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Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI
Abstract
We introduce MACACC-Mapping Anatomical Correlations Across Cerebral Cortex-to study correlated changes within and across different cortical networks. The principal topic of investigation is whether the thickness of one area of the cortex changes in a statistically correlated fashion with changes in thickness of other cortical regions. We further extend these methods by introducing techniques to test whether different population groupings exhibit significantly varying MACACC patterns. The methods are described in detail and applied to a normal childhood development population (n = 292), and show that association cortices have the highest correlation strengths. Taking Brodmann Area (BA) 44 as a seed region revealed MACACC patterns strikingly similar to tractography maps obtained from diffusion tensor imaging. Furthermore, the MACACC map of BA 44 changed with age, older subjects featuring tighter correlations with BA 44 in the anterior portions of the superior temporal gyri. Lastly, IQ-dependent MACACC differences were investigated, revealing steeper correlations between BA 44 and multiple frontal and parietal regions for the higher IQ group, most significantly (t = 4.0) in the anterior cingulate.
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Cites
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123
Mixed-Effects Models in S and S-PLUS
(2001) · Technometrics · doi:10.1198/tech.2001.s574 · Cited by 9,278
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256
Dynamic mapping of human cortical development during childhood through early adulthood
Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., Nugent, T. F., Herman, D. H., Clasen, L. S., Toga, A. W., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0402680101 · Cited by 5,604 · cited 2×
“The population used herein comes from a study of normal childhood brain development (Giedd, 2004; Gogtay et al., 2004).”
MethodSelf-citationA nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731
Self-citationAutomatic 3D intersubject registration of MR volumetric data in standardized Talairach space
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199403000-00005 · Cited by 3,431 · cited 1×
“, 1998) and registered into stereotaxic space using a nine parameter linear transformation (Collins et al., 1994).”
MethodSelf-citationNavigation-related structural change in the hippocampi of taxi drivers
Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S. J., & Frith, C. D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.070039597 · Cited by 3,225
The plastic human brain cortex
Pascual-Leone, A., Amedi, A., Fregni, F., & Merabet, L. B. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144216 · Cited by 1,929
Thinning of the Cerebral Cortex in Aging
Salat, D. H. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh032 · Cited by 1,851
Cited by
Neurodegenerative Diseases Target Large-Scale Human Brain Networks
Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L., & Greicius, M. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.024 · Cited by 2,350
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
Imaging structural co-variance between human brain regions
Alexander-Bloch, A., Giedd, J. N., & Bullmore, E. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3465 · Cited by 1,064
Self-citationHe, Y., Chen, Z., & Evans, A. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.0141-08.2008 · Cited by 965
Self-citationGraph analysis of the human connectome: Promise, progress, and pitfalls
Fornito, A., Zalesky, A., & Breakspear, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.087 · Cited by 743
Liang, X., Zou, Q., He, Y., & Yang, Y. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1214900110 · Cited by 677
Studying the human brain anatomical network via diffusion-weighted MRI and graph theory
Iturria-Medina, Y., Sotero, R. C., Canales-Rodríguez, E. J., Alemán-Gómez, Y., & Melie-García, L. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.10.060 · Cited by 506
