{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T00:04:10Z","timestamp":1771027450447,"version":"3.50.1"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health grants","doi-asserted-by":"crossref","award":["EB022880"],"award-info":[{"award-number":["EB022880"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health grants","doi-asserted-by":"crossref","award":["AG041721"],"award-info":[{"award-number":["AG041721"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health grants","doi-asserted-by":"crossref","award":["AG049371"],"award-info":[{"award-number":["AG049371"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health grants","doi-asserted-by":"crossref","award":["AG042599"],"award-info":[{"award-number":["AG042599"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6163301"],"award-info":[{"award-number":["6163301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["3137100"],"award-info":[{"award-number":["3137100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s12021-021-09554-3","type":"journal-article","created":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T17:02:41Z","timestamp":1637946161000},"page":"391-403","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Altered Connectedness of the Brain Chronnectome During the Progression to Alzheimer\u2019s Disease"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2131-7122","authenticated-orcid":false,"given":"Maryam","family":"Ghanbari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-Ming","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pew-Thian","family":"Yap","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,26]]},"reference":[{"issue":"1","key":"9554_CR1","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1523\/JNEUROSCI.3874-05.2006","volume":"26","author":"S Achard","year":"2006","unstructured":"Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. Journal of Neurosci, 26(1), 63\u201372.","journal-title":"Journal of Neurosci"},{"issue":"3","key":"9554_CR2","doi-asserted-by":"publisher","first-page":"187","DOI":"10.3233\/JAD-2005-7301","volume":"7","author":"H Adeli","year":"2005","unstructured":"Adeli, H., Ghosh-Dastidar, S., & Dadmehr, N. (2005a). Alzheimer\u2019s disease and models of computation: Imaging, classification, and neural models. Journal of Alzheimer\u2019s Disease, 7(3), 187\u2013199.","journal-title":"Journal of Alzheimer\u2019s Disease"},{"issue":"3","key":"9554_CR3","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1177\/155005940503600303","volume":"36","author":"H Adeli","year":"2005","unstructured":"Adeli, H., Ghosh-Dastidar, S., & Dadmehr, N. (2005b). Alzheimer\u2019s disease: Models of computation and analysis of EEGs. Clinical EEG and Neurosci, 36(3), 131\u2013140.","journal-title":"Clinical EEG and Neurosci"},{"issue":"1","key":"9554_CR4","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1038\/nrn.2017.149","volume":"19","author":"A Avena-Koenigsberger","year":"2018","unstructured":"Avena-Koenigsberger, A., Misic, B., & Sporns, O. (2018). Communication dynamics in complex brain networks. Nature Reviews Neuroscience, 19(1), 17.","journal-title":"Nature Reviews Neuroscience"},{"issue":"10","key":"9554_CR5","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1016\/j.tics.2013.08.012","volume":"17","author":"D Barulli","year":"2013","unstructured":"Barulli, D., & Stern, Y. (2013). Efficiency, capacity, compensation, maintenance, plasticity: Emerging concepts in cognitive reserve. Trends in Cognitive Sciences, 17(10), 502\u2013509.","journal-title":"Trends in Cognitive Sciences"},{"issue":"9","key":"9554_CR6","doi-asserted-by":"publisher","first-page":"2018","DOI":"10.1016\/j.neurobiolaging.2011.07.003","volume":"33","author":"MA Binnewijzend","year":"2012","unstructured":"Binnewijzend, M. A., Schoonheim, M. M., Sanz-Arigita, E., Wink, A. M., van der Flier, W. M., Tolboom, N., Adriaanse, S. M., Damoiseaux, J. S., Scheltens, P., van Berckel, B. N., et al. (2012). Resting-state fMRI changes in alzheimer\u2019s disease and mild cognitive impairment. Neurobiology of Aging, 33(9), 2018\u20132028.","journal-title":"Neurobiology of Aging"},{"issue":"3","key":"9554_CR7","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1038\/nrn2575","volume":"10","author":"E Bullmore","year":"2009","unstructured":"Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neurosci, 10(3), 186.","journal-title":"Nature Reviews Neurosci"},{"issue":"11","key":"9554_CR8","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1038\/s41583-018-0068-2","volume":"19","author":"R Cabeza","year":"2018","unstructured":"Cabeza, R., Albert, M., Belleville, S., Craik, F. I., Duarte, A., Grady, C. L., Lindenberger, U., Nyberg, L., Park, D. C., Reuter-Lorenz, P. A., & Rugg, M. D. (2018). Maintenance, reserve and compensation: The cognitive neuroscience of healthy ageing. Nature Reviews Neuroscience, 19(11), 701\u2013710.","journal-title":"Nature Reviews Neuroscience"},{"issue":"1","key":"9554_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s42003-021-02478-3","volume":"4","author":"AD Cascone","year":"2021","unstructured":"Cascone, A. D., Langella, S., Sklerov, M., & Dayan, E. (2021). Frontoparietal network resilience is associated with protection against cognitive decline in Parkinson\u2019s disease. Communications Biology, 4(1), 1\u201310.","journal-title":"Communications Biology"},{"issue":"1","key":"9554_CR10","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.neuroimage.2009.12.011","volume":"50","author":"C Chang","year":"2010","unstructured":"Chang, C., & Glover, G. H. (2010). Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage, 50(1), 81\u201398.","journal-title":"NeuroImage"},{"issue":"06","key":"9554_CR11","doi-asserted-by":"publisher","first-page":"1677","DOI":"10.1142\/S0218127410026757","volume":"20","author":"M Chavez","year":"2010","unstructured":"Chavez, M., Valencia, M., Latora, V., & Martinerie, J. (2010). Complex networks: New trends for the analysis of brain connectivity. International J of Bifurcation and Chaos, 20(06), 1677\u20131686.","journal-title":"International J of Bifurcation and Chaos"},{"issue":"1","key":"9554_CR12","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/s12883-016-0587-2","volume":"16","author":"GQ Chen","year":"2016","unstructured":"Chen, G. Q., Sheng, C., Li, Y. X., Yu, Y., Wang, X. N., Sun, Y., Li, H. Y., Li, X. Y., Xie, Y. Y., & Han, Y. (2016). Neuroimaging basis in the conversion of aMCI patients with apoe-\u03b54 to ad: Study protocol of a prospective diagnostic trial. BMC Neurology, 16(1), 64.","journal-title":"BMC Neurology"},{"key":"9554_CR13","doi-asserted-by":"crossref","unstructured":"Corson, F. (2010). Fluctuations and redundancy in optimal transport networks. Physical Review Letters, 104(4), 048703.","DOI":"10.1103\/PhysRevLett.104.048703"},{"issue":"3","key":"9554_CR14","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1006\/cbmr.1996.0014","volume":"29","author":"RW Cox","year":"1996","unstructured":"Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162\u2013173.","journal-title":"Computers and Biomedical Research"},{"issue":"4","key":"9554_CR15","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1016\/j.neuroimage.2012.03.070","volume":"61","author":"I Cribben","year":"2012","unstructured":"Cribben, I., Haraldsdottir, R., Atlas, L. Y., Wager, T. D., & Lindquist, M. A. (2012). Dynamic connectivity regression: Determining state-related changes in brain connectivity. NeuroImage, 61(4), 907\u2013920.","journal-title":"NeuroImage"},{"key":"9554_CR16","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.neurobiolaging.2018.11.005","volume":"75","author":"Z Dai","year":"2019","unstructured":"Dai, Z., Lin, Q., Li, T., Wang, X., Yuan, H., Yu, X., He, Y., & Wang, H. (2019). Disrupted structural and functional brain networks in alzheimer\u2019s disease. Neurobiology of Aging, 75, 71\u201382.","journal-title":"Neurobiology of Aging"},{"key":"9554_CR17","doi-asserted-by":"crossref","unstructured":"Damaraju, E., Allen, E. A., Belger, A., Ford, J. M., McEwen, S., Mathalon, D., Mueller, B., Pearlson, G., Potkin, S., Preda A, et al. (2014). Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. NeuroImage: Clinical, 5, 298\u2013308.","DOI":"10.1016\/j.nicl.2014.07.003"},{"issue":"8","key":"9554_CR18","doi-asserted-by":"publisher","first-page":"2918","DOI":"10.1002\/hbm.23215","volume":"37","author":"M Demirtas","year":"2016","unstructured":"Demirtas, M., Tornador, C., Falcon, C., Lopez-Sola, M., Hernandez-Ribas, R., Pujol, J., Menchon, J. M., Ritter, P., Cardoner, N., Soriano-Mas, C., et al. (2016). Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder. Human Brain Mapping, 37(8), 2918\u20132930.","journal-title":"Human Brain Mapping"},{"issue":"1","key":"9554_CR19","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s11065-014-9249-6","volume":"24","author":"EL Dennis","year":"2014","unstructured":"Dennis, E. L., & Thompson, P. M. (2014). Functional brain connectivity using fMRI in aging and alzheimer\u2019s disease. Neuropsychology Review, 24(1), 49\u201362.","journal-title":"Neuropsychology Review"},{"key":"9554_CR20","doi-asserted-by":"crossref","unstructured":"Di Lanzo, C., Marzetti, L., Zappasodi, F., De Vico Fallani, F., Pizzella, V. (2012). Redundancy as a graph-based index of frequency specific meg functional connectivity. Computational and mathematical methods in medicine 2012.","DOI":"10.1155\/2012\/207305"},{"issue":"5","key":"9554_CR21","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1016\/j.jalz.2018.12.009","volume":"15","author":"EC Edmonds","year":"2019","unstructured":"Edmonds, E. C., McDonald, C. R., Marshall, A., Thomas, K. R., Eppig, J., Weigand, A. J., Delano-Wood, L., Galasko, D. R., Salmon, D. P., Bondi, M. W., et al. (2019). Early versus late MCI\u202f: Improved MCI staging using a neuropsychological approach. Alzheim & Dem, 15(5), 699\u2013708.","journal-title":"Alzheim & Dem"},{"issue":"4","key":"9554_CR22","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1007\/s10548-010-0152-z","volume":"23","author":"FDV Fallani","year":"2011","unstructured":"Fallani, F. D. V., Rodrigues, F. A., da Fontoura, C. L., Astolfi, L., Cincotti, F., Mattia, D., Salinari, S., & Babiloni, F. (2011). Multiple pathways analysis of brain functional networks from EEG signals: An application to real data. Brain Topography, 23(4), 344\u2013354.","journal-title":"Brain Topography"},{"issue":"9518","key":"9554_CR23","doi-asserted-by":"publisher","first-page":"1262","DOI":"10.1016\/S0140-6736(06)68542-5","volume":"367","author":"S Gauthier","year":"2006","unstructured":"Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R. C., Ritchie, K., Broich, K., Belleville, S., Brodaty, H., Bennett, D., Chertkow, H., et al. (2006). Mild cognitive impairment. The Lancet, 367(9518), 1262\u20131270.","journal-title":"The Lancet"},{"key":"9554_CR24","doi-asserted-by":"crossref","unstructured":"H\u00e4rkeg\u00e5rd, O., Glad, S. T. (2005). Resolving actuator redundancy\u2014optimal control vs. control allocation. Automatica, 41(1), 137\u2013144.","DOI":"10.1016\/j.automatica.2004.09.007"},{"issue":"12","key":"9554_CR25","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1016\/j.tics.2013.09.012","volume":"17","author":"MP van den Heuvel","year":"2013","unstructured":"van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17(12), 683\u2013696.","journal-title":"Trends in Cognitive Sciences"},{"key":"9554_CR26","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.jneumeth.2017.03.006","volume":"282","author":"SH Hojjati","year":"2017","unstructured":"Hojjati, S. H., Ebrahimzadeh, A., Khazaee, A., Babajani-Feremi, A., Initiative, A. D. N., et al. (2017). Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM. Journal of Neurosci Methods, 282, 69\u201380.","journal-title":"Journal of Neurosci Methods"},{"key":"9554_CR27","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.neuroimage.2013.05.079","volume":"80","author":"RM Hutchison","year":"2013","unstructured":"Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013). Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage, 80, 360\u2013378.","journal-title":"NeuroImage"},{"issue":"4","key":"9554_CR28","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1002\/jmri.21049","volume":"27","author":"CR Jack Jr","year":"2008","unstructured":"Jack, C. R., Jr., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., Whitwell, L., J, Ward C, et al. (2008). The alzheimer\u2019s disease neuroimaging initiative (ADNI ): MRI methods. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 27(4), 685\u2013691.","journal-title":"Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine"},{"key":"9554_CR29","doi-asserted-by":"crossref","unstructured":"Kambeitz, J., Kambeitz-Ilankovic, L., Cabral, C., Dwyer, D. B., Calhoun, V. D., Van Den Heuvel, M. P. (2016). Aberrant functional whole-brain network architecture in patients with schizophrenia: a meta-analysis.\u00a0Schizophrenia Bulletin, 42(suppl_1), S13-S21.","DOI":"10.1093\/schbul\/sbv174"},{"key":"9554_CR30","doi-asserted-by":"publisher","first-page":"585","DOI":"10.3389\/fnins.2019.00585","volume":"13","author":"W Karwowski","year":"2019","unstructured":"Karwowski, W., Vasheghani Farahani, F., & Lighthall, N. (2019). Application of graph theory for identifying connectivity patterns in human brain networks: A systematic review. Frontiers in Neurosci, 13, 585.","journal-title":"Frontiers in Neurosci"},{"issue":"2","key":"9554_CR31","doi-asserted-by":"publisher","first-page":"89","DOI":"10.2478\/jaiscr-2014-0007","volume":"3","author":"D Kasthurirathna","year":"2013","unstructured":"Kasthurirathna, D., Piraveenan, M., & Thedchanamoorthy, G. (2013). On the influence of topological characteristics on robustness of complex networks. Journal of Artificial Intelligence and Soft Computing Research, 3(2), 89\u2013100.","journal-title":"Journal of Artificial Intelligence and Soft Computing Research"},{"key":"9554_CR32","doi-asserted-by":"crossref","unstructured":"Latora, V., Marchiori, M. (2001), Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701.","DOI":"10.1103\/PhysRevLett.87.198701"},{"key":"9554_CR33","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/j.neuroimage.2014.09.007","volume":"104","author":"N Leonardi","year":"2015","unstructured":"Leonardi, N., & Van De Ville, D. (2015). On spurious and real fluctuations of dynamic functional connectivity during rest. NeuroImage, 104, 430\u2013436.","journal-title":"NeuroImage"},{"key":"9554_CR34","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1016\/j.neuroimage.2014.06.052","volume":"101","author":"MA Lindquist","year":"2014","unstructured":"Lindquist, M. A., Xu, Y., Nebel, M. B., & Caffo, B. S. (2014). Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach. NeuroImage, 101, 531\u2013546.","journal-title":"NeuroImage"},{"key":"9554_CR35","doi-asserted-by":"publisher","first-page":"46","DOI":"10.3389\/fpsyt.2018.00046","volume":"9","author":"X Ma","year":"2018","unstructured":"Ma, X., Jiang, G., Fu, S., Fang, J., Wu, Y., Liu, M., Xu, G., & Wang, T. (2018). Enhanced network efficiency of functional brain networks in primary insomnia patients. Frontiers in Psychiatry, 9, 46.","journal-title":"Frontiers in Psychiatry"},{"issue":"1","key":"9554_CR36","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1002\/hbm.23346","volume":"38","author":"HA Marusak","year":"2017","unstructured":"Marusak, H. A., Calhoun, V. D., Brown, S., Crespo, L. M., Sala-Hamrick, K., Gotlib, I. H., & Thomason, M. E. (2017). Dynamic functional connectivity of neurocognitive networks in children. Human Brain Mapping, 38(1), 97\u2013108.","journal-title":"Human Brain Mapping"},{"key":"9554_CR37","unstructured":"MATLAB, Version 9.3.0.713579. (R2017b).,\u00a0The MathWorks Inc., Natick (2017)."},{"issue":"9","key":"9554_CR38","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1089\/brain.2014.0330","volume":"5","author":"J Meier","year":"2015","unstructured":"Meier, J., Tewarie, P., & Van Mieghem, P. (2015). The union of shortest path trees of functional brain networks. Brain Connectivity, 5(9), 575\u2013581.","journal-title":"Brain Connectivity"},{"issue":"4","key":"9554_CR39","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1016\/j.neuroimage.2008.10.031","volume":"44","author":"C Misra","year":"2009","unstructured":"Misra, C., Fan, Y., & Davatzikos, C. (2009). Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to ad: Results from ADNI. NeuroImage, 44(4), 1415\u20131422.","journal-title":"NeuroImage"},{"key":"9554_CR40","doi-asserted-by":"crossref","unstructured":"Musso, F., Brinkmeyer, J., Mobascher, A., Warbrick, T., Winterer, G. (2010). Spontaneous brain activity and EEG microstates. a novel EEG\/fMRI analysis approach to explore resting-state networks. NeuroImage, 52(4), 1149\u20131161.","DOI":"10.1016\/j.neuroimage.2010.01.093"},{"key":"9554_CR41","doi-asserted-by":"crossref","unstructured":"Newman, M. E. (2002). Assortative mixing in networks. Physical Review Letters, 89(20), 208701.","DOI":"10.1103\/PhysRevLett.89.208701"},{"issue":"23","key":"9554_CR42","doi-asserted-by":"publisher","first-page":"8577","DOI":"10.1073\/pnas.0601602103","volume":"103","author":"ME Newman","year":"2006","unstructured":"Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577\u20138582.","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"3","key":"9554_CR43","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1111\/j.1365-2796.2004.01388.x","volume":"256","author":"RC Petersen","year":"2004","unstructured":"Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256(3), 183\u2013194.","journal-title":"Journal of Internal Medicine"},{"issue":"12","key":"9554_CR44","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.1001\/archneur.58.12.1985","volume":"58","author":"RC Petersen","year":"2001","unstructured":"Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., Ritchie, K., Rossor, M., Thal, L., & Winblad, B. (2001a). Current concepts in mild cognitive impairment. Archives of Neurol, 58(12), 1985\u20131992.","journal-title":"Archives of Neurol"},{"issue":"12","key":"9554_CR45","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.1001\/archneur.58.12.1985","volume":"58","author":"RC Petersen","year":"2001","unstructured":"Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., Ritchie, K., Rossor, M., Thal, L., & Winblad, B. (2001b). Current concepts in mild cognitive impairment. Archives of Neurology, 58(12), 1985\u20131992.","journal-title":"Archives of Neurology"},{"issue":"3","key":"9554_CR46","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1111\/joim.12190","volume":"275","author":"RC Petersen","year":"2014","unstructured":"Petersen, R. C., Caracciolo, B., Brayne, C., Gauthier, S., Jelic, V., & Fratiglioni, L. (2014). Mild