Apolipoprotein E4 effects on topological brain network organization in mild cognitive impairment.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
12 01 2021
12 01 2021
Historique:
received:
12
06
2020
accepted:
30
12
2020
entrez:
13
1
2021
pubmed:
14
1
2021
medline:
26
8
2021
Statut:
epublish
Résumé
The Apolipoprotein E isoform E4 (ApoE4) is consistently associated with an elevated risk of developing late-onset Alzheimer's Disease (AD); however, less is known about the potential genetic modulation of the brain networks organization during prodromal stages like Mild Cognitive Impairment (MCI). To investigate this issue during this critical stage, we used a dataset with a cross-sectional sample of 253 MCI patients divided into ApoE4-positive (‛Carriers') and ApoE4-negative ('non-Carriers'). We estimated the cortical thickness (CT) from high-resolution T1-weighted structural magnetic images to calculate the correlation among anatomical regions across subjects and build the CT covariance networks (CT-Nets). The topological properties of CT-Nets were described through the graph theory approach. Specifically, our results showed a significant decrease in characteristic path length, clustering-index, local efficiency, global connectivity, modularity, and increased global efficiency for Carriers compared to non-Carriers. Overall, we found that ApoE4 in MCI shaped the topological organization of CT-Nets. Our results suggest that in the MCI stage, the ApoE4 disrupting the CT correlation between regions may be due to adaptive mechanisms to sustain the information transmission across distant brain regions to maintain the cognitive and behavioral abilities before the occurrence of the most severe symptoms.
Identifiants
pubmed: 33436948
doi: 10.1038/s41598-020-80909-7
pii: 10.1038/s41598-020-80909-7
pmc: PMC7804004
doi:
Substances chimiques
Apolipoprotein E4
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
845Références
2020 Alzheimer’s disease facts and figures. Alzheimer’s Dementia 16, 391–460 (2020)
Albert, M. S. et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dementia 7, 270–279 (2011).
pubmed: 21514249
doi: 10.1016/j.jalz.2011.03.008
Jansen, W. J. et al. Prevalence of cerebral amyloid pathology in persons without dementia: A meta-analysis. JAMA 313, 1924–1938 (2015).
pubmed: 25988462
pmcid: 4486209
doi: 10.1001/jama.2015.4668
Mueller, S. G. et al. Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s Dementia 1, 55–66 (2005).
pubmed: 17476317
doi: 10.1016/j.jalz.2005.06.003
Gao, W. et al. Intersubject variability of and genetic effects on the Brain’s functional connectivity during infancy. J. Neurosci. 34, 11288–11296 (2014).
pubmed: 25143609
pmcid: 4138339
doi: 10.1523/JNEUROSCI.5072-13.2014
Petersen, R. C. Mild cognitive impairment: Transition between aging and Alzheimer’s disease. Neurologia (Barcelona, Spain) 15, 93–101 (2000).
Rao, A. T., Degnan, A. J. & Levy, L. M. Genetics of Alzheimer disease. AJNR Am. J. Neuroradiol. 35, 457–458 (2014).
pubmed: 23538412
doi: 10.3174/ajnr.A3545
pmcid: 7964720
Farlow, M. R. et al. Impact of APOE in mild cognitive impairment. Neurology 63, 1898–1901 (2004).
pubmed: 15557508
doi: 10.1212/01.WNL.0000144279.21502.B7
Norberg, J. et al. Regional differences in effects of APOE ε4 on cognitive impairment in non-demented subjects. Dement. Geriatr. Cogn. Disord. 32, 135–142 (2011).
pubmed: 21952537
doi: 10.1159/000330492
Liu, C.-C., Liu, C.-C., Kanekiyo, T., Xu, H. & Bu, G. Apolipoprotein E and Alzheimer disease: Risk, mechanisms and therapy. Nat. Rev. Neurol. 9, 106–118 (2013).
pubmed: 23296339
pmcid: 3726719
doi: 10.1038/nrneurol.2012.263
Liu, Y., Cai, Z.-L., Xue, S., Zhou, X. & Wu, F. Proxies of cognitive reserve and their effects on neuropsychological performance in patients with mild cognitive impairment. J. Clin. Neurosci. 20, 548–553 (2013).
pubmed: 23406880
doi: 10.1016/j.jocn.2012.04.020
Cherbuin, N., Leach, L. S., Christensen, H. & Anstey, K. J. Neuroimaging and APOE genotype: A systematic qualitative review. Dement. Geriatr. Cogn. Disord. 24, 348–362 (2007).
pubmed: 17911980
doi: 10.1159/000109150
Delbeuck, X., Van der Linden, M. & Collette, F. Alzheimer’ disease as a disconnection syndrome?. Neuropsychol. Rev. 13, 79–92 (2003).
