Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease
Open accessAbstract
Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer’s disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of the graph analysis measures clustering, path length, global efficiency and transitivity in a cohort of AD ( N = 293) and control subjects ( N = 293). More specifically, we studied the effect that group size and composition, choice of neuroanatomical atlas, and choice of cortical measure (thickness or volume) have on binary and weighted network properties and relate them to the magnitude of the differences between groups of AD and control subjects. Our results showed that specific group composition heavily influenced the network properties, particularly for groups with less than 150 subjects. Weighted measures generally required fewer subjects to stabilize and all assessed measures showed robust significant differences, consistent across atlases and cortical measures. However, all these measures were driven by the average correlation strength, which implies a limitation of capturing more complex features in weighted networks. In binary graphs, significant differences were only found in the global efficiency and transitivity measures when using cortical thickness measures to define edges. The findings were consistent across the two atlases, but no differences were found when using cortical volumes. Our findings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be preferred in future AD studies if using binary networks. Further, studying cortical networks in small cohorts should be complemented by analyzing smaller, subsampled groups to reduce the risk that findings are spurious.
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Decreased local and global efficiency is a common characteristic of patients with AD.
“A decreased local (Zhou and Lui, 2013) and global (Martensson et al., 2018) efficiency is a common characteristic of patients with AD.”
From Disrupted structural connectivity in ArcAβ mouse model of Aβ amyloidosis (2020) · cites this paper as Martensson et al., 2018
The citation to Mårtensson et al. is attached to global efficiency, not local efficiency. The Results report significantly increased global efficiency in AD patients for binary networks generated from cortical thickness, and the Discussion reports a stable, significant increase—not the claimed decrease. (LLM, conf=0.91)
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Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324 · see citing works
Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease
Cappello, V., Marchetti, L., Parlanti, P., Landi, S., Tonazzini, I., Cecchini, M., Piazza, V., & Gemmi, M. (2016) · Scientific Reports · doi:10.1038/s41598-016-0001-8 · Cited by 13,097 · see citing works
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 · see citing works
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 · see citing works
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910 · see citing works
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610 · see citing works
Measuring the thickness of the human cerebral cortex from magnetic resonance images
Fischl, B. R., & Dale, A. M. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.200033797 · Cited by 5,789 · see citing works
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684 · see citing works
Efficient behavior of small-world networks
Latora, V., & Marchiori, M. (2001) · Physical Review Letters · doi:10.1103/physrevlett.87.198701 · Cited by 5,119 · see citing works
Automatically Parcellating the Human Cerebral Cortex
Fischl, B. R. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg087 · Cited by 4,208 · see citing works
Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature
Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.010 · Cited by 2,956 · see citing works
A hybrid approach to the skull stripping problem in MRI
Ségonne, F., Dale, A. M., Busa, E., Glessner, M., Salat, D. H., Hahn, H., & Fischl, B. R. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.03.032 · Cited by 2,227 · see citing works
Sequence-independent segmentation of magnetic resonance images
Fischl, B. R., Salat, D. H., van der Kouwe, A. J. W., Makris, N., Ségonne, F., Quinn, B. T., & Dale, A. M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.016 · Cited by 2,117 · see citing works
Fischl, B. R., Liu, A., & Dale, A. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906426 · Cited by 1,755 · see citing works
Highly accurate inverse consistent registration: A robust approach
Reuter, M., Rosas, H. D., & Fischl, B. R. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.020 · Cited by 1,378 · see citing works
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 · see citing works
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory
van Wijk, B. C. M., Stam, C. J., & Daffertshofer, A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013701 · Cited by 1,161 · see citing works
Intensity and coherence of motifs in weighted complex networks.
