Skip to content
2000
image of The Association between the rs6656401 Locus of the CR1 Gene and Structural Alterations of Brain Effects in Han Chinese Patients with Alzheimer's Disease

Abstract

Introduction

The complement receptor 1 (CR1) gene is identified as the one closely associated with Alzheimer's disease (AD). However, there has been no exploration of the imaging alterations associated with the CR1 gene in AD patients of the Han population. The purpose of this study is to investigate the association between the rs6656401 mutation and neuroimaging variations in Han AD patients.

Methods

We collected nuclear magnetic resonance images from 101 patients with AD and 98 healthy controls (HC). The subjects in this study, based on the different genotypes of rs6656401, were divided into three groups, with the number of AA, AG, and GG genotypes in the AD group being 1, 17, and 83, and 1, 8, and 89 in the HC group. Data were analyzed using the dominant model. Structural differences in the brain tissue between genotypes at the rs6656401 polymorphic locus were compared using voxel-based morphological analysis, cortical thickness, and graph-theoretic analysis to construct structural networks.

Results

Seven regions (namely, right precuneus, right caudal middle frontal cortical, right rostral middle frontal, right superior frontal, right bankssts, right superior parietal, and right paracentral) were significantly different across CR1 rs6656401 genotypes. The voxel-based morphometry analysis revealed that voxel cluster sizes in the left cerebellum, left superior temporal gyrus, right superior frontal gyrus orbital, right precuneus, and right superior parietal were significantly different in the AA, AG, and GG groups. The degree centrality (Dc) of the left inferior frontal gyrus was significantly greater in the GG group than in the AG group after false discovery rate correction in the structural network analysis.

Discussion

This study demonstrates that the rs6656401 AA genotype primarily induces structural alterations in the frontal, temporal, and parietal lobes of AD patients, with significant changes in the right middle frontal gyrus, precuneus, and superior parietal gyrus, along with Dc index alterations in the left inferior frontal gyrus affecting brain network function. Our findings confirm the association between the rs6656401 polymorphism and AD-related brain structural changes, providing the first evidence of these regional alterations in Han Chinese AD cohorts. Future studies will elucidate the locus's pathological mechanism to inform early diagnosis and targeted therapies.

Conclusion

Our study first indicated that CR1 rs6656401 genotypes significantly influenced the morphological and structural covariate networks in Han AD patients.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/car/10.2174/0115672050397092250823195514
2025-09-04
2025-11-02

