Fractal dimension (FD) analysis, a powerful tool that has significantly advanced our understanding of brain complexity, evolving from basic geometrical characterization to the nuanced analysis of neurophysiological signals. This review integrates the theoretical foundations of FD calculation with its practical applications in clinical neurophysiology, focusing on the Higuchi method. This method, widely recognized for its effectiveness in analyzing clinical time series datasets, is a crucial aspect of our research. Emphasizing the importance of fractal properties in interpreting brain function, we explore how FD analysis reveals the brain's physiological and pathological states. The review systematically examines FD analysis's role across various neurological conditions, drawing on a meta-analysis of existing literature, including studies on Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke, and schizophrenia. Additionally, we discuss its implications in aging and developmental research, particularly in elderly and young populations. By establishing FD analysis, particularly the Higuchi method, as an indispensable tool for evaluating brain dynamics, we highlight its potential for providing new insights and identifying biomarkers for these conditions. This exploration also underscores the ongoing challenges in synthesizing a unified model of brain function and the need for continued development of computational models that emulate the biological brain.
Fractal dimension and clinical neurophysiology fusion to gain a deeper brain signal understanding: A systematic review
Moaveninejad S.;Cauzzo S.;Porcaro C.
2025
Abstract
Fractal dimension (FD) analysis, a powerful tool that has significantly advanced our understanding of brain complexity, evolving from basic geometrical characterization to the nuanced analysis of neurophysiological signals. This review integrates the theoretical foundations of FD calculation with its practical applications in clinical neurophysiology, focusing on the Higuchi method. This method, widely recognized for its effectiveness in analyzing clinical time series datasets, is a crucial aspect of our research. Emphasizing the importance of fractal properties in interpreting brain function, we explore how FD analysis reveals the brain's physiological and pathological states. The review systematically examines FD analysis's role across various neurological conditions, drawing on a meta-analysis of existing literature, including studies on Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke, and schizophrenia. Additionally, we discuss its implications in aging and developmental research, particularly in elderly and young populations. By establishing FD analysis, particularly the Higuchi method, as an indispensable tool for evaluating brain dynamics, we highlight its potential for providing new insights and identifying biomarkers for these conditions. This exploration also underscores the ongoing challenges in synthesizing a unified model of brain function and the need for continued development of computational models that emulate the biological brain.
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