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Brain Imaging and Behavior

, Volume 12, Issue 5, pp 1221–1228 | Cite as

Quantitative analysis of grey matter degeneration in FTD patients using fractal dimension analysis

  • Raghavan Sheelakumari
  • Venkateswaran Rajagopalan
  • Anuvitha Chandran
  • Tinu Varghese
  • Luduan Zhang
  • Guang H. Yue
  • Pavagadha S. Mathuranath
  • Chandrasekharan Kesavadas
Original Research

Abstract

Fractal dimension (FD) is a quantitative parameter that can characterizes the complexity of human brain tissue. Extensive grey matter (GM) pathology has been previously identified in Frontotemporal dementia (FTD) and its variants. The aim of the present study was to investigate the GM morphometric abnormalities in the behavioral variant FTD (bvFTD) and primary progressive aphasia (PPA) using FD analysis. Twenty-seven bvFTD, 12 PPA and 20 controls were studied. SPM8 was used to segment the brain into GM tissue. Then the FD values were estimated for the GM skeleton, surface and general structure in patients and controls using our previously published algorithm. We found that patients with bvFTD had significant reduction in FD values of skeleton and general structure when compared to controls. In PPA, more significant decrease in FD was noted in the whole brain and left hemisphere skeleton along with left hemisphere general structure. Only the right hemisphere skeleton had a significant correlation with total score of Frontal Systems Behavior Scale (FrSBe). The results showed that the variants of FTD are associated with disease specific morphometric complexity patterns. These results indicate that FD can be used as a biomarker for the structural changes associated with neurodegenerative diseases.

Keywords

Fractal dimension (FD) Frontotemporal dementia (FTD) Behavioral variant frontotemporal dementia (bvFTD) Primary progressive aphasia (PPA) Voxel based morphometry (VBM) 

Notes

Acknowledgements

We wold like to thank all the participants of the study. This study was carried out by the funds to PSM from National Institute on Aging (NIA), USA (grant no. R21AG029799 and R01AG039330-01). S.R is supported by D.S.T for PhD fellowship.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Raghavan Sheelakumari
    • 1
  • Venkateswaran Rajagopalan
    • 2
  • Anuvitha Chandran
    • 1
  • Tinu Varghese
    • 1
  • Luduan Zhang
    • 3
  • Guang H. Yue
    • 3
  • Pavagadha S. Mathuranath
    • 4
  • Chandrasekharan Kesavadas
    • 5
  1. 1.Cognition and Behavioural Neurology Section, Department of NeurologySree Chitra Tirunal Institute for Medical Sciences and TechnologyTrivandrumIndia
  2. 2.Department of Electrical and Electronics EngineeringBirla Institute of Technology and Science PilaniHyderabadIndia
  3. 3.Human performance Engineering LaboratoryKessler foundationWest OrangeUSA
  4. 4.Department of NeurologyNational Institute of Mental Health and NeurosciencesBangloreIndia
  5. 5.Department of Imaging Sciences and Interventional RadiologySree Chitra Tirunal Institute for Medical Sciences and TechnologyTrivandrumIndia

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