No relationship between fornix and cingulum degradation and within-network decreases in functional connectivity in prodromal Alzheimer’s disease
Autoři:
Therese M. Gilligan aff001; Francesca Sibilia aff001; Dervla Farrell aff001; Declan Lyons aff003; Seán P. Kennelly aff002; Arun L. W. Bokde aff001
Působiště autorů:
Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
aff001; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
aff002; St Patrick’s University Hospital, Dublin, Ireland
aff003; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland
aff004; Memory Assessment and Support Service, Department of Age-related Healthcare, Tallaght University Hospital, Dublin, Ireland
aff005
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0222977
Souhrn
Introduction
The earliest changes in the brain due to Alzheimer’s disease are associated with the neural networks related to memory function. We investigated changes in functional and structural connectivity among regions that support memory function in prodromal Alzheimer’s disease, i.e., during the mild cognitive impairment (MCI) stage.
Methods
Twenty-three older healthy controls and 25 adults with MCI underwent multimodal MRI scanning. Limbic white matter tracts–the fornix, parahippocampal cingulum, retrosplenial cingulum, subgenual cingulum and uncinate fasciculus–were reconstructed in ExploreDTI using constrained spherical deconvolution-based tractography. Using a network-of-interest approach, resting-state functional connectivity time-series correlations among sub-parcellations of the default mode and limbic networks, the hippocampus and the thalamus were calculated in Conn.
Analysis
Controlling for age, education, and gender between group linear regressions of five diffusion-weighted measures and of resting state connectivity measures were performed per hemisphere. FDR-corrections were performed within each class of measures. Correlations of within-network Fisher Z-transformed correlation coefficients and the mean diffusivity per tract were performed. Whole-brain graph theory measures of cluster coefficient and average path length were inspecting using the resting state data.
Results & conclusion
MCI-related changes in white matter structure were found in the fornix, left parahippocampal cingulum, left retrosplenial cingulum and left subgenual cingulum. Functional connectivity decreases were observed in the MCI group within the DMN-a sub-network, between the hippocampus and sub-areas -a and -c of the DMN, between DMN-c and DMN-a, and, in the right hemisphere only between DMN-c and both the thalamus and limbic-a. No relationships between white matter tract ‘integrity’ (mean diffusivity) and within sub-network functional connectivity were found. Graph theory revealed that changes in the MCI group was mostly restricted to diminished between-neighbour connections of the hippocampi and of nodes within DMN-a and DMN-b.
Klíčová slova:
Cognitive impairment – Alzheimer's disease – Right hemisphere – Central nervous system – Neuropsychological testing – Graph theory – Hippocampus – Atrophy
Zdroje
1. Petersen RC, Stevens JC, Ganguli M, Tangalos EG, Cummings JL, DeKosky ST. Practice parameter: Early detection of dementia: Mild cognitive impairment (an evidence-based review): Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2001;56:1133–42. doi: 10.1212/wnl.56.9.1133 11342677
2. Bruscoli M, Lovestone S. Is MCI really just early dementia? A systematic review of conversion studies. Int Psychogeriatrics. 2004;16(2):129–40.
3. Ewers M, Frisoni GB, Teipel S, Grinberg LT, Amaro E Jr., Heinsen H, et al. Staging Alzheimer’s disease progression with multimodality neuroimaging. Prog Neurobiol. 2011;95(4):535–46. doi: 10.1016/j.pneurobio.2011.06.004 21718750
4. Dickerson BC, Bakkour A, Salat DH, Feczko E, Pacheco J, Greve DN, et al. The cortical signature of Alzheimer’s disease: Regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cereb Cortex. 2009;19(3):497–510. doi: 10.1093/cercor/bhn113 18632739
5. Dickerson BC, Feczko E, Augustinack JC, Pacheco J, Morris JC, Fischl B, et al. Differential effects of aging and Alzheimer’s disease on medial temporal lobe cortical thickness and surface area. Neurobiol Aging. 2009;30:432–40. doi: 10.1016/j.neurobiolaging.2007.07.022 17869384
6. Petersen RC, Parisi JE, Dickson DW, Johnson KA, Knopman DS, Boeve BF, et al. Neuropathologic Features of Amnestic Mild Cognitive Impairment. Arch Neurol. 2006;63(5):665. doi: 10.1001/archneur.63.5.665 16682536
7. Schneider JA, Arvanitakis Z, Leurgans SE, Bennett DA. The neuropathology of probable Alzheimer disease and mild cognitive impairment. Ann Neurol [Internet]. 2009 Aug;66(2):200–8. Available from: doi: 10.1002/ana.21706 19743450
8. Bokde ALW, Ewers M, Hampel H. Assessing neuronal networks: understanding Alzheimer’s disease. Prog Neurobiol. 2009;89(2):125–33. doi: 10.1016/j.pneurobio.2009.06.004 19560509
9. Nestor PJ, Scheltens P, Hodges JR. Advances in the early detectin of Alzheimer’s disease. Nat Rev Neurosci. 2004;7:S34–41.
