Skip to main content

Advertisement

SpringerLink
Log in

Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment

Detected by Neurite Orientation Dispersion and Density Imaging (NODDI) Combined with Machine Learning

  • Original Article
  • Published:
Clinical Neuroradiology Aims and scope Submit manuscript

Abstract

Purpose

This study investigated brain microstructural changes in patients with amnestic mild cognitive impairment (aMCI) by retrospectively analyzing neurite orientation dispersion and density imaging (NODDI) data with machine learning algorithms.

Methods

A total of 26 aMCI patients and 24 healthy controls (HC) underwent NODDI magnetic resonance imaging (MRI) examinations. The NODDI parameters including neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso) were estimated. Machine learning algorithms such as K‑nearest neighbor (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) were used to evaluate the diagnostic efficacy of NODDI parameters in predicting aMCI. The differences in the NODDI parameter values between the aMCI and HC groups were analyzed using the independent sample t‑test, False discovery rate (FDR) correction was used for multiple testing. After adjusting for age, sex, and educational years, partial correlation analysis was used to evaluate the relationship between NODDI parameters and clinical cognitive status of aMCI patients.

Results

The NDI, ODI, and Viso values of white matter (WM) and gray matter (GM) structure templates combined with the KNN, LR, RF and SVM machine learning algorithms accomplished the discrimination between aMCI and HC groups. The NDI and ODI values decreased (p value range, < 0.001–0.042) and Viso values increased (p value range, < 0.001–0.043) in the aMCI group compared to the HCs. The NDI, ODI, and Viso values of the WM and GM structure templates with significant differences were significantly correlated with mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scores.

Conclusion

NODDI combined with machine learning algorithms is a promising strategy for early diagnosis of aMCI. Moreover, NODDI parameters correlated with the clinical cognitive status of aMCI patients.

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

Similar content being viewed by others

References

  1. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CH. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:270–9. https://doi.org/10.1016/j.jalz.2011.03.008.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Petersen RC. Clinical practice. Mild cognitive impairment. N Engl J Med. 2011;364:2227–34. https://doi.org/10.1056/NEJMcp0910237.

    Article  CAS  PubMed  Google Scholar 

  3. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Iwatsubo T, Jack CR Jr., Kaye J, Montine TJ, Park DC, Reiman EM, Rowe CC, Siemers E, Stern Y, Yaffe K, Carrillo MC, Thies B, Morrison-Bogorad M, Wagster MV, Phelps CH. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:280–92. https://doi.org/10.1016/j.jalz.2011.03.003.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Jack CR Jr., Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, Holtzman DM, Jagust W, Jessen F, Karlawish J, Liu E, Molinuevo JL, Montine T, Phelps C, Rankin KP, Rowe CC, Scheltens P, Siemers E, Snyder HM, Sperling R. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14:535–62. https://doi.org/10.1016/j.jalz.2018.02.018.

    Article  PubMed  PubMed Central  Google Scholar 

  5. 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. 2017;83:405–16. https://doi.org/10.1016/j.neubiorev.2017.10.026.

    Article  PubMed  Google Scholar 

  6. Wirth M, Bejanin A, La Joie R, Arenaza-Urquijo EM, Gonneaud J, Landeau B, Perrotin A, Mézenge F, de La Sayette V, Desgranges B, Chételat G. Regional patterns of gray matter volume, hypometabolism, and beta-amyloid in groups at risk of Alzheimer’s disease. Neurobiol Aging. 2018;63:140–51. https://doi.org/10.1016/j.neurobiolaging.2017.10.023.

    Article  CAS  PubMed  Google Scholar 

  7. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 2012;61:1000–16. https://doi.org/10.1016/j.neuroimage.2012.03.072.

    Article  PubMed  Google Scholar 

  8. Reisert M, Kellner E, Dhital B, Hennig J, Kiselev VG. Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach. Neuroimage. 2017;147:964–75. https://doi.org/10.1016/j.neuroimage.2016.09.058.

    Article  PubMed  Google Scholar 

  9. Novikov DS, Fieremans E, Jespersen SN, Kiselev VG. Quantifying brain microstructure with diffusion MRI: theory and parameter estimation. Nmr Biomed. 2019;32:e3998. https://doi.org/10.1002/nbm.3998.