  • From This Site

    /content/journals/car/10.2174/0115672050397092250823195514
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Knopman D.S. Amieva H. Petersen R.C. Chételat G. Holtzman D.M. Hyman B.T. Nixon R.A. Jones D.T. Alzheimer disease. Nat Rev Dis Primers 2021 7 1 33 10.1038/s41572‑021‑00269‑y 33986301
    [Google Scholar]
  2. 2024 Alzheimer’s disease facts and figures. Alzheimers Dement 2024 20 5 3708 3821 10.1002/alz.13809 38689398
    [Google Scholar]
  3. Jia L. Du Y. Chu L. Zhang Z. Li F. Lyu D. Li Y. Li Y. Zhu M. Jiao H. Song Y. Shi Y. Zhang H. Gong M. Wei C. Tang Y. Fang B. Guo D. Wang F. Zhou A. Chu C. Zuo X. Yu Y. Yuan Q. Wang W. Li F. Shi S. Yang H. Zhou C. Liao Z. Lv Y. Li Y. Kan M. Zhao H. Wang S. Yang S. Li H. Liu Z. Wang Q. Qin W. Jia J. Quan M. Wang Y. Li W. Cao S. Xu L. Han Y. Liang J. Qiao Y. Qin Q. Qiu Q. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: A cross-sectional study. Lancet Public Health 2020 5 12 e661 e671 10.1016/S2468‑2667(20)30185‑7 33271079
    [Google Scholar]
  4. Ledig C. Schuh A. Guerrero R. Heckemann R.A. Rueckert D. Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: Biomarker analysis and shared morphometry database. Sci Rep 2018 8 1 11258 10.1038/s41598‑018‑29295‑9 30050078
    [Google Scholar]
  5. Scheltens P. Blennow K. Breteler M.M.B. de Strooper B. Frisoni G.B. Salloway S. Van der Flier W.M. Alzheimer’s disease. Lancet 2016 388 10043 505 517 10.1016/S0140‑6736(15)01124‑1 26921134
    [Google Scholar]
  6. Porsteinsson A.P. Isaacson R.S. Knox S. Sabbagh M.N. Rubino I. Diagnosis of early Alzheimer’s disease: Clinical practice in 2021. J Prev Alzheimers Dis 2021 8 3 371 386 10.14283/jpad.2021.23 34101796
    [Google Scholar]
  7. Khan S. Barve K.H. Kumar M.S. Recent advancements in pathogenesis, diagnostics and treatment of Alzheimer’s disease. Curr Neuropharmacol 2020 18 11 1106 1125 10.2174/1570159X18666200528142429 32484110
    [Google Scholar]
  8. Bateman R.J. Xiong C. Benzinger T.L.S. Fagan A.M. Goate A. Fox N.C. Marcus D.S. Cairns N.J. Xie X. Blazey T.M. Holtzman D.M. Santacruz A. Buckles V. Oliver A. Moulder K. Aisen P.S. Ghetti B. Klunk W.E. McDade E. Martins R.N. Masters C.L. Mayeux R. Ringman J.M. Rossor M.N. Schofield P.R. Sperling R.A. Salloway S. Morris J.C. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 2012 367 9 795 804 10.1056/NEJMoa1202753 22784036
    [Google Scholar]
  9. Matsuda H. MRI morphometry in Alzheimer’s disease. Ageing Res Rev 2016 30 17 24 10.1016/j.arr.2016.01.003 26812213
    [Google Scholar]
  10. Wang X. Huang W. Su L. Xing Y. Jessen F. Sun Y. Shu N. Han Y. Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer’s disease. Mol Neurodegener 2020 15 1 55 10.1186/s13024‑020‑00395‑3 32962744
    [Google Scholar]
  11. Zhao K. Lin J. Dyrba M. Wang D. Che T. Wu H. Wang J. Liu Y. Li S. Coupling of the spatial distributions between sMRI and PET reveals the progression of Alzheimer’s disease. Netw Neurosci 2023 7 1 86 101 10.1162/netn_a_00271 37334010
    [Google Scholar]
  12. Cai J. Xiong W. Wang X. Tan H. Genetic architecture of hippocampus subfields volumes in Alzheimer’s disease. CNS Neurosci Ther 2024 30 3 14110 10.1111/cns.14110 36756718
    [Google Scholar]
  13. Low A. Foo H. Yong T.T. Tan L.C.S. Kandiah N. Hippocampal subfield atrophy of CA1 and subicular structures predict progression to dementia in idiopathic Parkinson’s disease. J Neurol Neurosurg Psychiatry 2019 90 6 681 687 10.1136/jnnp‑2018‑319592 30683708
    [Google Scholar]
  14. Self W.K. Holtzman D.M. Emerging diagnostics and therapeutics for Alzheimer disease. Nat Med 2023 29 9 2187 2199 10.1038/s41591‑023‑02505‑2 37667136
    [Google Scholar]
  15. Breijyeh Z. Karaman R. Comprehensive review on Alzheimer’s disease: Causes and treatment. Molecules 2020 25 24 5789 10.3390/molecules25245789 33302541
