Disruption of white matter connectivity in chronic obstructive pulmonary disease

Autoři: Catherine A. Spilling aff001;  Paul W. Jones aff002;  James W. Dodd aff003;  Thomas R. Barrick aff001
Působiště autorů: Neuroscience Research Centre, Molecular and Clinical Sciences Research Institute, St George’s University of London, Tooting, London, United Kingdom aff001;  Institute of Infection and Immunity, St George's, University of London, Tooting, London, United Kingdom aff002;  Academic Respiratory Unit, Second Floor, Learning and Research, Southmead Hospital, University of Bristol, Westbury-on-Trym, Bristol, United Kingdom aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223297



Mild cognitive impairment is a common systemic manifestation of chronic obstructive pulmonary disease (COPD). However, its pathophysiological origins are not understood. Since, cognitive function relies on efficient communication between distributed cortical and subcortical regions, we investigated whether people with COPD have disruption in white matter connectivity.


Structural networks were constructed for 30 COPD patients (aged 54–84 years, 57% male, FEV1 52.5% pred.) and 23 controls (aged 51–81 years, 48% Male). Networks comprised 90 grey matter regions (nodes) interconnected by white mater fibre tracts traced using deterministic tractography (edges). Edges were weighted by the number of streamlines adjusted for a) streamline length and b) end-node volume. White matter connectivity was quantified using global and nodal graph metrics which characterised the networks connection density, connection strength, segregation, integration, nodal influence and small-worldness. Between-group differences in white matter connectivity and within-group associations with cognitive function and disease severity were tested.


COPD patients’ brain networks had significantly lower global connection strength (p = 0.03) and connection density (p = 0.04). There was a trend towards COPD patients having a reduction in nodal connection density and connection strength across the majority of network nodes but this only reached significance for connection density in the right superior temporal gyrus (p = 0.02) and did not survive correction for end-node volume. There were no other significant global or nodal network differences or within-group associations with disease severity or cognitive function.


COPD brain networks show evidence of damage compared to controls with a reduced number and strength of connections. This loss of connectivity was not sufficient to disrupt the overall efficiency of network organisation, suggesting that it has redundant capacity that makes it resilient to damage, which may explain why cognitive dysfunction is not severe. This might also explain why no direct relationships could be found with cognitive measures. Smoking and hypertension are known to have deleterious effects on the brain. These confounding effects could not be excluded.

Klíčová slova:

Central nervous system – Cognition – Cognitive impairment – Diffusion tensor imaging – Chronic obstructive pulmonary disease – Network analysis – Neural networks – Tractography


1. Kodl CT, Seaquist ER. Cognitive Dysfunction and Diabetes Mellitus. Endocr Rev. 2008;29: 494–511. doi: 10.1210/er.2007-0034 18436709

2. Berger I, Wu S, Masson P, Kelly PJ, Duthie FA, Whiteley W, et al. Cognition in chronic kidney disease: a systematic review and meta-analysis. BMC Med. 2016;14. doi: 10.1186/s12916-016-0745-9 27964726

3. Meade T, Manolios N, Cumming SR, Conaghan PG, Katz P. Cognitive Impairment in Rheumatoid Arthritis: A Systematic Review. Arthritis Care Res. 2018;70: 39–52. doi: 10.1002/acr.23243 28371512

4. Agusti A, Calverley PM, Celli B, Coxson HO, Edwards LD, Lomas DA, et al. Characterisation of COPD heterogeneity in the ECLIPSE cohort. Respir Res. 2010;11: 122. doi: 10.1186/1465-9921-11-122 20831787

5. Dodd JW. Lung disease as a determinant of cognitive decline and dementia. Alzheimers Res Ther. 2015;7: 32. doi: 10.1186/s13195-015-0116-3 25798202

6. Chang SS, Chen S, McAvay GJ, Tinetti ME. Effect of Coexisting Chronic Obstructive Pulmonary Disease and Cognitive Impairment on Health Outcomes in Older Adults. J Am Geriatr Soc. 2012;60: 1839–1846. doi: 10.1111/j.1532-5415.2012.04171.x 23035917

7. Antonelli Incalzi R. Verbal memory impairment in COPD: its mechanisms and clinical relevance. CHEST J. 1997;112: 1506. doi: 10.1378/chest.112.6.1506 9404746

