[1] 2024 Alzheimer's disease facts and figures. 2024. Alzheimers Dement, vol. 20, no. 5, pp. 3708-3821, May 2024, doi: 10.1002/alz.13809.
[2] Lamptey, R. N. L., Chaulagain, B., Trivedi, R., Gothwal, A., Layek, B., & Singh, J. 2022. A Review of the Common Neurodegenerative Disorders: Current Therapeutic Approaches and the Potential Role of Nanotherapeutics, Int J Mol Sci, vol. 23, no. 3, Feb 6 2022, doi: 10.3390/ijms23031851.
[3] Jack, Jr., C. R. et al., 2010. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol, vol. 9, no. 1, pp. 119-28, Jan 2010, doi: 10.1016/S1474-4422(09)70299-6.
[4] Sperling R. A. et al., 2011. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease, Alzheimers Dement, vol. 7, no. 3, pp. 280-92, May 2011, doi: 10.1016/j.jalz.2011.03.003.
[5] Jack, Jr. C. R. et al., 2013. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers, Lancet Neurol, vol. 12, no. 2, pp. 207-16, Feb 2013, doi: 10.1016/S1474-4422(12)70291-0.
[6] Reitz, C. 2012. Alzheimer's disease and the amyloid cascade hypothesis: a critical review," Int J Alzheimers Dis, vol. 2012, p. 369808, 2012, doi: 10.1155/2012/369808.
[7] Wu T., et al., 2022. Amyloid Cascade Hypothesis for the Treatment of Alzheimer's Disease: Progress and Challenges, Aging Dis, vol. 13, no. 6, pp. 1745-1758, Dec 1 2022, doi: 10.14336/AD.2022.0412.
[8] Hardy, J. A. & Higgins, G. A. 1992. Alzheimer's disease: the amyloid cascade hypothesis, Science, vol. 256, no. 5054, pp. 184-5, Apr 10 1992, doi: 10.1126/science.1566067.
[9] Palop, J. J. & Mucke, L. 2016. Network abnormalities and interneuron dysfunction in Alzheimer disease, Nat Rev Neurosci, vol. 17, no. 12, pp. 777-792, Dec 2016, doi: 10.1038/nrn.2016.141.
[10] Vogel, J. W. et al., 2023. Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight," Nat Rev Neurosci, vol. 24, no. 10, pp. 620-639, Oct 2023, doi: 10.1038/s41583-023-00731-8.
[11] Hafkemeijer, A., Van der Grond, J., & Rombouts, S. A. 2012. Imaging the default mode network in aging and dementia, Biochim Biophys Acta, vol. 1822, no. 3, pp. 431-41, Mar 2012, doi: 10.1016/j.bbadis. 2011.07.008.
[12] Hojjati, S. H., Feiz, F., Ozoria, S., Razlighi, Q. R., & Alzheimer's Disease Neuroimaging, I. 2021. "Topographical Overlapping of the Amyloid-beta and Tau Pathologies in the Default Mode Network Predicts Alzheimer's Disease with Higher Specificity," J Alzheimers Dis, vol. 83, no. 1, pp. 407-421, 2021, doi: 10.3233/JAD-210419.
[13] Palmqvist, S. et al., 2017. Earliest accumulation of beta-amyloid occurs within the default-mode network and concurrently affects brain connectivity, Nat Commun, vol. 8, no. 1, p. 1214, Oct 31 2017, doi: 10.1038/s41467-017-01150-x.
[14] Sorg, C., Neufang, S., Riedl, V., & Wohlschläger, A. 2009. Compensatory recruitment and intrinsic brain organization in Alzheimer's disease, NeuroImage, vol. 47, 2009, doi: 10.1016/s1053-8119(09)70488-4.
[15] Yuksel, D., Dal, Z., Yildirim, H., Gurvit, A., Kabakcioglu, & Acar, B. 2024. Reorganization of brain connectivity across the spectrum of clinical cognitive decline, Neurol Sci, vol. 45, no. 12, pp. 5719-5730, Dec 2024, doi: 10.1007/s10072-024-07688-1.
