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Networks of anatomical covariance
Abstract
Functional imaging or diffusion-weighted imaging techniques are widely used to understand brain connectivity at the systems level and its relation to normal neurodevelopment, cognition or brain disorders. It is also possible to extract information about brain connectivity from the covariance of morphological metrics derived from anatomical MRI. These covariance patterns may arise from genetic influences on normal development and aging, from mutual trophic reinforcement as well as from experience-related plasticity. This review describes the basic methodological strategies, the biological basis of the observed covariance as well as applications in normal brain and brain disease before a final review of future prospects for the technique.
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“Microscopic alterations in brain volume and anisotropy can be caused by changes in synaptogenesis, neuronal morphology, fiber density, and axonal branching as well as by extraneuronal changes, such as glial cell number or angiogenesis (65).”
From Neuroanatomic differences associated with stress susceptibility and resilience (2016) · cites this paper as [65]
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“Although changes in the cellular organization of a specific brain region can result from the molecular processes after neuronal activity (65,68)”
From Neuroanatomic differences associated with stress susceptibility and resilience (2016) · cites this paper as [65]
Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
Strogatz, S. H. (2001) · Nature · doi:10.1038/35065725 · Cited by 8,236
Error and attack tolerance of complex networks
Albert, R., Jeong, H., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35019019 · Cited by 7,534
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data
Smith, S. M., Jenkinson, M., Johansen‐Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., Watkins, K. E., Ciccarelli, O., Cader, M. Z., Matthews, P. M., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.024 · Cited by 6,598
Error and attack tolerance of complex networksPreprint
Albert, R., Jeong, H., & Barabási, A.-Ĺ. (2000) · Cited by 6,554
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Items 101–200 of 429
Structural brain network of gifted children has a more integrated and versatile topology
Solé‐Casals, J., Serra-Grabulosa, J. M., Romero-García, R., Vilaseca, G., Adán, A., Vilaró, N., Bargalló, N., & Bullmore, E. T. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01914-9 · Cited by 65
Thickness network features for prognostic applications in dementia
Raamana, P. R., Weiner, M. W., Wang, L., & Beg, M. F. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.05.040 · Cited by 50
Griffiths-King, D., & WOOD, A. G. (2019) · Network Neuroscience · doi:10.1162/netn_a_00123 · Cited by 45
Structural insight into the individual variability architecture of the functional brain connectome
Sun, L., Liang, X., Duan, D., Liu, J., Chen, Y., Wang, X., Liao, X., Xia, M., Zhao, T., & Yong He, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119387 · Cited by 48
Zhao, L., Wang, E., Zhang, X., Karama, S., Khundrakpam, B. S., Zhang, H., Guan, M., Wang, M., Cheng, J., Shi, D., et al. (2015) · BioMed Research International · doi:10.1155/2015/817595 · Cited by 47
Self-citationStructural covariance and cortical reorganisation in schizophrenia: a MRI-based morphometric study
Palaniyappan, L. K., Hodgson, O., Balain, V., Iwabuchi, S. J., Gowland, P. A., & Liddle, P. F. (2018) · Psychological Medicine · doi:10.1017/s0033291718001010 · Cited by 48
Griffiths, K. R., Grieve, S. M., Kohn, M. R., Clarke, S., Williams, L. M., & Korgaonkar, M. S. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.219 · Cited by 46
Li, C., Zuo, Z., Liu, D., Jiang, R., Li, Y., Li, H., Yin, X., Lai, Y., Wang, J., & Xiong, K. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00856 · 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
Resolving heterogeneity in depression using individualized structural covariance network analysis
