[1] Alzheimer's Association.  2017 Alzheimer's disease facts and figures[J]. Alzheimer's Dement, 2017, 13(4): 325-373.   doi: 10.1016/j.jalz.2017.02.001
[2] Scheltens P, Blennow K, Breteler MMB, et al.  Alzheimer's disease[J]. Lancet, 2016, 388(10043): 505-517.   doi: 10.1016/S0140-6736(15)01124-1
[3] Lista S, Hampel H.  Synaptic degeneration and neurogranin in the pathophysiology of Alzheimer's disease[J]. Expert Rev Neurother, 2017, 17(1): 47-57.   doi: 10.1080/14737175.2016.1204234
[4] Heneka MT, Carson MJ, El Khoury J, et al.  Neuroinflammation in Alzheimer's disease[J]. Lancet Neurol, 2015, 14(4): 388-405.   doi: 10.1016/S1474-4422(15)70016-5
[5] De La Monte SM, Tong M.  Brain metabolic dysfunction at the core of Alzheimer's disease[J]. Biochem Pharmacol, 2014, 88(4): 548-559.   doi: 10.1016/j.bcp.2013.12.012
[6] Jagust W.  Imaging the evolution and pathophysiology of Alzheimer disease[J]. Nat Rev Neurosci, 2018, 19(11): 687-700.   doi: 10.1038/s41583-018-0067-3
[7] Stam CJ.  Modern network science of neurological disorders[J]. Nat Rev Neurosci, 2014, 15(10): 683-695.   doi: 10.1038/nrn3801
[8] Bullmore E, Sporns O.  The economy of brain network organization[J]. Nat Rev Neurosci, 2012, 13(5): 336-349.   doi: 10.1038/nrn3214
[9] Sporns O.  Network attributes for segregation and integration in the human brain[J]. Curr Opin Neurobiol, 2013, 23(2): 162-171.   doi: 10.1016/j.conb.2012.11.015
[10] Watts DJ, Strogatz SH.  Collective dynamics of 'small-world' networks[J]. Nature, 1998, 393(6684): 440-442.   doi: 10.1038/30918
[11] Oh SW, Harris JA, Ng L, et al.  A mesoscale connectome of the mouse brain[J]. Nature, 2014, 508(7495): 207-214.   doi: 10.1038/nature13186
[12] Harris NG, Verley DR, Gutman BA, et al.  Disconnection and hyper-connectivity underlie reorganization after TBI: A rodent functional connectomic analysis[J]. Exp Neurol, 2016, 277: 124-138.   doi: 10.1016/j.expneurol.2015.12.020
[13] Albert R, Barabási AL.  Statistical mechanics of complex networks[J]. Rev Mod Phys, 2002, 74(1): 47-91.   doi: 10.1103/RevModPhys.74.47
[14] Giusti C, Ghrist R, Bassett DS.  Two's company, three (or more) is a simplex: algebraic-topological tools for understanding higher-order structure in neural data[J]. J Comput Neurosci, 2016, 41(1): 1-14.   doi: 10.1007/s10827-016-0608-6
[15] Calhoun VD, Miller R, Pearlson G, et al.  The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery[J]. Neuron, 2014, 84(2): 262-274.   doi: 10.1016/j.neuron.2014.10.015
[16] He Y, Chen Z, Evans A.  Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease[J]. J Neurosci, 2008, 28(18): 4756-4766.   doi: 10.1523/JNEUROSCI.0141-08.2008
[17]

Supekar K, Menon V, Rubin D, et al. Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease [J/OL]. PLoS Comput Biol, 2008, 4(6): e1000100[2019-11-08]. http://dx.doi.org/10.1371/journal.pcbi.1000100. DOI:10.1371/journal.pcbi.1000100.