10. Smith AD. Imaging the progression of Alzheimer pathology through the brain. Proc Natl Acad Sci. 2002;99(7):4135–7. doi: 10.1073/pnas.082107399 11929987
11. Villain N, Desgranges B, Viader F, de la Sayette V, Mezenge F, Landeau B, et al. Relationships between Hippocampal Atrophy, White Matter Disruption, and Gray Matter Hypometabolism in Alzheimer’s Disease. J Neurosci. 2008;28(24):6174–81. doi: 10.1523/JNEUROSCI.1392-08.2008 18550759
12. Bartzokis G. Age-related myelin breakdown: A developmental model of cognitive decline and Alzheimer’s disease. Neurobiol Aging. 2004;25:5–18. doi: 10.1016/j.neurobiolaging.2003.03.001 14675724
13. Reisberg B, Franssen EH, Souren LEM, Auer SR, Akram I, Kenowsky S. Evidence and mechanisms of retrogenesis in Alzheimer’s and other dementias: Management and treatment import. Am J Alzheimer’s Dis Other Dementiasr. 2002;17(4):202–12.
14. Bozzali M, Falini A, Franceschi M, M. C, Zuffi M, Scotti G, et al. White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging. J Neurol Neurosurg Psychiatry [Internet]. 2002;72:742–6. Available from: http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=emed7&NEWS=N&AN=34557017 doi: 10.1136/jnnp.72.6.742 12023417
15. Coleman MP, Perry VH. Axon pathology in neurological disease: a neglected therapeutic target. Trends Neurosci. 2002;25(20):532–7.
16. Englund E. Neuropathology of white matter lesions in vascular cognitive impairment. Cerebrovasc Dis. 2002;13(SUPPL. 2):11–5.
17. Jones DK, Knösche TR, Turner R. White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. Neuroimage [Internet]. 2013;73:239–54. Available from: doi: 10.1016/j.neuroimage.2012.06.081 22846632
18. Lancaster MA, Seidenberg M, Smith JC, Nielson KA, Woodard JL, Durgerian S, et al. Diffusion Tensor Imaging Predictors of Episodic Memory Decline in Healthy Elders at Genetic Risk for Alzheimer’s Disease. J Int Neuropsychol Soc. 2016;22(10):1005–15. doi: 10.1017/S1355617716000904 27903333
19. Agosta F, Pievani M, Sala S, Geroldi C, Galluzzi S, Frisoni GB, et al. White matter damage in Alzheimer Disease and Its relationship to gray matter atrophy. Radiology. 2011;258(3):853–63. doi: 10.1148/radiol.10101284 21177393
20. Amlien IK, Fjell AM. Diffusion tensor imaging of white matter degeneration in Alzheimer’s disease and mild cognitive impairment. Neuroscience [Internet]. 2014;276:206–15. Available from: doi: 10.1016/j.neuroscience.2014.02.017 24583036
21. Fletcher E, Raman M, Huebner P, Liu A, Mungas D, Carmichael O, et al. Loss of fornix white matter volume as a predictor of cognitive impairment in cognitively normal elderly individuals. JAMA Neurol. 2013;70(11):1389–95. doi: 10.1001/jamaneurol.2013.3263 24018960
22. Zhuang L, Sachdev PS, Trollor JN, Reppermund S, Kochan N, Brodaty H, et al. Microstructural White Matter Changes, Not Hippocampal Atrophy, Detect Early Amnestic Mild Cognitive Impairment. PLoS One. 2013;8(3):1–10.