Onnela, J., Saramäki, J., Kertész, J., & Kaski, K. (2004) · Physical review. E, Statistical, nonlinear, and soft matter physics · doi:10.1103/physreve.71.065103 · Cited by 1,087 · see citing works
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 · see citing works
He, Y., Chen, Z., & Evans, A. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.0141-08.2008 · Cited by 965 · see citing works
Human brain networks in health and disease
Bassett, D. S., & Bullmore, E. T. (2009) · Current Opinion in Neurology · doi:10.1097/wco.0b013e32832d93dd · Cited by 894 · see citing works
Small-World Brain Networks Revisited
Bassett, D. S., & Bullmore, E. T. (2016) · The Neuroscientist · doi:10.1177/1073858416667720 · Cited by 840 · see citing works
Graph 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 · see citing works
Generalizations of the clustering coefficient to weighted complex networks
Saramäki, J., Kivelä, M., Onnela, J.-P. “., Kaski, K. K., & Kertész, J. (2007) · Physical Review E · doi:10.1103/physreve.75.027105 · Cited by 727 · see citing works
Sanz-Arigita, E. J., Schoonheim, M. M., Damoiseaux, J. S., Rombouts, S. A. R. B., Maris, E., Barkhof, F., Scheltens, P., & Stam, C. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013788 · Cited by 608 · see citing works
A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging
Hutton, C., Draganski, B., Ashburner, J., & Weiskopf, N. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.06.043 · Cited by 566 · see citing works
Lo, C. Z., Wang, P.-N., Chou, K., Wang, J., Yong He, A., & Lin, C. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.4136-10.2010 · Cited by 538 · see citing works
Functional network disruption in the degenerative dementias
Pievani, M., de Haan, W., Wu, T., Seeley, W. W., & Frisoni, G. B. (2011) · The Lancet Neurology · doi:10.1016/s1474-4422(11)70158-2 · Cited by 481 · see citing works
Bernhardt, B. C., Chen, Z., Yong He, A., Evans, A. C., & Bernasconi, N. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq291 · Cited by 463 · see citing works
Abnormal Cortical Networks in Mild Cognitive Impairment and Alzheimer's Disease
Yao, Z., Zhang, Y., Lin, L., Zhou, Y., Xu, C., Jiang, T., & Initiative, T. A. D. N. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001006 · Cited by 442 · see citing works
On the use of correlation as a measure of network connectivity
Zalesky, A., Fornito, A., & Bullmore, E. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.001 · Cited by 430 · see citing works
Schwarz, C. G., Gunter, J. L., Wiste, H. J., Przybelski, S. A., Weigand, S. D., Ward, C. P., Senjem, M. L., Vemuri, P., Murray, M. E., Dickson, D. W., et al. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.05.017 · Cited by 422 · see citing works
Alzheimer's disease: connecting findings from graph theoretical studies of brain networks
Tijms, B. M., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., & Barkhof, F. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.02.020 · Cited by 416 · see citing works
de Haan, W., Pijnenburg, Y. A. L., STRIJERS, R. L. M., van der Made, Y., van der Flier, W. M., Scheltens, P., & Stam, C. J. (2009) · BMC Neuroscience · doi:10.1186/1471-2202-10-101 · Cited by 404 · see citing works
Small-World Propensity and Weighted Brain Networks
Muldoon, S. F., Bridgeford, E., & Bassett, D. S. (2016) · Scientific Reports · doi:10.1038/srep22057 · Cited by 305 · see citing works
Scales of Spatial Heterogeneity of Plastic Marine Debris in the Northeast Pacific Ocean
Goldstein, M. C., Titmus, A. J., & Ford, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0080020 · Cited by 262 · see citing works
BRAPH: A graph theory software for the analysis of brain connectivity
Mijalkov, M., Kakaei, E., Pereira, J. B., Westman, E., Volpe, G., & Initiative, A. D. N. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178798 · Cited by 257 · see citing works
Self-citationAddNeuroMed—The European Collaboration for the Discovery of Novel Biomarkers for Alzheimer's Disease
Lovestone, S., Francis, P. T., Kłoszewska, I., Mecocci, P., Simmons, A., Soininen, H. S., Spenger, C., Tsolaki, M., Vellas, B. J., Wahlund, L., et al. (2009) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2009.05064.x · Cited by 211 · see citing works
Self-citationWestman, E., Aguilar, C., Muehlboeck, J., & Simmons, A. (2012) · Brain Topography · doi:10.1007/s10548-012-0246-x · Cited by 206 · see citing works
Self-citationLi, Y., Wang, Y., Guorong Wu, A., Shi, F., Zhou, L., Lin, W., & Shen, D. G. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.11.008 · Cited by 199 · see citing works
