Skip to main content

Advertisement

SpringerLink
Log in

Identifying Leukoaraiosis with Mild Cognitive Impairment by Fusing Multiple MRI Morphological Metrics and Ensemble Machine Learning

  • Original Paper
  • Published:
Journal of Imaging Informatics in Medicine Aims and scope Submit manuscript

Abstract

Leukoaraiosis (LA) is strongly associated with impaired cognition and increased dementia risk. Determining effective and robust methods of identifying LA patients with mild cognitive impairment (LA-MCI) is important for clinical intervention and disease monitoring. In this study, an ensemble learning method that combines multiple magnetic resonance imaging (MRI) morphological features is proposed to distinguish LA-MCI patients from LA patients lacking cognitive impairment (LA-nCI). Multiple comprehensive morphological measures (including gray matter volume (GMV), cortical thickness (CT), surface area (SA), cortical volume (CV), sulcus depth (SD), fractal dimension (FD), and gyrification index (GI)) are extracted from MRI to enrich model training on disease characterization information. Then, based on the general extreme gradient boosting (XGBoost) classifier, we leverage a weighted soft-voting ensemble framework to ensemble a data-level resampling method (Fusion + XGBoost) and an algorithm-level focal loss (FL)-improved XGBoost model (FL-XGBoost) to overcome class-imbalance learning problems and provide superior classification performance and stability. The baseline XGBoost model trained on an original imbalanced dataset had a balanced accuracy (Bacc) of 78.20%. The separate Fusion + XGBoost and FL-XGBoost models achieved Bacc scores of 80.53 and 81.25%, respectively, which are clear improvements (i.e., 2.33% and 3.05%, respectively). The fused model distinguishes LA-MCI from LA-nCI with an overall accuracy of 84.82%. Sensitivity and specificity were also well improved (85.50 and 84.14%, respectively). This improved model has the potential to facilitate the clinical diagnosis of LA-MCI.

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

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Moran C, Phan TG, Srikanth VK: Cerebral small vessel disease: a review of clinical, radiological, and histopathological phenotypes. Int J Stroke 7:36-46, 2012

    Article  PubMed  Google Scholar 

  2. Debette S, Markus HS: The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. Brit Med J 341, 2010

  3. Mortamais M, Artero S, Ritchie K: Cerebral white matter hyperintensities in the prediction of cognitive decline and incident dementia. Int Rev Psychiatr 25:686-698, 2013

    Article  Google Scholar 

  4. Lee HK, Lee YM, Park JM, Lee BD, Moon ES, Chung YI: Amnestic multiple cognitive domains impairment and periventricular white matter hyperintensities are independently predictive factors progression to dementia in mild cognitive impairment. Int J Geriatr Psych 29:526-532, 2014

    Article  Google Scholar 

  5. Kynast J, et al.: White matter hyperintensities associated with small vessel disease impair social cognition beside attention and memory. J Cerebr Blood F Met 38:996-1009, 2018

    Article  Google Scholar 

  6. de Havenon A, Sheth KN, Yeatts SD, Turan TN, Prabhakaran S. White matter hyperintensity progression is associated with incident probable dementia or mild cognitive impairment. Stroke Vasc Neurol. 2022 Apr 29;7(4):364–6. https://doi.org/10.1136/svn-2021-001357. Epub ahead of print. PMID: 35487617

  7. Seo SW, et al.: Cortical thinning related to periventricular and deep white matter hyperintensities. Neurobiol Aging 33:1156-1167, 2012

    Article  PubMed  Google Scholar 

  8. Dey AK, Stamenova V, Turner G, Black SE, Levine B: Pathoconnectomics of cognitive impairment in small vessel disease: A systematic review. Alzheimers Dement 12:831-845, 2016

    Article  PubMed  Google Scholar 

  9. Nitkunan A, Lanfranconi S, Charlton RA, Barrick TR, Markus HS: Brain atrophy and cerebral small vessel disease: a prospective follow-up study. Stroke 42:133-138, 2011

    Article  PubMed  Google Scholar 

  10. Appelman AP, Exalto LG, van der Graaf Y, Biessels GJ, Mali WP, Geerlings MI: White matter lesions and brain atrophy: more than shared risk factors? A systematic review. Cerebrovasc Dis 28:227-242, 2009

    Article  PubMed  Google Scholar 

  11. Yuan JL, Feng L, Hu WL, Zhang YM: Use of Multimodal Magnetic Resonance Imaging Techniques to Explore Cognitive Impairment in Leukoaraiosis. Med Sci Monitor 24:8910-8915, 2018