Chen, Z. J., He, Y., Rosa-Neto, P., Germann, J., & Evans, A. C. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhn003 · Cited by 484
Self-citationNetwork-level structural covariance in the developing brain
Zielinski, B. A., Gennatas, E. D., Zhou, J., & Seeley, W. W. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1003109107 · Cited by 405
Networks of anatomical covariance
Evans, A. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.054 · Cited by 429
Self-citationStoodley, C. J., D’Mello, A. M., Ellegood, J., Jakkamsetti, V., Liu, P., Nebel, M. B., Gibson, J. M., Kelly, E., Meng, F., Cano, C. A., et al. (2017) · Nature Neuroscience · doi:10.1038/s41593-017-0004-1 · Cited by 387
Self-citationExploring the network dynamics underlying brain activity during rest
Cabral, J., Kringelbach, M. L., & Deco, G. (2014) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2013.12.005 · Cited by 351
Longitudinal changes in cortical thickness in autism and typical development
Zielinski, B. A., Prigge, M. B. D., Nielsen, J. A., Froehlich, A. L., Abildskov, T. J., Anderson, J. S., Fletcher, P. T., Zygmunt, K. M., Travers, B. G., Lange, N., et al. (2014) · Brain · doi:10.1093/brain/awu083 · Cited by 378
Gray matter differences correlate with spontaneous strategies in a human virtual navigation task
Bohbot, V. D., Lerch, J., Thorndycraft, B., Iaria, G., & Zijdenbos, A. P. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.1763-07.2007 · Cited by 347
Self-citationLenroot, R. K., Schmitt, J. E., Ordaz, S. J., Wallace, G. L., Neale, M. C., Lerch, J. P., Kendler, K. S., Evans, A. C., & Giedd, J. N. (2009) · Human Brain Mapping · doi:10.1002/hbm.20494 · Cited by 320
Self-citationImaging the Impact of Prenatal Alcohol Exposure on the Structure of the Developing Human Brain
Lebel, C., Roussotte, F., & Sowell, E. R. (2011) · Neuropsychology Review · doi:10.1007/s11065-011-9163-0 · Cited by 290
Developmental Changes in Organization of Structural Brain Networks
Khundrakpam, B. S., Reid, A., Brauer, J., Carbonell, F., Lewis, J., Ameis, S., Karama, S., Lee, J., Chen, Z., Das, S., et al. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs187 · Cited by 234
Self-citationVértes, P. E., & Bullmore, E. T. (2015) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12365 · Cited by 197
Childhood maltreatment: altered network centrality of cingulate, precuneus, temporal pole and insula
Teicher, M. H., Anderson, C. M., Ohashi, K., & Polcari, A. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.09.016 · Cited by 202
Relationship Between White Matter Hyperintensities, Cortical Thickness, and Cognition
Tuladhar, A. M., Reid, A. T., Shumskaya, E., de Laat, K. F., van Norden, A. G. W., van Dijk, E. J., Norris, D. G., & de Leeuw, F.-E. (2015) · Stroke · doi:10.1161/strokeaha.114.007146 · Cited by 195
Structural and Maturational Covariance in Early Childhood Brain Development
Geng, X., Li, G., Lu, Z., Gao, W., Wang, L., Shen, D., Zhu, H., & Gilmore, J. H. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw022 · Cited by 175
A review of attention-deficit/hyperactivity disorder from the perspective of brain networks
De La Fuente, A., Xia, S., Branch, C., & Li, X. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00192 · Cited by 159
Graph theoretical framework of brain networks in multiple sclerosis: a review of concepts
Fleischer, V., Radetz, A., Ciolac, D., Muthuraman, M., Gonzalez-Escamilla, G., Zipp, F., & Groppa, S. (2019) · Neuroscience · doi:10.1016/j.neuroscience.2017.10.033 · Cited by 151
Brain Neuroplasticity in Healthy, Hyperactive and Psychotic Children: Insights from Neuroimaging
Rapoport, J. L., & Gogtay, N. (2008) · Neuropsychopharmacology · doi:10.1038/sj.npp.1301553 · Cited by 132
Tamnes, C. K., Østby, Y., Walhovd, K. B., Westlye, L. T., Due‐Tønnessen, P., & Fjell, A. M. (2010) · Human Brain Mapping · doi:10.1002/hbm.20962 · Cited by 126
Single-Subject Grey Matter Graphs in Alzheimer's Disease
Tijms, B. M., Möller, C., Vrenken, H., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., & Barkhof, F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058921 · Cited by 132