23. Radanovic M, Ramos F, Pereira S, Stella F, Aprahamian I, Ferreira LF, et al. White matter abnormalities associated with Alzheimer’s disease and mild cognitive impairment: a critical review of MRI studies. Expert Rev Neurother. 2013;13(5):1–11.
24. Nir TM, Jahanshad N, Villalon-Reina JE, Toga AW, Jack CR, Weiner MW, et al. Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging. NeuroImage Clin [Internet]. 2013;3:180–95. Available from: doi: 10.1016/j.nicl.2013.07.006 24179862
25. Sexton CE, Kalu UG, Filippini N, Mackay CE, Ebmeier KP. A meta-analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer’s disease. Neurobiol Aging [Internet]. 2011;32(12):2322.e5–2322.e18. Available from: http://dx.doi.org/10.1016/j.neurobiolaging.2010.05.019
26. Kavcic V, Ni H, Zhu T, Zhong J, Duffy C. White matter integrity linked to functional impairments in aging and early Alzheimer’s disease. Alzheimer’s Dement. 2008;4(6):381–9.
27. Zhou Y, Dougherty JH, Hubner KF, Bai B, Cannon RL, Hutson RK. Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer’s disease and mild cognitive impairment. Alzheimer’s Dement. 2008;4(4):265–70.
28. O’Dwyer L, Lamberton F, Bokde ALW, Ewers M, Faluyi YO, Tanner C, et al. Multiple indices of diffusion identifies white matter damage in mild cognitive impairment and Alzheimer’s disease. PLoS One. 2011;6(6):1–13.
29. Cordes D, Haughton VM, Arfanakis K, Carew JD, Turski PA, Moritz CH, et al. Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. Am J Neuroradiol. 2001;22:1326–33. 11498421
30. Vecchio F, Miraglia F, Curcio G, Altavilla R, Scrascia F, Giambattistelli F, et al. Cortical Brain Connectivity Evaluated by Graph Theory in Dementia: A Correlation Study Between Functional and Structural Data. J Alzheimer’s Dis. 2015;45:745–56.
31. Lee MH, Hacker CD, Snyder AZ, Corbetta M, Zhang D, Leuthardt EC, et al. Clustering of resting state networks. PLoS One. 2012;7(7):1–12.
32. Lau WKW, Leung MK, Lee TMC, Law ACK. Resting-state abnormalities in amnestic mild cognitive impairment: A meta-analysis. Transl Psychiatry [Internet]. 2016;6:1–6. Available from: http://dx.doi.org/10.1038/tp.2016.55
33. Zheng D, Xia W, Yi ZQ, Zhao PW, Zhong JG, Shi HC, et al. Alterations of brain local functional connectivity in amnestic mild cognitive impairment. Transl Neurodegener. 2018;7(26):1–14.
34. Badhwar AP, Tam A, Dansereau C, Orban P, Hoffstaedter F, Bellec P. Resting-state network dysfunction in Alzheimer’s disease: A systematic review and meta-analysis. Alzheimer’s Dement Diagnosis, Assess Dis Monit [Internet]. 2017;8:73–85. Available from: https://doi.org/10.1016/j.dadm.2017.03.007
35. Wang Z, Liang P, Jia X, Jin G, Song H, Han Y, et al. The baseline and longitudinal changes of PCC connectivity in mild cognitive impairment: A combined structure and resting-state fMRI study. PLoS One. 2012;7(5):1–11.
36. Liang P, Wang Z, Yang Y, Jia X, Li K. Functional disconnection and compensation in mild cognitive impairment: Evidence from DLPFC connectivity using resting-state fMRI. PLoS One. 2011;6(7):1–12.
37. Sorg C, Riedl V, Mühlau M, Calhoun VD, Eichele T, Läer L, et al. Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proc Natl Acad Sci. 2007;104(47):18760–5. doi: 10.1073/pnas.0708803104 18003904
38. Li Y, Sun Y, Wang D, Jing B, Wang X, Xia M, et al. Abnormal Resting-State Functional Connectivity Strength in Mild Cognitive Impairment and Its Conversion to Alzheimer’s Disease. Neural Plast. 2016;4680972:1–12.