Age Correction in Dementia – Matching to a Healthy Brain
Dukart, J., Schroeter, M. L., Mueller, K., & Initiative, T. A. D. N. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022193 · Cited by 183 · see citing works
Drakesmith, M., Caeyenberghs, K., Dutt, A., Lewis, G., David, A. S., & Jones, D. K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.011 · Cited by 157 · see citing works
Acikalin, M. Y., Gorgolewski, K. J., & Poldrack, R. A. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00001 · Cited by 147 · see citing works
Pereira, J. B., Mijalkov, M., Kakaei, E., Mecocci, P., Vellas, B., Tsolaki, M., Kłoszewska, I., Soininen, H., Spenger, C., Lovestone, S., et al. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw128 · Cited by 142 · see citing works
Self-citationGraph Theory and Brain Connectivity in Alzheimer’s Disease
del'Etoile, J., & Adeli, H. (2017) · The Neuroscientist · doi:10.1177/1073858417702621 · Cited by 141 · see citing works
Complex Brain Network Analysis and Its Applications to Brain Disorders: A Survey
Liu, J., Li, M., Pan, Y. F., Lan, W., Zheng, R., Wu, F., & Wang, J. (2017) · Complexity · doi:10.1155/2017/8362741 · Cited by 141 · see citing works
Westman, E., Simmons, A., Muehlboeck, J., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Soininen, H. S., Weiner, M. W., Lovestone, S., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.065 · Cited by 136 · see citing works
Self-citationSimmons, A., Westman, E., Muehlboeck, S., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Wahlund, L., Soininen, H. S., Lovestone, S., et al. (2010) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.2491 · Cited by 134 · see citing works
Self-citationSingle-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 · see citing works
MRI Measures of Alzheimer's Disease and the AddNeuroMed Study
Simmons, A., Westman, E., Muehlboeck, S., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Wahlund, L., Soininen, H. S., Lovestone, S., et al. (2009) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2009.05063.x · Cited by 128 · see citing works
Self-citationFerreira, D., Cavallin, L., Larsson, E., Muehlboeck, J., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Soininen, H., Lovestone, S., et al. (2015) · Journal of Internal Medicine · doi:10.1111/joim.12358 · Cited by 119 · see citing works
Self-citationVecchio, F., Miraglia, F., Piludu, F., Granata, G., Romanello, R., Caulo, M., Onofrj, V., Bramanti, P., Colosimo, C., & Rossini, P. M. (2016) · Brain Imaging and Behavior · doi:10.1007/s11682-016-9528-3 · Cited by 116 · see citing works
TheHiveDB image data management and analysis framework
Muehlboeck, J., Westman, E., & Simmons, A. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00049 · Cited by 111 · see citing works
Self-citationThe influence of parametric forcing on non-equilibrium dynamics of wave patterns
Abarzhi, S. I., Desjardins, O., Nepomnuashchy, A., & Pitsch, H. G. (2007) · UWA Profiles and Research Repository (UWA) · Cited by 109 · see citing works
On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks
Liu, X., Liao, X., Li, S., Zheng, S. Q., Lin, B., Zhang, J., Shao, L., Huang, C., & Xiao, L. (2017) · Complexity · doi:10.1155/2017/5049836 · Cited by 103 · see citing works
A fast model independent method for automatic correction of intensity nonuniformity in MRI data
Vokurka, E. A., Thacker, N. A., & Jackson, A. (1999) · Journal of Magnetic Resonance Imaging · doi:10.1002/(sici)1522-2586(199910)10:4 · Cited by 92 · see citing works
Phillips, D. J., McGlaughlin, A., Ruth, D. M., Jager, L. R., & Soldan, A. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.01.007 · Cited by 89 · see citing works
Vecchio, F., Miraglia, F., Curcio, G., Altavilla, R., Scrascia, F., Giambattistelli, F., Quattrocchi, C. C., Bramanti, P., Vernieri, F., & Rossini, P. M. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-142484 · Cited by 68 · see citing works
Gray matter networks and clinical progression in subjects with predementia Alzheimer's disease
Tijms, B. M., ten Kate, M., Gouw, A. A., Borta, A., Verfaillie, S. C. J., Teunissen, C. E., Scheltens, P., Barkhof, F., & van der Flier, W. M. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.09.011 · Cited by 68 · see citing works
Spulber, G., Simmons, A., Muehlboeck, J., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Soininen, H., Spenger, C., Lovestone, S., et al. (2013) · Journal of Internal Medicine · doi:10.1111/joim.12028 · Cited by 63 · see citing works
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Papo, D., Zanin, M., Martínez, J. H., & Buldú, J. M. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00096 · Cited by 63 · see citing works
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Xiao, Y., Segal, M. R., & Yang, Y. H. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1117 · Cited by 62 · see citing works
Graph analysis of structural brain networks in Alzheimer’s disease: beyond small world properties
John, M., Ikuta, T., & Ferbinteanu, J. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1255-4 · Cited by 54 · see citing works
Gray matter network measures are associated with cognitive decline in mild cognitive impairment
Dicks, E., Tijms, B. M., ten Kate, M., Gouw, A. A., Benedictus, M. R., Teunissen, C. E., Barkhof, F., Scheltens, P., & van der Flier, W. M. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.09.029 · Cited by 51 · see citing works
Falahati, F., Ferreira, D., Soininen, H. S., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Lovestone, S., Eriksdotter, M., Wahlund, L., et al. (2015) · Brain Topography · doi:10.1007/s10548-015-0455-1 · Cited by 45 · see citing works
Self-citationDimitriadis, S. I., Drakesmith, M., Bells, S., Parker, G. D., Linden, D. E. J., & Jones, D. K. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00694 · Cited by 43 · see citing works
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Duan, H., Jiang, J., Xu, J. L., Zhou, H., Huang, Z., Yu, Z., & Yan, Z. (2016) · Brain Research · doi:10.1016/j.brainres.2016.11.019 · Cited by 42 · see citing works
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Voevodskaya, O., Pereira, J. B., Volpe, G., Lindberg, O. E., Stomrud, E., van Westen, D., Westman, E., & Hansson, O. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.11.014 · Cited by 37 · see citing works
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Hlinka, J., Hartman, D. G., Jajcay, N., Tomeček, D., Tintěra, J., & Paluš, M. (2017) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4977951 · Cited by 36 · see citing works
Bhardwaj, N., Carson, M. B., Abyzov, A. S., Yan, K.-K., Lu, H. J., & Gerstein, M. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000755 · Cited by 34 · see citing works
Assessing the Performance of a Computer-Based Policy Model of HIV and AIDS
Rydzak, C. E., Cotich, K. L., Sax, P. E., Hsu, H., Wang, B., Losina, E., Freedberg, K. A., Weinstein, M. C., Goldie, S. J., & for the CEPAC Investigators (2010) · PLoS ONE · doi:10.1371/journal.pone.0012647 · Cited by 28 · see citing works
Correction: 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/annotation/6a2e6405-ce1d-49e0-a88c-0017c680d597 · Cited by 21 · see citing works
Impaired Communication Between the Dorsal and Ventral Stream: Indications from Apraxia
Evans, C., Edwards, M., Taylor, L. J., & Ietswaart, M. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00008 · Cited by 14 · see citing works
On alternative formulations of the small-world metric in complex networksPreprint
Zanin, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.03689 · Cited by 7 · see citing works
High-resolution intersubject averaging and a coordinate system for the cortical surface
Fischl, B., Sereno, M. I., Tootell, R. B. H., & Dale, A. M. (1999) · Human Brain Mapping · doi:10.1002/(sici)1097-0193(1999)8:4
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Mårtensson, G., Ferreira, D., Granberg, T., Cavallin, L., Oppedal, K., Padovani, A., Rektorova, I., Bonanni, L., Pardini, M., Kramberger, M. G., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101714 · Cited by 176 · see citing works
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García-Cabello, E., Gonzalez‐Burgos, L., Pereira, J. B., Hernandez-Cabrera, J. A., Westman, E., Volpe, G., Barroso, J., & Ferreira, D. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.694254 · Cited by 29 · see citing works
Self-citationHub disruption in patients with chronic neck pain: a graph analytical approach
Pauw, R. D., Aerts, H., Šiugždaitė, R., Meeus, M., Coppieters, I., Caeyenberghs, K., & Cagnie, B. J. (2019) · Pain · doi:10.1097/j.pain.0000000000001762 · Cited by 29 · see citing works
Exploring Alzheimer's disease: a comprehensive brain connectome-based survey
Zhang, L., Qu, J., Ma, H., Chen, T., Liu, T., & Zhu, D. (2024) · Psychoradiology · doi:10.1093/psyrad/kkad033 · Cited by 25 · see citing works
Brain network topology early after stroke relates to recovery
Nemati, P. R., Backhaus, W., Feldheim, J. F., Bönstrup, M., Cheng, B., Thomalla, G., Gerloff, C. P., & Schulz, R. (2022) · Brain Communications · doi:10.1093/braincomms/fcac049 · Cited by 23 · see citing works
