Fractal Dimension Analysis in Neurological Disorders: An Overview

Díaz Beltrán, Leticia; Madan, Christopher R.; Finke, Carsten; Krohn, Stephan; Di Ieva, Antonio; Esteban, Francisco J.

doi:10.1007/978-3-031-47606-8_16

Cited by 7 publications

(2 citation statements)

References 68 publications

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“…Throughout the transition from mid to late life, the brain undergoes notable age-related alterations in both morphology and physiological dynamics that are intricately intertwined with changes in cognition and behavior (Phillips and Andrés, 2010; Harada et al, 2013). In recent years, there has been a burgeoning interest in non-linear dynamics-based approaches to understand the complexity of brain activity (Anokhin et al, 1996; Drachman, 2006; Rabinovich et al, 2006; Díaz Beltrán et al, 2024). Fractal dimension (FD) has emerged as a powerful tool to probe non-linear changes in the brain, offering valuable insights into various neurological conditions (Raghavendra and Dutt, 2011).…”

Section: Introductionmentioning

confidence: 99%

Age-related changes in Higuchi’s fractal dimension in healthy human EEG are anti-correlated with changes in oscillatory power and 1/f slope

Aggarwal,

Ray

2024

Preprint

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Non-linear dynamical methods such as Higuchi’s fractal dimension (HFD) are often used to study the complexities of brain activity. In human electroencephalogram (EEG), while power in the gamma band (30-70 Hz) and the slope of the power spectral density (PSD) have been shown to reduce with healthy aging, there are conflicting findings regarding how HFD and other measures of complexity vary with aging. Further, the dependence of HFD on features obtained from PSD (such as gamma power and slope) has not been thoroughly probed. To address these issues, we computed time and frequency resolved HFD for EEG data collected from elderly population (N=217), aged between 50-88 years, for baseline (BL) eyes open state and during a fixation task in which visual grating stimuli that induce strong gamma oscillations were presented. During BL, HFD increased with age at frequencies upto 150 Hz, but surprisingly showed an opposite trend at higher frequencies. Interestingly, this change in HFD was opposite to the age-related change in PSD 1/f slope. Further, stimulus-related changes in HFD were anti-correlated with the changes in oscillatory power. However, age classification using HFD was slightly better than classification using spectral features (power and slope). Therefore, HFD could effectively integrate various spectral features as well as some non-linearities not captured using spectral analysis, which could enhance our understanding of brain dynamics underlying healthy aging.Significance StatementHiguchi’s fractal dimension (HFD) is used widely to understand the complexity and non-linearities in brain signals. Previous studies have found inconsistent results regarding the change of HFD with aging. We tested whether this could be due to changes in spectral measures like oscillatory power and slope with aging by computing time-frequency resolved HFD for EEG data from elderly subjects (N=217; 50-88 years). We found that HFD increased with age upto 150 Hz, but decreased at higher frequencies. Interestingly, age-related changes in HFD were negatively correlated to the corresponding changes in slope and oscillatory power. Age classification using HFD was slightly better than spectral features, suggesting that HFD effectively integrates spectral as well as non-linear changes in brain signals with aging.

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Section: Introductionmentioning

confidence: 99%

Age-related changes in Higuchi’s fractal dimension in healthy human EEG are anti-correlated with changes in oscillatory power and 1/f slope

Aggarwal,

Ray

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Its applications span across several disciplines, with notable implications in the realm of neuroscience (Di Ieva, 2016 , 2024 ), where traditional analyses cannot sufficiently describe the morphological and functional complexity of the human brain. Indeed, fractal analysis, and especially its main index, the fractal dimension (FD), has evolved from describing brain architecture focusing on healthy (Madan and Kensinger, 2016 , 2017 , 2018 ; Marzi et al, 2020 , 2021 , 2024 ; Pani et al, 2022 ) and pathological (Marzi et al, 2018 ; Pantoni et al, 2019 ) conditions, to a comprehensive tool for analyzing brain functions under physiological (Cottone et al, 2017 ; Marino et al, 2019 ; Collantoni et al, 2020 ; Porcaro et al, 2020b , 2024 ), pathological conditions (Di Ieva et al, 2014 ; Smits et al, 2016 ; Porcaro et al, 2019 , 2020a , 2021 , 2022a , b , c ; Varley et al, 2020 ; Fiorenzato et al, 2024 )—and see Meregalli et al ( 2022 ) and Díaz Beltrán et al ( 2024 ) for in-depth reviews—and brain computer interfaces (BCIs) (Moaveninejad et al, 2024 ).…”

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confidence: 98%