Fractal dimension analysis of the cortical ribbon in mild Alzheimer's disease

Richard D. King, Brandon Brown, Michael Hwang, Tina Jeon, Anuh T. George

Research output: Contribution to journalArticlepeer-review

145 Scopus citations

Abstract

Fractal analysis methods are used to quantify the complexity of the human cerebral cortex. Many recent studies have focused on high resolution three-dimensional reconstructions of either the outer (pial) surface of the brain or the junction between the gray and white matter, but ignore the structure between these surfaces. This study uses a new method to incorporate the entire cortical thickness. Data were obtained from the Alzheimer's Disease (AD) Neuroimaging Initiative database (Control N= 35, Mild AD N= 35). Image segmentation was performed using a semi-automated analysis program. The fractal dimension of three cortical models (the pial surface, gray/white surface and entire cortical ribbon) were calculated using a custom cube-counting triangle-intersection algorithm. The fractal dimension of the cortical ribbon showed highly significant differences between control and AD subjects (p< 0.001). The inner surface analysis also found smaller but significant differences (p< 0.05). The pial surface dimensionality was not significantly different between the two groups. All three models had a significant positive correlation with the cortical gyrification index (r> 0.55, p< 0.001). Only the cortical ribbon had a significant correlation with cortical thickness (r= 0.832, p< 0.001) and the Alzheimer's Disease Assessment Scale cognitive battery (r= -0.513, p= 0.002). The cortical ribbon dimensionality showed a larger effect size (d= 1.12) in separating control and mild AD subjects than cortical thickness (d= 1.01) or gyrification index (d= 0.84). The methodological change shown in this paper may allow for further clinical application of cortical fractal dimension as a biomarker for structural changes that accrue with neurodegenerative diseases.

Original languageEnglish
Pages (from-to)471-479
Number of pages9
JournalNeuroImage
Volume53
Issue number2
DOIs
StatePublished - Nov 2010

Bibliographical note

Funding Information:
This paper was supported by the Center for Alzheimer's Care Imaging and Research at the University of Utah , and grants from the Robert Wood Johnson Foundation, National Institute of Aging ( 5-R37-AG006265-27 and 5-P30-AG012300-15 ), and the Alzheimer's Association . Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) ( National Institutes of Health Grant U01 AG024904 ). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI 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 the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 , K01 AG030514 , and the Dana Foundation .

Keywords

  • Alzheimer's disease
  • Complexity
  • Cortex
  • Cortical Thickness
  • Fractal Dimension
  • Gyrification Index

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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