    Article  PubMed  Google Scholar 

  10. Merluzzi AP, Dean DC, Adluru N, Suryawanshi GS, Okonkwo OC, Oh JM, Hermann BP, Sager MA, Asthana S, Zhang H, Johnson SC, Alexander AL, Bendlin BB. Age-dependent differences in brain tissue microstructure assessed with neurite orientation dispersion and density imaging. Neurobiol Aging. 2016;43:79–88. https://doi.org/10.1016/j.neurobiolaging.2016.03.026.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Jo T, Nho K, Saykin AJ. Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci. 2019;11:220. https://doi.org/10.3389/fnagi.2019.00220.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Sato M, Morimoto K, Kajihara S, Tateishi R, Shiina S, Koike K, Yatomi Y. Machine–learning approach for the development of a novel predictive model for the diagnosis of hepatocellular carcinoma. Sci Rep. 2019;9:7704. https://doi.org/10.1038/s41598-019-44022-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Katzman R, Zhang MY, Ouang YaQ, Wang ZY, Liu WT, Yu E, Wong SC, Salmon DP, Grant I. A Chinese version of the mini-mental state examination; impact of illiteracy in a Shanghai dementia survey. J Clin Epidemiol. 1988;41:971–8. https://doi.org/10.1016/0895-4356(88)90034-0.

    Article  CAS  PubMed  Google Scholar 

  14. Lu J, Li D, Li F, Zhou A, Wang F, Zuo X, Jia XF, Song H, Jia J. Montreal cognitive assessment in detecting cognitive impairment in Chinese elderly individuals: a population-based study. J Geriatr Psychiatry Neurol. 2011;24:184–90. https://doi.org/10.1177/0891988711422528.

    Article  PubMed  Google Scholar 

  15. Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology. 1993;43:2412–4. https://doi.org/10.1212/wnl.43.11.2412-a.

    Article  CAS  PubMed  Google Scholar 

  16. Castilla-Rilo J, López-Arrieta J, Bermejo-Pareja F, Ruiz M, Sánchez-Sánchez F, Trincado R. Instrumental activities of daily living in the screening of dementia in population studies: a systematic review and meta-analysis. Int J Geriatr Psychiatry. 2007;22:829–36. https://doi.org/10.1002/gps.1747.

    Article  PubMed  Google Scholar 

  17. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62:782-90. https://doi.org/10.1016/j.neuroimage.2011.09.015.

    Article  PubMed  Google Scholar 

  18. 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.e5–18. https://doi.org/10.1016/j.neurobiolaging.2010.05.019.

    Article  PubMed  Google Scholar 

  19. Li J, Pan P, Huang R, Shang H. A meta-analysis of voxel-based morphometry studies of white matter volume alterations in Alzheimer’s disease. Neurosci Biobehav Rev. 2012;36:757–63. https://doi.org/10.1016/j.neubiorev.2011.12.001.

    Article  PubMed  Google Scholar 

  20. Ferreira LK, Diniz BS, Forlenza OV, Busatto GF, Zanetti MV. Neurostructural predictors of Alzheimer’s disease: a meta-analysis of VBM studies. Neurobiol Aging. 2011;32:1733–41. https://doi.org/10.1016/j.neurobiolaging.2009.11.008.

    Article  PubMed  Google Scholar 

  21. Yang J, Pan P, Song W, Huang R, Li J, Chen K, Gong Q, Zhong J, Shi H, Shang H. Voxelwise meta-analysis of gray matter anomalies in Alzheimer’s disease and mild cognitive impairment using anatomic likelihood estimation. J Neurol Sci. 2012;316:21–9. https://doi.org/10.1016/j.jns.2012.02.010.

    Article  PubMed  Google Scholar 

  22. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273–89. https://doi.org/10.1006/nimg.2001.0978.

    Article  CAS  PubMed  Google Scholar 

  23. Wang N, Zhang J, Cofer G, Qi Y, Anderson RJ, White LE, Johnson AG. Neurite orientation dispersion and density imaging of mouse brain microstructure. Brain Struct Funct. 2019;224:1797–813. https://doi.org/10.1007/s00429-019-01877-x.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sepehrband F, Clark KA, Ullmann JF, Kurniawan ND, Leanage G, Reutens DC, Yang Z. Brain tissue compartment density estimated using diffusion-weighted MRI yields tissue parameters consistent with histology. Hum Brain Mapp. 2015;36:3687–702. https://doi.org/10.1002/hbm.22872.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Sato K, Kerever A, Kamagata K, Tsuruta K, Irie R, Tagawa K, Okazawa H, Arikawa-Hirasawa E, Nitta N, Aoki I, Aoki S. Understanding microstructure of the brain by comparison of neurite orientation dispersion and density imaging (NODDI) with transparent mouse brain. Acta Radiol Open. 2017;6:2058460117703816. https://doi.org/10.1177/2058460117703816.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Fu X, Shrestha S, Sun M, Wu Q, Luo Y, Zhang X, Yin J, Ni H. Microstructural white matter alterations in mild cognitive impairment and alzheimer’s disease : study based on neurite orientation dispersion and density imaging (NODDI). Clin Neuroradiol. 2020;30:569–79. https://doi.org/10.1007/s00062-019-00805-0.

    Article  PubMed  Google Scholar 

  27. Kamiya K, Hori M, Aoki S. NODDI in clinical research. J Neurosci Methods. 2020;346:108908. https://doi.org/10.1016/j.jneumeth.2020.108908.