pubmed: 12887040
doi: 10.1023/A:1023832305702
Xie, T. & He, Y. Mapping the Alzheimer’s brain with connectomics. Front. Psychiatry 2, 1–14 (2012).
doi: 10.3389/fpsyt.2011.00077
Bullmore, E. T. & Bassett, D. S. Brain Graphs: Graphical models of the human brain connectome. Annu. Rev. Clin. Psychol. 7, 113–140 (2011).
pubmed: 21128784
doi: 10.1146/annurev-clinpsy-040510-143934
Alexander-Bloch, A., Giedd, J. N. & Bullmore, E. Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci. 14, 322–336 (2013).
pubmed: 23531697
pmcid: 4043276
doi: 10.1038/nrn3465
Lerch, J. P. et al. Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. NeuroImage 31, 993–1003 (2006).
pubmed: 16624590
doi: 10.1016/j.neuroimage.2006.01.042
Tijms, B. M. et al. Alzheimer’s disease: Connecting findings from graph theoretical studies of brain networks. Neurobiol. Aging 34, 2023–2036 (2013).
pubmed: 23541878
doi: 10.1016/j.neurobiolaging.2013.02.020
He, Y., Chen, Z. & Evans, A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer’s disease. J. Neurosci. 28, 4756–4766 (2008).
pubmed: 18448652
pmcid: 6670444
doi: 10.1523/JNEUROSCI.0141-08.2008
Lo, C.-Y. et al. Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease. J. Neurosci. 30, 16876–16885 (2010).
pubmed: 21159959
pmcid: 6634928
doi: 10.1523/JNEUROSCI.4136-10.2010
Stam, C. J., Jones, B. F., Nolte, G., Breakspear, M. & Scheltens, P. Small-world networks and functional connectivity in Alzheimer’s disease. Cereb. Cortex 17, 92–99 (2007).
pubmed: 16452642
doi: 10.1093/cercor/bhj127
Supekar, K., Menon, V., Rubin, D., Musen, M. & Greicius, M. D. Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput. Biol. 4, e1000100 (2008).
pubmed: 18584043
pmcid: 2435273
doi: 10.1371/journal.pcbi.1000100
Sanabria-Diaz, G., Martínez-Montes, E. & Melie-Garcia, L. Glucose metabolism during resting state reveals abnormal brain networks organization in the Alzheimer’s disease and mild cognitive impairment. PLoS ONE 8, e68860 (2013).
pubmed: 23894356
pmcid: 3720883
doi: 10.1371/journal.pone.0068860
Brown, J. A. et al. Brain network local interconnectivity loss in aging APOE-4 allele carriers. Proc. Natl. Acad. Sci. USA 108, 20760–20765 (2011).
pubmed: 22106308
doi: 10.1073/pnas.1109038108
pmcid: 3251140
Goryawala, M., Duara, R., Loewenstein, D. A., Zhou, Q. & Barker, W. Apolipoprotein-E4 ( ApoE4) carriers show altered small-world properties in the default mode network of the brain. Biomed. Phys. Eng. Express 1, 15001 (2015).
doi: 10.1088/2057-1976/1/1/015001
Seo, E. H. et al. Influence of APOE genotype on whole-brain functional networks in cognitively normal elderly. PLoS ONE 8, 2–10 (2013).
Wang, J., Wang, X., He, Y., Yu, X. & Wang, H. Apolipoprotein E e 4 modulates functional brain connectome in Alzheimer’s disease. Hum. Brain Mapp. 36, 1828–1846 (2015).
pubmed: 25619771
pmcid: 6869368
doi: 10.1002/hbm.22740
Zhao, X. et al. Disrupted small-world brain networks in moderate Alzheimer’s disease: A resting-state fMRI study. PLoS ONE 7, e99540 (2012).
Ma, C. et al. Disrupted brain structural connectivity: Pathological interactions between genetic APOE ε4 status and developed MCI condition. Mol. Neurobiol. 54, 6999–7007 (2017).
pubmed: 27785756
doi: 10.1007/s12035-016-0224-5
Wang, Z. et al. APOE genotype effects on intrinsic brain network connectivity in patients with amnestic mild cognitive impairment. Sci. Rep. 7, 397 (2017).
pubmed: 28341847
pmcid: 5428452
doi: 10.1038/s41598-017-00432-0
Yao, Z. et al. A FDG-PET study of metabolic networks in apolipoprotein E ε4 allele carriers. PLoS ONE 10, 1–16 (2015).
doi: 10.1371/journal.pone.0132300
Petersen, R. C. et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization. Neurology 74, 201–209 (2010).
pubmed: 20042704
pmcid: 2809036
doi: 10.1212/WNL.0b013e3181cb3e25
Saykin, A. J. et al. Alzheimer’s disease neuroimaging initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans. Alzheimer’s Dementia 6, 265–273 (2010).
pubmed: 20451875
doi: 10.1016/j.jalz.2010.03.013
Serrano-Pozo, A., Qian, J., Monsell, S. E., Betensky, R. A. & Hyman, B. T. APOEε2 is associated with milder clinical and pathological Alzheimer disease. Ann. Neurol. 77, 917–929 (2015).
pubmed: 25623662
pmcid: 4447539
doi: 10.1002/ana.24369
Jovicich, J. et al. Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data. NeuroImage 30, 436–443 (2006).
pubmed: 16300968
doi: 10.1016/j.neuroimage.2005.09.046
Fornito, A. et al. Variability of the paracingulate sulcus and morphometry of the medial frontal cortex: Associations with cortical thickness, surface area, volume, and sulcal depth. Hum. Brain Mapp. 29, 222–236 (2008).
pubmed: 17497626
doi: 10.1002/hbm.20381
Sled, J. G., Zijdenbos, A. P. & Evans, A. C. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17, 87–97 (1998).
pubmed: 9617910
doi: 10.1109/42.668698
Fischl, B. et al. Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11–22 (2004).
pubmed: 14654453
doi: 10.1093/cercor/bhg087
Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53, 1–15 (2010).
pubmed: 20547229
doi: 10.1016/j.neuroimage.2010.06.010
Efron, B. & Tibshirani, R. An Introduction to the Bootstrap (Chapman & Hall, New York, 1994).
doi: 10.1201/9780429246593
Sanabria-Diaz, G. et al. Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks. NeuroImage 50, 1497–1510 (2010).
pubmed: 20083210
doi: 10.1016/j.neuroimage.2010.01.028
He, Y., Chen, Z. J. & Evans, A. C. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb. Cortex 17, 2407–2419 (2007).
pubmed: 17204824
doi: 10.1093/cercor/bhl149
Achard, S. & Bullmore, E. Efficiency and cost of economical brain functional networks. PLoS Comput. Biol. 3, 0174–0183 (2007).
doi: 10.1371/journal.pcbi.0030017
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. & Hwang, D. Complex networks: Structure and dynamics. Phys. Rep. 424, 175–308 (2006).
doi: 10.1016/j.physrep.2005.10.009
Watts, D. J. & Strogatz, S. H. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998).
pubmed: 9623998
doi: 10.1038/30918
Latora, V. & Marchiori, M. Efficient behavior of small-world networks. Phys. Rev. Lett. 87, 198701 (2001).
pubmed: 11690461
doi: 10.1103/PhysRevLett.87.198701
Freeman, L. C. A set of measures of centrality based on betweenness. Sociometry 40, 35 (1977).
doi: 10.2307/3033543
Castellano, C., Cecconi, F., Loreto, V., Parisi, D. & Radicchi, F. Self-contained algorithms to detect communities in networks. Eur. Phys. J. B 38, 311–319 (2004).
doi: 10.1140/epjb/e2004-00123-0
Newman, M. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 26113 (2004).
doi: 10.1103/PhysRevE.69.026113
Buldú, J. M. et al. Reorganization of functional networks in mild cognitive impairment. PLoS ONE 6, 1–8 (2011).
doi: 10.1371/journal.pone.0019584
Daianu, M. et al. In Algebraic Connectivity of Brain Networks Shows Patterns of Segregation Leading to Reduced Network Robustness in Alzheimer’s Disease BT: Computational Diffusion MRI (eds O’Donnell, L. et al.) 55–64 (Springer International Publishing, Berlin, 2014).
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52, 1059–1069 (2010).
pubmed: 19819337
doi: 10.1016/j.neuroimage.2009.10.003
Chung, M. K. et al. Cortical thickness analysis in autism with heat kernel smoothing. NeuroImage 25, 1256–1265 (2005).
pubmed: 15850743
doi: 10.1016/j.neuroimage.2004.12.052
He, Y. et al. Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. Brain 132, 3366–3379 (2009).
pubmed: 19439423
pmcid: 2792366
doi: 10.1093/brain/awp089
Efron, B. The Jackknife, the Bootstrap and Other Resampling Plans (Society for Industrial and Applied Mathematics, Philadelphia, 1982).
doi: 10.1137/1.9781611970319
Melie-Garcia, L., Sanabria-Diaz, G., Iturria-Medina, Y., Alemán-Gómez, Y. MorphoConnect: toolbox for studying structural brain networks using morphometric descriptors. In 16th Annual Meeting of the Organization for Human Brain Mapping (2010).