    [Google Scholar]
  16. Lei P. Ayton S. Bush A.I. The essential elements of Alzheimer’s disease. J Biol Chem 2021 296 100105 10.1074/jbc.REV120.008207 33219130
    [Google Scholar]
  17. Jansen I.E. Savage J.E. Watanabe K. Bryois J. Williams D.M. Steinberg S. Sealock J. Karlsson I.K. Hägg S. Athanasiu L. Voyle N. Proitsi P. Witoelar A. Stringer S. Aarsland D. Almdahl I.S. Andersen F. Bergh S. Bettella F. Bjornsson S. Brækhus A. Bråthen G. de Leeuw C. Desikan R.S. Djurovic S. Dumitrescu L. Fladby T. Hohman T.J. Jonsson P.V. Kiddle S.J. Rongve A. Saltvedt I. Sando S.B. Selbæk G. Shoai M. Skene N.G. Snaedal J. Stordal E. Ulstein I.D. Wang Y. White L.R. Hardy J. Hjerling-Leffler J. Sullivan P.F. van der Flier W.M. Dobson R. Davis L.K. Stefansson H. Stefansson K. Pedersen N.L. Ripke S. Andreassen O.A. Posthuma D. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet 2019 51 3 404 413 10.1038/s41588‑018‑0311‑9 30617256
    [Google Scholar]
  18. Adewale Q. Khan A.F. Carbonell F. Iturria-Medina Y. Integrated transcriptomic and neuroimaging brain model decodes biological mechanisms in aging and Alzheimer’s disease. eLife 2021 10 62589 10.7554/eLife.62589 34002691
    [Google Scholar]
  19. Hofer E. Roshchupkin G.V. Adams H.H.H. Knol M.J. Lin H. Li S. Zare H. Ahmad S. Armstrong N.J. Satizabal C.L. Bernard M. Bis J.C. Gillespie N.A. Luciano M. Mishra A. Scholz M. Teumer A. Xia R. Jian X. Mosley T.H. Saba Y. Pirpamer L. Seiler S. Becker J.T. Carmichael O. Rotter J.I. Psaty B.M. Lopez O.L. Amin N. van der Lee S.J. Yang Q. Himali J.J. Maillard P. Beiser A.S. DeCarli C. Karama S. Lewis L. Harris M. Bastin M.E. Deary I.J. Veronica Witte A. Beyer F. Loeffler M. Mather K.A. Schofield P.R. Thalamuthu A. Kwok J.B. Wright M.J. Ames D. Trollor J. Jiang J. Brodaty H. Wen W. Vernooij M.W. Hofman A. Uitterlinden A.G. Niessen W.J. Wittfeld K. Bülow R. Völker U. Pausova Z. Bruce Pike G. Maingault S. Crivello F. Tzourio C. Amouyel P. Mazoyer B. Neale M.C. Franz C.E. Lyons M.J. Panizzon M.S. Andreassen O.A. Dale A.M. Logue M. Grasby K.L. Jahanshad N. Painter J.N. Colodro-Conde L. Bralten J. Hibar D.P. Lind P.A. Pizzagalli F. Stein J.L. Thompson P.M. Medland S.E. Grasby K.L. Jahanshad N. Painter J.N. Colodro-Conde L. Bralten J. Hibar D.P. Lind P.A. Pizzagalli F. Ching C.R.K. McMahon M.A.B. Shatokhina N. Zsembik L.C.P. Agartz I. Alhusaini S. Almeida M.A.A. Alnæs D. Amlien I.K. Andersson M. Ard T. Armstrong N.J. Ashley-Koch A. Bernard M. Brouwer R.M. Buimer E.E.L. Bülow R. Bürger C. Cannon D.M. Chakravarty M. Chen Q. Cheung J.W. Couvy-Duchesne B. Dale A.M. Dalvie S. de Araujo T.K. de Zubicaray G.I. de Zwarte S.M.C. den Braber A. Doan N.T. Dohm K. Ehrlich S. Engelbrecht H-R. Erk S. Fan C.C. Fedko I.O. Foley S.F. Ford J.M. Fukunaga M. Garrett M.E. Ge T. Giddaluru S. Goldman A.L. Groenewold N.A. Grotegerd D. Gurholt T.P. Gutman B.A. Hansell N.K. Harris M.A. Harrison M.B. Haswell C.C. Hauser M. Herms S. Heslenfeld D.J. Ho N.F. Hoehn D. Hoffmann P. Holleran L. Hoogman M. Hottenga J-J. Ikeda M. Janowitz D. Jansen I.E. Jia T. Jockwitz C. Kanai R. Karama S. Kasperaviciute D. Kaufmann T. Kelly S. Kikuchi M. Klein M. Knapp M. Knodt A.R. Krämer B. Lam M. Lancaster T.M. Lee P.H. Lett T.A. Lewis L.B. Lopes-Cendes I. Luciano M. Macciardi F. Marquand A.F. Mathias S.R. Melzer T.R. Milaneschi Y. Mirza-Schreiber N. Moreira J.C.V. Mühleisen T.W. Müller-Myhsok B. Najt P. Nakahara S. Nho K. Olde Loohuis L.M. Orfanos D.P. Pearson J.F. Pitcher T.L. Pütz B. Ragothaman A. Rashid F.M. Redlich R. Reinbold C.S. Repple J. Richard G. Riedel B.C. Risacher S.L. Rocha C.S. Mota N.R. Salminen L. Saremi A. Saykin A.J. Schlag F. Schmaal L. Schofield P.R. Secolin R. Shapland C.Y. Shen L. Shin J. Shumskaya E. Sønderby