Clinical Sensitivity of Fractal Neurodynamics

Olejarczyk, Elzbieta; Cukic, Milena; Porcaro, Camillo; Zappasodi, Filippo; Tecchio, Franca

doi:10.1007/978-3-031-47606-8_15

Cited by 2 publications

(1 citation statement)

References 84 publications

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“…This “hidden information” captured by non-linear methods such as fractal dimension analysis may be additional and complementary to linear methods and could shed light on the physiological neural communication, computation, and cognition in healthy as well as patients with neuropathological conditions ( Goldberger, 2001 ; Goldberger et al, 2002 ; Zhang and Raichle, 2010 ; Rodríguez-Bermúdez and García-Laencina, 2015 ; Porcaro et al, 2017 , 2019 , 2020a , b , 2022 ). This is the reason why time-series fractal analysis is more and more used in different research fields ranging from basic neuroscience ( Di Ieva et al, 2014 , 2015 ; Moaveninejad et al, 2024 ), neurophysiology ( Adeli et al, 2008 ; Ahmadlou and Adeli, 2012 ; Ahmadlou et al, 2012a , b ), translational neuroscience ( Smits et al, 2016 ; Porcaro et al, 2020b , 2021 , 2022 ; Fiorenzato et al, 2024 ; Olejarczyk et al, 2024 ) to genetic variability in human phenotypes ( Cattani and Pierro, 2013 ; Lee, 2020 ; Borri et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

Porcaro,

Seppi,

Pellegrino

et al. 2024

Front. Neurosci.

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ObjectivesAn important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods.MaterialsTwenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC).MethodsEEG data were investigated from two different angles: frequency domain—spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain—FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups.ResultsThe δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ.DiscussionFD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes.ConclusionOur work suggests that FD is a promising measure to monitor the response to ASMs in FE.

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Section: Introductionmentioning

confidence: 99%

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

Porcaro,

Seppi,

Pellegrino

et al. 2024

Front. Neurosci.

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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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Clinical Sensitivity of Fractal Neurodynamics

Cited by 2 publications

References 84 publications

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

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

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