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Current Alzheimer Research

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ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

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Research Article

Disrupted Time-Dependent and Functional Connectivity Brain Network in Alzheimer's Disease: A Resting-State fMRI Study Based on Visibility Graph

Author(s): Zhongke Gao*, Yanhua Feng, Chao Ma*, Kai Ma, Qing Cai and and for the Alzheimer’s Disease Neuroimaging Initiative

Volume 17, Issue 1, 2020

Page: [69 - 79] Pages: 11

DOI: 10.2174/1567205017666200213100607

Price: $65

Abstract

Background: Alzheimer's Disease (AD) is a progressive neurodegenerative disease with insidious onset, which is difficult to be reversed and cured. Therefore, discovering more precise biological information from neuroimaging biomarkers is crucial for accurate and automatic detection of AD.

Methods: We innovatively used a Visibility Graph (VG) to construct the time-dependent brain networks as well as functional connectivity network to investigate the underlying dynamics of AD brain based on functional magnetic resonance imaging. There were 32 AD patients and 29 Normal Controls (NCs) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. First, the VG method mapped the time series of single brain region into networks. By extracting topological properties of the networks, the most significant features were selected as discriminant features into a supporting vector machine for classification. Furthermore, in order to detect abnormalities of these brain regions in the whole AD brain, functional connectivity among different brain regions was calculated based on the correlation of regional degree sequences.

Results: According to the topology abnormalities exploration of local complex networks, we found several abnormal brain regions, including left insular, right posterior cingulate gyrus and other cortical regions. The accuracy of characteristics of the brain regions extracted from local complex networks was 88.52%. Association analysis demonstrated that the left inferior opercular part of frontal gyrus, right middle occipital gyrus, right superior parietal gyrus and right precuneus played a tremendous role in AD.

Conclusion: These results would be helpful in revealing the underlying pathological mechanism of the disease.

Keywords: Alzheimer's disease, fMRI, visibility graph, functional networks, classification study, local complex network.

