Abstract
The role of magnetic resonance (MR) neuroimaging in studying brain development in the first three decades of life is reviewed, in terms of its relevance to pediatric neuropsychology. This review places an emphasis on displaying development neuroimaging findings in various types of growth plots, diagrams and figures. MR imaging (MRI) methods can be divided into both structural and functional approaches for brain development quantification. Since MRI methods can readily separate brain parenchyma into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) spaces, depending on the anatomical region or region of interest (ROI), MRI quantification is typically in the form of volume, surface area, shape, and/or thickness. Diffusion tensor imaging (DTI) permits the computation of various quantitative metrics, especially sensitive to WM integrity, including the extraction and assessment of WM tracts. Functional MRI (fMRI) techniques provide physiological metrics that examine maturation through connectivity profiles. Regardless of the MRI method used for image quantification, dynamic changes of the brain occur throughout the first three decades of life, dominated by GM reductions associated with cellular pruning and WM increases, reflecting myelination and connectivity. From a neuroimaging perspective, when quantitative metrics show stabilization, this may be an indication of a neuroimaging-derived “brain age” metric. Future directions and the importance of understanding brain development and neuroimaging findings in the context of neural networks and their maturation as applied to pediatric neuropsychology are discussed.
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Notes
This review assumes familiarity with neuroimaging methods and basic image analysis procedures. For additional background on the neuroimaging methods discussed in this review, the reader is referred to the following: Wilde et al., (2012b)
References
Ambrose, J., & Hounsfield, G. (1973). Computerized transverse axial tomography. The British Journal of Radiology, 46(542), 148–149 Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/4686818.
Anaturk, M., Kaufmann, T., Cole, J. H., Suri, S., Griffanti, L., Zsoldos, E., et al. (2020). Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging. Human Brain Mapping. https://doi.org/10.1002/hbm.25316.
Anda, R. F., Felitti, V. J., Bremner, J. D., Walker, J. D., Whitfield, C., Perry, B. D., et al. (2006). The enduring effects of abuse and related adverse experiences in childhood. A convergence of evidence from neurobiology and epidemiology. European Archives of Psychiatry and Clinical Neuroscience, 256(3), 174–186. https://doi.org/10.1007/s00406-005-0624-4.
Assaf, Y., Johansen-Berg, H., & Thiebaut de Schotten, M. (2019). The role of diffusion MRI in neuroscience. NMR in Biomedicine, 32(4), e3762. https://doi.org/10.1002/nbm.3762.
Ball Jr., W. S. (1991). Imaging of the brain in children. Current Opinion in Radiology, 3(6), 895–905 Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/1751299.
Baron, I. S. (2018). Neuropsychological evaluation of the child. Oxford University Press.
Becht, A. I., & Mills, K. L. (2020). Modeling individual differences in brain development. Biological Psychiatry, 88(1), 63–69. https://doi.org/10.1016/j.biopsych.2020.01.027.
Bells, S., Lefebvre, J., Longoni, G., Narayanan, S., Arnold, D. L., Yeh, E. A., & Mabbott, D. J. (2019). White matter plasticity and maturation in human cognition. Glia, 67(11), 2020–2037. https://doi.org/10.1002/glia.23661.
Bigler, E. D. (2017). Structural neuroimaging in neuropsychology: History and contemporary applications. Neuropsychology, 31(8), 934–953. https://doi.org/10.1037/neu0000418.
Bigler, E. D., Abildskov, T. J., Goodrich-Hunsaker, N. J., Black, G., Christensen, Z. P., Huff, T., et al. (2016). Structural neuroimaging findings in mild traumatic brain injury. Sports Medicine and Arthroscopy Review, 24(3), e42–e52. https://doi.org/10.1097/JSA.0000000000000119.
Blinkov, S. M., & Glezer, I. I. (1968). The human brain in figures and tables: A quantitative handbook. Basic Books.
Braun, K. (2011). The prefrontal-limbic system: Development, neuroanatomy, function, and implications for socioemotional development. Clinics in Perinatology, 38(4), 685–702. https://doi.org/10.1016/j.clp.2011.08.013.
Braun, K., & Bock, J. (2011). The experience-dependent maturation of prefronto-limbic circuits and the origin of developmental psychopathology: Implications for the pathogenesis and therapy of behavioural disorders. Developmental Medicine and Child Neurology, 53(Suppl 4), 14–18. https://doi.org/10.1111/j.1469-8749.2011.04056.x.
Coma, M., Valls, R., Mas, J. M., Pujol, A., Herranz, M. A., Alonso, V., & Naval, J. (2014). Methods for diagnosing perceived age on the basis of an ensemble of phenotypic features. Clinical, Cosmetic and Investigational Dermatology, 7, 133–137. https://doi.org/10.2147/CCID.S52257.
Courchesne, E., Chisum, H. J., Townsend, J., Cowles, A., Covington, J., Egaas, B., et al. (2000). Normal brain development and aging: Quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology, 216(3), 672–682. https://doi.org/10.1148/radiology.216.3.r00au37672.
Danese, A., & McEwen, B. S. (2012). Adverse childhood experiences, allostasis, allostatic load, and age-related disease. Physiology & Behavior, 106(1), 29–39. https://doi.org/10.1016/j.physbeh.2011.08.019.
Davison, A. N., & Dobbing, J. (1966). Myelination as a vulnerable period in brain development. British Medical Bulletin, 22(1), 40–44. https://doi.org/10.1093/oxfordjournals.bmb.a070434.
Dennis, E. L., Baron, D., Bartnik-Olson, B., Caeyenberghs, K., Esopenko, C., Hillary, F. G., et al. (2020). ENIGMA brain injury: Framework, challenges, and opportunities. Human Brain Mapping. https://doi.org/10.1002/hbm.25046.
Dosenbach, N. U., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., et al. (2010). Prediction of individual brain maturity using fMRI. Science, 329(5997), 1358–1361. https://doi.org/10.1126/science.1194144.
Dow-Edwards, D., MacMaster, F. P., Peterson, B. S., Niesink, R., Andersen, S., & Braams, B. R. (2019). Experience during adolescence shapes brain development: From synapses and networks to normal and pathological behavior. Neurotoxicology and Teratology, 76, 106834. https://doi.org/10.1016/j.ntt.2019.106834.
Ernhart, C. B. (1991). Clinical correlations between ethanol intake and fetal alcohol syndrome. Recent Developments in Alcoholism, 9, 127–150 Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/1758980.
Eskenazi, B., Gaylord, L., Bracken, M. B., & Brown, D. (1988). In utero exposure to organic solvents and human neurodevelopment. Developmental Medicine and Child Neurology, 30(4), 492–501. https://doi.org/10.1111/j.1469-8749.1988.tb04776.x.
Ewing-Cobbs, L., Johnson, C. P., Juranek, J., DeMaster, D., Prasad, M., Duque, G., et al. (2016). Longitudinal diffusion tensor imaging after pediatric traumatic brain injury: Impact of age at injury and time since injury on pathway integrity. Human Brain Mapping, 37(11), 3929–3945. https://doi.org/10.1002/hbm.23286.
Fish, A. M., Nadig, A., Seidlitz, J., Reardon, P. K., Mankiw, C., McDermott, C. L., et al. (2020). Sex-biased trajectories of amygdalo-hippocampal morphology change over human development. Neuroimage, 204, 116122. https://doi.org/10.1016/j.neuroimage.2019.116122.
Foulkes, L., & Blakemore, S. J. (2018). Studying individual differences in human adolescent brain development. Nature Neuroscience, 21(3), 315–323. https://doi.org/10.1038/s41593-018-0078-4.
Franke, K., & Gaser, C. (2019). Ten years of BrainAGE as a neuroimaging biomarker of brain aging: What insights have we gained? Frontiers in Neurology, 10, 789. https://doi.org/10.3389/fneur.2019.00789.
Freiwald, W. A. (2020). The neural mechanisms of face processing: Cells, areas, networks, and models. Current Opinion in Neurobiology, 60, 184–191. https://doi.org/10.1016/j.conb.2019.12.007.
Fu, Y., Guo, G., & Huang, T. S. (2010). Age synthesis and estimation via faces: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(11), 1955–1976. https://doi.org/10.1109/TPAMI.2010.36.
Geeraert, B. L., Lebel, R. M., & Lebel, C. (2019). A multiparametric analysis of white matter maturation during late childhood and adolescence. Human Brain Mapping, 40(15), 4345–4356. https://doi.org/10.1002/hbm.24706.
Gilmore, J. H., Knickmeyer, R. C., & Gao, W. (2018). Imaging structural and functional brain development in early childhood. Nature Reviews. Neuroscience, 19(3), 123–137. https://doi.org/10.1038/nrn.2018.1.
Gluhbegovic, N., & Williams, T. H. (1980). The human brain: A photographic guide. Harper & Row.
Goodkin, O., Pemberton, H., Vos, S. B., Prados, F., Sudre, C. H., Moggridge, J., et al. (2019). The quantitative neuroradiology initiative framework: Application to dementia. The British Journal of Radiology, 92(1101), 20190365. https://doi.org/10.1259/bjr.20190365.
Greenham, M., Botchway, E., Knight, S., Bonyhady, B., Tavender, E., Scheinberg, A., et al. (2020). Predictors of participation and quality of life following major traumatic injuries in childhood: A systematic review. Disability and Rehabilitation, 1–17. https://doi.org/10.1080/09638288.2020.1849425.
Haber, S. N., Tang, W., Choi, E. Y., Yendiki, A., Liu, H., Jbabdi, S., et al. (2020). Circuits, networks, and neuropsychiatric disease: Transitioning from anatomy to imaging. Biological Psychiatry, 87(4), 318–327. https://doi.org/10.1016/j.biopsych.2019.10.024.
Hayes, J. P., Bigler, E. D., & Verfaellie, M. (2016). Traumatic brain injury as a disorder of brain connectivity. Journal of the International Neuropsychological Society, 22(2), 120–137. https://doi.org/10.1017/S1355617715000740.
Herculano-Houzel, S. (2009). The human brain in numbers: A linearly scaled-up primate brain. Frontiers in Human Neuroscience, 3, 31. https://doi.org/10.3389/neuro.09.031.2009.
Herschkowitz, N., & Rossi, E. (1971). Critical periods in brain development. In: Lipids, malnutrition & the developing brain. Ciba Found Symp, 107–119. https://doi.org/10.1002/9780470719862.ch7.
Herzog, J. I., & Schmahl, C. (2018). Adverse childhood experiences and the consequences on neurobiological, psychosocial, and somatic conditions across the lifespan. Frontiers in Psychiatry, 9, 420. https://doi.org/10.3389/fpsyt.2018.00420.
Holtmaat, A., & Svoboda, K. (2009). Experience-dependent structural synaptic plasticity in the mammalian brain. Nature Reviews. Neuroscience, 10(9), 647–658. https://doi.org/10.1038/nrn2699.
Insel, T. R., & Landis, S. C. (2013). Twenty-five years of progress: The view from NIMH and NINDS. Neuron, 80(3), 561–567. https://doi.org/10.1016/j.neuron.2013.09.041.
Jiang, H., Lu, N., Chen, K., Yao, L., Li, K., Zhang, J., & Guo, X. (2019). Predicting brain age of healthy adults based on structural MRI parcellation using convolutional neural networks. Frontiers in Neurology, 10, 1346. https://doi.org/10.3389/fneur.2019.01346.
Jurkowski, M. P., Bettio, L., Woo, E. K., Patten, A., Yau, S. Y., & Gil-Mohapel, J. (2020). Beyond the hippocampus and the SVZ: Adult neurogenesis throughout the brain. Frontiers in Cellular Neuroscience, 14, 576444. https://doi.org/10.3389/fncel.2020.576444.
Karcher, N. R., & Barch, D. M. (2021). The ABCD study: Understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacology, 46(1), 131–142. https://doi.org/10.1038/s41386-020-0736-6.
Kast, R. J., & Levitt, P. (2019). Precision in the development of neocortical architecture: From progenitors to cortical networks. Progress in Neurobiology, 175, 77–95. https://doi.org/10.1016/j.pneurobio.2019.01.003.
Krogsrud, S. K., Fjell, A. M., Tamnes, C. K., Grydeland, H., Due-Tonnessen, P., Bjornerud, A., et al. (2018). Development of white matter microstructure in relation to verbal and visuospatial working memory-A longitudinal study. PLoS One, 13(4), e0195540. https://doi.org/10.1371/journal.pone.0195540.
Le, T. M., Huang, A. S., O’Rawe, J., & Leung, H. C. (2020). Functional neural network configuration in late childhood varies by age and cognitive state. Developmental Cognitive Neuroscience, 45, 100862. https://doi.org/10.1016/j.dcn.2020.100862.
Lebel, C., & Deoni, S. (2018). The development of brain white matter microstructure. Neuroimage, 182, 207–218. https://doi.org/10.1016/j.neuroimage.2017.12.097.
Lebel C, Walker L, Leemans A, Phillips L, Beaulieu C.Neuroimage (2008) Microstructural maturation of the human brain from childhood to adulthood 40(3):1044–1055. https://doi.org/10.1016/j.neuroimage.2007.12.053
Lebel, C., Treit, S., & Beaulieu, C. (2019). A review of diffusion MRI of typical white matter development from early childhood to young adulthood. NMR in Biomedicine, 32(4), e3778. https://doi.org/10.1002/nbm.3778.
Lindsey, H. M., Wilde, E. A., Caeyenberghs, K., & Dennis, E. L. (2019). Longitudinal neuroimaging in pediatric traumatic brain injury: Current state and consideration of factors that influence recovery. Frontiers in Neurology, 10, 1296. https://doi.org/10.3389/fneur.2019.01296.
Mah, A., Geeraert, B., & Lebel, C. (2017). Detailing neuroanatomical development in late childhood and early adolescence using NODDI. PLoS One, 12(8), e0182340. https://doi.org/10.1371/journal.pone.0182340.
Maxeiner, H., & Behnke, M. (2008). Intracranial volume, brain volume, reserve volume and morphological signs of increased intracranial pressure--A post-mortem analysis. Legal Medicine (Tokyo, Japan), 10(6), 293–300. https://doi.org/10.1016/j.legalmed.2008.04.001.
McDermott, C. L., Seidlitz, J., Nadig, A., Liu, S., Clasen, L. S., Blumenthal, J. D., et al. (2019). Longitudinally mapping childhood socioeconomic status associations with cortical and subcortical morphology. The Journal of Neuroscience, 39(8), 1365–1373. https://doi.org/10.1523/JNEUROSCI.1808-18.2018.
Meredith, H. V. (1946). Physical growth from birth to two years; head circumference; a review and synthesis of North American research on groups of infants. Child Development, 17(1-2), 1–61 Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/21002136.
Murphy, C. A. (2011). The role of perception in age estimation. Springer.
Novikov, D. S., Fieremans, E., Jespersen, S. N., & Kiselev, V. G. (2019). Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation. NMR in Biomedicine, 32(4), e3998. https://doi.org/10.1002/nbm.3998.
Oishi, K., Faria, A. V., Yoshida, S., Chang, L., & Mori, S. (2013). Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging. International Journal of Developmental Neuroscience, 31(7), 512–524. https://doi.org/10.1016/j.ijdevneu.2013.06.004.
Ostby Y, Tamnes CK, Fjell AM, Westlye LT, Due-Tønnessen P, Walhovd KB (2009) Heterogeneity in subcortical brain development: A structural magnetic resonance imaging study of brain maturation from 8 to 30 years. J Neurosci 29(38):11772–11782. https://doi.org/10.1523/JNEUROSCI.1242-09.2009
Pemberton, H. G., Goodkin, O., Prados, F., Das, R. K., Vos, S. B., Moggridge, J., et al. (2021). Automated quantitative MRI volumetry reports support diagnostic interpretation in dementia: A multi-rater, clinical accuracy study. European Radiology. https://doi.org/10.1007/s00330-020-07455-8.
Pfefferbaum, A., Mathalon, D. H., Sullivan, E. V., Rawles, J. M., Zipursky, R. B., & Lim, K. O. (1994). A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood. Archives of Neurology, 51(9), 874–887. https://doi.org/10.1001/archneur.1994.00540210046012.
Pinto, P. S., Meoded, A., Poretti, A., Tekes, A., & Huisman, T. A. (2012). The unique features of traumatic brain injury in children. Review of the characteristics of the pediatric skull and brain, mechanisms of trauma, patterns of injury, complications, and their imaging findings--Part 2. Journal of Neuroimaging, 22(2), e18–e41. https://doi.org/10.1111/j.1552-6569.2011.00690.x.
Prajapati, R., & Emerson, I. A. (2020). Construction and analysis of brain networks from different neuroimaging techniques. The International Journal of Neuroscience, 1–22. https://doi.org/10.1080/00207454.2020.1837802.
Pujol, J., Soriano-Mas, C., Ortiz, H., Sebastian-Galles, N., Losilla, J. M., & Deus, J. (2006). Myelination of language-related areas in the developing brain. Neurology, 66(3), 339–343. https://doi.org/10.1212/01.wnl.0000201049.66073.8d.
Raznahan, A., Shaw, P., Lalonde, F., Stockman, M., Wallace, G. L., Greenstein, D., et al. (2011). How does your cortex grow? The Journal of Neuroscience, 31(19), 7174–7177. https://doi.org/10.1523/JNEUROSCI.0054-11.2011.
Reynolds, C. R., & Fletcher-Janzen, E. (2009). Handbook of clinical child neuropsychology (3rd ed.). Springer.
Ryan, N. P., Anderson, V. A., Bigler, E. D., Dennis, M., Taylor, H. G., Rubin, K. H., et al. (2020). Delineating the nature and correlates of social dysfunction after childhood traumatic brain injury using common data elements: Evidence from an international multi-cohort study. Journal of Neurotrauma. https://doi.org/10.1089/neu.2020.7057.
Schmitt, J. E., Raznahan, A., Clasen, L. S., Wallace, G. L., Pritikin, J. N., Lee, N. R., et al. (2019). The dynamic associations between cortical thickness and general intelligence are genetically mediated. Cerebral Cortex, 29(11), 4743–4752. https://doi.org/10.1093/cercor/bhz007.
Schurz, M., Radua, J., Tholen, M. G., Maliske, L., Margulies, D. S., Mars, R. B., et al. (2020). Toward a hierarchical model of social cognition: A neuroimaging meta-analysis and integrative review of empathy and theory of mind. Psychological Bulletin. https://doi.org/10.1037/bul0000303.
Seki, T. (2020). Understanding the real state of human adult hippocampal neurogenesis from studies of rodents and non-human primates. Frontiers in Neuroscience, 14, 839. https://doi.org/10.3389/fnins.2020.00839.
Serru, M., Marechal, B., Kober, T., Ribier, L., Sembely Taveau, C., Sirinelli, D., et al. (2019). Improving diagnosis accuracy of brain volume abnormalities during childhood with an automated MP2RAGE-based MRI brain segmentation. Journal of Neuroradiology. https://doi.org/10.1016/j.neurad.2019.06.005.
Silk, T. J., Genc, S., Anderson, V., Efron, D., Hazell, P., Nicholson, J. M., et al. (2016). Developmental brain trajectories in children with ADHD and controls: A longitudinal neuroimaging study. BMC Psychiatry, 16, 59. https://doi.org/10.1186/s12888-016-0770-4.
Somerville, L. H. (2016). Searching for signatures of brain maturity: What are we searching for? Neuron, 92(6), 1164–1167. https://doi.org/10.1016/j.neuron.2016.10.059.
Sotiropoulos, S. N., & Zalesky, A. (2019). Building connectomes using diffusion MRI: Why, how and but. NMR in Biomedicine, 32(4), e3752. https://doi.org/10.1002/nbm.3752.
Steele, H., Bate, J., Steele, M., Dube, S. R., Danskin, K., Knafo, H., et al. (2016). Adverse childhood experiences, poverty, and parenting stress. Canadian Journal of Behavioural Science / Revue canadienne des sciences du comportement, 48(1), 32–38. https://doi.org/10.1037/cbs0000034.
Tamnes CK, Ostby Y, Fjell AM, Westlye LT, Due-Tønnessen P, Walhovd KB (2010) Brain maturation in adolescence and young adulthood: regional age-related changes in cortical thickness and white matter volume and microstructure. Cereb Cortex 20(3):534–548. https://doi.org/10.1093/cercor/bhp118
Tamnes, C. K., Roalf, D. R., Goddings, A. L., & Lebel, C. (2018). Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress. Developmental Cognitive Neuroscience, 33, 161–175. https://doi.org/10.1016/j.dcn.2017.12.002.
