Skip to main content

Advertisement

SpringerLink
Log in

Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer’s Disease

  • Original Article
  • Published:
Neuroscience Bulletin Aims and scope Submit manuscript

Abstract

Alzheimer’s disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen CE. Alzheimer’s disease. Lancet 2021, 397: 1577–1590.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Masters CL, Bateman R, Blennow K, Rowe CC, Sperling RA, Cummings JL. Alzheimer’s disease. Nat Rev Dis Primers 2015, 1: 15056.

    Article  PubMed  Google Scholar 

  3. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004, 256: 183–194.

    Article  CAS  PubMed  Google Scholar 

  4. Leifer BP. Early diagnosis of Alzheimer’s disease: Clinical and economic benefits. J Am Geriatr Soc 2003, 51: S281–S288.

    Article  PubMed  Google Scholar 

  5. Long JM, Holtzman DM. Alzheimer disease: An update on pathobiology and treatment strategies. Cell 2019, 179: 312–339.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Brun A, Englund E. A white matter disorder in dementia of the Alzheimer type: A pathoanatomical study. Ann Neurol 1986, 19: 253–262.

    Article  CAS  PubMed  Google Scholar 

  7. 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: 853–863.

    Article  PubMed  Google Scholar 

  8. Zlokovic BV. Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nat Rev Neurosci 2011, 12: 723–738.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bennett DA, Schneider JA, Wilson RS, Bienias JL, Arnold SE. Neurofibrillary tangles mediate the association of amyloid load with clinical Alzheimer disease and level of cognitive function. Arch Neurol 2004, 61: 378–384.

    Article  PubMed  Google Scholar 

  10. Zhu W, Huang H, Yang S, Luo X, Zhu W, Xu S, et al. Cortical and subcortical grey matter abnormalities in white matter hyperintensities and subsequent cognitive impairment. Neurosci Bull 2021, 37: 789–803.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Fellgiebel A, Dellani PR, Greverus D, Scheurich A, Stoeter P, Müller MJ. Predicting conversion to dementia in mild cognitive impairment by volumetric and diffusivity measurements of the hippocampus. Psychiatry Res 2006, 146: 283–287.

    Article  PubMed  Google Scholar 

  12. Amlien IK, Fjell AM. Diffusion tensor imaging of white matter degeneration in Alzheimer’s disease and mild cognitive impairment. Neuroscience 2014, 276: 206–215.

    Article  CAS  PubMed  Google Scholar 

  13. Delbeuck X, Van der Linden M, Collette F. Alzheimer’s disease as a disconnection syndrome? Neuropsychol Rev 2003, 13: 79–92.

    Article  CAS  PubMed  Google Scholar 

  14. Mayo CD, Mazerolle EL, Ritchie L, Fisk JD, Gawryluk JR. Alzheimer’s Disease Neuroimaging Initiative. Longitudinal changes in microstructural white matter metrics in Alzheimer’s disease. Neuroimage Clin 2017, 13: 330–338.

    Article  PubMed  Google Scholar 

  15. Sun H, Lui S, Yao L, Deng W, Xiao Y, Zhang W, et al. Two patterns of white matter abnormalities in medication-naive patients with first-episode schizophrenia revealed by diffusion tensor imaging and cluster analysis. JAMA Psychiatry 2015, 72: 678–686.

    Article  PubMed  Google Scholar 

  16. Yin RH, Tan L, Liu Y, Wang WY, Wang HF, Jiang T, et al. Multimodal voxel-based meta-analysis of white matter abnormalities in Alzheimer’s disease. J Alzheimers Dis 2015, 47: 495–507.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, et al. Diffusion tensor imaging: Concepts and applications. J Magn Reson Imaging 2001, 13: 534–546.

    Article  PubMed  Google Scholar 

  18. Beaulieu C. The basis of anisotropic water diffusion in the nervous system - a technical review. NMR Biomed 2002, 15: 435–455.

    Article  PubMed  Google Scholar 

  19. Song SK, Sun SW, Ju WK, Lin SJ, Cross AH, Neufeld AH. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. NeuroImage 2003, 20: 1714–1722.

    Article  PubMed  Google Scholar 

  20. Medina D, DeToledo-Morrell L, Urresta F, Gabrieli JD, Moseley M, Fleischman D, et al. White matter changes in mild cognitive impairment and AD: A diffusion tensor imaging study. Neurobiol Aging 2006, 27: 663–672.