« Previous Next »
[1]
Ma C, Wang J, Zhang J, Chen K, Li X, Shu N, et al. Disrupted brain structural connectivity: pathological interactions between genetic apoe ε4 status and developed MCI xondition. Mol Neurobiol 54(9): 6999-7007. (2017).
[http://dx.doi.org/10.1007/s12035-016-0224-5] [PMID: 27785756]
[2]
Yu H, Lei X, Song Z, Wang J, Wei X, Yu B. Functional brain connectivity in Alzheimer's disease: an EEG study based on permutation disalignment index. Physica a-Statistical Mechanics and Its ApplicationsPhysica a-Statistical Mechanics and Its Applications 506: 1093-3. (2018).
[3]
Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement 9(1): 63-75.e2. (2013).
[http://dx.doi.org/10.1016/j.jalz.2012.11.007] [PMID: 233058234]
[4]
McKeith IG, Galasko D, Kosaka K, Perry EK, Dickson DW, Hansen LA, et al. Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): report of the consortium on DLB international workshop. Neurology 47(5): 1113-24. (1996).
[http://dx.doi.org/10.1212/WNL.47.5.1113] [PMID: 8909416]
[5]
Rajasekhar K, Chakrabarti M, Govindaraju T. Function and toxicity of amyloid beta and recent therapeutic interventions targeting amyloid beta in Alzheimer’s disease. Chem Commun (Camb) 51(70): 13434-50. (2015).
[http://dx.doi.org/10.1039/C5CC05264E] [PMID: 26247608]
[6]
Maccioni RB, Farías G, Morales I, Navarrete L. The revitalized tau hypothesis on Alzheimer’s disease. Arch Med Res 41(3): 226-31. (2010).
[http://dx.doi.org/10.1016/j.arcmed.2010.03.007] [PMID: 20682182]
[7]
Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 297(5580): 353-6. (2002).
[http://dx.doi.org/10.1126/science.1072994] [PMID: 12130773]
[8]
Yao Z, Zhang Y, Lin L, Zhou Y, Xu C, Jiang T. Alzheimer’s Disease Neuroimaging Initiative. Abnormal cortical networks in mild cognitive impairment and Alzheimer’s disease. PLOS Comput Biol 6(11) e1001006 (2010).
[http://dx.doi.org/10.1371/journal.pcbi.1001006] [PMID: 21124954]
[9]
Seo EH, Lee DY, Lee J-M, Park J-S, Sohn BK, Lee DS, et al. Whole-brain functional networks in cognitively normal, mild cognitive impairment, and Alzheimer’s disease. PLoS One 8(1) e53922 (2013).
[http://dx.doi.org/10.1371/journal.pone.0053922] [PMID: 23335980]
[10]
Sun Y, Yin Q, Fang R, Yan X, Wang Y, Bezerianos A, et al. Disrupted functional brain connectivity and its association to structural connectivity in amnestic mild cognitive impairment and Alzheimer’s disease. PLoS One 9(5) e96505 (2014).
[http://dx.doi.org/10.1371/journal.pone.0096505] [PMID: 24806295]
[11]
Niu Y, Wang B, Zhou M, Xue J, Shapour H, Cao R, et al. Dynamic complexity of spontaneous BOLD activity in Alzheimer’s disease and mild cognitive impairment using multiscale entropy analysis. Front Neurosci 12: 677. (2018).
[http://dx.doi.org/10.3389/fnins.2018.00677]
[12]
Wang K, Liang M, Wang L, Tian L, Zhang X, Li K, et al. Altered functional connectivity in early Alzheimer’s disease: a resting-state fMRI study. Hum Brain Mapp 28(10): 967-78. (2007).
[http://dx.doi.org/10.1002/hbm.20324] [PMID: 17133390]
[13]
Sanz-Arigita EJ, Schoonheim MM, Damoiseaux JS, Rombouts SARB, Maris E, Barkhof F, et al. Loss of ‘small-world’ networks in Alzheimer’s disease: graph analysis of FMRI resting-state functional connectivity. PLoS One 5(11) e13788 (2010).
[http://dx.doi.org/10.1371/journal.pone.0013788] [PMID: 21072180]
[14]
Zhang H-Y, Wang S-J, Xing J, Liu B, Ma Z-L, Yang M, et al. Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer’s disease. Behav Brain Res 197(1): 103-8. (2009).
[http://dx.doi.org/10.1016/j.bbr.2008.08.012] [PMID: ]18786570]
[15]
