Skip to main content

Correlation of Diffusion Tensor Imaging Indices with MMSE Score in Alzheimer Patients: A Sub-anatomic Region Based Study on ADNI Database

  • Conference paper
Book cover Biomedical Informatics and Technology (ACBIT 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 404))

Included in the following conference series:

Abstract

In this study, an attempt has been made to find the correlation between Diffusion Tensor Imaging (DTI) indices of White Matter (WM) regions and Mini Mental State Examination (MMSE) score of Alzheimer patients. Diffusion weighted images are obtained from the ADNI database. These are preprocessed for eddy current correction and removal of non brain tissue. Fractional Anisotropy (FA), Mean Diffusivity (MD), Radial Diffusivity (RD) and Axial Diffusivity (DA) indices are computed over significant regions (Fornix left, Splenium of corpus callosum left, Splenium of corpus callosum right, Bilateral genu of the corpus callosum) affected by AD pathology. The correlation is computed between diffusion indices of the significant regions and MMSE score using linear fit technique so as to find the relation between clinical parameters and the image features. Binary classification has been employed using SVM, Decision Stumps and Simple Logistic classifiers on the extracted DTI indices along with MMSE score to classify Alzheimer patients from healthy controls. It is observed that distinct values of DTI indices exist for the range of MMSE score. However, there is no strong correlation (r varies from 0.0383 to -0.1924) between the MMSE score and the diffusion indices over the significant regions. Further, the performance evaluation of classifiers shows 94% accuracy using SVM in differentiating AD and Control. In isolation clinical and images features can be used for pre screening and diagnosis of AD but no sub anatomic region correlation exist between these features set. The discussion on the correlation of diffusion indices of WM with MMSE score is presented in this study.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ron, B., Elizabeth: Forecasting the global burden of Alzheimer’s disease. The Journal of the Alzheimer’s Association 3(3), 186–191 (2007)

    Google Scholar 

  2. Braak, H., Braak, E.: Evolution of neuronal changes in the course of Alzheimer’s disease. J. Neural Transm. Suppl. 127–140 (1998)

    Google Scholar 

  3. Alexander, A.L., Lee, J.E., Lazar, M., Field, A.S.: Diffusion Tensor Imaging of the Brain. The Journal of the American Society for Experimental NeuroTherapeutics 4(3), 316–329 (2007)

    Article  Google Scholar 

  4. Mori, S., Zhang, J.: Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research. Neuron, 527–539 (2006)

    Google Scholar 

  5. Folstein, M.F., Folstein, S.E., McHugh, P.R.: Mini-mental state A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 189–198 (1975)

    Google Scholar 

  6. Oishi, K., Faria, A.: Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer’s disease participants. Neuroimage 46(2), 486–499 (2009)

    Article  Google Scholar 

  7. O’Dwyer, L., Lamberton, F., Bokde, A.L.W., Ewers, M., Faluyi, Y.O., et al.: Using Support Vector Machines with Multiple Indices of Diffusion for Automated Classification of Mild Cognitive Impairment. PLoS ONE 7(2) (2012)

    Google Scholar 

  8. Granaa, M., Termenona, M., Savioa, A., et al.: Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson’s correlation. Neuroscience Letters 502, 225–229 (2011)

    Article  Google Scholar 

  9. Lerch, J.P., Evans, A.C.: Cortical thickness analysis examined through power analysis and a population simulation. Neuro Image 24, 163–173 (2005)

    Google Scholar 

  10. O’Dwyer, L., Lamberton, F., Bokde, A.L.W., Ewers, M., Faluyi, Y.O.: 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. Alzheimers Dis. 26, 667–682 (2011)

    Google Scholar 

  11. Bozzali, M., Falini, A., et al.: White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging. J. Neurol. Neurosurg. Psychiatry 72(6), 742–746 (2002)

    Article  Google Scholar 

  12. Westin, C.F., Maier, S.E., Mamata, H., et al.: Processing and visualization for diffusion tensor MRI. Medical Image Analysis 6(2), 93–108 (2002)

    Article  Google Scholar 

  13. Jenkinson, M., Beckmann, C.F., Behrens, T.E., et al.: FSL. NeuroImage 62, 782–790 (2012)

    Article  Google Scholar 

  14. Thomas, B., Eyssen, M., Peeters, R., Molenaers, G., Van Hecke, P.: Quantitative diffusion tensor imaging in cerebral palsy due to periventricular white matter injury. Brain 128, 2562–2577 (2005)

    Article  Google Scholar 

  15. Gold, B.T., Powell, D.K., Andersen, A.H., Smith, C.D.: Alterations in multiple measures of white matter integrity in normal women at high risk for Alzheimer’s disease. NeuroImage 52, 1487–1494 (2010)

    Article  Google Scholar 

  16. Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A.F.M., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 Algorithms in Data Mining. Knowledge and Information Systems 14(1), 1–37 (2008)

    Article  Google Scholar 

  17. Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. In: Neural Information Processing Systems (2002)

    Google Scholar 

  18. Rennie, J.: Boosting with decision stumps and binary features, Massachusetts Inst. Technol., Cambridge, MA, Tech. Rep. (2003)

    Google Scholar 

  19. Patil, R.B., Piyush, R., Ramakrishnan, S.: Identification of brain white matter regions for diagnosis of alzheimer using diffusion tensor imaging. In: 35th Annual International Conference of the IEEE EMBS, pp. 6535–6538 (2013)

    Google Scholar 

  20. Bozzali, M., Falini, A., et al.: White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging. J. Neurol. Neurosurg. Psychiatry 72(6), 742–746 (2002)

    Article  Google Scholar 

  21. Ibrahim, I., Horacek, J., Bartos, A., Hajek, M., Ripova, D., Brunovsky, M., Tintera, J.: Combination of voxel based morphometry and diffusion tensor imaging in patients with Alzheimer’s disease. Neuro. Endocrinol. Letter 30(1), 39–45 (2009)

    Google Scholar 

  22. Chou, Y.-Y., Leporé, N., Saharan, P., Madsen, S.K., Hua, X., et al.: Ventricular maps in 804 ADNI subjects: correlations with CSF biomarkers and clinical decline. Neurobiology of Aging 31, 1386–1400 (2010)

    Article  Google Scholar 

  23. Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., Akhter, K., Hua, K., Faria, A.V., Mahmood, A., Woods, R., Toga, A.W., Pike, G.B., Neto, P.R., Evans, A., Zhang, J., Huang, H., Miller, M.I., van Zijl, P., Mazziotta, J.: Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 40, 570–582 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Patil, R.B., Ramakrishnan, S. (2014). Correlation of Diffusion Tensor Imaging Indices with MMSE Score in Alzheimer Patients: A Sub-anatomic Region Based Study on ADNI Database. In: Pham, T.D., Ichikawa, K., Oyama-Higa, M., Coomans, D., Jiang, X. (eds) Biomedical Informatics and Technology. ACBIT 2013. Communications in Computer and Information Science, vol 404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54121-6_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-54121-6_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54120-9

  • Online ISBN: 978-3-642-54121-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics