» Articles » PMID: 20740072

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

Overview
Publisher Springer
Date 2011 Sep 28
PMID 20740072
Citations 43
Authors
Affiliations
Soon will be listed here.
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 (D(f)) of the cortical ribbons were then computed using a box-counting algorithm. The mean D(f) 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.

Citing Articles

Correlations Between Morpho-structural Properties of the Brain and Cognitive and Motor Deficits in Individuals with Traumatic Brain Injury.

Alivar A, Saleh S, Glassen M, Suviseshamuthu E, Handiru V, Allexandre D Neurotrauma Rep. 2025; 6(1):68-81.

PMID: 39990701 PMC: 11839535. DOI: 10.1089/neur.2024.0091.


Fractals in Neuroimaging.

Lahmiri S, Boukadoum M, Ieva A Adv Neurobiol. 2024; 36:429-444.

PMID: 38468046 DOI: 10.1007/978-3-031-47606-8_22.


Fractal Analysis in Neurodegenerative Diseases.

Pirici D, Mogoanta L, Ion D, Kumar-Singh S Adv Neurobiol. 2024; 36:365-384.

PMID: 38468042 DOI: 10.1007/978-3-031-47606-8_18.


Fractal Dimension Studies of the Brain Shape in Aging and Neurodegenerative Diseases.

Davidson J, Zhang L, Yue G, Ieva A Adv Neurobiol. 2024; 36:329-363.

PMID: 38468041 DOI: 10.1007/978-3-031-47606-8_17.


Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.

Marzi C, Giannelli M, Barucci A, Tessa C, Mascalchi M, Diciotti S Sci Data. 2024; 11(1):115.

PMID: 38263181 PMC: 10805868. DOI: 10.1038/s41597-023-02421-7.


References
1.
Thompson P, Mega M, Woods R, Zoumalan C, Lindshield C, Blanton R . Cortical change in Alzheimer's disease detected with a disease-specific population-based brain atlas. Cereb Cortex. 2000; 11(1):1-16. DOI: 10.1093/cercor/11.1.1. View

2.
Thompson P, Moussai J, Zohoori S, Goldkorn A, Khan A, Mega M . Cortical variability and asymmetry in normal aging and Alzheimer's disease. Cereb Cortex. 1998; 8(6):492-509. DOI: 10.1093/cercor/8.6.492. View

3.
Zhang L, Liu J, Dean D, Sahgal V, Yue G . A three-dimensional fractal analysis method for quantifying white matter structure in human brain. J Neurosci Methods. 2005; 150(2):242-53. DOI: 10.1016/j.jneumeth.2005.06.021. View

4.
Fischl B, Dale A . Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A. 2000; 97(20):11050-5. PMC: 27146. DOI: 10.1073/pnas.200033797. View

5.
Thompson P, Hayashi K, Dutton R, Chiang M, Leow A, Sowell E . Tracking Alzheimer's disease. Ann N Y Acad Sci. 2007; 1097:183-214. PMC: 3197831. DOI: 10.1196/annals.1379.017. View