cognitive impairment: A concept in evolution. Journal of Internal Medicine, 275(3), 214\u2013228.","journal-title":"Journal of Internal Medicine"},{"key":"9554_CR47","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.neuroimage.2013.08.048","volume":"84","author":"JD Power","year":"2014","unstructured":"Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320\u2013341.","journal-title":"NeuroImage"},{"key":"9554_CR48","doi-asserted-by":"publisher","first-page":"S121","DOI":"10.1016\/j.neurobiolaging.2014.04.037","volume":"36","author":"G Prasad","year":"2015","unstructured":"Prasad, G., Joshi, S. H., Nir, T. M., Toga, A. W., Thompson, P. M., ADNI, et al. (2015). Brain connectivity and novel network measures for alzheimer\u2019s disease classification. Neurobiology of Aging, 36, S121\u2013S131.","journal-title":"Neurobiology of Aging"},{"key":"9554_CR49","doi-asserted-by":"crossref","unstructured":"Quattrociocchi, W., Caldarelli, G., Scala, A. (2014a). Self-healing networks: redundancy and structure. PLoS One, 9(2).","DOI":"10.1371\/journal.pone.0087986"},{"key":"9554_CR50","doi-asserted-by":"crossref","unstructured":"Quattrociocchi, W., Caldarelli, G., Scala, A. (2014b). Self-healing networks: redundancy and structure.\u00a0PloS One, 9(2), e87986.","DOI":"10.1371\/journal.pone.0087986"},{"key":"9554_CR51","doi-asserted-by":"crossref","unstructured":"Ravasz, E., Barab\u00e1si, A. L. (2003). Hierarchical organization in complex networks. Physical Review E, 67(2), 026112.","DOI":"10.1103\/PhysRevE.67.026112"},{"issue":"02","key":"9554_CR52","doi-asserted-by":"publisher","first-page":"1650003","DOI":"10.1142\/S0129065716500039","volume":"26","author":"R Romero-Garcia","year":"2016","unstructured":"Romero-Garcia, R., Atienza, M., & Cantero, J. L. (2016). Different scales of cortical organization are selectively targeted in the progression to alzheimer\u2019s disease. International J of Neural Systems, 26(02), 1650003.","journal-title":"International J of Neural Systems"},{"issue":"3","key":"9554_CR53","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","volume":"52","author":"M Rubinov","year":"2010","unstructured":"Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059\u20131069.","journal-title":"NeuroImage"},{"key":"9554_CR54","doi-asserted-by":"crossref","unstructured":"Sadiq, M. U., Langella, S., Giovanello, K. S., Mucha, P. J., Dayan, E. (2021). Accrual of functional redundancy along the lifespan and its effects on cognition.\u00a0NeuroImage, 229, 117737.","DOI":"10.1016\/j.neuroimage.2021.117737"},{"issue":"5\u20136","key":"9554_CR55","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s10334-010-0197-8","volume":"23","author":"\u00dc Sako\u011flu","year":"2010","unstructured":"Sako\u011flu, \u00dc., Pearlson, G. D., Kiehl, K. A., Wang, Y. M., Michael, A. M., & Calhoun, V. D. (2010). A method for evaluating dynamic functional network connectivity and task-modulation: Application to schizophrenia. Magnetic Resonance Materials in Physics, Biology and Medicine, 23(5\u20136), 351\u2013366.","journal-title":"Magnetic Resonance Materials in Physics, Biology and Medicine"},{"key":"9554_CR56","doi-asserted-by":"crossref","unstructured":"Schwab, S., Afyouni, S., Chen, Y., Han, Z., Guo, Q., Dierks, T., Wahlund, L. O., Grieder, M. (2018). Functional connectivity alterations of the temporal lobe and hippocampus in semantic dementia and alzheimer\u2019s disease. BioRxiv p 322131.","DOI":"10.1101\/322131"},{"key":"9554_CR57","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.neuroimage.2013.05.081","volume":"82","author":"X Shen","year":"2013","unstructured":"Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. NeuroImage, 82, 