Faculty Collaboration Database

Robust estimation of fractal measures for characterizing the structural complexity of the human brain: optimization and reproducibility. Neuroimage 2013 Dec;83:646-57

Date

07/09/2013

Pubmed ID

23831414

Pubmed Central ID

PMC3897251

DOI

10.1016/j.neuroimage.2013.06.072

Scopus ID

2-s2.0-84881260477 (requires institutional sign-in at Scopus site)   30 Citations

Abstract

High-resolution isotropic three-dimensional reconstructions of human brain gray and white matter structures can be characterized to quantify aspects of their shape, volume and topological complexity. In particular, methods based on fractal analysis have been applied in neuroimaging studies to quantify the structural complexity of the brain in both healthy and impaired conditions. The usefulness of such measures for characterizing individual differences in brain structure critically depends on their within-subject reproducibility in order to allow the robust detection of between-subject differences. This study analyzes key analytic parameters of three fractal-based methods that rely on the box-counting algorithm with the aim to maximize within-subject reproducibility of the fractal characterizations of different brain objects, including the pial surface, the cortical ribbon volume, the white matter volume and the gray matter/white matter boundary. Two separate datasets originating from different imaging centers were analyzed, comprising 50 subjects with three and 24 subjects with four successive scanning sessions per subject, respectively. The reproducibility of fractal measures was statistically assessed by computing their intra-class correlations. Results reveal differences between different fractal estimators and allow the identification of several parameters that are critical for high reproducibility. Highest reproducibility with intra-class correlations in the range of 0.9-0.95 is achieved with the correlation dimension. Further analyses of the fractal dimensions of parcellated cortical and subcortical gray matter regions suggest robustly estimated and region-specific patterns of individual variability. These results are valuable for defining appropriate parameter configurations when studying changes in fractal descriptors of human brain structure, for instance in studies of neurological diseases that do not allow repeated measurements or for disease-course longitudinal studies.

Author List

Goñi J, Sporns O, Cheng H, Aznárez-Sanado M, Wang Y, Josa S, Arrondo G, Mathews VP, Hummer TA, Kronenberger WG, Avena-Koenigsberger A, Saykin AJ, Pastor MA

Author

Yang Wang MD Professor in the Radiology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Adult
Algorithms
Brain
Female
Fractals
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Male
Reproducibility of Results
Young Adult