Han, S., Zheng, R., Li, S., Zhou, B., Jiang, Y. B., Fang, K., Wei, Y., Pang, J., Li, H., Zhang, Y., et al. (2022) · Psychological Medicine · doi:10.1017/s0033291722002380 · Cited by 41
Kim, S., Jung, W. H., Kim, S. N., Jang, J. H., & Kwon, J. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0127118 · Cited by 44
He, C., Cortés, J. M., Kang, X., Cao, J., Chen, H., Guo, X., Wang, R., Kong, L., Huang, X., Xiao, J., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25434 · Cited by 42
Eisenberg, I. W., Wallace, G. L., Kenworthy, L. E., Gotts, S. J., & Martin, A. D. (2015) · Molecular Autism · doi:10.1186/s13229-015-0047-7 · Cited by 41
Grey matter networks in people at increased familial risk for schizophrenia
Tijms, B. M., Sprooten, E., Job, D. E., Johnstone, E. C., Owens, D. G. C., Willshaw, D. J., Seriès, P., & Lawrie, S. M. (2015) · Schizophrenia Research · doi:10.1016/j.schres.2015.08.025 · Cited by 42
Miskin, N. P., Thesen, T., Barr, W., Butler, T., Wang, X. H., Dugan, P. C., Kuzniecky, R., Doyle, W., Devinsky, O., & Blackmon, K. (2015) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9455-8 · Cited by 41
Characterizing the gradients of structural covariance in the human hippocampus
Masouleh, S. K., Plachti, A., Hoffstaedter, F., Eickhoff, S. B., & Genon, S. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116972 · Cited by 37
Kim, S., Kim, Y.-W., Jeon, H., Im, C., & Lee, S. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9061846 · Cited by 39
Zheng, W., Zhao, Z., Zhang, Z., Liu, T., Zhang, Y., Fan, J., & Wu, D. (2020) · Human Brain Mapping · doi:10.1002/hbm.25251 · Cited by 36
Morphological brain network assessed using graph theory and network filtration in deaf adults
Kim, E., Kang, H., Lee, H., Lee, H., Suh, M., Song, J., Oh, S. H., & Lee, D. S. (2014) · Hearing Research · doi:10.1016/j.heares.2014.06.007 · Cited by 40
Khundrakpam, B. S., Lewis, J. D., Jeon, S., Kostopoulos, P., Medina, Y. I., Chouinard-Decorte, F., & Evans, A. C. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx317 · Cited by 35
Self-citationThe Pathoconnectivity Profile of Alzheimer’s Disease: A Morphometric Coalteration Network Analysis
Manuello, J., Nani, A., Premi, E., Borroni, B., Costa, T., Tatu, K., Liloia, D., Duca, S., & Cauda, F. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2017.00739 · Cited by 38
Disruption of gray matter morphological networks in patients with paroxysmal kinesigenic dyskinesia
Li, X., Lei, D., Niu, R., Li, L., Suo, X., Li, W., Yang, C., Yang, T., Ren, J., Pinaya, W. H. L., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25230 · Cited by 35
Walhovd, K. B., Tamnes, C. K., Bjørnerud, A., Due‐Tønnessen, P., Holland, D., Dale, A. M., & Fjell, A. M. (2014) · Cerebral Cortex · doi:10.1093/cercor/bht424 · Cited by 36
Small vessel disease is linked to disrupted structural network covariance in Alzheimer's disease
Nestor, S. M., Mišić, B., Ramirez, J., Zhao, J., Graham, S. J., Verhoeff, N. P. L. G., Stuss, D. T., Masellis, M., & Black, S. E. (2017) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.12.007 · Cited by 37
Melie‐García, L., Slater, D., Ruef, A., Sanabria-Díaz, G., Preisig, M., Kherif, F., Draganski, B., & Lutti, A. (2017) · Human Brain Mapping · doi:10.1002/hbm.23929 · Cited by 36
Smith, S. M., Duff, E. P., Groves, A. R., Nichols, T. E., Jbabdi, S., Westlye, L. T., Tamnes, C. K., Engvig, A., Walhovd, K. B., Fjell, A. M., et al. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.2912-18.2019 · Cited by 39
Masouleh, S. K., Beyer, F., Lampe, L., Loeffler, M., Luck, T., Riedel-Heller, S. G., Schroeter, M. L., Stümvoll, M., Villringer, A., & Witte, A. V. (2017) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x17729111 · Cited by 35
Aboud, K. S., Huo, Y., Kang, H., Ealey, A., Resnick, S. M., Landman, B. A., & Cutting, L. E. (2018) · Human Brain Mapping · doi:10.1002/hbm.24359 · Cited by 34
Hahn, A., Lanzenberger, R. R., & Kasper, S. F. (2018) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyy100 · Cited by 40
Imaging local genetic influences on cortical folding
