Skip to main content
SpringerLink
Log in

Measures of Diffusion Tensor Tractography of Regions Associated with Default Mode Network in Alzheimer’s Disease

  • Conference paper
  • First Online:
ICTMI 2017

  • 266 Accesses

Abstract

Purpose Magnetic resonance diffusion tensor imaging (MR-DTI) was used to identify the imaging-based biomarker in Alzheimer’s disease based on the degree of degeneration of fiber tracts. In this study, we propose tracts from the regions associated with default mode network which will serve as a predictive biomarker for progression of Alzheimer’s disease. Procedure The diffusion tensor images were processed using DSI Studio, in which, deterministic algorithm was performed for fiber tracking at eight region of interests. The parameters like fractional anisotropy (FA) and mean diffusivity (MD) of the fiber tracts were assessed and were statistically evaluated for its ability to discriminate study cohorts (cognitively normal, early, and late mild cognitively impaired and Alzheimer’s disease) and its role disease progression. Later, connectivity network metrics were computed for each study group to evaluate the degree of degeneration of the fiber tracts that leads to cognitive impairment. Results The DTI parameters from the selected region of interests (ROI) could not classify the study groups significantly. Yet, the FA and MD of the tracts ending in ROIs significantly discriminate the study groups. Conclusion The tracts from hippocampus, posterior cingulate cortex, and precuneus are found to be the primary network that involves in the progression of Alzheimer’s disease.

Alzheimer’s Disease Neuroimaging Initiative (ADNI).

Data used in preparation of this article were obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.

A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Ferri CP, Prince M, Brayne C et al (2005) Global prevalence of dementia: a Delphi consensus study. Lancet 366:2112–2117

    Article  Google Scholar 

  2. Reitz C, Mayeux R (2014) Alzheimer disease: epidemiology, diagnostic criteria, risk factors and biomarkers. Biochem Pharmacol 88:640–651

    Article  Google Scholar 

  3. Risacher SL, Saykin AJ, West JD et al (2009) Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr Alzheimer Res 6:347–361

    Article  Google Scholar 

  4. Braak H, Braak E (1997) Frequency of stages of alzheimer-related lesions in different age categories. Neurobiol Aging 18:351–357

    Article  Google Scholar 

  5. Huang C, Wahlund L-O, Svensson L, Winblad B, Julin P (2002) Cingulate cortex hypoperfusion predicts Alzheimer’s disease in mild cognitive impairment. BMC Neurol 2:1–6

    Article  Google Scholar 

  6. Minoshima S, Giordani B, Berent S et al (1997) Metabolic reduction in the posterior cingulate cortex in very early Alzheimer’s disease. Ann Neurol 42:85–94

    Article  Google Scholar 

  7. Kogure D, Matsuda H, Ohnishi T et al (2000) Longitudinal evaluation of early Alzheimer’s disease using brain perfusion SPECT. J Nucl Med 41:1155–1162

    Google Scholar 

  8. Tomaiuolo F, Scapin M, Di Paola M et al (2007) Gross anatomy of the corpus callosum in Alzheimer’s disease: regions of degeneration and their neuropsychological correlates. Dement Geriatr Cogn Disord 23:96–103

    Article  Google Scholar 

  9. Wang PJ, Saykin AJ, Flashman LA et al (2006) Regionally specific atrophy of the corpus callosum in AD, MCI and cognitive complaints. Neurobiol Aging 27:1613–1617

    Article  Google Scholar 

  10. Teipel SJ, Bayer W, Alexander GE et al (2003) Regional pattern of hippocampus and corpus callosum atrophy in Alzheimer’s disease in relation to dementia severity: evidence for early neocortical degeneration. Neurobiol Aging 24:85–94

    Article  Google Scholar 

  11. Pierpaoli C, Basser PJ (1996) Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 36:893–906

    Article  Google Scholar 

  12. Basser PJ, Mattiello J, LeBihan D (1994) MR diffusion tensor spectroscopy and imaging. Biophys J 66:259–267

    Article  Google Scholar 

  13. Graña M, Termenon M, Savio A et al (2011) Computer aided diagnosis system for alzheimer disease using brain diffusion tensor imaging features selected by Pearson’s correlation. Neurosci Lett 502:225–229

    Article  Google Scholar 

  14. Nir TM, Jahanshad N, Villalon-Reina JE et al (2013) Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging. NeuroImage: Clin 3:180–195

    Article  Google Scholar 

  15. Leow AD, Zhu S, Zhan L et al (2009) The tensor distribution function. Magn Reson Med 61:205–214

    Article  Google Scholar 

  16. Villain N, Desgranges B, Viader F et al (2008) Relationships between hippocampal atrophy, white matter disruption, and gray matter hypometabolism in Alzheimer’s disease. J Neurosci 28:6174–6181

    Article  Google Scholar 

  17. Raichle ME, Snyder AZ (2007) A default mode of brain function: a brief history of an evolving idea. NeuroImage 37:1083–1090; discussion 1097–1089

    Article  Google Scholar 

  18. Daianu M, Mezher A, Jahanshad N et al (2015) Spectral graph theory and graph energy metrics show evidence for the alzheimer’s disease disconnection syndrome in APOE-4 risk gene carriers. In: 2015 IEEE 12th international symposium on biomedical imaging (ISBI), pp 458–461

    Google Scholar 

  19. Phillips DJ, McGlaughlin A, Ruth D, Jager LR, Soldan A (2015) Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer’s disease: the importance of graph creation methods. NeuroImage: Clin 7:377–390

