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Methods and considerations for longitudinal structural brain imaging analysis across development
Open accessAbstract
Magnetic resonance imaging (MRI) has allowed the unprecedented capability to measure the human brain in vivo. This technique has paved the way for longitudinal studies exploring brain changes across the entire life span. Results from these studies have given us a glimpse into the remarkably extended and multifaceted development of our brain, converging with evidence from anatomical and histological studies. Ever-evolving techniques and analytical methods provide new avenues to explore and questions to consider, requiring researchers to balance excitement with caution. This review addresses what MRI studies of structural brain development in children and adolescents typically measure and how. We focus on measurements of brain morphometry (e.g., volume, cortical thickness, surface area, folding patterns), as well as measurements derived from diffusion tensor imaging (DTI). By integrating finding from multiple longitudinal investigations, we give an update on current knowledge of structural brain development and how it relates to other aspects of biological development and possible underlying physiological mechanisms. Further, we review and discuss current strategies in image processing, analysis techniques and modeling of brain development. We hope this review will aid current and future longitudinal investigations of brain development, as well as evoke a discussion amongst researchers regarding best practices.
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Cites
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011) · Psychological Science · doi:10.1177/0956797611417632 · Cited by 6,567
The basis of anisotropic water diffusion in the nervous system—A technical review.
Beaulieu, C. (2002) · NMR in Biomedicine · doi:10.1002/nbm.782 · Cited by 4,628
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Why do many psychiatric disorders emerge during adolescence?
Paus, T., Keshavan, M., & Giedd, J. N. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2513 · Cited by 3,123
Synaptic density in human frontal cortex —Developmental changes and effects of aging
Peter R., H. (1979) · Brain Research · doi:10.1016/0006-8993(79)90349-4 · Cited by 2,645
Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water
Song, S.-K., Sun, S.-W., Ramsbottom, M. J., Chang, C., Russell, J., & Cross, A. H. (2002) · NeuroImage · doi:10.1006/nimg.2002.1267 · Cited by 2,582
Azevedo, F. A. C., Carvalho, L. R. B., Grinberg, L. T., Farfel, J. M., Ferretti, R. E. L., Leite, R. E. P., Filho, W. J., Lent, R., & Herculano‐Houzel, S. (2009) · Journal of Comparative Neurology · doi:10.1002/cne.21974 · Cited by 2,337
The social brain in adolescence
Blakemore, S.-J. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2353 · Cited by 1,896
Thinning of the Cerebral Cortex in Aging
Salat, D. H. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh032 · Cited by 1,851
The human brain in numbers: a linearly scaled-up primate brain
Herculano-Houzel, S. (2009) · Frontiers in Human Neuroscience · doi:10.3389/neuro.09.031.2009 · Cited by 1,778 · cited 2×
“However, primate and rodent brains differ on a number of levels, including neuronal size and packing density (Herculano-Houzel, 2009).”
BackgroundParent, A.-S., Teilmann, G., Juul, A., Skakkebaek, N. E., Toppari, J., & Bourguignon, J.-P. (2003) · Endocrine Reviews · doi:10.1210/er.2002-0019 · Cited by 1,762
Demyelination increases radial diffusivity in corpus callosum of mouse brain
Song, S.-K., Yoshino, J., Le, T. Q., Lin, S.-J., Sun, S.-W., Cross, A. H., & Armstrong, R. C. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.01.028 · Cited by 1,658
Intellectual ability and cortical development in children and adolescents
Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., Evans, A., Rapoport, J., & Giedd, J. (2006) · Nature · doi:10.1038/nature04513 · Cited by 1,543
Han, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Czanner, S., Busa, E., Pacheco, J., Albert, M., Killiany, R., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.051 · Cited by 1,508
Training induces changes in white-matter architecture
Scholz, J., Klein, M. C., Behrens, T. E. J., & Johansen-Berg, H. (2009) · Nature Neuroscience · doi:10.1038/nn.2412 · Cited by 1,508
Microstructural maturation of the human brain from childhood to adulthood
Lebel, C., Walker, L., Leemans, A., Phillips, L., & Beaulieu, C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.12.053 · Cited by 1,398
Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood
Lebel, C., & Beaulieu, C. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.5302-10.2011 · Cited by 1,193
Stably maintained dendritic spines are associated with lifelong memories
Yang, G., Pan, F., & Gan, W.-B. (2009) · Nature · doi:10.1038/nature08577 · Cited by 1,182
Winkler, A. M., Kochunov, P., Blangero, J., Almasy, L., Zilles, K., Fox, P. T., Duggirala, R., & Glahn, D. C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.028 · Cited by 1,172
The structural basis of inter-individual differences in human behaviour and cognition
Kanai, R., & Rees, G. (2011) · Nature reviews. Neuroscience · doi:10.1038/nrn3000 · Cited by 1,055
Regional and progressive thinning of the cortical ribbon in Huntington’s disease
Rosas, H. D., Liu, A. K., Hersch, S., Glessner, M., Ferrante, R. J., Salat, D. H., van der Kouwe, A., Jenkins, B. G., Dale, A. M., & Fischl, B. (2002) · Neurology · doi:10.1212/wnl.58.5.695 · Cited by 1,044
Regionally Localized Thinning of the Cerebral Cortex in Schizophrenia
Kuperberg, G. R., Broome, M. R., McGuire, P. K., David, A. S., Eddy, M., Ozawa, F., Goff, D., West, W. C., Williams, S. C. R., van der Kouwe, A. J. W., et al. (2003) · Archives of General Psychiatry · doi:10.1001/archpsyc.60.9.878 · Cited by 843
Tamnes, C. K., Østby, Y., Fjell, A. M., Westlye, L. T., Due-Tønnessen, P., & Walhovd, K. B. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhp118 · Cited by 779
Self-citationTraining of Working Memory Impacts Structural Connectivity
Takeuchi, H., Sekiguchi, A., Taki, Y., Yokoyama, S., Yomogida, Y., Komuro, N., Yamanouchi, T., Suzuki, S., & Kawashima, R. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.4611-09.2010 · Cited by 531
Østby, Y., Tamnes, C. K., Fjell, A. M., Westlye, L. T., Due-Tønnessen, P., & Walhovd, K. B. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.1242-09.2009 · Cited by 480
Self-citationPublication and other reporting biases in cognitive sciences: detection, prevalence, and prevention
Ioannidis, J. P. A., Munafò, M. R., Fusar-Poli, P., Nosek, B. A., & David, S. P. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.02.010 · Cited by 471 · cited 1×
“However, the large number of possible tests that could be run in large datasets make tive Neuroscience 9 (2014) 172–190 them vulnerable to practices that could bias our knowledge (Ioannidis et al., 2014).”
BackgroundFair, D. A., Nigg, J. T., Iyer, S., Bathula, D., Mills, K. L., Dosenbach, N. U. F., Schlaggar, B. L., Mennes, M., Gutman, D., Bangaru, S., et al. (2013) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00080 · Cited by 463
Self-citationGenetics of brain fiber architecture and intellectual performance
Chiang, M.-C., Barysheva, M., Shattuck, D. W., Lee, A. D., Madsen, S. K., Avedissian, C., Klunder, A. D., Toga, A. W., McMahon, K. L., de Zubicaray, G. I., et al. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.4184-08.2009 · Cited by 406
Diffusion tensor imaging of post mortem multiple sclerosis brain