Alexander‐Bloch, A. F., Raznahan, A., Vandekar, S., Seidlitz, J., Lu, Z., Mathias, S. R., Knowles, E. E. M., Mollon, J., Rodrigue, A., Curran, J. E., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912064117 · Cited by 31
Social cognitive network neuroscience
Krendl, A. C., & Betzel, R. F. (2022) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsac020 · Cited by 40
Zhao, L., Guan, M., Zhang, X., Karama, S., Khundrakpam, B. S., Wang, M., Dong, M., Qin, W., Tian, J., Evans, A. C., et al. (2015) · Human Brain Mapping · doi:10.1002/hbm.22925 · Cited by 33
Self-citationSone, D., Matsuda, H., Ota, M., Maikusa, N., Kimura, Y., Sumida, K., Yokoyama, K., Imabayashi, E., Watanabe, M., Watanabe, Y., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0158728 · Cited by 34
Hecht, E. E., Kukekova, A. V., Gutman, D. A., Acland, G. M., Preuss, T. M., & Trut, L. N. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.3114-20.2021 · Cited by 37
Lu, F., Cui, Q., Chen, Y., He, Z., Sheng, W., Tang, Q., Yang, Y., Luo, W., Yu, Y., Chen, J., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac105 · Cited by 34
Subirà, M., Cano, M., de Wit, S. J., Alonso, P., Cardoner, N., Hoexter, M. Q., Kwon, J. S., Nakamae, T., Löchner, C., Sato, J. R., et al. (2016) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.150012 · Cited by 34
Carmeli, C., Fornari, E., Jalili, M., Meuli, R. A., & Knyazeva, M. G. (2014) · Brain and Behavior · doi:10.1002/brb3.252 · Cited by 32
A seed-based cross-modal comparison of brain connectivity measures
Reid, A., Hoffstaedter, F., Gong, G., Laird, A. R., Fox, P. T., Evans, A. C., Amunts, K., & Eickhoff, S. B. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1264-3 · Cited by 32
Self-citationHuang, S.-R., Wu, Y.-T., Jao, C., Soong, B., Lirng, J.-F., Wu, H.-M., & Wang, P.-S. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.11.007 · Cited by 33
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Sharda, M., Foster, N. E. V., Tryfon, A., Doyle‐Thomas, K. A. R., Ouimet, T., Anagnostou, E. A., Evans, A. C., Zwaigenbaum, L., Lerch, J. P., Lewis, J. D., et al. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw024 · Cited by 36
Self-citationModular Development of Cortical Gray Matter Across Childhood and Adolescence
Krongold, M., Cooper, C., & Bray, S. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv307 · Cited by 32
Cross-population myelination covariance of human cerebral cortex
Ma, Z., & Zhang, N. (2017) · Human Brain Mapping · doi:10.1002/hbm.23698 · Cited by 33
Fu, Z., Zhao, M., He, Y., Wang, X., Lu, J., Li, S., Li, X., Kang, G., Han, Y., & Li, S. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.686598 · Cited by 31
Bray, S., Krongold, M., Cooper, C., & Lebel, C. A. (2015) · eNeuro · doi:10.1523/eneuro.0003-15.2015 · Cited by 34
Decreased centrality of cortical volume covariance networks in autism spectrum disorders
Balardin, J. B., Comfort, W. E., Daly, E., Murphy, C. M., Andrews, D. S., Murphy, D., Ecker, C., & Sato, J. R. (2015) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2015.08.003 · Cited by 31
Structural covariance networks in the mouse brain
Pagani, M., Bifone, A., & Gozzi, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.025 · Cited by 34
Smaller subcortical volume in Parkinson patients with rapid eye movement sleep behavior disorder
Kamps, S., van den Heuvel, O. A., van der Werf, Y. D., Berendse, H. W., Weintraub, D. A., & Vriend, C. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9939-4 · Cited by 31
Larabi, D. I., Renken, R. J., Cabral, J., Marsman, J.-B. C., Aleman, A., & Ćurĉić‐Blake, B. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116896 · Cited by 29
Han, S., Xu, Y., Guo, H., Fang, K., Wei, Y., Liu, L., Cheng, J., Zhang, Y., & Cheng, J. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac163 · Cited by 31
Multimodal connectivity mapping of the human left anterior and posterior lateral prefrontal cortex
Reid, A., Bzdok, D., Langner, R., Fox, P. T., Laird, A. R., Amunts, K., Eickhoff, S. B., & Eickhoff, C. R. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-1060-5 · Cited by 32