403\u2013415.","journal-title":"NeuroImage"},{"issue":"3","key":"9554_CR58","doi-asserted-by":"publisher","first-page":"247","DOI":"10.31887\/DCNS.2013.15.3\/osporns","volume":"15","author":"O Sporns","year":"2013","unstructured":"Sporns, O. (2013). Structure and function of complex brain networks. Dialogues in Clinical Neuroscience, 15(3), 247.","journal-title":"Dialogues in Clinical Neuroscience"},{"issue":"1","key":"9554_CR59","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1093\/cercor\/bhj127","volume":"17","author":"CJ Stam","year":"2006","unstructured":"Stam, C. J., Jones, B., Nolte, G., Breakspear, M., & Scheltens, P. (2006). Small-world networks and functional connectivity in alzheimer\u2019s disease. Cerebral Cortex, 17(1), 92\u201399.","journal-title":"Cerebral Cortex"},{"issue":"4","key":"9554_CR60","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1109\/TCT.1969.1083004","volume":"16","author":"K Steiglitz","year":"1969","unstructured":"Steiglitz, K., Weiner, P., & Kleitman, D. (1969). The design of minimum-cost survivable networks. IEEE Transactions on Circuit Theory, 16(4), 455\u2013460.","journal-title":"IEEE Transactions on Circuit Theory"},{"key":"9554_CR61","doi-asserted-by":"crossref","unstructured":"Supekar, K., Menon, V., Rubin, D., Musen, M., Greicius, M. D. (2008). Network analysis of intrinsic functional brain connectivity in alzheimer\u2019s disease. PLoS Computational Biology, 4(6), e1000100.","DOI":"10.1371\/journal.pcbi.1000100"},{"key":"9554_CR62","doi-asserted-by":"crossref","first-page":"386","DOI":"10.3389\/fpsyg.2018.00386","volume":"9","author":"J Wang","year":"2015","unstructured":"Wang, J., Wang, X., Xia, M., Liao, X., Evans, A., & He, Y. (2015). Gretna: A graph theoretical network analysis toolbox for imaging connectomics. Frontiers in Human Neurosci, 9, 386.","journal-title":"Frontiers in Human Neurosci"},{"key":"9554_CR63","doi-asserted-by":"crossref","unstructured":"Wang, J. H., Zuo, X. N., Gohel, S., Milham, M. P., Biswal, B. B., He, Y. (2011). Graph theoretical analysis of functional brain networks: test-retest evaluation on short-and long-term resting-state functional MRI data. PloS One, 6(7).","DOI":"10.1371\/journal.pone.0021976"},{"key":"9554_CR64","doi-asserted-by":"crossref","unstructured":"White, D. R., Newman, M. (2001). Fast approximation algorithms for finding node-independent paths in networks. Santa Fe Institute Working Papers Series","DOI":"10.2139\/ssrn.1831790"},{"key":"9554_CR65","doi-asserted-by":"publisher","first-page":"76","DOI":"10.3389\/fncom.2018.00076","volume":"12","author":"NJ Williams","year":"2018","unstructured":"Williams, N. J., Daly, I., & Nasuto, S. (2018). Markov model-based method to analyse time-varying networks in EEG task-related data. Frontiers in Computational Neuroscience, 12, 76.","journal-title":"Frontiers in Computational Neuroscience"},{"key":"9554_CR66","first-page":"13","volume":"4","author":"C Yan","year":"2010","unstructured":"Yan, C., & Zang, Y. (2010). DPARSF: A MATLAB toolbox for\u201d pipeline\u201d data analysis of resting-state fMRI. Frontiers in Systems Neurosci, 4, 13.","journal-title":"Frontiers in Systems Neurosci"},{"key":"9554_CR67","doi-asserted-by":"crossref","unstructured":"Yao, Z., Zhang, Y., Lin, L., Zhou, Y., Xu, C., Jiang, T., ADNI, et al. (2010). Abnormal cortical networks in mild cognitive impairment and alzheimer\u2019s disease. PLoS Computational Biology, 6(11), e1001006.","DOI":"10.1371\/journal.pcbi.1001006"},{"issue":"1","key":"9554_CR68","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1177\/1533317517731535","volume":"33","author":"Z Yao","year":"2018","unstructured":"Yao, Z., Hu, B., Chen, X., Xie, Y., Gutknecht, J., & Majoe, D. (2018). Learning metabolic brain networks in MCI and AD by robustness and