39. Brier M, Thomas JB, Snyder AZ, Benzinger TL, Zhang D, Raichle ME, et al. Loss of Intra- and Inter-Network Resting State Functional Connections with Alzheimer’s Disease Progression. J Neurosci. 2012;32(26):8890–9. doi: 10.1523/JNEUROSCI.5698-11.2012 22745490
40. Lin Q, Rosenberg MD, Yoo K, Hsu TW, O’Connell TP, Chun MM. Resting-state functional connectivity predicts cognitive impairment related to Alzheimer’s disease. Front Aging Neurosci. 2018;10:1–10. doi: 10.3389/fnagi.2018.00001
41. Contreras JA, Avena-Koenigsberger A, Risacher SL, West JD, Tallman EF, McDonald BC, et al. Resting state network modularity along the prodromal late onset Alzheimer’s disease continuum. NeuroImage Clin [Internet]. 2019;22:1–12. Available from: https://doi.org/10.1016/j.nicl.2019.101687
42. Dai Z, He Y. Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer’s disease. Neurosci Bull. 2014;30(2):217–32. doi: 10.1007/s12264-013-1421-0 24733652
43. Jovicich J, Babiloni C, Ferrari C, Marizzoni M, Moretti D V., Del Percio C, et al. Two-Year Longitudinal Monitoring of Amnestic Mild Cognitive Impairment Patients with Prodromal Alzheimer’s Disease Using Topographical Biomarkers Derived from Functional Magnetic Resonance Imaging and Electroencephalographic Activity. J Alzheimer’s Dis [Internet]. 2018;(November):1–21. Available from: doi: 10.3233/JAD-180158
44. de Haan W, Pijnenburg Y AL, Strijers RL, van der Made Y, van der Flier WM, Scheltens P, et al. Functional neural network analysis in frontotemporal dementia and Alzheimer’s disease using EEG and graph theory. BMC Neurosci. 2009;10(101):1–12.
45. Frantzidis CA, Vivas AB, Tsolaki A, Klados MA, Tsolaki M, Bamidis PD. Functional disorganization of small-world brain networks in mild Alzheimer’s disease and amnestic Mild cognitive impairment: An EEG study using Relative Wavelet Entropy (RWE). Front Aging Neurosci. 2014;6:1–11. doi: 10.3389/fnagi.2014.00001
46. Tijms BM, Wink AM, de Haan W, van der Flier WM, Stam CJ, Scheltens P, et al. Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurobiol Aging [Internet]. 2013;34(8):2023–36. Available from: doi: 10.1016/j.neurobiolaging.2013.02.020 23541878
47. Yao Z, Zhang Y, Lin L, Zhou Y, Xu C, Jiang T. Abnormal cortical networks in mild cognitive impairment and alzheimer’s disease. PLoS Comput Biol. 2010;6(11).
48. Pereira JB, Mijalkov M, Kakaei E, Mecocci P, Vellas B, Tsolaki M, et al. Disrupted Network Topology in Patients with Stable and Progressive Mild Cognitive Impairment and Alzheimer’s Disease. Cereb Cortex. 2016;26:3476–93. doi: 10.1093/cercor/bhw128 27178195
49. Reijmer YD, Leemans A, Caeyenberghs K, Heringa SM, Koek HL, Biessels GJ. Disruption of cerebral networks and cognitive impairment in Alzheimer disease. Neurology. 2013;80(15).
50. Liu Y, Yu C, Zhang X, Liu J, Duan Y, Alexander-Bloch AF, et al. Impaired Long Distance Functional Connectivity and Weighted Network Architecture in Alzheimer’s Disease. Cereb Cortex. 2014;24(6):1422–35. doi: 10.1093/cercor/bhs410 23314940
51. Supekar K, Menon V, Rubin D, Musen M, Greicius MD. Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput Biol. 2008;4(6).