    Article  PubMed  Google Scholar 

  28. Martinez-Heras E, Grussu F, Prados F, Solana E, Llufriu S. Diffusion-weighted imaging: recent advances and applications. Semin Ultrasound CT MR. 2021;42:490–506. https://doi.org/10.1053/j.sult.2021.07.006.

    Article  PubMed  Google Scholar 

  29. Bellucci A, Luccarini I, Scali C, Prosperi C, Giovannini MG, Pepeu G, Casamenti F. Cholinergic dysfunction, neuronal damage and axonal loss in TgCRND8 mice. Neurobiol Dis. 2006;23:260–72. https://doi.org/10.1016/j.nbd.2006.03.012.

    Article  CAS  PubMed  Google Scholar 

  30. Sun SW, Liang HF, Mei J, Xu D, Shi WX. In vivo diffusion tensor imaging of amyloid-beta-induced white matter damage in mice. J Alzheimers Dis. 2014;38:93–101. https://doi.org/10.3233/jad-130236.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wen Q, Risacher SL, Xie L, Li J, Harezlak J, Farlow MR, Unverzagt FW, Gao S, Apostolova LG, Saykin AJ, Wu YC. Tau-related white-matter alterations along spatially selective pathways. Neuroimage. 2021;226:117560. https://doi.org/10.1016/j.neuroimage.2020.117560.

    Article  PubMed  Google Scholar 

  32. Baloyannis SJ, Manolides SL, Manolides LS. Dendritic and spinal pathology in the acoustic cortex in Alzheimer’s disease: morphological estimation in Golgi technique and electron microscopy. Acta Otolaryngol. 2011;131:610–2. https://doi.org/10.3109/00016489.2010.539626.

    Article  PubMed  Google Scholar 

  33. Boros BD, Greathouse KM, Gentry EG, Curtis KA, Birchall EL, Gearing M, Herskowitz JH. Dendritic spines provide cognitive resilience against Alzheimer’s disease. Ann Neurol. 2017;82:602–14. https://doi.org/10.1002/ana.25049.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Wang SS, Zhang Z, Zhu TB, Chu SF, He WB, Chen NH. Myelin injury in the central nervous system and Alzheimer’s disease. Brain Res Bull. 2018;140:162–8. https://doi.org/10.1016/j.brainresbull.2018.05.003.

    Article  CAS  PubMed  Google Scholar 

  35. Colgan N, Siow B, O’Callaghan JM, Harrison IF, Wells JA, Holmes HE, Ismail O, Richardson S, Alexander DC, Collins EC, Fisher EM, Johnson R, Schwarz AJ, Ahmed Z, O’Neill MJ, Murray TK, Zhang H, Lythgoe MF. Application of neurite orientation dispersion and density imaging (NODDI) to a tau pathology model of Alzheimer’s disease. Neuroimage. 2016;125:739–44. https://doi.org/10.1016/j.neuroimage.2015.10.043.

    Article  CAS  PubMed  Google Scholar 

  36. Cao HL, Bernard S, Sabourin R, Heutte L. Random forest dissimilarity based multi-view learning for Radiomics application. Pattern Recognit. 2019;88:185–97. https://doi.org/10.1016/j.patcog.2018.11.011.

    Article  Google Scholar 

  37. Smith T, Gildeh N, Holmes C. The montreal cognitive assessment: validity and utility in a memory clinic setting. Can J Psychiatry. 2007;52:329–32. https://doi.org/10.1177/070674370705200508.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the Tianjin Municipal Health and Health Committee Project (grant number ZC20161).

Author information

Authors and Affiliations

  1. Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China

    Xiuwei Fu

  2. Department of Radiology, First Central Clinical institution, Tianjin Medical University, Tianjin, China

    Xiaonan Wang, Yu Zhang & Tongtong Li

  3. Institute of Medical Imaging, Tianjin Medical University, Tianjin, China

    Zixuan Tan

  4. Institute of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China

    Yuanyuan Chen

  5. MR Collaboration, Siemens Healthineers Ltd., Beijing, China

    Xianchang Zhang

  6. Department of Radiology, Tianjin First Central Hospital, 24 Fukang Road, 300192, Nankai District, Tianjin, China

    Hongyan Ni

Authors
  1. Xiuwei Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaonan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tongtong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zixuan Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuanyuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xianchang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hongyan Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongyan Ni.

Ethics declarations

Conflict of interest

X. Fu, X. Wang, Y. Zhang, T. Li, Z. Tan, Y. Chen, X. Zhang, and H. Ni declare that they have no conflict of interest.

Rights and permissions

Springer Nature or its licensor 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

Fu, X., Wang, X., Zhang, Y. et al. Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment. Clin Neuroradiol 33, 445–453 (2023). https://doi.org/10.1007/s00062-022-01226-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00062-022-01226-2

Keywords

Search

Navigation