Fractal Dimension Analysis in Brain Function: From Basic Mechanisms to Clinical Frontiers

A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "Life Science, Biophysics".

Deadline for manuscript submissions: 1 May 2026 | Viewed by 1971

Special Issue Editor


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Guest Editor
Department of Neuroscience and Padova Neuroscience Center, University of Padova, 35128 Padova, Italy
Interests: EEG; fMRI; MEG; fractal analysis (FA); brain–computer interface (BCI)
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Special Issue Information

Dear Colleagues,

Fractal dimension (FD) analysis is redefining the way we understand brain functions, providing a powerful lens through which to explore the dynamic, intricate, and often chaotic world of neural activity. This Special Issue of Fractal and Fractional seeks high-quality original research, comprehensive reviews, and insightful perspectives that demonstrate the versatility and impact of FD analysis in both basic neuroscience and clinical applications.

At the heart of brain functions is a delicate balance between order and chaos, a state known as criticality, which is thought to optimise processing, adaptability, and resilience. FD analysis provides a unique quantitative method of capturing this balance by measuring the complexity, regularity, and predictability of neural signals. From traditional modalities such as EEG, MEG, and fMRI to advanced multimodal setups such as TMS-EEG, the FD is emerging as a key biomarker with broad translational potential.

This Special Issue highlights the latest advances in FD-based methods and their application in cutting-edge technologies such as brain–computer interfaces (BCIs) and non-invasive brain stimulation (NIBS), including their use in diagnostics, therapy, and neurorehabilitation. By bridging theory, computation, and clinical practice, this collection aims to accelerate innovation at the intersection of brain complexity and real-world impacts.

Prof. Dr. Camillo Porcaro
Guest Editor

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Keywords

  • innovative methods for FD extraction and analysis in neural signals
  • FD-driven improvements in BCI design and decoding strategies
  • personalised neuromodulation by tailoring NIBS interventions using FD metrics
  • FD as a non-invasive biomarker for early diagnosis and monitoring of neurological and psychiatric disorders
  • multimodal and cross-modal applications combining FD with EEG, MEG, fMRI, and TMS
  • new theoretical frameworks linking brain criticality, fractal geometry, and functional connectivity

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Published Papers (3 papers)

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Research

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15 pages, 2013 KB  
Article
Detrended Fluctuation Analysis Complements Spectral Features in Characterizing Functional Brain Aging
by Simone Cauzzo, Sadaf Moaveninejad, Angelo Antonini, Maurizio Corbetta and Camillo Porcaro
Fractal Fract. 2026, 10(4), 224; https://doi.org/10.3390/fractalfract10040224 - 27 Mar 2026
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Abstract
Aging is a significant risk factor for several neurodegenerative diseases. Understanding brain aging processes is a fundamental step in identifying the early signs of pathological dysfunction. Nonetheless, regional functional changes are still poorly characterized. In this study, we employed Detrended Fluctuation Analysis (DFA) [...] Read more.
Aging is a significant risk factor for several neurodegenerative diseases. Understanding brain aging processes is a fundamental step in identifying the early signs of pathological dysfunction. Nonetheless, regional functional changes are still poorly characterized. In this study, we employed Detrended Fluctuation Analysis (DFA) to investigate age-related changes in the scale-free temporal dynamics of blood oxygen level-dependent (BOLD) signal fluctuations derived from resting-state networks. We compared DFA to fractional amplitude of low-frequency fluctuations (fALFF) to assess their ability to discriminate between young and old adults. Significant decreases (p < 0.01) in fALFF in the visuospatial and dorsal default mode networks and in DFA in the salience network, were identified as key predictors of functional brain aging. Using machine learning, we showed that DFA and fALFF provide complementary information for predicting aging, with an accuracy of approximately 80% achieved only through their combined use. Overall, DFA captures alterations in scale-free temporal organization that complement conventional spectral measures, providing additional insight into network-specific functional aging. Full article
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20 pages, 1319 KB  
Article
Complexity and Persistence of Electrical Brain Activity Estimated by Higuchi Fractal Dimension
by Pierpaolo Croce and Filippo Zappasodi
Fractal Fract. 2026, 10(2), 88; https://doi.org/10.3390/fractalfract10020088 - 27 Jan 2026
Viewed by 524
Abstract
Brain electrical activity, as recorded through electroencephalography (EEG), displays scale-free temporal fluctuations indicative of fractal behavior and complex dynamics. This study explores the use of the Higuchi Fractal Dimension (HFD) as a proxy of two complementary aspects of EEG temporal organization: local signal [...] Read more.
Brain electrical activity, as recorded through electroencephalography (EEG), displays scale-free temporal fluctuations indicative of fractal behavior and complex dynamics. This study explores the use of the Higuchi Fractal Dimension (HFD) as a proxy of two complementary aspects of EEG temporal organization: local signal irregularity, interpreted within a Kolmogorov-type framework, and persistence related to temporal structure, associated with statistical complexity. The latter can be used to evidence persistence in the EEG signal, serving as an alternative to previously used approaches for estimating the Hurst exponent. Thirty-eight healthy participants underwent resting-state EEG recordings in open- and closed-eyes conditions. HFD was computed for the original signals to assess Kolmogorov complexity and for the signals’ cumulative envelopes to evaluate statistical complexity and, consequently, persistence. The results confirmed that HFD values align with theoretical expectations: higher for random noise in the Kolmogorov model (~2) and lower in the statistical model (~1.5). EEG data showed condition-dependent and topographically specific variations in HFD, with parieto-occipital regions exhibiting greater complexity and persistence. The HFD values in the statistical model fall within the 1–1.5 range, indicating long-term correlation. These findings support HFD as a reliable tool for assessing both the local roughness and global temporal structure of brain activity, with implications for physiological modeling and clinical applications. Full article
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Review

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25 pages, 1526 KB  
Review
An Evolution of Our Understanding of Decomplexification Estimation for Early Detection, Monitoring and Modeling of Human Physiology
by Milena Čukić Radenković, Camillo Porcaro and Victoria Lopez
Fractal Fract. 2026, 10(3), 169; https://doi.org/10.3390/fractalfract10030169 - 4 Mar 2026
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Abstract
Human physiology is among the most complex systems in nature, characterized by intricate structural and functional networks and rich temporal dynamics. Electrophysiological signals produced by different tissues/organs reflect physiological activity, and are inherently non-stationary, non-linear, and noisy. This work focuses on fractal analysis, [...] Read more.
Human physiology is among the most complex systems in nature, characterized by intricate structural and functional networks and rich temporal dynamics. Electrophysiological signals produced by different tissues/organs reflect physiological activity, and are inherently non-stationary, non-linear, and noisy. This work focuses on fractal analysis, a framework that captures the self-similar and scale-free properties of electrophysiological signals, which is considered to act as an output of complex physiological structures that generate complex processes. Central to this approach is the principle of ‘decomplexification’, whereby aging and disease are associated with a loss of physiological complexity. We discuss key algorithms, particularly Higuchi’s fractal dimension, which is often combined with other nonlinear measures and machine-learning models for real-time analysis of electrophysiological signals. Evidence shows that fractal metrics enable the early detection and monitoring of neurological and psychiatric disorders, outperforming traditional spectral measures. In movement disorders and mood disorders, fractal and nonlinear features show high diagnostic accuracy. Beyond diagnostics, we discuss therapeutic applications, including the prediction of responsiveness to non-invasive brain stimulation. Here, we envisage the evolution of one fractal or nonlinear measure use, to several measures applied, then use it as a feature for machine learning, and then realize that a whole cluster of biomarkers must be used to reflect the state of autonomic profile, which then can be used for ontology-based application profiles that can be machine-actionable. In addition, we discuss the fractal and fractional description of transport processes, which offer innovative improvement for a much more accurate description of physiological reality as a prerequisite for further modeling: for example, this is needed for digital twins to support the clinical translation of fractal analysis for personalized medicine. In essence, if one is trying to mathematically describe or quantify structures or processes in human physiology, fractal and fractional are the supreme and adequate approach to accurately model that reality. Full article
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