    Article  PubMed  Google Scholar 

  21. Zhang Y, Schuff N, Du AT, Rosen HJ, Kramer JH, Gorno-Tempini ML, et al. White matter damage in frontotemporal dementia and Alzheimer’s disease measured by diffusion MRI. Brain 2009, 132: 2579–2592.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Xie S, Xiao JX, Gong GL, Zang YF, Wang YH, Wu HK, et al. Voxel-based detection of white matter abnormalities in mild Alzheimer disease. Neurology 2006, 66: 1845–1849.

    Article  CAS  PubMed  Google Scholar 

  23. Araque Caballero MÁ, Suárez-Calvet M, Duering M, Franzmeier N, Benzinger T, Fagan AM, et al. White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease. Brain 2018, 141: 3065–3080.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Acosta-Cabronero J, Nestor PJ. Diffusion tensor imaging in Alzheimer’s disease: Insights into the limbic-diencephalic network and methodological considerations. Front Aging Neurosci 2014, 6: 266.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Yeatman JD, Dougherty RF, Myall NJ, Wandell BA, Feldman HM. Tract profiles of white matter properties: Automating fiber-tract quantification. PLoS One 2012, 7: e49790.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yeatman JD, Richie-Halford A, Smith JK, Keshavan A, Rokem A. A browser-based tool for visualization and analysis of diffusion MRI data. Nat Commun 2018, 9: 940.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Dou X, Yao H, Feng F, Wang P, Zhou B, Jin D, et al. Characterizing white matter connectivity in Alzheimer’s disease and mild cognitive impairment: An automated fiber quantification analysis with two independent datasets. Cortex 2020, 129: 390–405.

    Article  PubMed  Google Scholar 

  28. Zhang X, Sun Y, Li W, Liu B, Wu W, Zhao H, et al. Characterization of white matter changes along fibers by automated fiber quantification in the early stages of Alzheimer’s disease. Neuroimage Clin 2019, 22: 101723.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Jin Y, Huang C, Daianu M, Zhan L, Dennis EL, Reid RI, et al. 3D tract-specific local and global analysis of white matter integrity in Alzheimer’s disease. Hum Brain Mapp 2017, 38: 1191–1207.

    Article  PubMed  Google Scholar 

  30. 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 2011, 32: 2322.e5-2322.e18.

    Article  PubMed  Google Scholar 

  31. Clerx L, Visser PJ, Verhey F, Aalten P. New MRI markers for Alzheimer’s disease: A meta-analysis of diffusion tensor imaging and a comparison with medial temporal lobe measurements. J Alzheimers Dis 2012, 29: 405–429.

    Article  PubMed  Google Scholar 

  32. Qin L, Guo Z, McClure MA, Mu Q. White matter changes from mild cognitive impairment to Alzheimer’s disease: A meta-analysis. Acta Neurol Belg 2021, 121: 1435–1447.

    Article  PubMed  Google Scholar 

  33. Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. Neuroimage 2017, 155: 530–548.

    Article  PubMed  Google Scholar 

  34. Dyrba M, Grothe M, Kirste T, Teipel SJ. Multimodal analysis of functional and structural disconnection in Alzheimer’s disease using multiple kernel SVM. Hum Brain Mapp 2015, 36: 2118–2131.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Teipel S, Drzezga A, Grothe MJ, Barthel H, Chételat G, Schuff N, et al. Multimodal imaging in Alzheimer’s disease: Validity and usefulness for early detection. Lancet Neurol 2015, 14: 1037–1053.

    Article  PubMed  Google Scholar 

  36. Nir TM, Villalon-Reina JE, Prasad G, Jahanshad N, Joshi SH, Toga AW, et al. Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer’s disease. Neurobiol Aging 2015, 36: S132–S140.

    Article  PubMed  Google Scholar 

  37. Jin D, Wang P, Zalesky A, Liu B, Song C, Wang D, et al. Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer’s Disease. Hum Brain Mapp 2020, 41: 3379–3391.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Li J, Jin D, Li A, Liu B, Song C, Wang P, et al. ASAF: Altered spontaneous activity fingerprinting in Alzheimer’s disease based on multisite fMRI. Sci Bull 2019, 64: 998–1010.

    Article  Google Scholar 

  39. Jin D, Zhou B, Han Y, Ren J, Han T, Liu B, et al. Generalizable, reproducible, and neuroscientifically interpretable imaging biomarkers for Alzheimer’s disease. Adv Sci (Weinh) 2020, 7: 2000675.

    Article  CAS  PubMed  Google Scholar 

  40. Chen P, Yao H, Tijms BM, Wang P, Wang D, Song C, et al. Four distinct subtypes of Alzheimer’s disease based on resting-state connectivity biomarkers. Biol Psychiatry 2022, 2022: S0006-S3223.

    Google Scholar 

  41. Qu Y, Wang P, Liu B, Song C, Wang D, Yang H, et al. AI4AD: Artificial intelligence analysis for Alzheimer’s disease classification based on a multisite DTI database. Brain Disord 2021, 1: 100005.

    Article  CAS  Google Scholar 

  42. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale: Lawrence Erlbaum Associates, 1988.

  43. Yu M, Linn KA, Cook PA, Phillips ML, McInnis M, Fava M, et al. Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Hum Brain Mapp 2018, 39: 4213–4227.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Fan L, Li H, Zhuo J, Zhang Y, Wang J, Chen L, et al. The human brainnetome atlas: A new brain atlas based on connectional architecture. Cereb Cortex 2016, 26: 3508–3526.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 2011, 106: 1125–1165.

    Article  PubMed  Google Scholar 

  46. 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 2013, 51: 67–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Preti MG, Baglio F, Laganà MM, Griffanti L, Nemni R, Clerici M, et al. Assessing corpus callosum changes in Alzheimer’s disease: Comparison between tract-based spatial statistics and atlas-based tractography. PLoS One 2012, 7: e35856.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lee SH, Coutu JP, Wilkens P, Yendiki A, Rosas HD, Salat DH, et al. Tract-based analysis of white matter degeneration in Alzheimer’s disease. Neuroscience 2015, 301: 79–89.

    Article  CAS  PubMed  Google Scholar 

  49. Mito R, Raffelt D, Dhollander T, Vaughan DN, Tournier JD, Salvado O, et al. Fibre-specific white matter reductions in Alzheimer’s disease and mild cognitive impairment. Brain 2018, 141: 888–902.

    Article  PubMed  Google Scholar 

  50. Buckner RL, DiNicola LM. The brain’s default network: Updated anatomy, physiology and evolving insights. Nat Rev Neurosci 2019, 20: 593–608.

    Article  CAS  PubMed  Google Scholar 

  51. Seguin C, Razi A, Zalesky A. Inferring neural signalling directionality from undirected structural connectomes. Nat Commun 2019, 10: 4289.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Eyler LT, Elman JA, Hatton SN, Gough S, Mischel AK, Hagler DJ, et al. Resting state abnormalities of the default mode network in mild cognitive impairment: A systematic review and meta-analysis. J Alzheimers Dis 2019, 70: 107–120.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, Hedden T, et al. Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci 2009, 29: 1860–1873.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Huang J, Beach P, Bozoki A, Zhu DC. Alzheimer’s disease progressively reduces visual functional network connectivity. J Alzheimers Dis Rep 2021, 5: 549–562.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Mandal PK, Joshi J, Saharan S. Visuospatial perception: An emerging biomarker for Alzheimer’s disease. J Alzheimers Dis 2012, 31: S117–S135.

    Article  PubMed  Google Scholar 

  56. Braak H, Del Tredici K. Spreading of tau pathology in sporadic Alzheimer’s disease along cortico-cortical top-down connections. Cereb Cortex 2018, 28: 3372–3384.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Catani M, Howard RJ, Pajevic S, Jones DK. Virtual in vivo interactive dissection of white matter fasciculi in the human brain. NeuroImage 2002, 17: 77–94.

    Article  PubMed  Google Scholar 

  58. Wakana S, Jiang H, Nagae-Poetscher LM, van Zijl PC, Mori S. Fiber tract-based atlas of human white matter anatomy. Radiology 2004, 230: 77–87.

    Article  PubMed  Google Scholar 

  59. Larroza A, Moratal D, D’ocón Alcañiz V, Arana E. por la Alzheimer’s Disease Neuroimaging Initiative. Tractography of the uncinate fasciculus and the posterior cingulate fasciculus in patients with mild cognitive impairment and Alzheimer’s disease. Neurologia 2014, 29: 11–20.

    Article  CAS  PubMed  Google Scholar 

  60. Hau J, Sarubbo S, Perchey G, Crivello F, Zago L, Mellet E, et al. Cortical terminations of the inferior Fronto-occipital and uncinate fasciculi: Anatomical stem-based virtual dissection. Front Neuroanat 2016, 10: 58.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Teipel SJ, Bokde AL, Meindl T, Amaro E Jr, Soldner J, Reiser MF, et al. White matter microstructure underlying default mode network connectivity in the human brain. Neuroimage 2010, 49: 2021–2032.