leave-one-out analysis: An FDG-PET study. American Journal of Alzheimer\u2019s Disease & Other Dementias, 33(1), 42\u201354.","journal-title":"American Journal of Alzheimer\u2019s Disease & Other Dementias"},{"key":"9554_CR69","doi-asserted-by":"publisher","first-page":"10057","DOI":"10.1038\/srep12125","volume":"5","author":"SW Yoo","year":"2015","unstructured":"Yoo, S. W., Han, C. E., Shin, J. S., Seo, S. W., Na, D. L., Kaiser, M., Jeong, Y., & Seong, J. K. (2015). A network flow-based analysis of cognitive reserve in normal ageing and alzheimer\u2019s disease. Scientific Reports, 5, 10057.","journal-title":"Scientific Reports"},{"issue":"4","key":"9554_CR70","doi-asserted-by":"publisher","first-page":"2062","DOI":"10.1016\/j.neuroimage.2012.02.031","volume":"60","author":"H Yuan","year":"2012","unstructured":"Yuan, H., Zotev, V., Phillips, R., Drevets, W. C., & Bodurka, J. (2012). Spatiotemporal dynamics of the brain at rest\u2014exploring EEG microstates as electrophysiological signatures of bold resting state networks. NeuroImage, 60(4), 2062\u20132072.","journal-title":"NeuroImage"},{"issue":"4","key":"9554_CR71","first-page":"S729","volume":"7","author":"Y Zhou","year":"2011","unstructured":"Zhou, Y., Ge, Y., & Dougherty, J. (2011). Small world network properties changes in mild cognitive impairment and early alzheimer\u2019s disease. Alzheim & Dem: THe Journal of the Alzheimer\u2019s Association, 7(4), S729.","journal-title":"Alzheim & Dem: THe Journal of the Alzheimer\u2019s Association"},{"key":"9554_CR72","doi-asserted-by":"publisher","first-page":"148","DOI":"10.3389\/fncom.2015.00148","volume":"9","author":"AG Zippo","year":"2015","unstructured":"Zippo, A. G., Castiglioni, I., Borsa, V. M., & Biella, G. E. (2015). The compression flow as a measure to estimate the brain connectivity changes in resting state fMRI and 18FDG-PET alzheimer\u2019s disease connectomes. Frontiers in Computational Neurosci, 9, 148.","journal-title":"Frontiers in Computational Neurosci"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-021-09554-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12021-021-09554-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-021-09554-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T10:18:01Z","timestamp":1665137881000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12021-021-09554-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,26]]},"references-count":72,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["9554"],"URL":"https:\/\/doi.org\/10.1007\/s12021-021-09554-3","relation":{},"ISSN":["1539-2791","1559-0089"],"issn-type":[{"value":"1539-2791","type":"print"},{"value":"1559-0089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,26]]},"assertion":[{"value":"3 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The experiments and data collection were approved by the local ethics committees, as mentioned in ADNI data sharing website http:\/\/ad ni.loni.usc.edu. For the Xuanwu hospital\u2019s data, ethical approval has been obtained from the medical research ethics committee and institutional review board of XuanWu Hospital, Capital Medical University (approval number: [2014]011).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Data used from ADNI is publicly available, so this is not applicable. For the Xuanwu hospital\u2019s data, all participation is based on written informed consent and the participants will be able to withdraw from the study at any time.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The publisher has the permission from the authors to publish the paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare that they have no conflict of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest\/Competing Interests"}}]}}