    Article  CAS  Google Scholar 

  12. Mok VCT, et al.: Neuroimaging predictors of cognitive impairment in confluent white matter lesion: Volumetric analyses of 99 brain regions. Dement Geriatr Cogn 25:67-73, 2008

    Article  Google Scholar 

  13. Peng Y, et al.: Density abnormalities in normal-appearing gray matter in the middle-aged brain with white matter hyperintense lesions: a DARTEL-enhanced voxel-based morphometry study. Clin Interv Aging 11:615-622, 2016

    PubMed  PubMed Central  Google Scholar 

  14. Tuladhar AM, et al.: Relationship Between White Matter Hyperintensities, Cortical Thickness, and Cognition. Stroke 46:425-432, 2015

    Article  PubMed  Google Scholar 

  15. Rizvi B, et al.: The effect of white matter hyperintensities on cognition is mediated by cortical atrophy. Neurobiol Aging 64:25-32, 2018

    Article  CAS  PubMed  Google Scholar 

  16. Zhuang Y, Zeng X, Wang B, Huang M, Gong H, Zhou F: Cortical Surface Thickness in the Middle-Aged Brain with White Matter Hyperintense Lesions. Front Aging Neurosci 9:225, 2017

    Article  PubMed  PubMed Central  Google Scholar 

  17. Iniesta R, Stahl D, McGuffin P: Machine learning, statistical learning and the future of biological research in psychiatry. Psychol Med 46:2455-2465, 2016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lemm S, Blankertz B, Dickhaus T, Muller KR: Introduction to machine learning for brain imaging. Neuroimage 56:387-399, 2011

    Article  PubMed  Google Scholar 

  19. Morra JH, Tu ZW, Apostolova LG, Green AE, Toga AW, Thompson PM: Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer’s Disease Through Automated Hippocampal Segmentation. Ieee T Med Imaging 29:30-43, 2010

    Article  Google Scholar 

  20. Chen HF, et al.: Microstructural disruption of the right inferior fronto-occipital and inferior longitudinal fasciculus contributes to WMH-related cognitive impairment. Cns Neurosci Ther 26:576-588, 2020

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zhu WH, et al.: Cortical and Subcortical Grey Matter Abnormalities in White Matter Hyperintensities and Subsequent Cognitive Impairment. Neurosci Bull 37:789-803, 2021

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hopkins WD, Li X, Crow T, Roberts N: Vertex- and atlas-based comparisons in measures of cortical thickness, gyrification and white matter volume between humans and chimpanzees. Brain Struct Funct 222:229-245, 2017

    Article  PubMed  Google Scholar 

  23. Yushkevich PA, et al.: Automated Volumetry and Regional Thickness Analysis of Hippocampal Subfields and Medial Temporal Cortical Structures in Mild Cognitive Impairment. Hum Brain Mapp 36:258-287, 2015

    Article  PubMed  Google Scholar 

  24. Ma Z, et al.: Identifying Mild Cognitive Impairment with Random Forest by Integrating Multiple MRI Morphological Metrics. J Alzheimers Dis 73:991-1002, 2020

    Article  PubMed  Google Scholar 

  25. Dimitriadis SI, Liparas D, Tsolaki MN, Initia ADN: Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and Alzheimer’s disease patients: From the Alzheimer’s disease neuroimaging initiative (ADNI) database. J Neurosci Meth 302:14-23, 2018

    Article  CAS  Google Scholar 

  26. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA: MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR American journal of roentgenology 149:351–356, 1987

  27. Ribaldi F, et al.: Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study. Magn Reson Imaging 76:108-115, 2021

    Article  PubMed  Google Scholar 

  28. Seiger R, Ganger S, Kranz GS, Hahn A, Lanzenberger R: Cortical Thickness Estimations of FreeSurfer and the CAT12 Toolbox in Patients with Alzheimer’s Disease and Healthy Controls. J Neuroimaging 28:515-523, 2018

    Article  PubMed  PubMed Central  Google Scholar 

  29. Dahnke R, Yotter RA, Gaser C: Cortical thickness and central surface estimation. Neuroimage 65:336-348, 2013

    Article  PubMed  Google Scholar 

  30. Luders E, Thompson PM, Narr KL, Toga AW, Jancke L, Gaser C: A curvature-based approach to estimate local gyrification on the cortical surface. Neuroimage 29:1224-1230, 2006

    Article  CAS  PubMed  Google Scholar 

  31. Mascalchi M, et al.: The burden of microstructural damage modulates cortical activation in elderly subjects with MCI and leuko-araiosis. A DTI and fMRI study. Hum Brain Mapp 35:819–830, 2014