Zugman, A., Assunção-Leme, I. B., Vieira, G., Gadelha, A., White, T. P., Oliveira, P. P. M., Noto, C. D. S., Crossley, N. A., McGuire, P. K., Cordeiro, Q., et al. (2015) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2015.09.018 · Cited by 28
Nordin, K., Persson, J., Stening, E., Herlitz, A., Larsson, E., & Söderlund, H. (2017) · Hippocampus · doi:10.1002/hipo.22817 · Cited by 29
Xu, X., Wang, T., Li, W., Li, H., Xu, B., Zhang, M., Yue, L., Wang, P., & Xiao, S. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.688113 · Cited by 31
Hansen, J. Y., Shafiei, G., Markello, R. D., Smart, K. A., Cox, S. M. L., Wu, Y., Gallezot, J., Aumont, É., Servaes, S., Scala, S. G., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1040925/v1 · Cited by 30
Nassar, R., Kaczkurkin, A. N., Xia, C. H., Sotiras, A., Pehlivanova, M., Moore, T. M., de la Garza, Á. G., Roalf, D. R., Rosen, A. F. G., Lorch, S. A., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy091 · Cited by 27
Vuksanović, V., Staff, R. T., Ahearn, T. S., Murray, A. D., & Wischik, C. M. (2018) · International Journal of Neural Systems · doi:10.1142/s0129065718500557 · Cited by 31
Lai, H., Kong, X., Zhao, Y., Pan, N., Zhang, X., He, M., Wang, S., & Gong, Q. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119009 · Cited by 28
Li, Z., Li, J., Wang, N., Lv, Y., Zou, Q., & Wang, J. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120434 · Cited by 29
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Wang, X., Bernhardt, B. C., Karapanagiotidis, T., de Caso, I., Alam, T. R. G., Cotter, Z., Smallwood, J., & Jefferies, E. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.044 · Cited by 30
van Duinkerken, E., IJzerman, R. G., Klein, M., Moll, A. C., Snoek, F. J., Scheltens, P., Pouwels, P. J. W., Barkhof, F., Diamant, M., & Tijms, B. M. (2015) · Human Brain Mapping · doi:10.1002/hbm.23096 · Cited by 28
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Zhao, L., Guan, M., Zhu, X., Karama, S., Khundrakpam, B. S., Wang, M., Dong, M., Qin, W., Tian, J., Evans, A. C., et al. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00675 · Cited by 28
Self-citationModern Methods for Interrogating the Human Connectome
Lowe, M. J., Sakaie, K., Beall, E. B., Calhoun, V. D., Bridwell, D. A., Rubinov, M., & Rao, S. M. (2016) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617716000060 · Cited by 29
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Pelkmans, W., Ossenkoppele, R., Dicks, E., Strandberg, O. T., Barkhof, F., Tijms, B. M., Pereira, J. B., & Hansson, O. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00876-7 · Cited by 26
Xu, Q., Zhang, Q., Yang, F., Weng, Y., Xie, X., Hao, J., Qi, R., Gumenyuk, V., Stufflebeam, S. M., Bernhardt, B. C., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25279 · Cited by 25
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Kim, D. J., Lim, M., Kim, J. S., & Chung, C. K. (2021) · Scientific Reports · doi:10.1038/s41598-021-02616-1 · Cited by 26
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Fradkin, Y., Khadka, S., Bessette, K. L., & Stevens, M. C. (2017) · Brain Imaging and Behavior · doi:10.1007/s11682-016-9612-8 · Cited by 26
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Masuda, N., Boyd, Z. M., Garlaschelli, D., & Mucha, P. J. (2025) · Physics Reports · doi:10.1016/j.physrep.2025.06.002 · Cited by 40
Li, X., Cao, Q., Pu, F., Li, D., Fan, Y., Li, A., Wang, P., Wu, Z.-M., Sun, L., Li, S., et al. (2015) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2015.01.006 · Cited by 27
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Lin, X., Chen, Y., Wang, M., Song, C., Lin, B., Yuan, X., Liu, Q., Yang, H., & Jiang, N. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00541 · Cited by 26
Mancuso, L., Fornito, A., Costa, T., Ficco, L., Liloia, D., Manuello, J., Duca, S., & Cauda, F. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117220 · Cited by 30
Liloia, D., Mancuso, L., Uddin, L. Q., Costa, T., Nani, A., Keller, R., Manuello, J., Duca, S., & Cauda, F. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102583 · Cited by 24