8. van Dijk EJ. Arterial oxygen saturation, COPD, and cerebral small vessel disease. J Neurol Neurosurg Psychiatry. 2004;75: 733–736. doi: 10.1136/jnnp.2003.022012 15090569

9. Lahousse L, Vernooij MW, Darweesh SKL, Akoudad S, Loth DW, Joos GF, et al. Chronic obstructive pulmonary disease and cerebral microbleeds. The Rotterdam study. Am J Respir Crit Care Med. 2013;188: 783–788. doi: 10.1164/rccm.201303-0455OC 23885754

10. Dodd JW, Chung AW, van den Broek MD, Barrick TR, Charlton RA, Jones PW. Brain Structure and Function in Chronic Obstructive Pulmonary Disease: A Multimodal Cranial Magnetic Resonance Imaging Study. Am J Respir Crit Care Med. 2012;186: 240–245. doi: 10.1164/rccm.201202-0355OC 22652026

11. Spilling CA, Jones PW, Dodd JW, Barrick TR. White matter lesions characterise brain involvement in moderate to severe chronic obstructive pulmonary disease, but cerebral atrophy does not. BMC Pulm Med. 2017;17. doi: 10.1186/s12890-017-0435-1 28629404

12. Ryu CW, Jahng GH, Choi CW, Rhee HY, Kim M-J, Kim SM, et al. Microstructural change of the brain in chronic obstructive pulmonary disease. 2013;10: 357–366.

13. Zhang H, Wang X, Lin J, Sun Y, Huang Y, Yang T, et al. Grey and white matter abnormalities in chronic obstructive pulmonary disease: a case-control study. BMJ Open. 2012;2: e000844–e000844. doi: 10.1136/bmjopen-2012-000844 22535793

14. Zeestraten EA, Lawrence AJ, Lambert C, Benjamin P, Brookes RL, Mackinnon AD, et al. Change in multimodal MRI markers predicts dementia risk in cerebral small vessel disease. Neurology. 2017;89: 1869–1876. doi: 10.1212/WNL.0000000000004594 28978655

15. Nave RD, Foresti S, Pratesi A, Ginestroni A, Inzitari M, Salvadori E, et al. Whole-Brain Histogram and Voxel-Based Analyses of Diffusion Tensor Imaging in Patients with Leukoaraiosis: Correlation with Motor and Cognitive Impairment. Am J Neuroradiol. 2007;28: 1313–1319. doi: 10.3174/ajnr.A0555 17698534

16. Nitkunan A, Charlton RA, McIntyre DJO, Barrick TR, Howe FA, Markus HS. Diffusion tensor imaging and MR spectroscopy in hypertension and presumed cerebral small vessel disease. Magn Reson Med. 2008;59: 528–534. doi: 10.1002/mrm.21461 18224697

17. Mesulam M-M. Large-scale neurocognitive networks and distributed processing for attention, language and memory. Ann Neurol. 1990;28: 597–613. 2260847

18. Park H-J, Friston K. Structural and Functional Brain Networks: From Connections to Cognition. Science. 2013;342: 1238411–1238411. doi: 10.1126/science.1238411 24179229

19. Meunier D, Lambiotte R, Bullmore ET. Modular and Hierarchically Modular Organization of Brain Networks. Front Neurosci. 2010;4. doi: 10.3389/fnins.2010.00200 21151783

20. Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci. 2012; doi: 10.1038/nrn3214 22498897

21. Lawrence AJ, Chung AW, Morris RG, Markus HS, Barrick TR. Structural network efficiency is associated with cognitive impairment in small-vessel disease. Neurology. 2014;83: 304–311. doi: 10.1212/WNL.0000000000000612 24951477

22. Vogelmeier CF, Criner GJ, Martinez FJ, Anzueto A, Barnes PJ, Bourbeau J, et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary. Am J Respir Crit Care Med. 2017;195: 557–582. doi: 10.1164/rccm.201701-0218PP 28128970

23. Sabit R, Bolton CE, Edwards PH, Pettit RJ, Evans WD, McEniery CM, et al. Arterial stiffness and osteoporosis in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2007;175: 1259–1265. doi: 10.1164/rccm.200701-067OC 17363772

24. D’Agostino R B. Stroke risk profile: adjustment for antihypertensive medication. The Framingham Study. Stroke. 1994;25: 40–43. doi: 10.1161/01.str.25.1.40 8266381

25. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40: 373–383. doi: 10.1016/0021-9681(87)90171-8 3558716

26. Jones PW, Quirk FH, Baveystock CM, Littlejohns P. A self-complete measure of health status for chronic airflow limitation. The St. George’s Respiratory Questionnaire. Am Rev Respir Dis. 1992;145: 1321–1327. doi: 10.1164/ajrccm/145.6.1321 1595997

27. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand. 1983;67: 361–370. doi: 10.1111/j.1600-0447.1983.tb09716.x 6880820

28. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. NeuroImage. 2012;62: 782–790. doi: 10.1016/j.neuroimage.2011.09.015 21979382

29. Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17: 143–155. doi: 10.1002/hbm.10062 12391568

30. Lambert C, Sam Narean J, Benjamin P, Zeestraten E, Barrick TR, Markus HS. Characterising the grey matter correlates of leukoaraiosis in cerebral small vessel disease. NeuroImage Clin. 2015;9: 194–205. doi: 10.1016/j.nicl.2015.07.002 26448913

31. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10: 186–198. doi: 10.1038/nrn2575 19190637

32. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain. NeuroImage. 2002;15: 273–289. doi: 10.1006/nimg.2001.0978 11771995

33. Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. NeuroImage. 2009;48: 63–72. doi: 10.1016/j.neuroimage.2009.06.060 19573611

34. Holmes CJ, Hoge R, Collins L, Woods R, Toga AW, Evans AC. Enhancement of MR images using registration for signal averaging. J Comput Assist Tomogr. 1998;22: 324–333. doi: 10.1097/00004728-199803000-00032 9530404

35. Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage. 2011;54: 2033–2044. doi: 10.1016/j.neuroimage.2010.09.025 20851191

36. Calamante F, Tournier J-D, Jackson GD, Connelly A. Track-density imaging (TDI): super-resolution white matter imaging using whole-brain track-density mapping. NeuroImage. 2010;53: 1233–1243. doi: 10.1016/j.neuroimage.2010.07.024 20643215

37. Hagmann P, Kurant M, Gigandet X, Thiran P, Wedeen VJ, Meuli R, et al. Mapping Human Whole-Brain Structural Networks with Diffusion MRI. Sporns O, editor. PLoS ONE. 2007;2: e597. doi: 10.1371/journal.pone.0000597 17611629

38. van den Heuvel MP, Sporns O. Rich-club organization of the human connectome. J Neurosci Off J Soc Neurosci. 2011;31: 15775–15786. doi: 10.1523/JNEUROSCI.3539-11.2011 22049421

39. Rubinov M, Sporns O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage. 2010;52: 1059–1069. doi: 10.1016/j.neuroimage.2009.10.003 19819337

40. Maslov S, Sneppen K. Specificity and stability in topology of protein networks. Science. 2002;296: 910–913. doi: 10.1126/science.1065103 11988575

41. Garrison KA, Scheinost D, Finn ES, Shen X, Constable RT. The (in)stability of functional brain network measures across thresholds. NeuroImage. 2015;118: 651–661. doi: 10.1016/j.neuroimage.2015.05.046 26021218

42. Ginestet CE, Fournel AP, Simmons A. Statistical network analysis for functional MRI: summary networks and group comparisons. Front Comput Neurosci. 2014;8. doi: 10.3389/fncom.2014.00051 24834049

43. Drakesmith M, Caeyenberghs K, Dutt A, Lewis G, David AS, Jones DK. Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data. NeuroImage. 2015;118: 313–333. doi: 10.1016/j.neuroimage.2015.05.011 25982515

44. Drakesmith M, Caeyenberghs K, Dutt A, Zammit S, Evans CJ, Reichenberg A, et al. Schizophrenia-like topological changes in the structural connectome of individuals with subclinical psychotic experiences: Connectome Changes in Subclinical Psychosis. Hum Brain Mapp. 2015;36: 2629–2643. doi: 10.1002/hbm.22796 25832856

45. Bassett DS, Bullmore E. Small-world brain networks. Neurosci Rev J Bringing Neurobiol Neurol Psychiatry. 2006;12: 512–523. doi: 10.1177/1073858406293182 17079517

46. Zhang J, Wang Y, Wang J, Zhou X, Shu N, Wang Y, et al. White matter integrity disruptions associated with cognitive impairments in type 2 diabetes patients. Diabetes. 2014; DB_140342. doi: 10.2337/db14-0342 24947353

47. van Duinkerken E, Schoonheim MM, IJzerman RG, Klein M, Ryan CM, Moll AC, et al. Diffusion tensor imaging in type 1 diabetes: decreased white matter integrity relates to cognitive functions. Diabetologia. 2012;55: 1218–1220. doi: 10.1007/s00125-012-2488-2 22327286

48. Kennedy KM, Raz N. Pattern of normal age-related regional differences in white matter microstructure is modified by vascular risk. Brain Res. 2009;1297: 41–56. doi: 10.1016/j.brainres.2009.08.058 19712671

49. Matsuda-Abedini M, Fitzpatrick K, Harrell WR, Gipson DS, Hooper SR, Belger A, et al. Brain abnormalities in children and adolescents with chronic kidney disease. Pediatr Res. 2018; doi: 10.1038/s41390-018-0037-5 29967532

50. Vemuri P, Knopman DS, Jack CR, Lundt ES, Weigand SD, Zuk SM, et al. Association of Kidney Function Biomarkers with Brain MRI Findings: The BRINK Study. J Alzheimers Dis JAD. 2017;55: 1069–1082. doi: 10.3233/JAD-160834 27767995

51. Wardlaw JM, Smith C, Dichgans M. Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol. 2013;12: 483–497. doi: 10.1016/S1474-4422(13)70060-7 23602162

52. Li J, Fei G-H. The unique alterations of hippocampus and cognitive impairment in chronic obstructive pulmonary disease. Respir Res. 2013;14: 1–9. doi: 10.1186/1465-9921-14-1

53. Esser RW, Stoeckel MC, Kirsten A, Watz H, Taube K, Lehmann K, et al. Structural Brain Changes in Patients With COPD. Chest. 2016;149: 426–434. doi: 10.1378/chest.15-0027 26203911

54. Wang C, Ding Y, Shen B, Gao D, An J, Peng K, et al. Altered Gray Matter Volume in Stable Chronic Obstructive Pulmonary Disease with Subclinical Cognitive Impairment: an Exploratory Study. Neurotox Res. 2017;31: 453–463. doi: 10.1007/s12640-016-9690-9 28005183

55. Liao D, Higgins M, Bryan NR, Eigenbrodt ML, Chambless LE, Lamar V, et al. Lower pulmonary function and cerebral subclinical abnormalities detected by MRI: the Atherosclerosis Risk in Communities study. Chest. 1999;116: 150–156. doi: 10.1378/chest.116.1.150 10424519

56. Taki Y, Kinomura S, Ebihara S, Thyreau B, Sato K, Goto R, et al. Correlation between pulmonary function and brain volume in healthy elderly subjects. Neuroradiology. 2013;55: 689–695. doi: 10.1007/s00234-013-1157-6 23440433

57. Sachdev PS, Anstey KJ, Parslow RA, Wen W, Maller J, Kumar R, et al. Pulmonary Function, Cognitive Impairment and Brain Atrophy in a Middle-Aged Community Sample. Dement Geriatr Cogn Disord. 2006;21: 300–308. doi: 10.1159/000091438 16484809

58. Murray AD, Staff RT, Shenkin SD, Deary IJ, Starr JM, Whalley LJ. Brain white matter hyperintensities: relative importance of vascular risk factors in nondemented elderly people. Radiology. 2005;237: 251–257. doi: 10.1148/radiol.2371041496 16126931

59. van Dijk EJ, Prins ND, Vrooman HA, Hofman A, Koudstaal PJ, Breteler MMB. Progression of Cerebral Small Vessel Disease in Relation to Risk Factors and Cognitive Consequences: Rotterdam Scan Study. Stroke. 2008;39: 2712–2719. doi: 10.1161/STROKEAHA.107.513176 18635849

60. Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ. 2010;341: c3666–c3666. doi: 10.1136/bmj.c3666 20660506

61. Lucas P, Izquierdo JL, Gonzalez-Moro, Frances Fernandez, Lopez V, Cano Bellon. Chronic obstructive pulmonary disease as a cardiovascular risk factor. Results of a case–control study (CONSISTE study). Int J Chron Obstruct Pulmon Dis. 2012; 679. doi: 10.2147/COPD.S36222 23055717

62. Lahousse L, Tiemeier H, Ikram MA, Brusselle GG. Chronic obstructive pulmonary disease and cerebrovascular disease: A comprehensive review. Respir Med. 2015;109: 1371–1380. doi: 10.1016/j.rmed.2015.07.014 26342840

63. Tuladhar AM, van Dijk E, Zwiers MP, van Norden AGW, de Laat KF, Shumskaya E, et al. Structural network connectivity and cognition in cerebral small vessel disease: Structural Network and Cognition. Hum Brain Mapp. 2016;37: 300–310. doi: 10.1002/hbm.23032 26466741

64. Wei R, Li C, Fogelson N, Li L. Prediction of Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using MRI and Structural Network Features. Front Aging Neurosci. 2016;8. doi: 10.3389/fnagi.2016.00076 27148045

65. Eijlers AJC, van Geest Q, Dekker I, Steenwijk MD, Meijer KA, Hulst HE, et al. Predicting cognitive decline in multiple sclerosis: a 5-year follow-up study. Brain. 2018;141: 2605–2618. doi: 10.1093/brain/awy202 30169585

66. Zeestraten EA, Lawrence AJ, Lambert C, Benjamin P, Brookes RL, Mackinnon AD, et al. Change in multimodal MRI markers predicts dementia risk in cerebral small vessel disease. Neurology. 2017;89: 1869–1876. doi: 10.1212/WNL.0000000000004594 28978655

67. Lawrence AJ, Zeestraten EA, Benjamin P, Lambert CP, Morris RG, Barrick TR, et al. Longitudinal decline in structural networks predicts dementia in cerebral small vessel disease. Neurology. 2018;90: e1898–e1910. doi: 10.1212/WNL.0000000000005551 29695593

68. Berlot R, Metzler-Baddeley C, Ikram MA, Jones DK, O’Sullivan MJ. Global Efficiency of Structural Networks Mediates Cognitive Control in Mild Cognitive Impairment. Front Aging Neurosci. 2016;8. doi: 10.3389/fnagi.2016.00292 28018208

69. Du J, Wang Y, Zhi N, Geng J, Cao W, Yu L, et al. Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease. NeuroImage Clin. 2019;22: 101712. doi: 10.1016/j.nicl.2019.101712 30772684

70. Satz P. Brain reserve capacity on symptom onset after brain injury: A formulation and review of evidence for threshold theory. Neuropsychology. 1993;7: 273–295. doi: 10.1037/0894-4105.7.3.273

71. Stern Y. What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc. 2002;8: 448–460. 11939702

72. Stern Y. Cognitive reserve☆. Neuropsychologia. 2009;47: 2015–2028. doi: 10.1016/j.neuropsychologia.2009.03.004 19467352

73. Lezak M D., Howieson D B., Loring D W. Neuropsychological Assessment, 4th Edn. 2004.

74. Wolf H, Julin P, Gertz H-J, Winblad B, Wahlund L-O. Intracranial volume in mild cognitive impairment, Alzheimer’s disease and vascular dementia: evidence for brain reserve? Int J Geriatr Psychiatry. 2004;19: 995–1007. doi: 10.1002/gps.1205 15449362

75. Dodd JW, Getov SV, Jones PW. Cognitive function in COPD. European Respiratory Journal. 2010;35: 913–922. doi: 10.1183/09031936.00125109 20356988

76. Aras YG, Tunç A, Güngen BD, Güngen AC, Aydemir Y, Demiyürek BE. The effects of depression, anxiety and sleep disturbances on cognitive impairment in patients with chronic obstructive pulmonary disease. Cogn Neurodyn. 2017;11: 565–571. doi: 10.1007/s11571-017-9449-x 29147148

77. Omachi TA, Blanc PD, Claman DM, Chen H, Yelin EH, Julian L, et al. Disturbed Sleep among COPD Patients is Longitudinally Associated with Mortality and Adverse COPD Outcomes. Sleep Med. 2012;13: 476–483. doi: 10.1016/j.sleep.2011.12.007 22429651