Baghernezhad, S., & Daliri, M. R. (2024) · GeroScience · doi:10.1007/s11357-024-01128-w · Cited by 21 · see citing works
Cui, H., Dai, W., Zhu, Y., Li, X., He, L., & Yang, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05097 · Cited by 19 · see citing works
Callow, D. D., & Smith, J. C. (2023) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2023.108527 · Cited by 18 · see citing works
Xu, F., Garai, S., Duong-Tran, D. A., Saykin, A. J., Zhao, Y., & Shen, L. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995657 · Cited by 17 · see citing works
White Matter Brain Network Research in Alzheimer’s Disease Using Persistent Features
Kuang, L., Gao, Y. X., Chen, Z., Xing, J., Xiong, F., & Xie, H. (2020) · Molecules · doi:10.3390/molecules25112472 · Cited by 14 · see citing works
Safai, A., Buckingham, W. R., Jonaitis, E. M., Langhough, R. E., Johnson, S. C., Powell, W. R., Kind, A., Bendlin, B. B., & Tiwari, P. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70095 · Cited by 12 · see citing works
Robalo, B. M. D. B., Vlegels, N., Meier, J. M., Leemans, A., Biessels, G. J., Reijmer, Y. D., on Behalf of the Utrecht VCI Study Group, van den Berg, E., Biesbroek, J. M., Brundel, M., et al. (2020) · Brain Connectivity · doi:10.1089/brain.2019.0686 · Cited by 10 · see citing works
Virtual Region based Multi-robot Path Planning in an Unknown Occluded Environment
Roy, D., Chowdhury, A., Maitra, M., & Bhattacharya, S. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968177 · Cited by 9 · see citing works
Structural brain splitting is a hallmark of Granulin-related frontotemporal dementia
Gazzina, S., Grassi, M., Premi, E., Alberici, A., Benussi, A., Archetti, S., Gasparotti, R., Bocchetta, M., Cash, D. M., Todd, E. G., et al. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.02.009 · Cited by 8 · see citing works
Grey matter networks in women and men with dementia with Lewy bodies
Habich, A., Oltra, J., Schwarz, C. G., Przybelski, S. A., Oppedal, K., Inguanzo, A., Blanc, F., Lemstra, A. W., Hort, J., Westman, E., et al. (2024) · npj Parkinson s Disease · doi:10.1038/s41531-024-00702-5 · Cited by 8 · see citing works
Self-citationWhite Matter Network Alterations in Alzheimer’s Disease Patients
Lama, R. K., & Lee, S.-W. (2020) · Applied Sciences · doi:10.3390/app10030919 · Cited by 8 · see citing works
Structural network disruption of corticothalamic pathways in cerebral small vessel disease
Jia, X., Li, Y., Jia, X., & Yang, Q. (2024) · Brain Imaging and Behavior · doi:10.1007/s11682-024-00889-4 · Cited by 7 · see citing works
The effect of cognitive reserve on the cognitive connectome in healthy ageing
Habich, A., García-Cabello, E., Abbatantuono, C., Gonzalez‐Burgos, L., Taurisano, P., Dierks, T., Barroso, J., & Ferreira, D. (2024) · GeroScience · doi:10.1007/s11357-024-01328-4 · Cited by 7 · see citing works
Exploring Functional Brain Networks in Alzheimer’s Disease Using Resting State EEG Signals
Oikonomou, V. P., Georgiadis, K., Lazarou, I., Nikolopoulos, S., & Kompatsiaris, I. Y. (2025) · Journal of dementia and Alzheimer's disease · doi:10.3390/jdad2020012 · Cited by 7 · see citing works
Integrative Computational Framework for Understanding Metabolic Modulation in Leishmania
Chauhan, N., & Singh, S. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00336 · Cited by 7 · see citing works
Yang, Z., Chen, Y., Hou, X., Xu, Y., & Bai, F. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e15389 · Cited by 7 · see citing works
Irastorza-Valera, L., Gómez, E. S., Benítez, J. M., Montáns, F. J., & Saucedo‐Mora, L. (2024) · Biomimetics · doi:10.3390/biomimetics9060362 · Cited by 7 · see citing works
Irastorza-Valera, L., Benítez, J. M., Montáns, F. J., & Saucedo‐Mora, L. (2024) · Biomimetics · doi:10.3390/biomimetics9020101 · Cited by 4 · see citing works
MvHo-IB: Multi-view Higher-Order Information Bottleneck for Brain Disorder Diagnosis
Zhang, K., Li, Q., & Yu, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05182-0_40 · Cited by 3 · see citing works
Sharma, R., & Joshi, S. D. (2023) · Annals of Neurosciences · doi:10.1177/09727531231186503 · Cited by 3 · see citing works
Lazarou, I., Nikolopoulos, S., Dimitriadis, S. I., Kompatsiaris, I. Y., Spylioti, M., & Tsolaki, M. (2019) · doi:10.31219/osf.io/dtqeh · Cited by 3 · see citing works
Self-citationDisrupted structural connectivity in ArcAβ mouse model of Aβ amyloidosis
Al-Amin, M. M., Grandjean, J., Klohs, J., & Kim, J. (2020) · doi:10.1101/2020.04.27.064634 · Cited by 2 · see citing works
“A decreased local (Zhou and Lui, 2013) and global (Martensson et al., 2018) efficiency is a common characteristic of patients with AD.”