Libero, L. E., DeRamus, T. P., Deshpande, H. D., & Kana, R. K. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.07.001 · Cited by 95
Brain connectivity in normally developing children and adolescents
Khundrakpam, B. S., Lewis, J. D., Zhao, L., Chouinard-Decorte, F., & Evans, A. C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.062 · Cited by 91
Self-citationMulticenter mapping of structural network alterations in autism
Valk, S. L., Di Martino, A., Milham, M. P., & Bernhardt, B. C. (2015) · Human Brain Mapping · doi:10.1002/hbm.22776 · Cited by 85
Richmond, S., Johnson, K. A., Seal, M. L., Allen, N. B., & Whittle, S. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.08.024 · Cited by 70
Development of cortical anatomical properties from early childhood to early adulthood
Nie, J., Li, G., & Shen, D. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.021 · Cited by 70
Hosseini, S. M. H., & Kesler, S. R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.032 · Cited by 59
Gray matter network disruptions and amyloid beta in cognitively normal adults
Tijms, B. M., Kate, M. T., Wink, A. M., Visser, P. J., Ecay, M., Clerigue, M., Estanga, A., Garcia Sebastian, M., Izagirre, A., Villanua, J., et al. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.10.015 · Cited by 59
The brain dynamics of intellectual development: Waxing and waning white and gray matter
Tamnes, C. K., Fjell, A. M., Østby, Y., Westlye, L. T., Due-Tønnessen, P., Bjørnerud, A., & Walhovd, K. B. (2011) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2011.09.012 · Cited by 55
Lim, H. K., Jung, W. S., & Aizenstein, H. J. (2013) · International Psychogeriatrics · doi:10.1017/s104161021300149x · Cited by 44
Kim, H.-J., Shin, J.-H., Han, C. E., Kim, H. J., Na, D. L., Seo, S. W., & Seong, J.-K. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00394 · Cited by 44
Gray Matter Network Disruptions and Regional Amyloid Beta in Cognitively Normal Adults
ten Kate, M., Visser, P. J., Bakardjian, H., Barkhof, F., Sikkes, S. A. M., van der Flier, W. M., Scheltens, P., Hampel, H., Habert, M.-O., Dubois, B., et al. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00067 · Cited by 41
Vignando, M., ffytche, D., Lewis, S. J. G., Lee, P. H., Chung, S. J., Weil, R. S., Hu, M. T., Mackay, C. E., Griffanti, L., Pins, D., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28087-0 · Cited by 42
Lee, N. R., Wallace, G. L., Raznahan, A., Clasen, L. S., & Giedd, J. N. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00496 · Cited by 30
Self-citationIQ Modulates Coupling Between Diverse Dimensions of Psychopathology in Children and Adolescents
Mahony, B. W., Tu, D., Rau, S., Liu, S., Lalonde, F. M., Alexander-Bloch, A. F., Satterthwaite, T. D., Shinohara, R. T., Bassett, D. S., Milham, M. P., et al. (2023) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2022.06.015 · Cited by 15
Dang, M., Yang, C., Chen, K., Lu, P., Li, H., Zhang, Z., & for the Beijing Aging Brain Rejuvenation Initiative, F. T. A. D. N. I. (2023) · Alzheimer's Research & Therapy · doi:10.1186/s13195-023-01167-z · Cited by 16
“…local GM degeneration, but also showed loss of synchronous GM changes among distributed brain regions, a phenomenon known as structural covariance [22], that is, interindividual differences in the structure of brain regions often covary with interindividual differences in other brain regions [23].”
BackgroundJiang, L., Zhang, T., Lv, F., Li, S., Liu, H., Zhang, Z., & Luo, T. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00010 · Cited by 13
Metadata
- Authors
- Lerch, J. P., Worsley, K., Shaw, W. P., Greenstein, D. K., Lenroot, R. K., Giedd, J., & Evans, A. C.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2006-07
- Volume
- 31
- Issue
- 3
- Pages
- 993-1003
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2086568497
- Cited by
- 537
- References
- 24
- FWCI
- 10.08
- Type
- journal article
- Language
- en
- Open access
- closed
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