Ajnakina, O., Das, T. K., Lally, J., Forti, M. D., Pariante, C. M., Marques, T. R., Mondelli, V., David, A. S., Murray, R. M., Palaniyappan, L. K., et al. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbab035 · Cited by 26
Abnormal cortical thickness connectivity persists in childhood absence epilepsy
Curwood, E. K., Pedersen, M., Carney, P. W., Berg, A. T., Abbott, D. F., & Jackson, G. D. (2015) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.178 · Cited by 25
Liu, X., Yuan, J., Guang, Y., Wang, X., & Feng, Z. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00613 · Cited by 25
Prenatal stress and its association with amygdala-related structural covariance patterns in youth
Marečková, K., Miles, A. E., Liao, Z., Andrýsková, L., Brázdil, M., Paus, T., & Nikolova, Y. S. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.102976 · Cited by 29
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Chen, Y., Li, P., Gu, B., Liu, Z., Li, X., Evans, A. C., Gong, G., & Zhang, Z. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1946-14.2015 · Cited by 27
Self-citationWagstyl, K., & Lerch, J. P. (2018) · Neuromethods · doi:10.1007/978-1-4939-7647-8_3 · Cited by 30
Large-scale structural network change correlates with clinical response to rTMS in depression
Nestor, S. M., Mir-Moghtadaei, A., Vila‐Rodriguez, F., Giacobbe, P., Daskalakis, Z. J., Blumberger, D. M., & Downar, J. H. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01256-3 · Cited by 24
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Carbonell, F., Charil, A., Zijdenbos, A. P., Evans, A. C., Bedell, B. J., & Initiative, A. D. N. (2014) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2014.165 · Cited by 24
Self-citationWalker, S. E., Wood, T., Cash, D., Mesquita, M. S., Williams, S., & Sandi, C. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.14061 · Cited by 25
Structural cortical network reorganization associated with early conversion to multiple sclerosis
Tur, C., Eshaghi, A., Altmann, D. R., Jenkins, T. M., Prados, F., Grussu, F., Charalambous, T., Schmidt, A., Sebastien Ourselin, A., Clayden, J. D., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-29017-1 · Cited by 24
Watanabe, K., Kakeda, S., Katsuki, A., Ueda, I., Ikenouchi, A., Yoshimura, R., & Korogi, Y. (2020) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2020.111083 · Cited by 24
Zhao, Y., Niu, R., Lei, D., Shah, C., Xiao, Y., Zhang, W., Chen, Z., Lui, S., & Gong, Q. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00172 · Cited by 23
Veldsman, M., Cheng, H.-J., Ji, F., Werden, E., Khlif, M. S., Ng, K. K., Lim, J. K. W., Qian, X., Yu, H., Zhou, J. H., et al. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa155 · Cited by 23
Reliable brain morphometry from contrast‐enhanced T1w‐MRI in patients with multiple sclerosis
Rebsamen, M., McKinley, R., Radojewski, P., Pistor, M., Friedli, C., Hoepner, R., Salmen, A., Chan, A., Reyes, M., Wagner, F., et al. (2022) · Human Brain Mapping · doi:10.1002/hbm.26117 · Cited by 22
Lai, H., Zhao, Y., Li, J., Gong, Q., & Wang, S. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad211 · Cited by 22
Cai, S., Wang, X., Yang, F., Chen, D., & Huang, L. (2021) · Autism Research · doi:10.1002/aur.2464 · Cited by 19
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Metadata
- Authors
- Evans, A. C.
- Affiliations
- Montreal Neurological Institute and Hospital (CA)
- Corresponding author
- Evans, Alan C.
- ORCID
- Evans, Alan C.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 80
- Pages
- 489-504
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2081828863
- Cited by
- 429
- References
- 261
- Citations by year
- 2026: 35 · 2025: 28 · 2024: 29 · 2023: 34 · 2022: 30 · 2021: 43 · 2020: 38 · 2019: 29 · 2018: 41 · 2017: 34 · 2016: 33 · 2015: 36 · 2014: 15 · 2013: 4
- FWCI
- 2.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- brain connectivity, morphometrics, anatomical MRI, brain development, brain aging, experience-dependent plasticity, brain disorders
- MeSH terms
- Animals, Brain, Brain Diseases, Humans, Models, Anatomic, Models, Neurological, Nerve Net, Models, Statistical, Connectome
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