BackgroundLi, Z., Han, Y., & Jiang, J. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630421 · Cited by 2 · see citing works
Influence of individual’s age on the characteristics of brain effective connectivity
Nourzadegan, N., Baghernezhad, S., & Daliri, M. R. (2024) · GeroScience · doi:10.1007/s11357-024-01436-1 · Cited by 2 · see citing works
Patients with dementia with Lewy bodies display a signature alteration of their cognitive connectome
Yánez-Pérez, R., García-Cabello, E., Habich, A., Cedrés, N., Diaz‐Galvan, P., Ruiz, C. A., Toledo, J. B., Barroso, J., & Ferreira, D. (2025) · Scientific Reports · doi:10.1038/s41598-024-84946-4 · Cited by 2 · see citing works
Cognitive Analytics of Apple Products from Crowdsourced Tweets
Rasheed, A. A. (2021) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc51019.2021.9418302 · Cited by 2 · see citing works
Dyrba, M., Mohammadi, R., Grothe, M. J., Kirste, T., & Teipel, S. J. (2018) · arXiv (Cornell University) · Cited by 2 · see citing works
Yánez-Pérez, R., Habich, A., Toledo, J. B., Barroso, J., & Ferreira, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-24157-7 · Cited by 1 · see citing works
Cognitive Test Scores and Progressive Cognitive Decline in the Aberdeen 1921 and 1936 Birth Cohorts
Whalley, L. J., Staff, R. T., Lemmon, H. A., Fox, H. C., McNeil, C. J., & Murray, A. D. (2022) · Brain Sciences · doi:10.3390/brainsci12030318 · Cited by 1 · see citing works
Riedmann, U., Fink, A., Weber, B., & Koschutnig, K. (2022) · Brain Sciences · doi:10.3390/brainsci12081092 · Cited by 1 · see citing works
Zhao, J., Duan, Y., Zhao, K., Li, H., Wang, D., Yao, H., Zhou, B., Lu, J., Wang, P., Chen, Y., et al. (2025) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.70428 · Cited by 1 · see citing works
Sex differences in grey matter networks in dementia with Lewy bodiesPreprint
Habich, A., Oltra, J., Schwarz, C. G., Przybelski, S. A., Oppedal, K., Inguanzo, A., Blanc, F., Lemstra, A. W., Hort, J., Westman, E., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2519935/v1 · Cited by 1 · see citing works
Self-citationIrastorza-Valera, L., Benítez, J. M., Montáns, F. J., & Saucedo‐Mora, L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.10.566538 · Cited by 1 · see citing works
Varela, L. I., Benítez, J. M., Montáns, F. J., & Saucedo‐Mora, L. (2023) · Preprints.org · doi:10.20944/preprints202312.0489.v1 · Cited by 1 · see citing works
Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models
Legramanti, S., Rigon, T., & Durante, D. (2020) · Sankhya A · doi:10.1007/s13171-020-00231-2
Evaluation of Metabolic Network for Alzheimer's Disease
Bu, X., Yao, X., Lv, Y., Xu, S., & Huang, G. (2019) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai48974.2019.9010574
The Cognitive Connectome in Dementia with Lewy BodiesPreprint
Yánez-Pérez, R., García-Cabello, E., Habich, A., Cedrés, N., Diaz‐Galvan, P., Ruiz, C. A., Toledo, J. B., Barroso, J., & Ferreira, D. (2023) · medRxiv · doi:10.1101/2023.11.14.23298515
Safai, A., Jonaitis, E. M., Langhough, R. E., Buckingham, W. R., Johnson, S. C., Powell, W. R., Kind, A., Bendlin, B. B., & Tiwari, P. (2024) · PubMed · doi:10.48550/arxiv.2406.13822
Khatri, U., Kim, J., & Kwon, G. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113693
Diogo, V. S., Ferreira, H. A., & Prata, D. P. (2022) · Figshare · doi:10.6084/m9.figshare.20430647
Liang, J., Gao, Y., Li, Y., Yuan, M., Yan, F.-X., Qu, S., Liu, L., & Ma, X. Y. (2026) · Research Square · doi:10.21203/rs.3.rs-9862612/v1
Topology-constrained graph transformer network for structural and functional brain organization
Ji, J., Liu, M., Chen, N., Sun, D., & Qiu, A. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104250
- Authors
- Mårtensson, G., Pereira, J. B., Mecocci, P., Vellas, B., Tsolaki, M., Kłoszewska, I., Soininen, H., Lovestone, S., Simmons, A., Volpe, G., & Westman, E.
