Fractal dimensions of the cerebral hemispheres: anatomical correlations, age-related changes, and application prospects in clinical practice
Year: 2024
Type: article
Abstract: Background. Fractal analysis is a promising image analysis method that can be used as a morphometric tool in neuromorphology, allowing for the quantitative assessment of the extent to which space is filled by structures with geometrically complex configurations. There are various types of fractal dimensions (FD) of brain structures, but data on the anatomical correlations of FD and the structural features of brain that affect FD values are lacking. The purpose of the study is to identify the factors that influence the FD values of the cerebral hemispheres by analyzing structural models and conducting a correlation analysis of FD values and quantitative parameters of skeletonized images. Methods. Structural models of tomographic sections of the cerebral hemispheres were developed with simulation of reduced gyrification and imitation of age-related changes. Fractal analysis and quantitative analysis of the skeletonized images were then conducted. A comprehensive correlation analysis of the studied parameters was also performed. Results and conclusion. The primary factors influencing different FD values are the structural complexity and age-related changes of the cerebral hemispheres. Structural complexity (the number and complexity of gyri) positively correlated with the FD of the cortex, digital skeleton, and contours, while showing negative correlations with the FD of the white matter. The FD of the cortex, the FD of the contour determined by contour smoothing method, and the FD of the brain tissue as a whole (FD of silhouettes) were the most sensitive to age-related changes. The most promising areas for the application of fractal analysis in clinical practice include the identification and quantitative characterization of atrophic changes, the differentiation of atrophy in normal versus pathological aging, and the diagnosis of brain malformations.
Source: Morphologia
Author: N.I. Maryenko
Institution: Kharkiv National Medical University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411098754