ICTMI 2017 pp 29-40 | Cite as

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

  • J. Joy Sebastian Prakash
  • Karunanithi RajamanickamEmail author
  • R. M. Arunnath
Conference paper


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.


Diffusion tensor imaging Alzheimer’s disease Default mode network Diffusion tensor tractography Fractional anisotropy Mean diffusivity Global and local efficiency 



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 ( 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.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • J. Joy Sebastian Prakash
    • 1
  • Karunanithi Rajamanickam
    • 1
    Email author
  • R. M. Arunnath
    • 1
  1. 1.Faculty of Allied Health SciencesChettinad Academy of Research and EducationKelambakkam, ChennaiIndia

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