Liu Z, Zhang Y, Yan H, Bai L, Dai R, Wei W, et al. Altered topological patterns of brain networks in mild cognitive impairment and Alzheimer’s disease: a resting-state fMRI study. Psychiatry Res 202(2): 118-25. (2012).
[http://dx.doi.org/10.1016/j.pscychresns.2012.03.002] [PMID: 22695315]
[16]
He Y, Chen Z, Evans A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease. J Neurosci 28(18): 4756-66. (2008).
[http://dx.doi.org/10.1523/JNEUROSCI.0141-08.2008] [PMID: 18448652]
[17]
Grieder M, Wang DJJ, Dierks T, Wahlund L-O, Jann K. Default mode network complexity and cognitive decline in mild Alzheimer’s disease. Front in Neurosci 12: 770. (2018).
[http://dx.doi.org/10.3389/fnins.2018.00770]
[18]
Ma C, Zhang Y, Li X, Chen Y, Zhang J, Liu Z, et al. The TT allele of rs405509 synergizes with APOE ε4 in the impairment of cognition and its underlying default mode network in non-demented elderly. Curr Alzheimer Res 13(6): 708-17. (2016).
[http://dx.doi.org/10.2174/1567205013666160129100350] [PMID: 26825091]
[19]
Wang L, Zang Y, He Y, Liang M, Zhang X, Tian L, et al. Changes in hippocampal connectivity in the early stages of Alzheimer’s disease: evidence from resting state fMRI. Neuroimage 31(2): 496-504. (2006).
[http://dx.doi.org/10.1016/j.neuroimage.2005.12.033] [PMID: 16473024]
[20]
Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, et al. Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 25(34): 7709-17. (2005).
[http://dx.doi.org/10.1523/JNEUROSCI.2177-05.2005] [PMID: 16120771]
[21]
Supekar K, Menon V, Rubin D, Musen M, Greicius MD. Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLOS Comput Biol 4(6) e1000100 (2008).
[http://dx.doi.org/10.1371/journal.pcbi.1000100] [PMID: 18584043]
[22]
Gao ZK, Small M, Kurths J. Complex network analysis of time series. Epl-Europhys Lett 116(5) (2016).
[http://dx.doi.org/10.1209/0295-5075/116/50001]
[23]
Gao ZK, Zhang KL, Dang WD, Yang YX, Wang ZB, Duan HB, et al. An adaptive optimal-Kernel time-frequency representation-based complex network method for characterizing fatigued behavior using the SSVEP-based BCI system. Knowl Base Syst 152: 163-71. (2018).
[http://dx.doi.org/10.1016/j.knosys.2018.04.013]
[24]
Gao Z, Wang X, Yang Y, Mu C, Cai Q, Dang W, et al. EEG-Based spatio-temporal convolutional neural network for driver fatigue evaluation. IEEE Trans Neural Netw Learn Syst 30(9): 2755-63. (2019).
[http://dx.doi.org/10.1109/TNNLS.2018.2886414] [PMID: 30640634]
[25]
Lacasa L, Luque B, Ballesteros F, Luque J, Nuño JC. From time series to complex networks: the visibility graph. Proc Natl Acad Sci USA 105(13): 4972-5. (2008).
[http://dx.doi.org/10.1073/pnas.0709247105] [PMID: 18362361]
[26]
Yu M, Hillebrand A, Gouw AA, Stam CJ. Horizontal visibility graph transfer entropy (HVG-TE): a novel metric to characterize directed connectivity in large-scale brain networks. Neuroimage 156: 249-64. (2017).
[http://dx.doi.org/10.1016/j.neuroimage.2017.05.047] [PMID: 28539247]
[27]
Ahmadlou M, Adeli H, Adeli A. Improved visibility graph fractality with application for the diagnosis of Autism Spectrum Disorder. Physica a-Statistical Mechanics and Its Applications 391(20): 4720-26. (2012).
[http://dx.doi.org/10.1016/j.physa.2012.04.025]
[28]
Gao ZK, Cai Q, Yang YX, Dong N, Zhang SS. Visibility graph from adaptive optimal kernel time-frequency representation for classification of epileptiform EEG. Int J Neural Syst 27(4) 1750005 (2017).
[http://dx.doi.org/10.1142/S0129065717500058] [PMID: 27832712]
[29]
Cai Q, Gao ZK, Yang YX, Dang WD, Grebogi C. Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue Detection. Int J Neural Syst 29(5) 1850057 (2019).