[16] Bullmore, E., & Sporns, O. 2009. Complex brain networks: graph theoretical analysis of structural and functional systems, Nat Rev Neurosci, vol. 10, no. 3, pp. 186-98, Mar 2009, doi: 10.1038/nrn2575.
[17] Badhwar, A., Tam, A., Dansereau, C., Orban, P., Hoffstaedter, F., & Bellec, P. 2017. Resting-state network dysfunction in Alzheimer's disease: A systematic review and meta-analysis, Alzheimers Dement (Amst), vol. 8, pp. 73-85, 2017, doi: 10.1016/j.dadm.2017.03.007.
[18] Engels, M. M., Stam, C. J., Van der Flier, W. M., Scheltens, P., De Waal, H., & van Straaten, E. C. 2015. Declining functional connectivity and changing hub locations in Alzheimer's disease: an EEG study, BMC Neurol, vol. 15, p. 145, Aug 20 2015, doi: 10.1186/s12883-015-0400-7.
[19] Jin, S., Wang, J., & He, Y. 2024. The brain network hub degeneration in Alzheimer's disease, Biophys Rep, vol. 10, no. 4, pp. 213-229, Aug 31 2024, doi: 10.52601/bpr.2024.230025.
[20] Li, C. et al., 2019. Abnormal Brain Network Connectivity in a Triple-Network Model of Alzheimer's Disease," J Alzheimers Dis, vol. 69, no. 1, pp. 237-252, 2019, doi: 10.3233/JAD-181097.
[21] Zhang, X., Liu, J., Chen, Y., Jin, Y., Cheng, J., & Alzheimer's Disease Neuroimaging, I. 2021. Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly-available nodes approach, Brain Behav, vol. 11, no. 3, p. e02027, Mar 2021, doi: 10.1002/brb3.2027.
[22] Franzmeier, N. et al., 2020. Patient-centered connectivity-based prediction of tau pathology spread in Alzheimer's disease, Sci Adv, vol. 6, no. 48, Nov 2020, doi: 10.1126/sciadv.abd1327.
[23] Lee, W. J. et al., 2022. Regional Abeta-tau interactions promote onset and acceleration of Alzheimer's disease tau spreading, Neuron, vol. 110, no. 12, pp. 1932-1943 e5, Jun 15 2022, doi: 10.1016/j.neuron.2022.03.034.
[24] Vogel, J. W. et al., 2020. Spread of pathological tau proteins through communicating neurons in human Alzheimer's disease, Nat Commun, vol. 11, no. 1, p. 2612, May 26 2020, doi: 10.1038/s41467-020-15701-2.
[25] Hampton O. L. et al., 2020. Resting-state functional connectivity and amyloid burden influence longitudinal cortical thinning in the default mode network in preclinical Alzheimer’s disease, NeuroImage: Clinical, vol. 28, 2020, doi: 10.1016/j.nicl.2020.102407.
[26] Hojjati, S. H. et al., 2025. Inter-network functional connectivity increases by beta-amyloid and may facilitate the early stage of tau accumulation," Neurobiol Aging, vol. 148, pp. 16-26, Apr 2025, doi: 10.1016/j.neurobiolaging.2025.01.005.
[27] Saberi, M., Khosrowabadi, R., Khatibi, A., Misic, B., & Jafari, G. 2021. Topological impact of negative links on the stability of resting-state brain network, Sci Rep, vol. 11, no. 1, p. 2176, Jan 26 2021, doi: 10.1038/s41598-021-81767-7.
[28] Zhan, L.et al., 2017. The significance of negative correlations in brain connectivity," J Comp Neurol, vol. 525, no. 15, pp. 3251-3265, Oct 15 2017, doi: 10.1002/cne.24274.
[29] Zhao, Y., Ma, B., Jiang, P., Zeng, D., Wang, X., & Li, S. 2020. Prediction of Alzheimer's Disease Progression with Multi-Information Generative Adversarial Network, IEEE J Biomed Health Inform, vol. 25, no. 3, pp. 711-719, Mar 2021, doi: 10.1109/JBHI.2020.3006925.