Xia, M., Wang, J. & He, Y. BrainNet viewer: A network visualization tool for human brain connectomics. PLoS ONE 8, e68910 (2013).
pubmed: 23861951
pmcid: 3701683
doi: 10.1371/journal.pone.0068910
Bastian, M., Heymann, S. & Jacomy, M. Gephi : An open source software for exploring and manipulating networks visualization and exploration of large graphs. Icwsm 8, 361–362 (2009).
doi: 10.1609/icwsm.v3i1.13937
Verghese, P. B., Castellano, J. M. & Holtzman, D. M. Apolipoprotein E in Alzheimer’s disease and other neurological disorders. Lancet Neurol 10, 241–252 (2011).
pubmed: 21349439
pmcid: 3132088
doi: 10.1016/S1474-4422(10)70325-2
Dickerson, B. C. et al. Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths. NeuroImage 39, 10–18 (2008).
pubmed: 17942325
doi: 10.1016/j.neuroimage.2007.08.042
Gong, G., He, Y., Chen, Z. J. & Evans, A. C. Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex. NeuroImage 59, 1239–1248 (2012).
pubmed: 21884805
doi: 10.1016/j.neuroimage.2011.08.017
Tijms, B. M. et al. Gray matter networks and clinical progression in subjects with predementia Alzheimer ’ s disease. Neurobiol. Aging 61, 75–81 (2018).
pubmed: 29040871
doi: 10.1016/j.neurobiolaging.2017.09.011
Pereira, J. B. et al. Disrupted network topology in patients with stable and progressive mild cognitive impairment and Alzheimer’s disease. Cereb. Cortex 26, 3476–3493 (2016).
pubmed: 27178195
pmcid: 4961019
doi: 10.1093/cercor/bhw128
Sporns, O. & Zwi, J. D. The Small World of the Cerebral Cortex 145–162 (Springer, Berlin, 2004).
Tuminello, E. R. & Han, S. D. The Apolipoprotein E antagonistic pleiotropy hypothesis: Review and recommendations. Int. J. Alzheimer’s Dis. 2011, 1–12 (2011).
doi: 10.4061/2011/726197
Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain’s default network: Anatomy, function, and relevance to disease. Ann. N. Y. Acad. Sci. 1124, 1–38 (2008).
pubmed: 18400922
doi: 10.1196/annals.1440.011
Raichle, M. E. et al. A default mode of brain function. Proc. Natl. Acad. Sci. USA 98, 676–682 (2001).
pubmed: 11209064
doi: 10.1073/pnas.98.2.676
pmcid: 14647
Bero, A. W. et al. Neuronal activity regulates the regional vulnerability to amyloid-β deposition. Nat. Neurosci. 14, 750–756 (2011).
pubmed: 21532579
pmcid: 3102784
doi: 10.1038/nn.2801
Sun, Z.-W. et al. Decreased cerebral blood flow velocity in apolipoprotein E epsilon4 allele carriers with mild cognitive impairment. Eur. J. Neurol. 14, 150–155 (2007).
pubmed: 17250722
doi: 10.1111/j.1468-1331.2006.01579.x
Curtis, C. E. & D’Esposito, M. Persistent activity in the prefrontal cortex during working memory. Trends Cogn. Sci. 7, 415–423 (2003).
pubmed: 12963473
doi: 10.1016/S1364-6613(03)00197-9
Lau, H. C., Rogers, R. D. & Passingham, R. E. On measuring the perceived onsets of spontaneous actions. J. Neurosci. 26, 7265–7271 (2006).
pubmed: 16822984
pmcid: 6673952
doi: 10.1523/JNEUROSCI.1138-06.2006
Ridderinkhof, K. R., Ullsperger, M., Crone, E. A. & Nieuwenhuis, S. The role of the medial frontal cortex in cognitive control. Science 306, 443–447 (2004).
pubmed: 15486290
doi: 10.1126/science.1100301
Salathé, M. & Jones, J. H. Dynamics and control of diseases in networks with community structure. PLoS Comput. Biol. 6, e1000736 (2010).
pubmed: 20386735
pmcid: 2851561
doi: 10.1371/journal.pcbi.1000736
Fair, D. A. et al. The maturing architecture of the brain’s default network. Proc. Natl. Acad. Sci. USA 105, 4028–4032 (2008).
pubmed: 18322013
doi: 10.1073/pnas.0800376105
pmcid: 2268790
Calhoun, V. D., Miller, R., Pearlson, G. & Adali, T. The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84, 262–274 (2014).
pubmed: 25374354
pmcid: 4372723
doi: 10.1016/j.neuron.2014.10.015
Zalesky, A., Fornito, A. & Bullmore, E. T. Network-based statistic: Identifying differences in brain networks. NeuroImage 53, 1197–1207 (2010).
pubmed: 20600983
doi: 10.1016/j.neuroimage.2010.06.041