[http://dx.doi.org/10.1142/S0129065718500570] [PMID: 30776986]
[30]
Ahmadlou M, Adeli H, Adeli A. New diagnostic EEG markers of the Alzheimer’s disease using visibility graph. J Neural Transm (Vienna) 117(9): 1099-109. (2010).
[http://dx.doi.org/10.1007/s00702-010-0450-3] [PMID: 20714909 ]
[31]
Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3): 186-98. (2009).
[http://dx.doi.org/10.1038/nrn2575] [PMID: 19190637]
[32]
Dai Z, Yan C, Li K, Wang Z, Wang J, Cao M, et al. Identifying and mapping connectivity patterns of brain network hubs in Alzheimer’s disease. Cereb Cortex 25(10): 3723-42. (2015).
[http://dx.doi.org/10.1093/cercor/bhu246] [PMID: 25331602]
[33]
Lacasa L, Luque B, Luque J, Nuno JC. The visibility graph: a new method for estimating the Hurst exponent of fractional Brownian motion. Epl-Europhys Lett 86(3) (2009).
[http://dx.doi.org/10.1209/0295-5075/86/30001]
[34]
Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3): 1059-69. (2010).
[http://dx.doi.org/10.1016/j.neuroimage.2009.10.003] [PMID: 19819337]
[35]
Goel K, Singh RR, Iyengar S, Gupta S. A faster algorithm to update betweenness centrality after node alteration. Internet Math 11(4-5): 403-20. (2015).
[http://dx.doi.org/10.1080/15427951.2014.982311]
[36]
Chang P, Li X, Ma C, Zhang S, Liu Z, Chen K, et al. The effects of an APOE promoter polymorphism on human white matter connectivity during non-demented aging. J Alzheimers Dis 55(1): 77-87. (2017).
[http://dx.doi.org/10.3233/JAD-160447] [PMID: 27636845]
[37]
Opsahl T. Triadic closure in two-mode networks: redefining the global and local clustering coefficients. Soc Networks 35(2): 159-67. (2013).
[http://dx.doi.org/10.1016/j.socnet.2011.07.001]
[38]
Feng X, Zhao Y, Zhang C, Cheng P, He Y. Discrimination of transgenic maize kernel using NIR hyperspectral imaging and multivariate data analysis. Sensors (Basel) 17(8) E1894 (2017).
[http://dx.doi.org/10.3390/s17081894] [PMID: 28817075]
[39]
Wang S, Liu S, Che X, Wang Z, Zhang J, Kong D. Recognition of polycyclic aromatic hydrocarbons using fluorescence spectrometry combined with bird swarm algorithm optimization support vector machine. Spectrochimica Acta Part a-Molecular and Biomolecular Spectroscopy 22(4) (2020).
[http://dx.doi.org/10.1016/j.saa.2019.117404]
[40]
Dai S, Niu D, Han Y. Forecasting of Power grid investment in china based on support vector machine optimized by differential evolution algorithm and grey wolf optimization algorithm applied sciences-basel 84 (2018)..
[http://dx.doi.org/10.3390/app8040636]
[41]
Cervantes J, Garcia-Lamont F, Rodriguez-Mazahua L, Lopez A, Ruiz-Castilla J, Trueba A. PSO-based method for SVM classification on skewed data sets. Neurocomputing 228: 187-97. (2017).
[http://dx.doi.org/10.1016/j.neucom.2016.10.041]
[42]
Chang C-C, Lin C-J. LIBSVM: A Library for Support Vector Machines. ACM Trans Intell Syst Technol 2(3) (2011).
[http://dx.doi.org/10.1145/1961189.1961199]
[43]
Wang J, Yang C, Wang RF, Yu HT, Cao YB, Liu J. Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method. Physica a-Statistical Mechanics App 460: 174-87. (2016).
[44]
Naqvi NH, Rudrauf D, Damasio H, Bechara A. Damage to the insula disrupts addiction to cigarette smoking. Science 315(5811): 531-4. (2007).
[http://dx.doi.org/10.1126/science.1135926] [PMID: 17255515]
[45]
Kurth F, Zilles K, Fox PT, Laird AR, Eickhoff SB. A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct Funct 214(5-6): 519-34. (2010).
[http://dx.doi.org/10.1007/s00429-010-0255-z] [PMID: 20512376]
[46]
Zhao Z-L, Fan F-M, Lu J, Li H-J, Jia L-F, Han Y, et al. Changes of gray matter volume and amplitude of low-frequency oscillations in amnestic MCI: An integrative multi-modal MRI study. Acta Radiol 56(5): 614-21. (2015).