I.E. Sprooten E. Strike L.T. Tansey K.E. Teumer A. Thalamuthu A. Thomopoulos S.I. Tordesillas-Gutiérrez D. Turner J.A. Uhlmann A. Vallerga C.L. van der Meer D. van Donkelaar M.M.J. van Eijk L. van Erp T.G.M. van Haren N.E.M. van Rooij D. van Tol M-J. Veldink J.H. Verhoef E. Walton E. Wang M. Wang Y. Wardlaw J.M. Wen W. Westlye L.T. Whelan C.D. Witt S.H. Wittfeld K. Wolf C. Wolfers T. Yasuda C.L. Zaremba D. Zhang Z. Zhu A.H. Zwiers M.P. Artiges E. Assareh A.A. Ayesa-Arriola R. Belger A. Brandt C.L. Brown G.G. Cichon S. Curran J.E. Davies G.E. Degenhardt F. Dietsche B. Djurovic S. Doherty C.P. Espiritu R. Garijo D. Gil Y. Gowland P.A. Green R.C. Häusler A.N. Heindel W. Ho B-C. Hoffmann W.U. Holsboer F. Homuth G. Hosten N. Jack C.R. Jang M.H. Jansen A. Kolskår K. Koops S. Krug A. Lim K.O. Luykx J.J. Mathalon D.H. Mather K.A. Mattay V.S. Matthews S. Son J.M.V. McEwen S.C. Melle I. Morris D.W. Mueller B.A. Nauck M. Nordvik J.E. Nöthen M.M. O’Leary D.S. Opel N. Martinot M-L.P. Pike G.B. Preda A. Quinlan E.B. Ratnakar V. Reppermund S. Steen V.M. Torres F.R. Veltman D.J. Voyvodic J.T. Whelan R. White T. Yamamori H. Alvim M.K.M. Ames D. Anderson T.J. Andreassen O.A. Arias-Vasquez A. Bastin M.E. Baune B.T. Blangero J. Boomsma D.I. Brodaty H. Brunner H.G. Buckner R.L. Buitelaar J.K. Bustillo J.R. Cahn W. Calhoun V. Caseras X. Caspers S. Cavalleri G.L. Cendes F. Corvin A. Crespo-Facorro B. Dalrymple-Alford J.C. Dannlowski U. de Geus E.J.C. Deary I.J. Delanty N. Depondt C. Desrivières S. Donohoe G. Espeseth T. Fernández G. Fisher S.E. Flor H. Forstner A.J. Francks C. Franke B. Glahn D.C. Gollub R.L. Grabe H.J. Gruber O. Håberg A.K. Hariri A.R. Hartman C.A. Hashimoto R. Heinz A. Hillegers M.H.J. Hoekstra P.J. Holmes A.J. Hong L.E. Hopkins W.D. Hulshoff Pol H.E. Jernigan T.L. Jönsson E.G. Kahn R.S. Kennedy M.A. Kircher T.T.J. Kochunov P. Kwok J.B.J. Hellard S.L. Martin N.G. Martinot J-L. McDonald C. McMahon K.L. Meyer-Lindenberg A. Morey R.A. Nyberg L. Oosterlaan J. Ophoff R.A. Paus T. Pausova Z. Penninx B.W.J.H. Polderman T.J.C. Posthuma D. Rietschel M. Roffman J.L. Rowland L.M. Sachdev P.S. Sämann P.G. Schumann G. Sim K. Sisodiya S.M. Smoller J.W. Sommer I.E. Pourcain B.S. Stein D.J. Toga A.W. Trollor J.N. Van der Wee N.J.A. van ’t Ent D. Völzke H. Walter H. Weber B. Weinberger D.R. Wright M.J. Zhou J. Stein J.L. Thompson P.M. Medland S.E. Sachdev P.S. Kremen W.S. Wardlaw J.M. Villringer A. van Duijn C.M. Grabe H.J. Longstreth W.T. Fornage M. Paus T. Debette S. Ikram M.A. Schmidt H. Schmidt R. Seshadri S. Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults. Nat Commun 2020 11 1 4796 10.1038/s41467‑020‑18367‑y 32963231
    [Google Scholar]
  20. Moon S.W. Zhao L. Matloff W. Hobel S. Berger R. Kwon D. Kim J. Toga A.W. Dinov I.D. Brain structure and allelic associations in Alzheimer’s disease. CNS Neurosci Ther 2023 29 4 1034 1048 10.1111/cns.14073 36575854
    [Google Scholar]
  21. Chandra A. Dervenoulas G. Politis M. Magnetic resonance imaging in Alzheimer’s disease and mild cognitive impairment. J Neurol 2019 266 6 1293 1302 10.1007/s00415‑018‑9016‑3 30120563
    [Google Scholar]
  22. Kim J. Jeong M. Stiles W.R. Choi H.S. Neuroimaging modalities in Alzheimer’s disease: Diagnosis and clinical features. Int J Mol Sci 2022 23 11 6079 10.3390/ijms23116079 35682758
    [Google Scholar]
  23. Márquez F. Yassa M.A. Neuroimaging biomarkers for Alzheimer’s disease. Mol Neurodegener 2019 14 1 21 10.1186/s13024‑019‑0325‑5 31174557
    [Google Scholar]
  24. Nho K. Nudelman K. Allen M. Hodges A. Kim S. Risacher S.L. Apostolova L.G. Lin K. Lunnon K. Wang X. Burgess J.D. Ertekin-Taner N. Petersen R.C. Wang L. Qi Z. He A. Neuhaus I. Patel V. Foroud T. Faber K.M. Lovestone S. Simmons A. Weiner M.W. Saykin A.J. Genome‐wide transcriptome analysis identifies novel dysregulated genes implicated in Alzheimer’s pathology. Alzheimers Dement 2020 16 9 1213 1223 10.1002/alz.12092 32755048