  32. Fan LZ, et al.: The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cereb Cortex 26:3508-3526, 2016

    Article  PubMed  PubMed Central  Google Scholar 

  33. Glasser MF, et al.: A multi-modal parcellation of human cerebral cortex. Nature 536:171-+, 2016

  34. Han H, Wang WY, Mao BH: Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. Lect Notes Comput Sc 3644:878-887, 2005

    Article  Google Scholar 

  35. Lin WC, Tsai CF, Hu YH, Jhang JS: Clustering-based undersampling in class-imbalanced data. Inform Sciences 409:17-26, 2017

    Article  Google Scholar 

  36. Batista GE, Prati RC, Monard MCJASen: A study of the behavior of several methods for balancing machine learning training data6:20–29, 2004

  37. Lin TY, Goyal P, Girshick R, He KM, Dollar P: Focal loss for dense object detection. Ieee T Pattern Anal 42:318-327, 2020

    Article  Google Scholar 

  38. Wang C, Deng CY, Wang SZ: Imbalance-XGBoost: leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost. Pattern Recogn Lett 136:190-197, 2020

    Article  Google Scholar 

  39. Lundberg SM, Lee SI: A unified approach to interpreting model predictions. Adv Neur In 30, 2017

  40. Wang, L et al.: Alterations in cortical thickness and white matter integrity in mild cognitive impairment measured by whole-brain cortical thickness mapping and diffusion tensor imaging. American journal of neuroradiology vol. 30,5 (2009): 893-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Seo, Sang Won et al.: Cortical thinning related to periventricular and deep white matter hyperintensities. Neurobiology of aging vol. 33,7 (2012): 1156–67.

  42. Guo, Shengwen et al.: Conversion discriminative analysis on mild cognitive impairment using multiple cortical features from MR images. Frontiers in aging neuroscience vol. 9 146. 18 May. 2017.

  43. Lin, Sung-Han et al.: Increased water diffusion in the parcellated cortical regions from the patients with amnestic mild cognitive impairment and Alzheimer’s disease. Frontiers in aging neuroscience vol. 8 325. 11 Jan. 2017.

  44. Burge, Wesley K et al.: Cortical thickness in human V1 associated with central vision loss. Scientific reports vol. 6 23268. 24 Mar. 2016.

  45. Won, Yu Deok et al.: The frontal skull Hounsfield unit value can predict ventricular enlargement in patients with subarachnoid haemorrhage. Scientific reports vol. 8,1 10178. 5 Jul. 2018.

  46. Du AT, et al.: White matter lesions are associated with cortical atrophy more than entorhinal and hippocampal atrophy. Neurobiol Aging 26:553-559, 2005

    Article  PubMed  Google Scholar 

  47. Gouw AA, et al.: Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations. J Neurol Neurosur Ps 82:126-135, 2011

    Article  Google Scholar 

  48. Ma, Zhe et al.: Identifying mild cognitive impairment with random forest by integrating multiple MRI morphological metrics. Journal of Alzheimer’s disease : JAD vol. 73,3 (2020): 991–1002.

  49. Wee, Chong-Yaw et al.: Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations. NeuroImage. Clinical vol. 23 (2019): 101929.

Download references

Funding

This study has received funding by grant from the National Natural Science Foundation of China (Grant No. 82271286), the Key Program of National Natural Science Foundation of China (Grant No.81830052); the Science and Technology Innovation Action Plan of Shanghai (Grant No.18441900500), and the Natural Science Foundation of Shanghai (Grant 20ZR1438300).

Author information

Authors and Affiliations

  1. School of Health Science and Engineering, University of Shanghai for Science and Technology, 200093, Shanghai, People’s Republic of China

    Yifeng Yang, Yang Chen & Shengdong Nie

  2. Department of Radiology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 200127, Shanghai, People’s Republic of China

    Ying Hu

  3. Department of Anesthesiology, Huadong Hospital, Fudan University, 200040, Shanghai, People’s Republic of China

    Weidong Gu

Authors
  1. Yifeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weidong Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shengdong Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Weidong Gu or Shengdong Nie.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 22.6 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

Yang, Y., Hu, Y., Chen, Y. et al. Identifying Leukoaraiosis with Mild Cognitive Impairment by Fusing Multiple MRI Morphological Metrics and Ensemble Machine Learning. J Digit Imaging. Inform. med. 37, 666–678 (2024). https://doi.org/10.1007/s10278-023-00958-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-023-00958-y

Keywords

Search

Navigation