- Affiliations
- Karolinska Institutet (SE)
- University of Perugia (IT)
- Inserm (FR)
- Aristotle University of Thessaloniki (GR)
- Medical University of Lodz (PL)
- University of Eastern Finland (FI)
- Warneford Hospital (GB)
- King's College London (GB)
- University of Gothenburg (SE)
- Corresponding author
- Mårtensson, Gustav
- ORCID
- Venue
- Scientific Reports
- Published
- 2018-08-02
- Volume
- 8
- Issue
- 1
- Pages
- 11592-11592
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6072788
- OpenAlex
- W2883196872
- Cited by
- 60
- References
- 75
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 9 · 2023: 7 · 2022: 13 · 2021: 5 · 2020: 8 · 2019: 7 · 2018: 1
- FWCI
- 3.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, other-oa)
- UEF eRepo (University of Eastern Finland) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH-12, W81XWH1220012, AG024904)
- Foundation for the National Institutes of Health (W81XWH-12-2-0012, U01AG024904, Grant U01 AG024904, AG024904)
- Alzheimer's Association (U01 AG024904, W81XWH-12-2-0012)
- Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Pfizer (AG024904, W81XWH-12-2-0012, U01 AG024904)
- Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
- University of Southern California (W81XWH-12-2-0012, U01 AG024904)
- F. Hoffmann-La Roche (W81XWH-12-2-0012, AG024904, U01 AG024904)
- Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Alzheimer's Disease Neuroimaging Initiative (W81XWH-12-2–0012, U01AG024904, AG024904, W81XWH-12-2-, Department of Defense award number W81XWH-12-2-0012)
- Åke Wiberg Stiftelse
- BioClinica (U01 AG024904, W81XWH-12-2-0012, AG024904)
- Novartis Pharmaceuticals Corporation (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Northern California Institute for Research and Education (U01 AG024904, W81XWH-12-2-0012)
- Stiftelsen för Strategisk Forskning
- Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Hjärnfonden
- Karolinska Institutet
- Stockholms Läns Landsting
- Vetenskapsrådet
- Swedish Brain Power
- Alzheimerfonden
- Servier (AG024904, W81XWH-12-2-0012, U01 AG024904)
- H. Lundbeck A/S (U01 AG024904, AG024904, W81XWH-12-2-0012)
- IXICO (AG024904, W81XWH-12-2-0012, U01 AG024904)
- National Institutes of Health (3u01ag024904-10s2, W81XWH, W81XWH-12-2-0012, U01-AG024904, Grant U01 AG024904, AG024904)
- Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
- Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012)
- National Institute on Aging (U01-AG-024904, W81XWH-12-2–0012, AG 024904)
- National Institute of Biomedical Imaging and Bioengineering (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- Alzheimer's disease, structural brain networks, graph theory, network stability, clustering coefficient, path length, global efficiency, transitivity, weighted networks, binary networks, brain atlas, cortical thickness, cortical volume, sample size, group composition, subsampling
- MeSH terms
- Aged, Alzheimer Disease, Cerebral Cortex, Female, Humans, Male, Models, Neurological, Nerve Net
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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