    [Google Scholar]
  25. Jia J. Ning Y. Chen M. Wang S. Yang H. Li F. Ding J. Li Y. Zhao B. Lyu J. Yang S. Yan X. Wang Y. Qin W. Wang Q. Li Y. Zhang J. Liang F. Liao Z. Wang S. Biomarker Changes during 20 Years Preceding Alzheimer’s Disease. N Engl J Med 2024 390 8 712 722 10.1056/NEJMoa2310168 38381674
    [Google Scholar]
  26. Sintini I. Whitwell J.L. Update on neuroimaging in Alzheimer’s disease. Curr Opin Neurol 2021 34 4 525 531 10.1097/WCO.0000000000000947 33928929
    [Google Scholar]
  27. Lambert J.C. Heath S. Even G. Campion D. Sleegers K. Hiltunen M. Combarros O. Zelenika D. Bullido M.J. Tavernier B. Letenneur L. Bettens K. Berr C. Pasquier F. Fiévet N. Barberger-Gateau P. Engelborghs S. De Deyn P. Mateo I. Franck A. Helisalmi S. Porcellini E. Hanon O. de Pancorbo M.M. Lendon C. Dufouil C. Jaillard C. Leveillard T. Alvarez V. Bosco P. Mancuso M. Panza F. Nacmias B. Bossù P. Piccardi P. Annoni G. Seripa D. Galimberti D. Hannequin D. Licastro F. Soininen H. Ritchie K. Blanché H. Dartigues J.F. Tzourio C. Gut I. Van Broeckhoven C. Alpérovitch A. Lathrop M. Amouyel P. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet 2009 41 10 1094 1099 10.1038/ng.439 19734903
    [Google Scholar]
  28. Hossain R. Noonong K. Nuinoon M. Lao-On U. Norris C.M. Sompol P. Rahman M.A. Majima H.J. Tangpong J. Alzheimer’s diseases in America, Europe, and Asian regions: A global genetic variation. PeerJ 2024 12 17339 10.7717/peerj.17339 38756443
    [Google Scholar]
  29. Li Y. Laws S.M. Miles L.A. Wiley J.S. Huang X. Masters C.L. Gu B.J. Genomics of Alzheimer’s disease implicates the innate and adaptive immune systems. Cell Mol Life Sci 2021 78 23 7397 7426 10.1007/s00018‑021‑03986‑5 34708251
    [Google Scholar]
  30. Torvell M. Carpanini S.M. Daskoulidou N. Byrne R.A.J. Sims R. Morgan B.P. Genetic insights into the impact of complement in Alzheimer’s disease. Genes 2021 12 12 1990 10.3390/genes12121990 34946939
    [Google Scholar]
  31. Yuan H. Du L. Ge P. Complement receptor 1 genetic polymorphism contributes to sporadic Alzheimer’s disease susceptibility in Caucasians: A meta-analysis. Biosci Rep 2020 40 6 BSR20200321 10.1042/BSR20200321 32432316
    [Google Scholar]
  32. Almeida J.F.F. dos Santos L.R. Trancozo M. de Paula F. Updated meta-analysis of BIN1, CR1, MS4A6A, CLU, and ABCA7 variants in Alzheimer’s disease. J Mol Neurosci 2018 64 3 471 477 10.1007/s12031‑018‑1045‑y 29504051
    [Google Scholar]
  33. Luo J. Li S. Qin X. Song L. Peng Q. Chen S. Xie Y. Xie L. Li T. He Y. Deng Y. Wang J. Zeng Z. Meta-analysis of the association between CR1 polymorphisms and risk of late-onset Alzheimer’s disease. Neurosci Lett 2014 578 165 170 10.1016/j.neulet.2014.06.055 24996192
    [Google Scholar]
  34. Chen L.H. Kao P.Y.P. Fan Y.H. Ho D.T.Y. Chan C.S.Y. Yik P.Y. Ha J.C.T. Chu L.W. Song Y.Q. Polymorphisms of CR1, CLU and PICALM confer susceptibility of Alzheimer’s disease in a southern Chinese population. Neurobiol Aging 2012 33 1 210.e1 210.e7 10.1016/j.neurobiolaging.2011.09.016 22015308
    [Google Scholar]
  35. Jin C. Li W. Yuan J. Xu W. Cheng Z. Association of the CR1 polymorphism with late-onset Alzheimer’s disease in Chinese Han populations: A meta-analysis. Neurosci Lett 2012 527 1 46 49 10.1016/j.neulet.2012.08.032 22960360
    [Google Scholar]
  36. Shen N. Chen B. Jiang Y. Feng R. Liao M. Zhang L. Li F. Ma G. Chen Z. Zhao B. Li K. Liu G. An updated analysis with 85,939 samples confirms the association between CR1 rs6656401 polymorphism and Alzheimer’s disease. Mol Neurobiol 2015 51 3 1017 1023 10.1007/s12035‑014‑8761‑2 24878768
    [Google Scholar]
  37. Zhu X. Dai W. Ma T. Impacts of CR1 genetic variants on cerebrospinal fluid and neuroimaging biomarkers in Alzheimer’s disease. BMC Med Genet 2020 21 1 181 10.1186/s12881‑020‑01114‑x 32919460
    [Google Scholar]
  38. Zhu X.C. Wang H.F. Jiang T. Lu H. Tan M.S. Tan C.C. Tan L. Tan L. Yu J.T. Effect of CR1 genetic variants on cerebrospinal fluid and neuroimaging biomarkers in healthy, mild cognitive impairment and Alzheimer’s disease cohorts. Mol Neurobiol 2017 54 1 551 562 10.1007/s12035‑015‑9638‑8 26742530
    [Google Scholar]
  39. Zhu X.C. Yu J.T. Jiang T. Wang P. Cao L. Tan L. CR1 in Alzheimer’s disease. Mol Neurobiol 2015 51 2 753 765 10.1007/s12035‑014‑8723‑8 24794147
    [Google Scholar]
  40. Sun X. Ding H. Hung K. Guo B. A new MALDI-TOF based mini-sequencing assay for genotyping of SNPS. Nucleic Acids Res 2000 28 12 68e 68 10.1093/nar/28.12.e68 10871391
    [Google Scholar]
  41. Fischl B. Dale A.M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA 2000 97 20 11050 11055 10.1073/pnas.200033797 10984517
    [Google Scholar]
  42. Ashburner J. Friston K.J. Voxel-based morphometry--the methods. Neuroimage 2000 11 6 805 821 10.1006/nimg.2000.0582 10860804
    [Google Scholar]
  43. Watts D.J. Strogatz S.H. Collective dynamics of ‘small-world’ networks. Nature 1998 393 6684 440 442 10.1038/30918 9623998
    [Google Scholar]
  44. Bullmore E.T. Bassett D.S. Brain graphs: Graphical models of the human brain connectome. Annu Rev Clin Psychol 2011 7 1 113 140 10.1146/annurev‑clinpsy‑040510‑143934 21128784
    [Google Scholar]
  45. Zhu X.C. Cao L. Tan M.S. Jiang T. Wang H.F. Lu H. Tan C.C. Zhang W. Tan L. Yu J.T. Association of Parkinson’s disease GWAS-linked loci with Alzheimer’s disease in han chinese. Mol Neurobiol 2017 54 1 308 318 10.1007/s12035‑015‑9649‑5 26738859
    [Google Scholar]
  46. Chang H.I. Chang Y.T. Huang C.W. Huang K.L. Hsu J.L. Hsu S.W. Tsai S.J. Chang W.N. Lee C.C. Huang S.H. Chang C.C. Structural covariance network as an endophenotype in Alzheimer’s disease-susceptible single-nucleotide polymorphisms and the correlations with cognitive outcomes. Front Aging Neurosci 2021 13 721217 10.3389/fnagi.2021.721217 34975449
    [Google Scholar]
  47. Crehan H. Holton P. Wray S. Pocock J. Guerreiro R. Hardy J. Complement receptor 1 (CR1) and Alzheimer’s disease. Immunobiology 2012 217 2 244 250 10.1016/j.imbio.2011.07.017 21840620
    [Google Scholar]
  48. Lu L. Yao Q. Ruan S.S. Hu J.W. Long W. Dai W.Z. Ma T. Zhu X.C. Explore the role of CR1 genetic variants in late-onset Alzheimer’s disease susceptibility. Psychiatr Genet 2021 31 6 216 229 10.1097/YPG.0000000000000291 34347684
    [Google Scholar]
  49. Leocadi M. Canu E. Paldino A. Agosta F. Filippi M. Awareness impairment in Alzheimer’s disease and frontotemporal dementia: A systematic MRI review. J Neurol 2023 270 4 1880 1907 10.1007/s00415‑022‑11518‑9 36512063
    [Google Scholar]
  50. Ferreira D. Verhagen C. Hernández-Cabrera J.A. Cavallin L. Guo C.J. Ekman U. Muehlboeck J.S. Simmons A. Barroso J. Wahlund L.O. Westman E. Distinct subtypes of Alzheimer’s disease based on patterns of brain atrophy: Longitudinal trajectories and clinical applications. Sci Rep 2017 7 1 46263 10.1038/srep46263 28417965
    [Google Scholar]
  51. Hu Y. Zhu T. Zhang W. The characteristics of brain atrophy prior to the onset of Alzheimer’s disease: A longitudinal study. Front Aging Neurosci 2024 16 1344920 10.3389/fnagi.2024.1344920 38863784
    [Google Scholar]
  52. Chauveau L. Kuhn E. Palix C. Felisatti F. Ourry V. de La Sayette V. Chételat G. de Flores R. Medial temporal lobe subregional atrophy in aging and Alzheimer’s disease: A longitudinal study. Front Aging Neurosci 2021 13 750154 10.3389/fnagi.2021.750154 34720998
    [Google Scholar]
  53. Mofrad S.A. Lundervold A.J. Vik A. Lundervold A.S. Cognitive and MRI trajectories for prediction of Alzheimer’s disease. Sci Rep 2021 11 1 2122 10.1038/s41598‑020‑78095‑7 33483535
    [Google Scholar]
  54. Marino S. Bonanno L. Lo Buono V. Ciurleo R. Corallo F. Morabito R. Chirico G. Marra A. Bramanti P. Longitudinal analysis of brain atrophy in Alzheimer’s disease and frontotemporal dementia. J Int Med Res 2019 47 10 5019 5027 10.1177/0300060519830830 31524019
    [Google Scholar]
  55. Du A-T. Schuff N. Kramer J.H. Rosen H.J. Different regional patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia. Brain 2007 130 Pt 4 1159 1166 10.1093/brain/awm016
    [Google Scholar]
  56. Wu B.S. Zhang Y.R. Li H.Q. Kuo K. Chen S.D. Dong Q. Liu Y. Yu J.T. Cortical structure and the risk for Alzheimer’s disease: A bidirectional Mendelian randomization study. Transl Psychiatry 2021 11 1 476 10.1038/s41398‑021‑01599‑x 34526483
    [Google Scholar]
  57. Rizvi B. Lao P.J. Chesebro A.G. Dworkin J.D. Amarante E. Beato J.M. Gutierrez J. Zahodne L.B. Schupf N. Manly J.J. Mayeux R. Brickman A.M. Association of regional white matter hyperintensities with longitudinal Alzheimer-like pattern of neurodegeneration in older adults. JAMA Netw Open 2021 4 10 2125166 10.1001/jamanetworkopen.2021.25166 34609497
    [Google Scholar]
  58. Iannopollo E. Garcia K. Enhanced detection of cortical atrophy in Alzheimer’s disease using structural MRI with anatomically constrained longitudinal registration. Hum Brain Mapp 2021 42 11 3576 3592 10.1002/hbm.25455 33988265
    [Google Scholar]
  59. Coleman M.M. Keith C.M. Wilhelmsen K. Mehta R.I. Vieira Ligo Teixeira C. Miller M. Ward M. Navia R.O. McCuddy W.T. D’Haese P.F. Haut M.W. Surface-based correlates of cognition along the Alzheimer’s continuum in a memory clinic population. Front Neurol 2023 14 1214083 10.3389/fneur.2023.1214083 37731852
    [Google Scholar]
  60. Li J.Q. Wang H.F. Zhu X.C. Sun F.R. Tan M.S. Tan C.C. Jiang T. Tan L. Yu J.T. GWAS-linked loci and neuroimaging measures in Alzheimer’s disease. Mol Neurobiol 2017 54 1 146 153 10.1007/s12035‑015‑9669‑1 26732597
    [Google Scholar]
  61. Sebenius I. Seidlitz J. Warrier V. Bethlehem R.A.I. Alexander-Bloch A. Mallard T.T. Garcia R.R. Bullmore E.T. Morgan S.E. Robust estimation of cortical similarity networks from brain MRI. Nat Neurosci 2023 26 8 1461 1471 10.1038/s41593‑023‑01376‑7 37460809
    [Google Scholar]
  62. Jiang J. Zhao K. Li W. Zheng P. Jiang S. Ren Q. Duan Y. Yu H. Kang X. Li J. Hu K. Jiang T. Zhao M. Wang L. Yang S. Zhang H. Liu Y. Wang A. Liu Y. Xu J. Multiomics reveals biological mechanisms linking macroscale structural covariance network dysfunction with neuropsychiatric symptoms across the Alzheimer’s disease continuum. Biol Psychiatry 2025 97 11 1067 1078 10.1016/j.biopsych.2024.08.027 39419461
    [Google Scholar]
  63. Zheng C. Zhao W. Yang Z. Tang D. Feng M. Guo S. Resolving heterogeneity in Alzheimer’s disease based on individualized structural covariance network. Prog Neuropsychopharmacol Biol Psychiatry 2024 129 110873 10.1016/j.pnpbp.2023.110873 37827426
    [Google Scholar]
  64. Gonuguntla V. Yang E. Guan Y. Koo B.B. Kim J.H. Brain signatures based on structural MRI : Classification for MCI, PMCI, and AD. Hum Brain Mapp 2022 43 9 2845 2860 10.1002/hbm.25820 35289025
    [Google Scholar]
  65. Li Y. Seger C. Chen Q. Mo L. Left inferior frontal gyrus integrates multisensory information in category learning. Cereb Cortex 2020 30 8 4410 4423 10.1093/cercor/bhaa029 32133488
    [Google Scholar]
  66. Zheng Q. Wang X. Alzheimer’s disease: Insights into pathology, molecular mechanisms, and therapy. Protein Cell 2025 16 2 83 120 10.1093/procel/pwae026 38733347
    [Google Scholar]
  67. Twarowski B. Herbet M. Inflammatory processes in Alzheimer’s disease—pathomechanism, diagnosis and treatment: A review. Int J Mol Sci 2023 24 7 6518 10.3390/ijms24076518 37047492
    [Google Scholar]
  68. Schjeide BM Schnack C Lambert JC Lill CM Kirchheiner J Tumani H Otto M Tanzi RE Lehrach H Amouyel P von Arnim CA Bertram L The role of clusterin, complement receptor 1, and phosphatidylinositol binding clathrin assembly protein in Alzheimer disease risk and cerebrospinal fluid biomarker levels. Arch Gen Psychiatry 2011 68 2 207 213 10.1001/archgenpsychiatry.2010.196 21300948
    [Google Scholar]
  69. Nosheny R.L. Insel P.S. Mattsson N. Tosun D. Buckley S. Truran D. Schuff N. Aisen P.S. Weiner M.W. Associations among amyloid status, age, and longitudinal regional brain atrophy in cognitively unimpaired older adults. Neurobiol Aging 2019 82 110 119 10.1016/j.neurobiolaging.2019.07.005 31437719
    [Google Scholar]
  70. Barthélemy N.R. Salvadó G. Schindler S.E. He Y. Janelidze S. Collij L.E. Saef B. Henson R.L. Chen C.D. Gordon B.A. Li Y. La Joie R. Benzinger T.L.S. Morris J.C. Mattsson-Carlgren N. Palmqvist S. Ossenkoppele R. Rabinovici G.D. Stomrud E. Bateman R.J. Hansson O. Highly accurate blood test for Alzheimer’s disease is similar or superior to clinical cerebrospinal fluid tests. Nat Med 2024 30 4 1085 1095 10.1038/s41591‑024‑02869‑z 38382645
    [Google Scholar]
  71. Serdar C.C. Cihan M. Yücel D. Serdar M.A. Sample size, power and effect size revisited: Simplified and practical approaches in pre-clinical, clinical and laboratory studies. Biochem Med 2021 31 1 27 53 10.11613/BM.2021.010502 33380887
    [Google Scholar]
  72. Zhu J.D. Huang C.W. Chang H.I. Tsai S.J. Huang S.H. Hsu S.W. Lee C.C. Chen H.J. Chang C.C. Yang A.C. Functional MRI and ApoE4 genotype for predicting cognitive decline in amyloid-positive individuals. Ther Adv Neurol Disord 2022 15 17562864221138154 10.1177/17562864221138154 36419870
    [Google Scholar]
  73. Haney M.S. Pálovics R. Munson C.N. Long C. Johansson P.K. Yip O. Dong W. Rawat E. West E. Schlachetzki J.C.M. Tsai A. Guldner I.H. Lamichhane B.S. Smith A. Schaum N. Calcuttawala K. Shin A. Wang Y.H. Wang C. Koutsodendris N. Serrano G.E. Beach T.G. Reiman E.M. Glass C.K. Abu-Remaileh M. Enejder A. Huang Y. Wyss-Coray T. APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia. Nature 2024 628 8006 154 161 10.1038/s41586‑024‑07185‑7 38480892
    [Google Scholar]
  74. Lin E. Lin C.H. Lane H.Y. Deep learning with neuroimaging and genomics in Alzheimer’s disease. Int J Mol Sci 2021 22 15 7911 10.3390/ijms22157911 34360676
    [Google Scholar]
  75. Rostagno A.A. Pathogenesis of Alzheimer’s Disease. Int J Mol Sci 2022 24 1 107 10.3390/ijms24010107 36613544
    [Google Scholar]
  76. Graff-Radford J. Yong K.X.X. Apostolova L.G. Bouwman F.H. Carrillo M. Dickerson B.C. Rabinovici G.D. Schott J.M. Jones D.T. Murray M.E. New insights into atypical Alzheimer’s disease in the era of biomarkers. Lancet Neurol 2021 20 3 222 234 10.1016/S1474‑4422(20)30440‑3 33609479
    [Google Scholar]
/content/journals/car/10.2174/0115672050397092250823195514
Loading
The Association between the rs6656401 Locus of the CR1 Gene and Structural Alterations of Brain Effects in Han Chinese Patients with Alzheimer's Disease
Bentham Science Publishers ; https://doi.org/10.2174/0115672050397092250823195514
/content/journals/car/10.2174/0115672050397092250823195514
/content/journals/car/10.2174/0115672050397092250823195514
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

file format download Download

  • Article Type:     Research Article
Keywords: cortical thickness ; voxel-based morphological analysis ; structural networks ; Complement receptor 1 ; rs6656401 ; Alzheimer’s disease ; magnetic resonance images

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test