    Article  Google Scholar 

  20. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52:1059–1069

    Article  Google Scholar 

  21. Olde Dubbelink KTE, Hillebrand A, Stoffers D et al (2014) Disrupted brain network topology in Parkinson’s disease: a longitudinal magnetoencephalography study. Brain: J Neurol 137:197–207

    Article  Google Scholar 

  22. Phillips DJ, McGlaughlin A, Ruth D et al (2015) Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer’s disease: the importance of graph creation methods. NeuroImage: Clin 7:377–390

    Article  Google Scholar 

  23. Daianu M, Jahanshad N, Nir TM et al (2013) Breakdown of brain connectivity between normal aging and Alzheimer’s disease: a structural k-core network analysis. Brain Connectivity 3:407–422

    Article  Google Scholar 

  24. Wang Z, Liu J, Zhong N et al (2012) Changes in the brain intrinsic organization in both on-task state and post-task resting state. NeuroImage 62:394–407

    Article  Google Scholar 

  25. Rorden C, Karnath H-O, Bonilha L (2007) Improving lesion-symptom mapping. J Cogn Neurosci 19:1081–1088

    Article  Google Scholar 

  26. Yeh F-C, Verstynen TD, Wang Y, Fernández-Miranda JC, Tseng W-YI (2013) Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS ONE 8:e80713

    Article  Google Scholar 

  27. Bozzali M, Franceschi M, Falini A et al (2001) Quantification of tissue damage in AD using diffusion tensor and magnetization transfer MRI. Neurology 57:1135–1137

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Hanyu H, Sakurai H, Iwamoto T et al (1998) Diffusion-weighted MR imaging of the hippocampus and temporal white matter in Alzheimer’s disease. J Neurol Sci 156:195–200

    Article  Google Scholar 

  30. Rose SE, Chen F, Chalk JB et al (2000) Loss of connectivity in Alzheimer’s disease: an evaluation of white matter tract integrity with colour coded MR diffusion tensor imaging. J Neurol Neurosurg Psychiatry 69:528–530

    Article  Google Scholar 

  31. Liu Y, Spulber G, Lehtimaki KK et al (2011) Diffusion tensor imaging and tract-based spatial statistics in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging 32:1558–1571

    Article  Google Scholar 

  32. Pievani M, Agosta F, Pagani E et al (2010) Assessment of white matter tract damage in mild cognitive impairment and Alzheimer’s disease. Hum Brain Mapp 31:1862–1875

    Article  Google Scholar 

  33. Englund E (1998) Neuropathology of white matter changes in Alzheimer’s disease and vascular dementia. Dement Geriatr Cogn Disord 9(Suppl 1):6–12

    Article  Google Scholar 

  34. Kantarci K, Jack CR, Xu YC et al (2001) Mild cognitive impairment and Alzheimer disease: regional diffusivity of water. Radiology 219:101–107

    Article  Google Scholar 

  35. Fellgiebel A, Wille P, Muller MJ et al (2004) Ultrastructural hippocampal and white matter alterations in mild cognitive impairment: a diffusion tensor imaging study. Dement Geriatr Cogn Disord 18:101–108

    Article  Google Scholar 

  36. Hagmann P, Cammoun L, Gigandet X et al (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6:e159

    Article  Google Scholar 

  37. Raichle ME, MacLeod AM, Snyder AZ et al (2001) A default mode of brain function. Proc Natl Acad Sci U S A 98:676–682

    Article  Google Scholar 

  38. Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124:1–38

    Article  Google Scholar 

  39. Nakata Y, Sato N, Abe O et al (2008) Diffusion abnormality in posterior cingulate fiber tracts in Alzheimer’s disease: tract-specific analysis. Radiat Med 26:466

    Article  Google Scholar 

  40. Palesi F, Vitali P, Chiarati P et al (2012) DTI and MR volumetry of Hippocampus-PC/PCC circuit. In search of early micro- and macrostructural signs of Alzheimers’s disease. Neurol Res Int 2012:9

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Chettinad Academy of Research and Education (CARE), Director—Research, and The Principal, FAHS, CARE, for the support.

Data collection and sharing for this project were funded by Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE.

Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuroimaging at the University of Southern California.

Author information

Authors and Affiliations

  1. Faculty of Allied Health Sciences, Chettinad Academy of Research and Education, Kelambakkam, Chennai, 603103, Tamil Nadu, India

    J. Joy Sebastian Prakash, Karunanithi Rajamanickam & R. M. Arunnath

Authors
  1. J. Joy Sebastian Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karunanithi Rajamanickam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. M. Arunnath
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karunanithi Rajamanickam .

Editor information

Editors and Affiliations

  1. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    Balázs Gulyás

  2. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    Parasuraman Padmanabhan

  3. Mar Ephraem College Of Engineering and Technology , Kanyakumari Dt, Tamil Nadu, India

    A. Lenin Fred

  4. Rajiv Gandhi Centre for Biotechnology , Thiruvananthapuram, Kerala, India

    T. R. Santhosh Kumar

  5. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    Sundramurthy Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Joy Sebastian Prakash, J., Rajamanickam, K., Arunnath, R.M. (2019). Measures of Diffusion Tensor Tractography of Regions Associated with Default Mode Network in Alzheimer’s Disease. In: Gulyás, B., Padmanabhan, P., Fred, A., Kumar, T., Kumar, S. (eds) ICTMI 2017. Springer, Singapore. https://doi.org/10.1007/978-981-13-1477-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1477-3_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1476-6

  • Online ISBN: 978-981-13-1477-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation