Literature DB >> 19635573

Fractal dimension analysis for quantifying cerebellar morphological change of multiple system atrophy of the cerebellar type (MSA-C).

Yu-Te Wu1, Kuo-Kai Shyu, Chii-Wen Jao, Zun-Yun Wang, Bing-Wen Soong, Hsiu-Mei Wu, Po-Shan Wang.   

Abstract

Multiple system atrophy of the cerebellar type (MSA-C) is a degenerative neurological disease of the central nervous system. This study aims to demonstrate that the morphological changes of cerebellar structure, specifically, the cerebellum white matter (CBWM) and cerebellum gray matter (CBGM) from T1-weighted magnetic resonance (MR) images, can be quantified by three-dimensional (3D) fractal dimension (FD) analysis, which is a measure of complexity. Twenty-three MSA-C patients and twenty-one normal subjects participated in this study. The results of this study show that MSA-C patients presented significantly lower FD values compared to the control group, and that morphological change in the CBWM dominates the cerebellar degeneration. In addition, the FD analysis method is superior to conventional volumetric methods in quantifying the structural changes of WM and GM because it exhibits smaller variances and less gender effect. Since a decrease of cerebellar FD value indicates degeneration of the cerebellar structure, this study further suggests that the morphological changes of cerebellar structures (CBGM and CBWM) can be characterized by FD analysis.

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Year:  2009        PMID: 19635573     DOI: 10.1016/j.neuroimage.2009.07.042

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  19 in total

1.  Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI.

Authors:  Stephan Krohn; Martijn Froeling; Alexander Leemans; Dirk Ostwald; Pablo Villoslada; Carsten Finke; Francisco J Esteban
Journal:  Hum Brain Mapp       Date:  2019-05-15       Impact factor: 5.038

2.  3D structural complexity analysis of cerebellum in Chiari malformation type I.

Authors:  Engin Akar; Sadık Kara; Hidayet Akdemir; Adem Kırış
Journal:  Med Biol Eng Comput       Date:  2017-06-07       Impact factor: 2.602

3.  Cortical complexity as a measure of age-related brain atrophy.

Authors:  Christopher R Madan; Elizabeth A Kensinger
Journal:  Neuroimage       Date:  2016-04-19       Impact factor: 6.556

4.  Fractal dimension analysis of MDCT images for quantifying the morphological changes of the pulmonary artery tree in patients with pulmonary hypertension.

Authors:  Sun Haitao; Li Ning; Guo Lijun; Gao Fei; Liu Cheng
Journal:  Korean J Radiol       Date:  2011-04-25       Impact factor: 3.500

5.  The fractal spatial distribution of pancreatic islets in three dimensions: a self-avoiding growth model.

Authors:  Junghyo Jo; Andreas Hörnblad; German Kilimnik; Manami Hara; Ulf Ahlgren; Vipul Periwal
Journal:  Phys Biol       Date:  2013-04-29       Impact factor: 2.583

6.  Predicting age from cortical structure across the lifespan.

Authors:  Christopher R Madan; Elizabeth A Kensinger
Journal:  Eur J Neurosci       Date:  2018-02-12       Impact factor: 3.386

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

Authors:  Richard D King; Brandon Brown; Michael Hwang; Tina Jeon; Anuh T George
Journal:  Neuroimage       Date:  2010-06-25       Impact factor: 6.556

8.  Longitudinal Effects of Combination Antiretroviral Therapy on Cognition and Neuroimaging Biomarkers in Treatment-Naive People With HIV.

Authors:  Miriam T Weber; Alan Finkelstein; Md Nasir Uddin; Elizabeth Asiago Reddy; Roberto C Arduino; Lu Wang; Madalina E Tivarus; Jianhui Zhong; Xing Qiu; Giovanni Schifitto
Journal:  Neurology       Date:  2022-06-22       Impact factor: 11.800

9.  The physico-chemical properties and biostimulative activities of humic substances regenerated from lignite.

Authors:  Jan David; Daniela Smejkalová; Sárka Hudecová; Oldřich Zmeškal; Ray von Wandruszka; Tomáš Gregor; Jiří Kučerík
Journal:  Springerplus       Date:  2014-03-21

10.  Cortical shape and curvedness analysis of structural deficits in remitting and non-remitting depression.

Authors:  Yuan-Lin Liao; Po-Shan Wang; Chia-Feng Lu; Chih-I Hung; Cheng-Ta Li; Ching-Po Lin; Jen-Chuen Hsieh; Tung-Ping Su; Yu-Te Wu
Journal:  PLoS One       Date:  2013-07-16       Impact factor: 3.240

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