    Article  PubMed  Google Scholar 

  62. Davatzikos C. Machine learning in neuroimaging: Progress and challenges. Neuroimage 2019, 197: 652–656.

    Article  PubMed  Google Scholar 

  63. Lian C, Liu M, Zhang J, Shen D. Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE Trans Pattern Anal Mach Intell 2020, 42: 880–893.

    Article  PubMed  Google Scholar 

  64. Ding Y, Zhao K, Che T, Du K, Sun H, Liu S, et al. Quantitative radiomic features as new biomarkers for Alzheimer’s disease: An amyloid PET study. Cereb Cortex 2021, 31: 3950–3961.

    Article  PubMed  Google Scholar 

  65. Pinto MS, Paolella R, Billiet T, van Dyck P, Guns PJ, Jeurissen B, et al. Harmonization of brain diffusion MRI: Concepts and methods. Front Neurosci 2020, 14: 396.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 2018, 167: 104–120.

    Article  PubMed  Google Scholar 

  67. Fortin JP, Parker D, Tunç B, Watanabe T, Elliott MA, Ruparel K, et al. Harmonization of multi-site diffusion tensor imaging data. Neuroimage 2017, 161: 149–170.

    Article  PubMed  Google Scholar 

  68. Tax CM, Grussu F, Kaden E, Ning L, Rudrapatna U, John Evans C, et al. Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms. Neuroimage 2019, 195: 285–299.

    Article  PubMed  Google Scholar 

  69. Dinsdale NK, Jenkinson M, Namburete AIL. Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal. Neuroimage 2021, 228: 117689.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the Science and Technology Innovation 2030 Major Projects (2022ZD0211600), the Beijing Natural Science Funds for Distinguished Young Scholars (JQ20036), the Beijing Nova Program (20220484177), the Fundamental Research Funds for the Central Universities (2021XD-A03), and the National Natural Science Foundation of China (82172018 and 81871438). In addition, data collection and sharing for this project were funded by the National Natural Science Foundation of China (61633018, 81571062, 81400890, 81471120, and 81701781).

Author information

Author notes

  1. Yida Qu and Pan Wang contributed equally to this work.

Authors and Affiliations

  1. Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Yida Qu, Pindong Chen, Xiaopeng Kang, Kai Du, Lingzhong Fan, Nianming Zuo, Tianzi Jiang, Bing Liu & Yong Liu

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China

    Yida Qu, Pindong Chen, Xiaopeng Kang, Kai Du, Lingzhong Fan, Nianming Zuo, Tianzi Jiang & Bing Liu

  3. Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China

    Pan Wang & Yuying Zhou

  4. Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China

    Hongxiang Yao

  5. Department of Radiology, Department of Epidemiology and Health Statistics, School of Public Health, Qilu Hospital of Shandong University, Ji’nan, 250063, China

    Dawei Wang

  6. Department of Neurology, Qilu Hospital of Shandong University, Ji’nan, 250063, China

    Chengyuan Song

  7. Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China

    Hongwei Yang & Jie Lu

  8. Branch of Chinese, PLA General Hospital, Sanya, 572022, China

    Zengqiang Zhang

  9. Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China

    Bo Zhou & Xi Zhang

  10. Department of Radiology, Tianjin Huanhu Hospital, Tianjin, 300222, China

    Tong Han

  11. Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China

    Chunshui Yu

  12. Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China

    Ying Han

  13. Beijing Institute of Geriatrics, Beijing, 100053, China

    Ying Han

  14. National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China

    Ying Han

  15. Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China

    Ying Han

  16. State Key Lab of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, 100091, China

    Bing Liu

  17. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yong Liu

Authors
  1. Yida Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongxiang Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dawei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chengyuan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hongwei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zengqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Pindong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xiaopeng Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kai Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Lingzhong Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Bo Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Tong Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Chunshui Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Xi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Nianming Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Tianzi Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Yuying Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Bing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Ying Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Jie Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Yong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

Multi-Center Alzheimer’s Disease Imaging (MCADI) Consortium

Corresponding authors

Correspondence to Jie Lu or Yong Liu.

Ethics declarations

Conflict of Interest

The authors declare that there are no conflicts of interest.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 1576 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qu, Y., Wang, P., Yao, H. et al. Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer’s Disease. Neurosci. Bull. 39, 1533–1543 (2023). https://doi.org/10.1007/s12264-023-01041-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12264-023-01041-w

Keywords

Search

Navigation