Teicher, M. H., Samson, J. A., Anderson, C. M., & Ohashi, K. (2016). The effects of childhood maltreatment on brain structure, function and connectivity. Nature Reviews. Neuroscience, 17(10), 652–666. https://doi.org/10.1038/nrn.2016.111.
Thomason, M. E. (2020). Development of brain networks in utero: Relevance for common neural disorders. Biological Psychiatry, 88(1), 40–50. https://doi.org/10.1016/j.biopsych.2020.02.007.
van Osch, M. J., Teeuwisse, W. M., Chen, Z., Suzuki, Y., Helle, M., & Schmid, S. (2018). Advances in arterial spin labelling MRI methods for measuring perfusion and collateral flow. Journal of Cerebral Blood Flow and Metabolism, 38(9), 1461–1480. https://doi.org/10.1177/0271678X17713434.
Veraart, J., Nunes, D., Rudrapatna, U., Fieremans, E., Jones, D. K., Novikov, D. S., & Shemesh, N. (2020). Nonivasive quantification of axon radii using diffusion MRI. Elife, 9. https://doi.org/10.7554/eLife.49855.
Vidal-Pineiro, D., Parker, N., Shin, J., French, L., Grydeland, H., Jackowski, A. P., et al. (2020). Cellular correlates of cortical thinning throughout the lifespan. Scientific Reports, 10(1), 21803. https://doi.org/10.1038/s41598-020-78471-3.
Walhovd, K. B., Fjell, A. M., Giedd, J., Dale, A. M., & Brown, T. T. (2017). Through thick and thin: A need to reconcile contradictory results on trajectories in human cortical development. Cerebral Cortex, 27(2), 1472–1481. https://doi.org/10.1093/cercor/bhv301.
Wierenga, L. M., Doucet, G. E., Dima, D., Agartz, I., Aghajani, M., Akudjedu, T. N., et al. (2020). Greater male than female variability in regional brain structure across the lifespan. Human Brain Mapping. https://doi.org/10.1002/hbm.25204.
Wilde, E. A., McCauley, S. R., Barnes, A., Wu, T. C., Chu, Z., Hunter, J. V., & Bigler, E. D. (2012a). Serial measurement of memory and diffusion tensor imaging changes within the first week following uncomplicated mild traumatic brain injury. Brain Imaging and Behavior, 6(2), 319–328. https://doi.org/10.1007/s11682-012-9174-3.
Wilde, E. A., Hunter, J. V., & Bigler, E.D. (2012b). A primer of neuroimaging analysis in neurorehabilitation outcome research. NeuroRehabilitation, 31(3), 227–242. https://doi.org/10.3233/NRE-2012-0793.
Wilde, E. A., Merkley, T. L., Lindsey, H. M., Bigler, E. D., Hunter, J. V., Ewing-Cobbs, L., et al. (2020). Developmental alterations in cortical organization and socialization in adolescents who sustained a traumatic brain injury in early childhood. Journal of Neurotrauma. https://doi.org/10.1089/neu.2019.6698.
Willerman, L., Schultz, R., Rutledge, J. N., & Bigler, E. D. (1991). In vivo brain size and intelligence. Intelligence, 15(2), 223–228. https://doi.org/10.1016/0160-2896(91)90031-8.
Yamada, S., Esaki, Y., & Mizutani, T. (1999). Intracranial cavity volume can be accurately estimated from the weights of intracranial contents: Confirmation by the dental plaster casting method. Neuropathology and Applied Neurobiology, 25(4), 341–344. https://doi.org/10.1046/j.1365-2990.1999.00183.x.
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., et al. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.2011.
Yeo, B. T., Krienen, F. M., Chee, M. W., & Buckner, R. L. (2014). Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex. Neuroimage, 88, 212–227. https://doi.org/10.1016/j.neuroimage.2013.10.046.
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Dr. Bigler is retired with emeritus and adjunct status at the institutions listed above. Dr. Bigler provides forensic consultation.
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Bigler, E.D. Charting Brain Development in Graphs, Diagrams, and Figures from Childhood, Adolescence, to Early Adulthood: Neuroimaging Implications for Neuropsychology. J Pediatr Neuropsychol 7, 27–54 (2021). https://doi.org/10.1007/s40817-021-00099-6
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DOI: https://doi.org/10.1007/s40817-021-00099-6