Schmierer, K., Wheeler-Kingshott, C. A. M., Boulby, P. A., Scaravilli, F., Altmann, D. R., Barker, G. J., Tofts, P. S., & Miller, D. H. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.12.010 · Cited by 395
Neuroanatomical Assessment of Biological Maturity
Brown, T. T., Kuperman, J. M., Chung, Y., Erhart, M., McCabe, C., Hagler, D. J., Venkatraman, V. K., Akshoomoff, N., Amaral, D. G., Bloss, C. S., et al. (2012) · Current Biology · doi:10.1016/j.cub.2012.07.002 · Cited by 332
Longitudinal Growth Curves of Brain Function Underlying Inhibitory Control through Adolescence
Ordaz, S. J., Foran, W., Velanova, K., & Luna, B. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.1741-13.2013 · Cited by 302
Maze training in mice induces MRI-detectable brain shape changes specific to the type of learning
Lerch, J. P., Yiu, A. P., Martinez-Canabal, A., Pekar, T., Bohbot, V. D., Frankland, P. W., Henkelman, R. M., Josselyn, S. A., & Sled, J. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.086 · Cited by 300
Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation
Schmierer, K., Wheeler‐Kingshott, C. A. M., Tozer, D. J., Boulby, P. A., Parkes, H. G., Yousry, T. A., Scaravilli, F., Barker, G. J., Tofts, P. S., & Miller, D. H. (2008) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21487 · Cited by 281
Urošević, S., Collins, P., Muetzel, R., Lim, K., & Luciana, M. (2012) · Developmental Psychology · doi:10.1037/a0027502 · Cited by 258
Engvig, A., Fjell, A. M., Westlye, L. T., Moberget, T., Sundseth, Ø., Larsen, V. A., & Walhovd, K. B. (2012) · Human Brain Mapping · doi:10.1002/hbm.21370 · Cited by 250
Experience-dependent plasticity of white-matter microstructure extends into old age
Lövdén, M., Bodammer, N. C., Kühn, S., Kaufmann, J., Schütze, H., Tempelmann, C., Heinze, H.-J., Düzel, E., Schmiedek, F., & Lindenberger, U. (2010) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2010.08.026 · Cited by 222
Quantitative tract-based white matter development from birth to age 2 years
Geng, X., Gouttard, S., Sharma, A., Gu, H., Styner, M., Lin, W., Gerig, G., & Gilmore, J. H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.057 · Cited by 212
Brouwer, R. M., Mandl, R. C. W., Schnack, H. G., van Soelen, I. L. C., van Baal, G. C., Peper, J. S., Kahn, R. S., Boomsma, D. I., & Pol, H. E. H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032316 · Cited by 107
How environment and genes shape the adolescent brain
Paus, T. (2013) · Hormones and Behavior · doi:10.1016/j.yhbeh.2013.04.004 · Cited by 59
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Tamnes, C. K., Herting, M. M., Goddings, A.-L., Meuwese, R., Blakemore, S.-J., Dahl, R. E., Güroğlu, B., Raznahan, A., Sowell, E. R., Crone, E. A., et al. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3302-16.2017 · Cited by 685
Self-citationInvestigating white matter fibre density and morphology using fixel-based analysis.
Raffelt, D. A., Tournier, J.-D., Smith, R. E., Vaughan, D. N., Jackson, G., Ridgway, G. R., & Connelly, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.029 · Cited by 620
“However, most white matter voxels contain contributions from multiple fibre populations (often referred to as crossing fibres), and therefore voxel-averaged quantitative measures (e.g. fractional anisotropy) are not fibre-specific and have poor interpretability.”
BackgroundInfluentialMills, K. L., Goddings, A.-L., Herting, M. M., Meuwese, R., Blakemore, S.-J., Crone, E. A., Dahl, R. E., Güroğlu, B., Raznahan, A., Sowell, E. R., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.044 · Cited by 600
Self-citationPuberty and the human brain: insights into adolescent development
Vijayakumar, N., Op de Macks, Z., Shirtcliff, E. A., & Pfeifer, J. H. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.06.004 · Cited by 437
Studying individual differences in human adolescent brain development
Foulkes, L., & Blakemore, S.-J. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0078-4 · Cited by 413
Metadata
- Authors
- Mills, K. L., & Tamnes, C. K.
- Venue
- Developmental Cognitive Neuroscience
- Publisher
- Elsevier BV
- Published
- 2014-07
- Volume
- 9
- Pages
- 172-190
- ISSN
- 1878-9293 (ISSN-L)
- OpenAlex
- W2069239049
- Cited by
- 266
- References
- 42
- FWCI
- 16.84
- Type
- journal article
- Language
- en
- Open access
- gold
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