[http://dx.doi.org/10.1177/0284185114533329] [PMID: 24792358]
[47]
Karas GB, Scheltens P, Rombouts SA, Visser PJ, van Schijndel RA, Fox NC, et al. Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage 23(2): 708-16. (2004).
[http://dx.doi.org/10.1016/j.neuroimage.2004.07.006] [PMID: 15488420]
[48]
Braak H, Alafuzoff I, Arzberger T, Kretzschmar H, Del Tredici K. Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol 112(4): 389-404. (2006).
[http://dx.doi.org/10.1007/s00401-006-0127-z] [PMID: 16906426]
[49]
He W, Liu D, Radua J, Li G, Han B, Sun Z. Meta-analytic comparison between PIB-PET and FDG-PET results in Alzheimer’s disease and MCI. Cell Biochem Biophys 71(1): 17-26. (2015).
[http://dx.doi.org/10.1007/s12013-014-0138-7] [PMID: 25370296]
[50]
Xie C, Bai F, Yu H, Shi Y, Yuan Y, Chen G, et al. Abnormal insula functional network is associated with episodic memory decline in amnestic mild cognitive impairment. Neuroimage 63(1): 320-7. (2012).
[http://dx.doi.org/10.1016/j.neuroimage.2012.06.062] [PMID: 22776459]
[51]
Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 27(9): 2349-56. (2007).
[http://dx.doi.org/10.1523/JNEUROSCI.5587-06.2007] [PMID: 17329432]
[52]
Li H, Jia X, Qi Z, Fan X, Ma T, Ni H, et al. Altered functional connectivity of the basal nucleus of meynert in mild cognitive impairment: a resting-state FMRI Study. Front Aging Neurosci 9: 127. (2017).
[53]
Mesulam MM, Volicer L, Marquis JK, Mufson EJ, Green RC. Systematic regional differences in the cholinergic innervation of the primate cerebral cortex: distribution of enzyme activities and some behavioral implications. Ann Neurol 19(2): 144-51. (1986).
[http://dx.doi.org/10.1002/ana.410190206] [PMID: 3963756]
[54]
Maddock RJ. The retrosplenial cortex and emotion: new insights from functional neuroimaging of the human brain. Trends Neurosci 22(7): 310-6. (1999).
[http://dx.doi.org/10.1016/S0166-2236(98)01374-5] [PMID: 10370255]
[55]
Xu L, Wu X, Li R, Chen KW, Long ZY, Zhang JC, et al. Alzheimer’s disease neuroimaging initiative. Prediction of progressive mild cognitive impairment by multi-modal neuroimaging biomarkers. J Alzheimers Dis 51(4): 1045-56. (2016).
[http://dx.doi.org/10.3233/JAD-151010] [PMID: 26923024]
[56]
Camus V, Payoux P, Barré L, Desgranges B, Voisin T, Tauber C, et al. Using PET with 18F-AV-45 (florbetapir) to quantify brain amyloid load in a clinical environment. Eur J Nucl Med Mol Imaging 39(4): 621-31. (2012).
[http://dx.doi.org/10.1007/s00259-011-2021-8] [PMID: 22252372]
[57]
Farrow TFD, Thiyagesh SN, Wilkinson ID, Parks RW, Ingram L, Woodruff PWR. Fronto-temporal-lobe atrophy in early-stage Alzheimer’s disease identified using an improved detection methodology. Psychiatry Res 155(1): 11-9. (2007).
[http://dx.doi.org/10.1016/j.pscychresns.2006.12.013] [PMID: 17399959]
[58]
Ting WK-C, Fischer CE, Millikin CP, Ismail Z, Chow TW, Schweizer TA. Grey matter atrophy in mild cognitive impairment / early Alzheimer disease associated with delusions: a voxel-based morphometry study. Curr Alzheimer Res 12(2): 165-72. (2015).
[http://dx.doi.org/10.2174/1567205012666150204130456] [PMID: 25654501]
[59]
Mazoyer B, Zago L, Mellet E, Bricogne S, Etard O, Houde O, et al. Cortical networks for working memory and executive functions sustain the conscious resting state in man. Brain Res Bull 54(3): 287-98. (2001).
[http://dx.doi.org/10.1016/S0361-9230(00)00437-8] [PMID: 11287133]
[60]
McKiernan KA, Kaufman JN, Kucera-Thompson J, Binder JR. A parametric manipulation of factors affecting task-induced deactivation in functional neuroimaging. J Cogn Neurosci 15(3): 394-408. (2003).