[30] Ho, N. H., Yang, H. J., Kim, J., Dao, D. P., Park, H. R., & Pant, S. 2022. Predicting progression of Alzheimer's disease using forward-to-backward bi-directional network with integrative imputation, Neural Netw, vol. 150, pp. 422-439, Jun 2022, doi: 10.1016/j.neunet.2022.03.016.
[31] Costantini, G., & Perugini, M. 2014. Generalization of clustering coefficients to signed correlation networks, PLoS One, vol. 9, no. 2, p. e88669, 2014, doi: 10.1371/journal.pone.0088669.
[32] Amiri, S., Van den Berg, M., Nazem-Zadeh, M. R., Verhoye, M., Amiri, M., & Keliris, G. A. 2024. Nodal degree centrality in the default mode-like network of the TgF344-AD Alzheimer's disease rat model as a measure of early network alterations, NPJ Aging, vol. 10, no. 1, p. 29, Jun 20 2024, doi: 10.1038/s41514-024-00151-7.
[33] Chiang, S. et al., 2016. Time-dependence of graph theory metrics in functional connectivity analysis, Neuroimage, vol. 125, pp. 601-615, Jan 15 2016, doi: 10.1016/j.neuroimage.2015.10.070.
[34] Zhang, M. et al., 2022. Disrupted coupling between salience network segregation and glucose metabolism is associated with cognitive decline in Alzheimer's disease - A simultaneous resting-state FDG-PET/fMRI study, Neuroimage Clin, vol. 34, p. 102977, 2022, doi: 10.1016/j.nicl.2022.102977.
[35] Liu, Y. et al., 2014. Impaired long distance functional connectivity and weighted network architecture in Alzheimer's disease, Cereb Cortex, vol. 24, no. 6, pp. 1422-35, Jun 2014, doi: 10.1093/cercor/bhs410.
[36] Klepl, D., He, F., Wu, M., Blackburn, D. J., & Sarrigiannis, P. 2022. EEG-Based Graph Neural Network Classification of Alzheimer's Disease: An Empirical Evaluation of Functional Connectivity Methods, IEEE Trans Neural Syst Rehabil Eng, vol. 30, pp. 2651-2660, 2022, doi: 10.1109/TNSRE.2022.3204913.
[37] Hallquist, M. N. & Hillary, F. G. 2019. Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world, Netw Neurosci, vol. 3, no. 1, pp. 1-26, 2019, doi: 10.1162/netn_a_00054.
[38] Freeman, L. C. 1978. Centrality in social networks conceptual clarification, Social Networks, vol. 1, no. 3, pp. 215-239, 1978, doi: doi.org/10.1016/0378-8733(78)90021-7.
[39] Opsahl, T., Agneessens, F., & Skvoretz, J. 2010. Node centrality in weighted networks: Generalizing degree and shortest paths, Social Networks, vol. 32, no. 3, pp. 245-251, 2010, doi: 10.1016/j.socnet.2010.03.006.
[40] Heider, F. 1946. Attitudes and cognitive organization, J Psychol, vol. 21, pp. 107-12, Jan 1946, doi: 10.1080/00223980.1946.9917275.
[41] Cartwright, D., & Harary, F. 1956. Structural balance: a generalization of Heider's theory, Psychol Rev, vol. 63, no. 5, pp. 277-93, Sep 1956, doi: 10.1037/h0046049.
[42] Facchetti, G., Iacono, G., & Altafini, C. 2011. Computing global structural balance in large-scale signed social networks, Proc Natl Acad Sci U S A, vol. 108, no. 52, pp. 20953-8, Dec 27 2011, doi: 10.1073/pnas.1109521108.
[43] Aref, S. M., Andrew, J., & Wilson, Mark C. 2019. A modelling and computational study of the frustration index in signed networks, arXiv preprint arXiv:1611.09030, 2019, doi: 10.48550/arXiv.1611.09030.
[44] Watts, D. J. & Strogatz, S. H. 1998. Collective dynamics of 'small-world' networks, Nature, vol. 393, no. 6684, pp. 440-2, Jun 4 1998, doi: 10.1038/30918.