[18] Zhao QH, Lu H, Metmer H, et al.  Evaluating functional connectivity of executive control network and frontoparietal network in Alzheimer's disease[J]. Brain Res, 2018, 1678: 262-272.   doi: 10.1016/j.brainres.2017.10.025
[19] Liu Y, Yu CS, Zhang XQ, et al.  Impaired Long Distance Functional Connectivity and Weighted Network Architecture in Alzheimer's Disease[J]. Cereb Cortex, 2014, 24(6): 1422-1435.   doi: 10.1093/cercor/bhs410
[20] Hahn K, Myers N, Prigarin S, et al.  Selectively and progressively disrupted structural connectivity of functional brain networks in Alzheimer's disease — revealed by a novel framework to analyze edge distributions of networks detecting disruptions with strong statistical evidence[J]. Neuroimage, 2013, 81: 96-109.   doi: 10.1016/j.neuroimage.2013.05.011
[21] Bernard C, Dilharreguy B, Helmer C, et al.  PCC characteristics at rest in 10-year memory decliners[J]. Neurobiol Aging, 2015, 36(10): 2812-2820.   doi: 10.1016/j.neurobiolaging.2015.07.002
[22] Dai ZJ, Yan CG, Li KC, et al.  Identifying and Mapping Connectivity Patterns of Brain Network Hubs in Alzheimer's Disease[J]. Cereb Cortex, 2015, 25(10): 3723-3742.   doi: 10.1093/cercor/bhu246
[23] Huang S, Li J, Sun L, et al.  Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation[J]. NeuroImage, 2010, 50(3): 935-949.   doi: 10.1016/j.neuroimage.2009.12.120
[24] Titov D, Diehl-Schmid J, Shi KY, et al.  Metabolic connectivity for differential diagnosis of dementing disorders[J]. J Cereb Blood Flow Metab, 2017, 37(1): 252-262.   doi: 10.1177/0271678x15622465
[25] Duan HQ, Jiang JH, Xu J, et al.  Differences in Aβ brain networks in Alzheimer's disease and healthy controls[J]. Brain Res, 2017, 1655: 77-89.   doi: 10.1016/j.brainres.2016.11.019
[26] Lo CY, Wang PN, Chou KH, et al.  Diffusion Tensor Tractography Reveals Abnormal Topological Organization in Structural Cortical Networks in Alzheimer's Disease[J]. J Neurosci, 2010, 30(50): 16876-16885.   doi: 10.1523/JNEUROSCI.4136-10.2010
[27] Stam CJ, Jones BF, Nolte G, et al.  Small-World Networks and Functional Connectivity in Alzheimer's Disease[J]. Cereb Cortex, 2006, 17(1): 92-99.   doi: 10.1093/cercor/bhj127
[28] Xue C, Yuan B, Yue Y, et al.  Distinct Disruptive Patterns of Default Mode Subnetwork Connectivity Across the Spectrum of Preclinical Alzheimer's Disease[J]. Front Aging Neurosci, 2019, 11: 307-.   doi: 10.3389/fnagi.2019.00307
[29] Dillen KNH, Jacobs HIL, Kukolja J, et al.  Aberrant functional connectivity differentiates retrosplenial cortex from posterior cingulate cortex in prodromal Alzheimer's disease[J]. Neurobiol Aging, 2016, 44: 114-126.   doi: 10.1016/j.neurobiolaging.2016.04.010
[30] Yan TY, Wang WH, Yang L, et al.  Rich club disturbances of the human connectome from subjective cognitive decline to Alzheimer's disease[J]. Theranostics, 2018, 8(12): 3237-3255.   doi: 10.7150/thno.23772
[31] Buckner RL, Sepulcre J, Talukdar T, et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease[J]. J Neurosci, 2009, 29(6): 1860-1873.   doi: 10.1523/JNEUROSCI.5062-08.2009
[32] Filippi M, Van Den Heuvel MP, Fornito A, et al.  Assessment of system dysfunction in the brain through MRI-based connectomics[J]. Lancet Neurol, 2013, 12(12): 1189-1199.   doi: 10.1016/S1474-4422(13)70144-3
[33] Evans A, He Y, Chen Z.  P1-241: Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease[J]. Alzheimer's Dement, 2008, 4(4S): T284-T285.   doi: 10.1016/j.jalz.2008.05.830
[34] Zhou HC, Jiang JH, Lu JY, et al.  Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer's Disease[J]. Front Neurosci, 2019, 12: 1045-.   doi: 10.3389/fnins.2018.01045
[35]

Zeidán-Chuliá F, De Oliveira BH, Salmina AB, et al. Altered expression of Alzheimer's disease-related genes in the cerebellum of autistic patients: a model for disrupted brain connectome and therapy[J/OL]. Cell Death Dis, 2014, 5(5): e1250[2019-11-08]. https://www.nature.com/articles/cddis2014227. DOI: 10.1038/cddis.2014.227.

[36] Solé-Padullés C, Bartrés-Faz D, Junqué C, et al.  Brain structure and function related to cognitive reserve variables in normal aging, mild cognitive impairment and Alzheimer's disease[J]. Neurobiol Aging, 2009, 30(7): 1114-1124.   doi: 10.1016/j.neurobiolaging.2007.10.008
[37] Kivelä M, Arenas A, Barthelemy M, et al.  Multilayer networks[J]. J Complex Networks, 2014, 2(3): 203-271.   doi: 10.1093/comnet/cnu016
[38] Linnman C, Catana C, Petkov MP, et al.  Molecular and functional PET-fMRI measures of placebo analgesia in episodic migraine: Preliminary findings[J]. Neuroimage Clin, 2018, 17: 680-690.   doi: 10.1016/j.nicl.2017.11.011
[39] Marchitelli R, Aiello M, Cachia A, et al.  Simultaneous resting-state FDG-PET/fMRI in Alzheimer Disease: Relationship between glucose metabolism and intrinsic activity[J]. NeuroImage, 2018, 176: 246-258.   doi: 10.1016/j.neuroimage.2018.04.048
[40] Qin PM, Duncan NW, Chen DYT, et al.  Vascular-metabolic and GABAergic Inhibitory Correlates of Neural Variability Modulation. A Combined fMRI and PET Study[J]. Neuroscience, 2018, 379: 142-151.   doi: 10.1016/j.neuroscience.2018.02.041