[http://dx.doi.org/10.1162/089892903321593117] [PMID: 12729491]
[61]
Greicius MD, Menon V. Default-mode activity during a passive sensory task: uncoupled from deactivation but impacting activation. J Cogn Neurosci 16(9): 1484-92. (2004).
[http://dx.doi.org/10.1162/0898929042568532] [PMID: 15601513]
[62]
Duarte-Abritta B, Villarreal MF, Abulafia C, et al. Cortical thickness, brain metabolic activity, and in vivo amyloid deposition in asymptomatic, middle-aged offspring of patients with late-onset Alzheimer’s disease. J Psychiatr Res 107: 11-8. (2018).
[http://dx.doi.org/10.1016/j.jpsychires.2018.10.008] [PMID: 30308328]
[63]
Binnewijzend MAA, Adriaanse SM, Van der Flier WM, et al. Brain network alterations in Alzheimer’s disease measured by eigenvector centrality in fMRI are related to cognition and CSF biomarkers. Hum Brain Mapp 35(5): 2383-93. (2014).
[http://dx.doi.org/10.1002/hbm.22335] [PMID: 24039033]
[64]
Wang Z, Jia X, Liang P, Qi Z, Yang Y, Zhou W, et al. Changes in thalamus connectivity in mild cognitive impairment: evidence from resting state fMRI. Eur J Radiol 81(2): 277-85. (2012).
[http://dx.doi.org/10.1016/j.ejrad.2010.12.044] [PMID: 21273022]
[65]
Holroyd S, Shepherd ML, Downs JH III. Occipital atrophy is associated with visual hallucinations in Alzheimer’s disease. J Neuropsychiatry Clin Neurosci 12(1): 25-8. (2000).
[http://dx.doi.org/10.1176/jnp.12.1.25] [PMID: 10678508]
[66]
Wang B, Niu Y, Miao L, Cao R, Yan P, Guo H, et al. Decreased Complexity in Alzheimer’s Disease: Resting-State fMRI Evidence of Brain Entropy Mapping. Front Aging Neurosci 9: 378. (2017).
[67]
Zheng W, Yao Z, Hu B, Gao X, Cai H, Moore P. Novel cortical thickness pattern for accurate detection of Alzheimer’s disease. J Alzheimers Dis 48(4): 995-1008. (2015).
[http://dx.doi.org/10.3233/JAD-150311] [PMID: 26444768]
[68]
Chételat G, Landeau B, Eustache F, enge F, Viader F, de la Sayette V, et al. Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. Neuroimage 27(4): 934-46. (2005).
[http://dx.doi.org/10.1016/j.neuroimage.2005.05.015] [PMID: 15979341]
[69]
Cavanna AE, Trimble MR. The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129(Pt 3): 564-83. (2006).
[http://dx.doi.org/10.1093/brain/awl004] [PMID: 16399806]
[70]
Delbeuck X. Van dL, M, Collette FJNR. Alzheimer’. Disease as a Disconnection Syndrome 13(2): 79-92. (2003).
[71]
Zhou J, Gennatas ED, Kramer JH, Miller BL, Seeley WW. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 73(6): 1216-27. (2012).
[http://dx.doi.org/10.1016/j.neuron.2012.03.004] [PMID: 22445348]
[72]
Raj A, Kuceyeski A, Weiner M. A network diffusion model of disease progression in dementia. Neuron 73(6): 1204-15. (2012).
[http://dx.doi.org/10.1016/j.neuron.2011.12.040] [PMID: 22445347]
[73]
Bi X-a, Jiang Q, Sun Q, Shu Q, Liu Y. Analysis of Alzheimer’s disease based on the random neural network cluster in FMRI. Front Neuroinform 12: 60. (2018).
[http://dx.doi.org/10.3389/fninf.2018.00060]
[74]
Ju R, Hu C, Zhou P, Li Q. Early diagnosis of Alzheimer’s disease based on resting-state brain networks and deep learning. IEEE/ACM Trans Comput Biol Bioinformatics 16(1): 244-57. (2019).
[http://dx.doi.org/10.1109/TCBB.2017.2776910] [PMID: 29989989]
[75]
Wang J, Zuo X, Dai Z, Xia M, Zhao Z, Zhao X, et al. Disrupted functional brain connectome in individuals at risk for Alzheimer’s disease. Biol Psychiatry 73(5): 472-81. (2013).
[http://dx.doi.org/10.1016/j.biopsych.2012.03.026] [PMID: 22537793]

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