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Alzheimer's disease: connecting findings from graph theoretical studies of brain networks
Abstract
The interrelationships between pathological processes and emerging clinical phenotypes in Alzheimer's disease (AD) are important yet complicated to study, because the brain is a complex network where local disruptions can have widespread effects. Recently, properties in brain networks obtained with neuroimaging techniques have been studied in AD with tools from graph theory. However, the interpretation of graph alterations remains unclear, because the definition of connectivity depends on the imaging modality used. Here we examined which graph properties have been consistently reported to be disturbed in AD studies, using a heuristically defined "graph space" to investigate which theoretical models can best explain graph alterations in AD. Findings from structural and functional graphs point to a loss of highly connected areas in AD. However, studies showed considerable variability in reported group differences of most graph properties. This suggests that brain graphs might not be isometric, which complicates the interpretation of graph measurements. We highlight confounding factors such as differences in graph construction methods and provide recommendations for future research.
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“Inconsistencies in connectome findings are a well-known issue in the literature (for reviews, see Dai & He, 2014; Tijms, Wink et al., 2013) and often reflect differences in methodology such as imaging modality and/or choice of thresholding method.”
From Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease (2018) · cites this paper as Tijms, Wink et al., 2013
Citations
Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
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
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123 · cited 1×
“At later stages of the disease, cortical atrophy progresses in an orderly fashion from subcortical structures such as the hippocampus into associative cortical areas and finally primary sensory areas (Braak and Braak, 1991; Jack et al., 2010).”
BackgroundComplex 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
A set of measures of centrality based upon betweenness
Freeman, L. (1977) · Sociometry · doi:10.2307/3033543 · Cited by 10,557
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980
Cited by
Sorted by citations
Items 101–200 of 416
Sedeño, L., Piguet, O., Abrevaya, S., Desmaras, H., García‐Cordero, I., Baez, S., Alethia de la Fuente, L., Reyes, P., Tu, S., Moguilner, S., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23627 · Cited by 55
Microstate connectivity alterations in patients with early Alzheimer’s disease
Hatz, F., Hardmeier, M., Benz, N., Ehrensperger, M. M., Gschwandtner, U., Rüegg, S. J., Schindler, C., Monsch, A. U., & Fuhr, P. (2015) · Alzheimer s Research & Therapy · doi:10.1186/s13195-015-0163-9 · Cited by 55
Wei, R., Li, C., Fogelson, N., & Li, L. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00076 · Cited by 51
van Straaten, E. C. W., Scheltens, P., Gouw, A. A., & Stam, C. J. (2014) · Alzheimer s Research & Therapy · doi:10.1186/s13195-014-0086-x · Cited by 52
Self-citationGraph 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
Verfaillie, S. C. J., Slot, R. E. R., Dicks, E., Prins, N. D., Overbeek, J. M., Teunissen, C. E., Scheltens, P., Barkhof, F., van der Flier, W. M., & Tijms, B. M. (2018) · Human Brain Mapping · doi:10.1002/hbm.24065 · Cited by 50
Self-citationYu, K., Wang, X., Li, Q., Zhang, X., Li, X., & Li, S. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00204 · Cited by 51
Janssen, T. W. P., Hillebrand, A., Gouw, A. A., Geladé, K., van Mourik, R., Maras, A., & Oosterlaan, J. (2017) · Clinical Neurophysiology · doi:10.1016/j.clinph.2017.09.004 · Cited by 54
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 52
Self-citationSmit, D. J. A., de Geus, E. J. C., Boersma, M., Boomsma, D. I., & Stam, C. J. (2016) · Brain Connectivity · doi:10.1089/brain.2015.0359 · Cited by 55
Self-citationEEG/MEG- and imaging-based diagnosis of Alzheimer’s disease
Hulbert, S., & Adeli, H. (2013) · Reviews in the Neurosciences · doi:10.1515/revneuro-2013-0042 · Cited by 50
McCarthy, P., Benuskova, L., & Franz, E. A. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00301 · Cited by 48
Hub overload and failure as a final common pathway in neurological brain network disorders
Stam, C. J. (2023) · Network Neuroscience · doi:10.1162/netn_a_00339 · Cited by 52
Self-citationSchmidt, B., Ghuman, A. S., & Huppert, T. J. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00141 · Cited by 52
Maudsley, S. R., Devanarayan, V., Martin, B., Geerts, H., & (BHMI), O. B. O. T. B. H. M. I. (2018) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2018.01.014 · Cited by 48
The road ahead in clinical network neuroscience
Douw, L., van Dellen, E., Gouw, A. A., Griffa, A., de Haan, W., van den Heuvel, M. P., Hillebrand, A., Mieghem, P. V., Nissen, I. A., Otte, W. M., et al. (2019) · Network Neuroscience · doi:10.1162/netn_a_00103 · Cited by 48
Self-citationStefanovski, L., Meier, J. M., Pai, R. K., Triebkorn, P., Lett, T. A. P., Martin, L., Bülau, K., Hofmann‐Apitius, M., Solodkin, A., McIntosh, A. R., et al. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.630172 · Cited by 47
Magnetoencephalography as a Tool in Psychiatric Research: Current Status and Perspective
Uhlhaas, P. J., Liddle, P. F., Linden, D. E. J., Nobre, A. C., Singh, K. D., & Groß, J. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.01.005 · Cited by 46
Cai, L., Wei, X., Wang, J., Yu, H., Deng, B., & Wang, R. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.07.019 · Cited by 47
Alderson, T. H., Bokde, A. L. W., Kelso, J. A. S., Maguire, L., & Coyle, D. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.033 · Cited by 46
Zilidou, V. I., Frantzidιs, C. A., Romanopoulou, E. D., Paraskevopoulos, E., Douka, S., & Bamidis, P. D. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00422 · Cited by 46
Aydın, S., & Onbaşı, L. (2024) · Cognitive Neurodynamics · doi:10.1007/s11571-023-09931-5 · Cited by 46
Miraglia, F., Vecchio, F., Pappalettera, C., Nucci, L., Cotelli, M., Judica, E., Ferreri, F., & Rossini, P. M. (2022) · Brain Sciences · doi:10.3390/brainsci12030402 · Cited by 49
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
Smith, K., Abásolo, D., & Escudero, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0186164 · Cited by 46
Aydın, S. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09843-w · Cited by 46
Blomsma, N., de Rooy, B., Gerritse, F. L., van der Spek, R., Tewarie, P. K. B., Hillebrand, A., Otte, W. M., Stam, C. J., & van Dellen, E. (2022) · Network Neuroscience · doi:10.1162/netn_a_00245 · Cited by 44
Self-citationPapma, J. M., Smits, M., de Groot, M., Mattace-Raso, F. U. S., van der Lugt, A., Vrooman, H. A., Niessen, W. J., Koudstaal, P. J., Swieten, J. C. V., van der Veen, F. M., et al. (2017) · European Radiology · doi:10.1007/s00330-017-4768-1 · Cited by 39
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
Self-citationYoussef, N., Xiao, S., Liu, M., Lian, H., Li, R., Chen, X., Zhang, W., Zheng, X., Li, Y., & Li, Y. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.698386 · Cited by 44
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Amoroso, N., Rocca, M. L., Bruno, S., Maggipinto, T., Monaco, A., Bellotti, R., & Tangaro, S. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00365 · Cited by 59
Sun, Y., Bi, Q., Wang, X., Hu, X., Li, H., Li, X., Ma, T., Lu, J., Chan, P., Shu, N., et al. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2018.01178 · Cited by 40
Minati, L., Chan, D., Mastropasqua, C., Serra, L., Spanò, B., Marra, C., Caltagirone, C., Cercignani, M., & Bozzali, M. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-131766 · Cited by 39
Epicentral Disruption of Structural Connectivity in Alzheimer's Disease
Mallio, C. A., Schmidt, R., de Reus, M. A., Vernieri, F., Quintiliani, L., Curcio, G., Beomonte Zobel, B., Quattrocchi, C. C., & van den Heuvel, M. P. (2015) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.12397 · Cited by 39
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López, M. E., Engels, M. M. A., van Straaten, E. C. W., Bajo, R., Delgado‐Losada, M. L., Scheltens, P., Hillebrand, A., Stam, C. J., & Maestú, F. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00107 · Cited by 39
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Han, J., Zeng, K., Kang, J., Tong, Z., Cai, E., Chen, H., Ding, M., Gu, Y., Ouyang, G., & Li, X. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.10.015 · Cited by 40
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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
Contreras, J. A., Aslanyan, V., Sweeney, M. D., Sanders, L. M. J., Sagare, A. P., Zloković, B. V., Toga, A. W., Han, S. D., Morris, J. C., Fagan, A. M., et al. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.10.013 · Cited by 42
A clustering coefficient for complete weighted networks
McAssey, M. P., & Bijma, F. (2015) · Network Science · doi:10.1017/nws.2014.26 · Cited by 37
Altered structural network organization in cognitively normal individuals with amyloid pathology
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 41
Lin, S.-Y., Lin, C., Hsieh, T.-J., Lin, C.-F., Chen, S.-H., Chao, Y., Chen, Y., Hsu, C.-C., & Kuo, L. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101680 · Cited by 37
Kucikova, L., Goerdten, J., Dounavi, M., Mak, E., Su, L., Waldman, A. D., Danso, S., Terrera, G. M., & Ritchie, C. W. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.07.024 · Cited by 42
EEG Functional Connectivity Underlying Emotional Valance and Arousal Using Minimum Spanning Trees
Cao, R., Hao, Y., Wang, X., Gao, Y., Shi, H., Huo, S., Wang, B., Guo, H., & Xiang, J. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00355 · Cited by 35
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Bahrami, N., Seibert, T. M., Karunamuni, R. A., Bartsch, H., Krishnan, A. P., Farid, N., Hattangadi-Gluth, J. A., & McDonald, C. R. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0494 · Cited by 36
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Niu, R., Lei, D., Chen, F., Chen, Y., Suo, X., Li, L., Lui, S., Huang, X., Sweeney, J. A., & Gong, Q. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.03.030 · Cited by 35
Schmieder, F., Klapper, S. D., Koukourakis, N., Busskamp, V., & Czarske, J. W. (2018) · Applied Sciences · doi:10.3390/app8071180 · Cited by 35
Jacini, F., Sorrentino, P., Lardone, A., Rucco, R., Baselice, F., Cavaliere, C., Aiello, M., Orsini, M., Iavarone, A., Manzo, V., et al. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00400 · Cited by 33
Synchronization dependent on spatial structures of a mesoscopic whole-brain network
Choi, H., & Mihalaş, Ş. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006978 · Cited by 36
Fodor, Z., Horvath, A. A., Hidasi, Z., Gouw, A. A., Stam, C. J., & Csukly, G. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.680200 · Cited by 37
Self-citationKesler, S. R., Acton, P., Rao, V., & Ray, W. J. (2018) · Network Neuroscience · doi:10.1162/netn_a_00048 · Cited by 34
Effects of Amyloid and Small Vessel Disease on White Matter Network Disruption
Kim, H. J., Im, K., Kwon, H., Lee, J. M., Ye, B. S., Kim, Y. J., Cho, H., Choe, Y. S., Lee, K. H., Kim, S. T., et al. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-141623 · Cited by 35
Sone, 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
A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework
Dragomir, A., Vrahatis, A. G., & Bezerianos, A. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2863202 · Cited by 34
Welton, T., Constantinescu, C. S., Auer, D. P., & Dineen, R. A. (2020) · Brain Connectivity · doi:10.1089/brain.2019.0717 · Cited by 32
Luo, Y., Sun, T., Ma, C., Zhang, X., Ji, Y., Fu, X., & Ni, H. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.10.023 · Cited by 32
Non-monotonic reorganization of brain networks with Alzheimer's disease progression
Kim, H., Yoo, K., Na, D. L., Seo, S. W., Jeong, J., & Jeong, Y. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00111 · Cited by 32
Functional network topology associated with apathy in Alzheimer's disease
Tumati, S., Marsman, J.-B. C., Deyn, P. P. D., Martens, S., & Aleman, A. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.01.158 · Cited by 31
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Yu, H., Zhu, L., Cai, L., Wang, J., Liu, J., Wang, R., & Zhang, Z. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00641 · Cited by 31
Dong, S.-Y., Choi, J., Park, Y., Baik, S. Y., Jung, M., Kim, Y., & Lee, S. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.659814 · Cited by 31
An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease
Amoroso, N., Quarto, S., Rocca, M. L., Tangaro, S., Monaco, A., & Bellotti, R. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1238065 · Cited by 33
Smith, K., Azami, H., Parra, M. A., Starr, J. M., & Escudero, J. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318983 · Cited by 30
Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s disease
Guillon, J., Chávez, M., Battiston, F., Attal, Y., Corte, V. L., de Schotten, M. T., Dubois, B., Schwartz, D., Olivier Colliot, A., & Fallani, F. D. V. (2019) · Network Neuroscience · doi:10.1162/netn_a_00087 · Cited by 33
Zhang, L., Ni, H., Yu, Z., Wang, J., Qin, J., Hou, F., Yang, A. C., & Initiative, A. D. N. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.558434 · Cited by 28
Rosales Jubal, E., Schwalm, M., dos Santos Guilherme, M., Schuck, F., Reinhardt, S., Tose, A., Barger, Z., Roesler, M. K., Ruffini, N., Wierczeiko, A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-85912-0 · Cited by 30
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Wright, L. M., Marco, M. D., & Venneri, A. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.676618 · Cited by 32
Sporns, O. (2020) · doi:10.1093/med/9780198713005.003.0013 · Cited by 29
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Aydın, S. (2024) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-024-00917-0 · Cited by 32
The complex hierarchical topology of EEG functional connectivity
Smith, K., & Escudero, J. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.11.003 · Cited by 29
Wang, Z., Jie, B., Feng, C., Wang, T., Bian, W., Ding, X., Zhou, W. J., & Liu, M. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3107305 · Cited by 30
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Fei, F., Jie, B., & Zhang, D. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0214 · Cited by 28
Abazid, M., Houmani, N., Boudy, J., Dorizzi, B., Mariani, J., & Kinugawa, K. (2021) · Entropy · doi:10.3390/e23111553 · Cited by 28
A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer’s Disease Data
Kundu, S., Lukemire, J., Wang, Y., Guo, Y., Initiative, T. A. D. N., Weiner, M. W., Schuff, N. W., Rosen, H. J., Miller, B. L., Neylan, T. C., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-55818-z · Cited by 25
Functional Reorganizations of Brain Network in Prelingually Deaf Adolescents
Li, W., Li, J., Wang, J., Zhou, P., Wang, Z., Xian, J., & He, H. (2015) · Neural Plasticity · doi:10.1155/2016/9849087 · Cited by 27
Reduced local segregation of single‐subject gray matter networks in adult PTSD
Niu, R., Lei, D., Chen, F., Chen, Y., Suo, X., Li, L., Lui, S., Huang, X., Sweeney, J. A., & Gong, Q. (2018) · Human Brain Mapping · doi:10.1002/hbm.24330 · Cited by 26
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Hebbink, J., van Blooijs, D., Huiskamp, G. J. M., Leijten, F. S. S., van Gils, S. A., & Meijer, H. G. E. (2018) · Brain Topography · doi:10.1007/s10548-018-0692-1 · Cited by 27
Xiang, J., Xue, J., Guo, H., Li, D., Cui, X., Niu, Y., Yan, T., Cao, R., Ma, Y., Yang, Y., et al. (2019) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00042-6 · Cited by 28
Divergent topological networks in Alzheimer’s disease: a diffusion kurtosis imaging analysis
Cheng, J.-X., Zhang, H., Peng, Z.-K., Xu, Y. Q., Tang, H., Wu, J., & Xu, J. L. (2018) · Translational Neurodegeneration · doi:10.1186/s40035-018-0115-y · Cited by 27
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Zhang, Z., Ding, J., Xu, J., Tang, J., & Guo, F. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2983456 · Cited by 26
Zamani, J., Sadr, A., & Javadi, A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0267608 · Cited by 26
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Gong, D., Ma, W., Gong, J., He, H., Dong, L., Zhang, D., Li, J., Luo, C., & Yao, D. (2017) · Neural Plasticity · doi:10.1155/2017/7543686 · Cited by 26
Yu, H., Lei, X., Song, Z., Wang, J., Wei, X., & Yu, B. (2018) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2018.05.009 · Cited by 27
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Kabbara, A., Paban, V., & Hassan, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117674 · Cited by 27
Simfukwe, C., Han, S., Jeong, H. T., & Youn, Y. C. (2023) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s433207 · Cited by 26
Lei, D., Ma, J., Zhang, J., Wang, M., Zhang, K., Chen, F., Suo, X., Gong, Q., & Du, X. (2015) · BioMed Research International · doi:10.1155/2015/463708 · Cited by 27
Rossini, P. M., Iorio, R. D., Granata, G., Miraglia, F., & Vecchio, F. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160482 · Cited by 25
Structural connectivity centrality changes mark the path toward Alzheimer's disease
Peraza, L. R., Díaz‐Parra, A., Kennion, O., Moratal, D., Taylor, J., Kaiser, M., Bauer, R., & Initiative, A. D. N. (2019) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.12.004 · Cited by 24
Požar, R., Giordani, B., & Kavcic, V. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230099 · Cited by 25
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Babiloni, C., Arakaki, X., Báez, S., Barry, R. J., Benussi, A., Blinowska, K. J., Bonanni, L., Borroni, B., Bosch‐Bayard, J., Bruno, G., et al. (2025) · Clinical Neurophysiology · doi:10.1016/j.clinph.2025.02.256 · Cited by 27
Wang, X., Cui, X., Ding, C., Li, D., Chen, C., Wang, B., & Xiang, J. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27453 · Cited by 24
Lei, H., Hu, R., Luo, G., Yang, T., Shen, H., Deng, H., Chen, C., Zhao, H., & Liu, J. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.755017 · Cited by 24
Fu, Z., Batta, I., Wu, L., Abrol, A., Agcaoglu, O., Salman, M. S., Du, Y., Iraji, A., Shultz, S., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., et al. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120617 · Cited by 26
Functional brain network centrality is related to APOE genotype in cognitively normal elderly
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Self-citationHigh-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases
Hendrickx, J. O., van Gastel, J., Leysen, H., Martin, B., & Maudsley, S. R. (2019) · Pharmacological Reviews · doi:10.1124/pr.119.017921 · Cited by 25
Mijalkov, M., Volpe, G., & Pereira, J. B. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab237 · Cited by 26
Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis
Peña, D. A. C., Collazos-Huertas, D. F., & Castellanos-Dominguez, G. (2016) · Computational and Mathematical Methods in Medicine · doi:10.1155/2016/9523849 · Cited by 23
Dicks, E., van der Flier, W. M., Scheltens, P., Barkhof, F., & Tijms, B. M. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.05.008 · Cited by 25
Self-citationA statistical model for brain networks inferred from large-scale electrophysiological signals
Obando, C., & Fallani, F. D. V. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2016.0940 · Cited by 26
Millan, M. J., Goodwin, G. M., Hamon, M., Meyer‐Lindenberg, A., & Ögren, S. O. (2015) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2015.01.015 · Cited by 23
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Dachena, C., Casu, S., Fanti, A., Lodi, M. B., & Mazzarella, G. (2019) · Applied Sciences · doi:10.3390/app9153156 · Cited by 22
Thumuluri, D., Lyday, R. G., Babcock, P., Ip, E. H., Kraft, R., Laurienti, P. J., Barnstaple, R., Soriano, C. T., & Hugenschmidt, C. E. (2022) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2021.796101 · Cited by 24
Metadata
- Authors
- Tijms, B. M., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., & Barkhof, F.
- Affiliations
- Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
- Corresponding author
- Tijms, Betty M.
- ORCID
- Venue
- Neurobiology of Aging
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 34
- Issue
- 8
- Pages
- 2023-2036
- ISSN
- 0197-4580 (ISSN-L)
- OpenAlex
- W2087938247
- Cited by
- 416
- References
- 135
- Citations by year
- 2026: 17 · 2025: 23 · 2024: 29 · 2023: 18 · 2022: 27 · 2021: 39 · 2020: 33 · 2019: 30 · 2018: 49 · 2017: 41 · 2016: 38 · 2015: 36 · 2014: 30 · 2013: 5
- FWCI
- 3.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurobiology of Aging (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Alzheimer Nederland and Stichting VUmc fonds
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- Alzheimer's disease, brain networks, graph theory, structural connectivity, functional connectivity, graph space, graph construction, network topology, clinical phenotype
- MeSH terms
- Alzheimer Disease, Brain, Electroencephalography, Humans, Magnetic Resonance Imaging, Models, Theoretical, Nerve Net, Magnetoencephalography, Neuroimaging
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