acad. Sci. U.S.A. 109: 11372–11377, https://doi.org/10.1073/pnas.1203593109.Search in Google Scholar PubMed PubMed Central

Van Den Heuvel, M.P., Kersbergen, K.J., De Reus, M.A., Keunen, K., Kahn, R.S., Groenendaal, F., De Vries, L.S., and Benders, M.J.N.L. (2015). The neonatal connectome during preterm brain development. Cerebr. Cortex 25: 3000–3013, https://doi.org/10.1093/cercor/bhu095.Search in Google Scholar PubMed PubMed Central

Van Den Heuvel, M.P., Stam, C.J., Kahn, R.S., and Pol, H.E.H. (2009). Efficiency of functional brain networks and intellectual performance. J. Neurosci. 29: 7619–7624, https://doi.org/10.1523/jneurosci.1443-09.2009.Search in Google Scholar

Varela-nieto, I., Rosa, E.J. De, Valenciano, A.I., and León, Y. (2003). Cell death in the nervous system physiological and pathological cell death in the nervous system. Mol. Neurobiol. 28: 23–49, https://doi.org/10.1385/mn:28:1:23.10.1385/MN:28:1:23Search in Google Scholar

Varela, F., Lachaux, J.-P., Rodriguez, E., and Martinerie, J. (2001). The brainweb: phase synchronization and large-scale integration. Nat. Rev. Neurosci. 2: 229–239, https://doi.org/10.1038/35067550.Search in Google Scholar PubMed

Vasung, L., Charvet, C.J., Shiohama, T., Gagoski, B., Levman, J., and Takahashi, E. (2019a). Ex vivo fetal brain MRI: recent advances, challenges, and future directions. Neuroimage 195: 23–37, https://doi.org/10.1016/j.neuroimage.2019.03.034.Search in Google Scholar PubMed PubMed Central

Vasung, L., Turk, E.A., Ferradal, S.L., Sutin, J., Stout, J.N., Ahtam, B., Lin, P.-Y., and Grant, P.E. (2019b). Exploring early human brain development with structural and physiological neuroimaging. Neuroimage 187: 226–254, https://doi.org/10.1016/j.neuroimage.2018.07.041.Search in Google Scholar PubMed PubMed Central

Vestergaard, M., Madsen, K.S., Baaré, W.F.C., Skimminge, A., Ejersbo, L.R., Ramsøy, T.Z., Gerlach, C., Åkeson, P., Paulson, O.B., and Jernigan, T.L. (2011). White matter microstructure in superior longitudinal fasciculus associated with spatial working memory performance in children. J. Cognit. Neurosci. 23: 2135–2146, https://doi.org/10.1162/jocn.2010.21592.Search in Google Scholar PubMed

Viviano, R.P., Raz, N., Yuan, P., and Damoiseaux, J.S. (2017). Associations between dynamic functional connectivity and age, metabolic risk, and cognitive performance. Neurobiol. Aging 59: 135–143, https://doi.org/10.1016/j.neurobiolaging.2017.08.003.Search in Google Scholar PubMed PubMed Central

Wang, D. and Fawcett, J. (2012). The perineuronal net and the control of CNS plasticity. Cell Tissue Res. 349: 147–160, https://doi.org/10.1007/s00441-012-1375-y.Search in Google Scholar PubMed

Wen, H., Liu, Y., Zhang, J., Peng, Y., and He, H. (2018). Altered structural-functional coupling of large-scale brain networks in early Tourette syndrome children. Med. Imaging 2018 Biomed. Appl. Mol. Struct. Funct. Imaging 10578, 1057828.Search in Google Scholar

Wendelken, C., Ferrer, E., Ghetti, S., Bailey, S.K., Cutting, L., and Bunge, S.A. (2017). Frontoparietal structural connectivity in childhood predicts development of functional connectivity and reasoning ability: a large-scale longitudinal investigation. J. Neurosci. 37: 8549–8558, https://doi.org/10.1523/jneurosci.3726-16.2017.Search in Google Scholar

Wendelken, C., O’Hare, E.D., Whitaker, K.J., Ferrer, E., and Bunge, S.A. (2011). Increased functional selectivity over development in rostrolateral prefrontal cortex. J. Neurosci. 31: 17260–17268, https://doi.org/10.1523/jneurosci.1193-10.2011.Search in Google Scholar

Whitford, T.J., Rennie, C.J., Grieve, S.M., Clark, C.R., Gordon, E., and Williams, L.M. (2007). Brain maturation in adolescence: concurrent changes in neuroanatomy and neurophysiology. Hum. Brain Mapp. 28: 228–237, https://doi.org/10.1002/hbm.20273.Search in Google Scholar PubMed PubMed Central

Wilcox, T. and Biondi, M. (2015). fNIRS in the developmental sciences. Wiley Interdiscip. Rev. Cogn. Sci. 6: 263–283, https://doi.org/10.1002/wcs.1343.Search in Google Scholar PubMed PubMed Central

Wunderlich, J.L. and Cone-Wesson, B.K. (2006). Maturation of CAEP in infants and children: a review. Hear. Res. 212: 212–223, https://doi.org/10.1016/j.heares.2005.11.008.Search in Google Scholar PubMed

Xifra-Porxas, A., Niso, G., Larivière, S., Kassinopoulos, M., Baillet, S., Mitsis, G.D., and Boudrias, M.-H. (2019). Older adults exhibit a more pronounced modulation of beta oscillations when performing sustained and dynamic handgrips. Neuroimage 201: 116037, https://doi.org/10.1016/j.neuroimage.2019.116037.Search in Google Scholar PubMed PubMed Central

Yap, P.-T., Fan, Y., Chen, Y., Gilmore, J.H., Lin, W., and Shen, D. (2011). Development trends of white matter connectivity in the first years of life. PLoS One 6: e24678, https://doi.org/10.1371/journal.pone.0024678.Search in Google Scholar PubMed PubMed Central

Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., and Alexander, D.C. (2012). NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61: 1000–1016, https://doi.org/10.1016/j.neuroimage.2012.03.072.Search in Google Scholar PubMed

Zhang, H., Wang, Y., Lu, T., Qiu, B., Tang, Y., Ou, S., Tie, X., Sun, C., Xu, K., and Wang, Y. (2013). Differences between generalized q-sampling imaging and diffusion tensor imaging in the preoperative visualization of the nerve fiber tracts within peritumoral edema in brain. Neurosurgery 73: 1044–1053, https://doi.org/10.1227/neu.0000000000000146.Search in Google Scholar

Zielinski, B.A., Gennatas, E.D., Zhou, J., and Seeley, W.W. (2010). Network-level structural covariance in the developing brain. Proc. Natl. Acad. Sci. U.S.A. 107: 18191–18196, https://doi.org/10.1073/pnas.1003109107.Search in Google Scholar PubMed PubMed Central

Zimmermann, J., Ritter, P., Shen, K., Rothmeier, S., Schirner, M., and McIntosh, A.R. (2016). Structural architecture supports functional organization in the human aging brain at a regionwise and network level. Hum. Brain Mapp. 37: 2645–2661, https://doi.org/10.1002/hbm.23200.Search in Google Scholar PubMed PubMed Central

Received: 2022-02-15
Accepted: 2022-02-17
Published Online: 2022-03-21
Published in Print: 2022-10-26

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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