52. Alderson T, Kehoe E, Maguire L, Farrell D, Lawlor B, Kenny RA, et al. Disrupted thalamus white matter anatomy and posterior default mode network effective connectivity in amnestic mild cognitive impairment. Front Aging Neurosci. 2017;9:1–15. doi: 10.3389/fnagi.2017.00001
53. Kehoe EG, Farrell D, Metzler-Baddeley C, Lawlor BA, Kenny RA, Lyons D, et al. Fornix white matter is correlated with resting-state functional connectivity of the thalamus and hippocampus in healthy aging but not in mild cognitive impairment—A preliminary study. Front Aging Neurosci. 2015;7:1–10. doi: 10.3389/fnagi.2015.00001
54. Reijmer YD, Leemans A, Heringa SM, Wielaard I, Jeurissen B, Koek HL, et al. Improved Sensitivity to Cerebral White Matter Abnormalities in Alzheimer’s Disease with Spherical Deconvolution Based Tractography. PLoS One. 2012;7(8):1–8.
55. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild Cognitive Impairment. Arch Neurol. 1999;56:303–9. doi: 10.1001/archneur.56.3.303 10190820
56. Morris JC, Heyman A, Mohs R, Hughes J, van Belle G, Fillenbaum G, et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology. 1989;39(9):1159–65. doi: 10.1212/wnl.39.9.1159 2771064
57. Welsh K, Butters N, Hughes J. Detection and Staging of Dementia in Alzheimer’s Disease. Use of the Neuropsychological Measures Developed for the Consortium to Establish a Registry for Alzheimer’s Disease. Arch Neurol. 1992;49(5):448–52. doi: 10.1001/archneur.1992.00530290030008 1580805
58. Folstein MF, Folstein SE, McHugh PR. Mini-mental state: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(13):189–98.
59. Yesavage J. Geriatric Depression Scale. Psychopharmacol Bull. 1988;24(4):709–11. 3249773
60. Rami L, Valls-Pedret C, Bartrés-Faz D, Carpille C, Solé-Padullés C, Castellvi M, et al. Cognitive reserve questionnaire: Scores obtained in a healthy elderly population and in one with Alzheimer’s disease. Rev Neurol. 2011;52(4):195–201. 21312165
61. Yuen KK. The two-sample trimmed t for unequal population variances. Biometrika. 1974;61(1):165–70.
62. Mair P, Wilcox R. ‘ WRS2 ‘: A collection of robust statistical methods. CRAN; 2018.
63. Richard E, Reitz C, Honig LH, Schupf N, Tang MX, Manly JJ, et al. Late-life depression, mild cognitive impairment, and dementia. JAMA Neurol. 2013;70(3):383–9.
64. Mourao RJ, Mansur G, Malloy-Diniz LF, Castro Costa E, Diniz BS. Depressive symptoms increase the risk of progression to dementia in subjects with mild cognitive impairment: systematic review and meta-analysis. Int J Geriatr Psychiatry. 2016;31(8):905–11. doi: 10.1002/gps.4406 26680599
65. Pruessmann KP, Weiger M, Scheidegger MB, Boseiger P. SENSE: Sensitivity encoding for fast MRI. Magn Reson Med. 1999;42:952–62. 10542355
66. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62:782–90. doi: 10.1016/j.neuroimage.2011.09.015 21979382
67. Li X, Morgan PS, Ashburner J, Smith J, Rorden C. The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J Neurosci Methods [Internet]. 2016;264:47–56. Available from: doi: 10.1016/j.jneumeth.2016.03.001 26945974
68. Leemans A, Jeurissen B, Sijbers J, Jones DK. ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In: 17th Annual Meeting of Inl Soc Mag Reson Med, Hawaii, USA. 2009. p. 3537.
69. Leemans A, Jones DK. The B -Matrix Must Be Rotated When Correcting for Subject Motion in DTI Data. Magn Reson Med. 2009;61:1336–49. doi: 10.1002/mrm.21890 19319973
70. Irfanoglu MO, Walker L, Sarlls J, Marenco S, Pierpaoli C. Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results. Neuroimage. 2012;61(1):1–31. doi: 10.1016/j.neuroimage.2012.02.057
71. Tax CMW, Jeurissen B, Vos SB, Viergever MA, Leemans A. Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data. Neuroimage [Internet]. 2014;86:67–80. Available from: doi: 10.1016/j.neuroimage.2013.07.067 23927905
72. Tournier JD, Mori S, Leemans A. Diffusion tensor imaging and beyond. Magn Reson Med. 2011;65(6):1532–56. doi: 10.1002/mrm.22924 21469191
73. Metzler-Baddeley C, Jones DK, Belaroussi B, Aggleton JP, O’Sullivan MJ. Frontotemporal Connections in Episodic Memory and Aging: A Diffusion MRI Tractography Study. J Neurosci [Internet]. 2011;31(37):13236–45. Available from: doi: 10.1523/JNEUROSCI.2317-11.2011 21917806
74. Metzler-Baddeley C, Baddeley RJ, Jones DK, Aggleton JP, O’Sullivan MJ. Individual Differences in Fornix Microstructure and Body Mass Index. PLoS One. 2013;8(3):1–8.