78. Dag E, Bulcun E, Turkel Y, Ekici A, Ekici M. Factors Influencing Cognitive Function in Subjects With COPD. Respiratory Care. 2016;61: 1044–1050. doi: 10.4187/respcare.04403 26932385

79. Emery CF, Shermer RL, Hauck ER, Hsiao ET, MacIntyre NR. Cognitive and psychological outcomes of exercise in a 1-year follow-up study of patients with chronic obstructive pulmonary disease. Health Psychol. 2003;22: 598–604. doi: 10.1037/0278-6133.22.6.598 14640857

80. Xie X, Shi Y, Zhang J. Structural network connectivity impairment and depressive symptoms in cerebral small vessel disease. J Affect Disord. 2017;220: 8–14. doi: 10.1016/j.jad.2017.05.039 28575716

81. Lin F, Wu G, Zhu L, Lei H. Heavy smokers show abnormal microstructural integrity in the anterior corpus callosum: A diffusion tensor imaging study with tract-based spatial statistics. Drug Alcohol Depend. 2013;129: 82–87. doi: 10.1016/j.drugalcdep.2012.09.013 23062873

82. Lin L, Xue Y, Duan Q, Sun B, Lin H, Chen X, et al. Microstructural White Matter Abnormalities and Cognitive Dysfunction in Subcortical Ischemic Vascular Disease: an Atlas-Based Diffusion Tensor Analysis Study. J Mol Neurosci MN. 2015;56: 363–370. doi: 10.1007/s12031-015-0550-5 25859933

83. Zhang H, Wang X, Lin J, Sun Y, Huang Y, Yang T, et al. Reduced regional gray matter volume in patients with chronic obstructive pulmonary disease: a voxel-based morphometry study. Am J Neuroradiol. 2013;34: 334–339. doi: 10.3174/ajnr.A3235 22859277

84. Heinen R, Vlegels N, de Bresser J, Leemans A, Biessels GJ, Reijmer YD. The cumulative effect of small vessel disease lesions is reflected in structural brain networks of memory clinic patients. NeuroImage Clin. 2018;19: 963–969. doi: 10.1016/j.nicl.2018.06.025 30003033

85. Ciccarelli O, Parker GJM, Toosy AT, Wheeler-Kingshott C a. M, Barker GJ, Boulby PA, et al. From diffusion tractography to quantitative white matter tract measures: a reproducibility study. NeuroImage. 2003;18: 348–359. 12595188

86. Behrens TEJ, Berg HJ, Jbabdi S, Rushworth MFS, Woolrich MW. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage. 2007;34: 144–155. doi: 10.1016/j.neuroimage.2006.09.018 17070705

87. Metzler-Baddeley C, O’Sullivan MJ, Bells S, Pasternak O, Jones DK. How and how not to correct for CSF-contamination in diffusion MRI. NeuroImage. 2012;59: 1394–1403. doi: 10.1016/j.neuroimage.2011.08.043 21924365

88. Berlot R, Metzler-Baddeley C, Jones DK, O’Sullivan MJ. CSF contamination contributes to apparent microstructural alterations in mild cognitive impairment. NeuroImage. 2014;92: 27–35. doi: 10.1016/j.neuroimage.2014.01.031 24503415

89. Côté M-A, Girard G, Boré A, Garyfallidis E, Houde J-C, Descoteaux M. Tractometer: towards validation of tractography pipelines. Med Image Anal. 2013;17: 844–857. doi: 10.1016/j.media.2013.03.009 23706753

90. Tournier J-D, Calamante F, Gadian DG, Connelly A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage. 2004;23: 1176–1185. doi: 10.1016/j.neuroimage.2004.07.037 15528117

91. Tournier J-D, Calamante F, Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage. 2007;35: 1459–1472. doi: 10.1016/j.neuroimage.2007.02.016 17379540

92. Farquharson S, Tournier J-D, Calamante F, Fabinyi G, Schneider-Kolsky M, Jackson GD, et al. White matter fiber tractography: why we need to move beyond DTI. J Neurosurg. 2013;118: 1367–1377. doi: 10.3171/2013.2.JNS121294 23540269

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