[45] Zhang, B. & Horvath, S. 2005. A general framework for weighted gene co-expression network analysis, Stat Appl Genet Mol Biol, vol. 4, p. Article17, 2005, doi: 10.2202/1544-6115.1128.
[46] Barabasi, A. L. & Albert, R. 1999. Emergence of scaling in random networks, Science, vol. 286, no. 5439, pp. 509-12, Oct 15 1999, doi: 10.1126/science.286.5439.509.
[47] Newman, M. E. 2002. Assortative mixing in networks, Phys Rev Lett, vol. 89, no. 20, p. 208701, Nov 11 2002, doi: 10.1103/PhysRevLett.89.208701.
[48] bctpy — Brain Connectivity Toolbox (Python port). https://github.com/aestrivex/bctpy (accessed.
[49] Newman, M. E., 2003. Mixing patterns in networks, Phys Rev E Stat Nonlin Soft Matter Phys, vol. 67, no. 2 Pt 2, p. 026126, Feb 2003, doi: 10.1103/PhysRevE.67.026126.
[50] Gurobi Optimization, L. Gurobi Optimizer Reference Manual. https://www.gurobi.com (accessed.
[51] Hojjati S. H., et al., 2023. Distinct and joint effects of low and high levels of Abeta and tau deposition on cortical thickness, Neuroimage Clin, vol. 38, p. 103409, 2023, doi: 10.1016/j.nicl.2023.103409.
[52] Hojjati S. H. et al., 2022. Early accumulation of Aβ and Tau is associated with increase in cortical thickness, Alzheimer's & Dementia, vol. 18, no. S5, 2022, doi: 10.1002/alz.063715.
[53] Forner, S., Baglietto-Vargas, D., Martini, A. C., Trujillo-Estrada, L. & LaFerla, F. M. 2017. Synaptic Impairment in Alzheimer's Disease: A Dysregulated Symphony, Trends Neurosci, vol. 40, no. 6, pp. 347-357, Jun 2017, doi: 10.1016/j.tins.2017.04.002.
[54] Bero, A. W. et al., 2011. Neuronal activity regulates the regional vulnerability to amyloid-beta deposition, Nat Neurosci, vol. 14, no. 6, pp. 750-6, Jun 2011, doi: 10.1038/nn.2801.
[55] Busche, M. A. et al., 2012. Critical role of soluble amyloid-beta for early hippocampal hyperactivity in a mouse model of Alzheimer's disease, Proc Natl Acad Sci U S A, vol. 109, no. 22, pp. 8740-5, May 29 2012, doi: 10.1073/pnas.1206171109.
[56] Li, Y. et al., 2020. Microglia: A Potential Therapeutic Target for Sepsis-Associated Encephalopathy and Sepsis-Associated Chronic Pain," Front Pharmacol, vol. 11, p. 600421, 2020, doi: 10.3389/fphar. 2020.600421.
[57] Park, J., Lee, C., & Kim, Y. T. 2023. Effects of Natural Product-Derived Compounds on Inflammatory Pain via Regulation of Microglial Activation, Pharmaceuticals (Basel), vol. 16, no. 7, Jun 29 2023, doi: 10.3390/ph16070941.
[58] Rogers, J., Strohmeyer, R., Kovelowski, C. J., & Li, R. 2002. Microglia and inflammatory mechanisms in the clearance of amyloid beta peptide, Glia, vol. 40, no. 2, pp. 260-269, Nov 2002, doi: 10.1002/glia.10153.
[59] Klempin, F. & Kempermann, G. 2007. Adult hippocampal neurogenesis and aging, Eur Arch Psychiatry Clin Neurosci, vol. 257, no. 5, pp. 271-80, Aug 2007, doi: 10.1007/s00406-007-0731-5.
[60] Perry, E. K., Johnson, M., Ekonomou, A., Perry, R. H., Ballard, C., & Attems, J. 2012. Neurogenic abnormalities in Alzheimer's disease differ between stages of neurogenesis and are partly related to cholinergic pathology, Neurobiol Dis, vol. 47, no. 2, pp. 155-62, Aug 2012, doi: 10.1016/j.nbd. 2012.03.033.