75. Jones DK, Christiansen KF, Chapman RJ, Aggleton JP. Distinct subdivisions of the cingulum bundle revealed by diffusion MRI fibre tracking: Implications for neuropsychological investigations. Neuropsychologia [Internet]. 2013;51(1):67–78. Available from: doi: 10.1016/j.neuropsychologia.2012.11.018 23178227
76. Sibilia F, Kehoe EG, Farrell D, Kerskens C, O’Neill D, McNulty JP, et al. Aging-Related Microstructural Alterations Along the Length of the Cingulum Bundle. Brain Connect [Internet]. 2017;7(6):366–72. Available from: doi: 10.1089/brain.2017.0493 28583034
77. Whitfield-Gabrieli S, Nieto-Castanon A. Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2012;2(3):125–41. doi: 10.1089/brain.2012.0073 22642651
78. Penny W, Friston K, Ashburner J, Kiebel S, Nichols T. Statistical Parametric Mapping: The Analysis of Functional Brain Images. Elsevier; 2006. 656 p.
79. Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, et al. Brain atrophy in Alzheimer’s Disease and aging. Ageing Res Rev [Internet]. 2016;30:25–48. Available from: doi: 10.1016/j.arr.2016.01.002 26827786
80. Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo X, Holmes AJ, et al. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex. 2018;28:3095–114. doi: 10.1093/cercor/bhx179 28981612
81. Field AP, Wilcox RR. Robust statistical methods: A primer for clinical psychology and experimental psychopathology researchers. Behav Res Ther [Internet]. 2017;98:19–38. Available from: doi: 10.1016/j.brat.2017.05.013 28577757
82. Maecheler M, Rousseeuw P, Croux C, Todorov V, Rucksuhl A, Salibian-Barrera M, et al. “robustbase”: Basic robust statistics. R package [Internet]. 2018. Available from: http://cran.r-project.org/package=robustbase
83. Koller M, Stahel WA. Sharpening Wald-type inference in robust regression for small samples. Comput Stat Data Anal [Internet]. 2011;55(8):2504–15. Available from: http://dx.doi.org/10.1016/j.csda.2011.02.014
84. Dong HS, Han C, Jeon SW, Yoon S, Jeong HG, Huh YJ, et al. Characteristics of neurocognitive functions in mild cognitive impairment with depression. Int Psychogeriatrics. 2016;28(7):1–10.
85. Van Der Mussele S, Fransen E, Struyfs H, Luyckx J, Mariën P, Saerens J, et al. Depression in mild cognitive impairment is associated with progression to Alzheimer’s disease: A longitudinal study. J Alzheimer’s Dis. 2014;42(4):1239–50.
86. Team RC. R: A language and environment for statistical computing. [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2013. Available from: http://www.r-project.org/
87. Wickham H, François R, Henry L, Müller K. dyplr: A grammar of data manipulation. R package. 2019.
88. Wickham H. ggplot2: Elegant Graphics for Data Analysis [Internet]. New York: Springer-Verlag; 2016. Available from: http://ggplot2.org
89. Wickham H. stringr: simple, consistent wrappers for common string operations. R package. 2019.
90. Yu J, Lam CLM, Lee TMC. White matter microstructural abnormalities in amnestic mild cognitive impairment: A meta-analysis of whole-brain and ROI-based studies. Neurosci Biobehav Rev [Internet]. 2017;83:405–16. Available from: doi: 10.1016/j.neubiorev.2017.10.026 29092777
91. Damoiseaux JS, Greicius MD. Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity. Brain Struct Funct. 2009;213:525–33. doi: 10.1007/s00429-009-0208-6 19565262
92. Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, et al. Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci. 2009;106(6):2035–40. doi: 10.1073/pnas.0811168106 19188601
93. Nowrangi MA, Lyketsos CG, Leoutsakos J-MS, Oishi K, Albert M, Mori S, et al. Longitudinal, region-specific course of diffusion tensor imaging measures in mild cognitive impairment and Alzheimer’s disease. Alzheimers Dement. 2013;9(5):519–28. doi: 10.1016/j.jalz.2012.05.2186 23245561
94. van Wijk BCM, Stam CJ, Daffertshofer A. Comparing brain networks of different size and connectivity density using graph theory. PLoS One. 2010;5(10).
