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Characterization of Atrophic Changes in the Cerebral Cortex Using Fractal Dimensional Analysis

Authors
  • King, Richard D.1, 2
  • George, Anuh T.1, 2
  • Jeon, Tina1, 2
  • Hynan, Linda S.3, 4
  • Youn, Teddy S.1
  • Kennedy, David N.5
  • Dickerson, Bradford6
  • 1 University of Texas Southwestern Medical Center, Department of Neurology, 5323 Harry Hines Blvd, Dallas, TX, 75390-9129, USA , Dallas (United States)
  • 2 University of Texas at Dallas, Center for BrainHealth, Dallas, TX, USA , Dallas (United States)
  • 3 University of Texas Southwestern Medical Center, Department of Clinical Sciences, Division of Biostatistics, Dallas, TX, USA , Dallas (United States)
  • 4 University of Texas Southwestern Medical Center, Department of Psychiatry, Dallas, TX, USA , Dallas (United States)
  • 5 Harvard Medical School, Center for Morphometric Analysis, Massachusetts General Hospital, Boston, MA, USA , Boston (United States)
  • 6 Harvard Medical School, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA , Boston (United States)
Type
Published Article
Journal
Brain Imaging and Behavior
Publisher
Springer-Verlag
Publication Date
Jan 25, 2009
Volume
3
Issue
2
Pages
154–166
Identifiers
DOI: 10.1007/s11682-008-9057-9
Source
Springer Nature
Keywords
License
Yellow

Abstract

The purpose of this project is to apply a modified fractal analysis technique to high-resolution T1 weighted magnetic resonance images in order to quantify the alterations in the shape of the cerebral cortex that occur in patients with Alzheimer’s disease. Images were selected from the Alzheimer’s Disease Neuroimaging Initiative database (Control N = 15, Mild-Moderate AD N = 15). The images were segmented using a semi-automated analysis program. Four coronal and three axial profiles of the cerebral cortical ribbon were created. The fractal dimensions (Df) of the cortical ribbons were then computed using a box-counting algorithm. The mean Df of the cortical ribbons from AD patients were lower than age-matched controls on six of seven profiles. The fractal measure has regional variability which reflects local differences in brain structure. Fractal dimension is complementary to volumetric measures and may assist in identifying disease state or disease progression.

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