95. Acosta-Cabronero J, Williams GB, Pengas G, Nestor PJ. Absolute diffusivities define the landscape of white matter degeneration in Alzheimer’s disease. Brain. 2010;133(2):529–39.
96. Adluru N, Destiche DJ, Lu SY, Doran ST, Birdsill AC, Melah KE, et al. White matter microstructure in late middle-age: Effects of apolipoprotein E4 and parental family history of Alzheimer ‘ s disease. NeuroImage Clin [Internet]. 2014;4:730–42. Available from: doi: 10.1016/j.nicl.2014.04.008 24936424
97. Kiuchi K, Morikawa M, Taoka T, Nagashima T, Yamauchi T, Makinodan M, et al. Abnormalities of the uncinate fasciculus and posterior cingulate fasciculus in mild cognitive impairment and early Alzheimer’s disease: A diffusion tensor tractography study. Brain Res [Internet]. 2009;1287:184–91. Available from: doi: 10.1016/j.brainres.2009.06.052 19559010
98. Fellgiebel A, Matthias JM, Wille P, Dellani PR, Scheurich A, Schmidt LG, et al. Color-coded diffusion-tensor-imaging of posterior cingulate fiber tracts in mild cognitive impairment. Neurobiol Aging. 2005;26:1193–8. doi: 10.1016/j.neurobiolaging.2004.11.006 15917103
99. Choo IH, Lee DY, Oh JS, Lee JS, Lee DS, Song IC, et al. Posterior cingulate cortex atrophy and regional cingulum disruption in mild cognitive impairment and Alzheimer’s disease. Neurobiol Aging [Internet]. 2010;31(5):772–9. Available from: doi: 10.1016/j.neurobiolaging.2008.06.015 18687503
100. Damoiseaux JS, Smith SM, Witter MP, Sanz-Arigita EJ, Barkhof F, Scheltens P, et al. White matter tract integrity in aging and alzheimer’s disease. Hum Brain Mapp. 2009;30:1051–9. doi: 10.1002/hbm.20563 18412132
101. Kitamura S, Kiuchi K, Taoka T, Hashimoto K, Ueda S, Yasuno F, et al. Longitudinal white matter changes in Alzheimer’s disease: A tractography-based analysis study. Brain Res [Internet]. 2013;1515:12–8. Available from: doi: 10.1016/j.brainres.2013.03.052 23583480
102. Chua TC, Wen W, Chen X, Kochan N, Slavin MJ, Trollor JN, et al. Diffusion Tensor Imaging of the Posterior Cingulate is a Useful Biomarker of Mild Cognitive Impairment. Am J Geriatr Psychiatry [Internet]. 2009;17(7):602–13. Available from: doi: 10.1097/JGP.0b013e3181a76e0b 19546655
103. Liu Y, Spulber G, Lehtimäki KK, Könönen M, Hallikainen I, Gröhn H, et al. Diffusion tensor imaging and Tract-Based Spatial Statistics in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging [Internet]. 2011;32:1558–71. Available from: doi: 10.1016/j.neurobiolaging.2009.10.006 19913331
104. Pievani M, Agosta F, Pagani E, Canu E, Sala S, Absinta M, et al. Assessment of white matter tract damage in mild cognitive impairment and Alzheimer’s disease. Hum Brain Mapp. 2010;31:1862–75. doi: 10.1002/hbm.20978 20162601
105. Villain N, Fouquet M, Baron JC, Mézenge F, Landeau B, De La Sayette V, et al. Sequential relationships between grey matter and white matter atrophy and brain metabolic abnormalities in early Alzheimer’s disease. Brain. 2010;133:3301–14. doi: 10.1093/brain/awq203 20688814
106. Fischer FU, Wolf D, Scheurich A, Fellgiebel A. Altered whole-brain white matter networks in preclinical Alzheimer’s disease. NeuroImage Clin [Internet]. 2015;8:660–6. Available from: doi: 10.1016/j.nicl.2015.06.007 26288751
107. Bartzokis G, Lu PH, Mintz J. Human brain myelination and amyloid beta deposition in Alzheimer’s disease. Alzheimer’s Dement. 2007;3(2):122–5.
108. O’Dwyer L, Lamberton F, Bokde ALW, Ewers M, Faluyi YO, Tanner C, et al. Using diffusion tensor imaging and mixed-effects models to investigate primary and secondary white matter degeneration in Alzheimer’s disease and mild cognitive impairment. J Alzheimer’s Dis. 2011;26(4):667–82.
109. Liao W, Long X, Jiang C, Diao Y, Liu X, Zheng H, et al. Discerning Mild Cognitive Impairment and Alzheimer Disease from Normal Aging: Morphologic characterization based on univariate and multivariate models. Acad Radiol [Internet]. 2014;21(5):597–604. Available from: doi: 10.1016/j.acra.2013.12.001 24433704
110. Mahjoub I, Mahjoub MA, Rekik I. Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Sci Rep. 2018;8(1):1–14. doi: 10.1038/s41598-017-17765-5
111. Konukoglu E, Coutu J, Salat DH, Fischl B. Multivariate Statistical Analysis of Diffusion Imaging Parameters using Partial Least Squares: Application to White Matter Variations in Alzheimer’s Disease. Neuroimage. 2016;134:573–86. doi: 10.1016/j.neuroimage.2016.04.038 27103138
112. Reginold W, Luedke AC, Itorralba J, Fernandez-Ruiz J, Islam O, Garcia A. Altered Superficial White Matter on Tractography MRI in Alzheimer’s Disease. Dement Geriatr Cogn Dis Extra. 2016;6:233–41. doi: 10.1159/000446770 27489557
113. Phillips OR, Clark KA, Luders E, Azhir R, Joshi SH, Woods RP, et al. Superficial White Matter: Effects of Age, Sex, and Hemisphere. Brain Connect. 2013;3(2):146–59. doi: 10.1089/brain.2012.0111 23461767
114. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci [Internet]. 2009;10:186–98. Available from: doi: 10.1038/nrn2575 19190637
115. Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci. 2012;13(5):336–49. doi: 10.1038/nrn3214 22498897
116. Berg AI, Wallin A, Nordlund A, Johansson B. Living with stable MCI: Experiences among 17 individuals evaluated at a memory clinic. Aging Ment Heal. 2013;17(3):293–9.
117. Champely S. pwr: Basic functions for Power Analysis. r package verions 1.2–2. 2018.
118. Bijsterbosch J, Smith SM, Beckmann CF. Introduction to Resting State fMRI Functional Connectivity. First. Jenkinson M, Chappell M, editors. Oxford: Oxford University Press; 2017. 141 p.
119. Phillips DJ, McGlaughlin A, Ruth D, Jager LR, Soldan A. Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer’s disease: The importance of graph creation methods. NeuroImage Clin [Internet]. 2015;7:377–90. Available from: doi: 10.1016/j.nicl.2015.01.007 25984446
120. van den Heuvel MP, de Lange SC, Zalesky A, Seguin C, Yeo BTT, Schmidt R. Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations. Neuroimage [Internet]. 2017;152:437–49. Available from: doi: 10.1016/j.neuroimage.2017.02.005 28167349
Článok vyšiel v časopise
PLOS One
2019 Číslo 10
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Těžké menstruační krvácení může značit poruchu krevní srážlivosti. Jaký management vyšetření a léčby je v takovém případě vhodný?
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie v tomto čísle
- Correction: Low dose naltrexone: Effects on medication in rheumatoid and seropositive arthritis. A nationwide register-based controlled quasi-experimental before-after study
- Combining CDK4/6 inhibitors ribociclib and palbociclib with cytotoxic agents does not enhance cytotoxicity
- Experimentally validated simulation of coronary stents considering different dogboning ratios and asymmetric stent positioning
- Risk factors associated with IgA vasculitis with nephritis (Henoch–Schönlein purpura nephritis) progressing to unfavorable outcomes: A meta-analysis