Brain activity of women is more fractal than men

Ahmadi, Khodabakhsh; Ahmadlou, Mehran; Rezazade, Majid; Azad-Marzabadi, Esfandiar; Sajedi, Firoozeh

doi:10.1016/j.neulet.2012.12.043

Cited by 23 publications

(24 citation statements)

References 39 publications

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“…Nevertheless, in this study, significant sex differences were observed in a cluster located in the central and posterior regions of the brain. Such findings are in agreement with the increase of ApEn values over parieto-occipital sensors within females in the EEG reported by Jausovec and Jausovec [20], and the FD results obtained by Ahmadi and colleagues [21]. Similarly, a previous MEG study revealed higher LZC scores in central and posterior sensor groups in females [22].…”

Section: Discussionsupporting

confidence: 92%

“…Figure 3 shows an illustration of the cluster which was positively correlated (average rho = 0.4343, p = Figure 3. Illustration of the central cluster with complexity values, in the high beta frequency range (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), which significantly correlated (average rho = 0.4343, p = 0.022) with right-hippocampal volume scores for females.…”

Section: Resultsmentioning

confidence: 99%

“…In this regard, the first studies investigating sex differences in complexity or entropy estimates reported higher values in females [18,19]. More recent investigations [20,21] supported this notion by calculating approximate entropy (ApEn) and fractal dimension (FD) values. Our group performed an exhaustive investigation of complexity evolution across the life span, using Lempel-Ziv complexity (LZC) as an estimator of complexity and MEG as a scanning technique [22].…”

Section: Introductionmentioning

confidence: 94%

“…In this study, preliminary analyses were performed in broadband, using sensor space analysis, as a first step in identifying channels over regions of the brain with differing PLZC between males and females. Following this investigation, more detailed analyses in source space were then performed using PLZC in the broadband (2-45 Hz), as well as the theta (4-8 Hz), alpha (8-12 Hz), low beta (12-20 Hz), high beta (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-45 Hz) bands.…”

Section: Statistical Analysesmentioning

confidence: 99%

See 3 more Smart Citations

Sex Differences in the Complexity of Healthy Older Adults’ Magnetoencephalograms

Shumbayawonda

Abásolo

López‐Sanz

et al. 2019

Entropy

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The analysis of resting-state brain activity recording in magnetoencephalograms (MEGs) with new algorithms of symbolic dynamics analysis could help obtain a deeper insight into the functioning of the brain and identify potential differences between males and females. Permutation Lempel-Ziv complexity (PLZC), a recently introduced non-linear signal processing algorithm based on symbolic dynamics, was used to evaluate the complexity of MEG signals in source space. PLZC was estimated in a broad band of frequencies (2–45 Hz), as well as in narrow bands (i.e., theta (4–8 Hz), alpha (8–12 Hz), low beta (12–20 Hz), high beta (20–30 Hz), and gamma (30–45 Hz)) in a sample of 98 healthy elderly subjects (49 males, 49 female) aged 65–80 (average age of 72.71 ± 4.22 for males and 72.67 ± 4.21 for females). PLZC was significantly higher for females than males in the high beta band at posterior brain regions including the precuneus, and the parietal and occipital cortices. Further statistical analyses showed that higher complexity values over highly overlapping regions than the ones mentioned above were associated with larger hippocampal volumes only in females. These results suggest that sex differences in healthy aging can be identified from the analysis of magnetoencephalograms with novel signal processing methods.

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Section: Discussionsupporting

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Section: Statistical Analysesmentioning

confidence: 99%

See 2 more Smart Citations

Sex Differences in the Complexity of Healthy Older Adults’ Magnetoencephalograms

Shumbayawonda

Abásolo

López‐Sanz

et al. 2019

Entropy

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“…So far, FD analysis of EEG was used to explore nonlinear features of underlying cortical activity in several cognitive-behavioral tasks such as a visual recognition task (Tong et al 2005;Lutzenberger et al 1995), during creative thinking (Molle et al 1996), and arithmetic tasks (Molnar et al 2009). A study by Ahmadi et al (2013) explored gender differences in FD. The majority of studies examined FD changes in EEG in neurological and cognitive/behavioral disorders such as Alzheimer's disease (Ahmadlou et al 2011), Parkinson disease (Dastgheib et al 2011Stam et al 1995), autism (Catarino et al 2011;Ahmadlou et al 2010), ADHD (Sohn et al 2010), schizofrenia (Ha et al 2005), sleep disorders (Kronholm et al 2007), epileptic seizures detection (Litt and Echauz 2002), and mental fatigue (Liu et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Memory load effect in auditory–verbal short-term memory task: EEG fractal and spectral analysis

et al. 2015

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The objective of this preliminary study was to quantify changes in complexity of EEG using fractal dimension (FD) alongside linear methods of spectral power, event-related spectral perturbations, coherence, and source localization of EEG generators for theta (4-7 Hz), alpha (8-12 Hz), and beta (13-23 Hz) frequency bands due to a memory load effect in an auditory-verbal short-term memory (AVSTM) task for words. We examined 20 healthy individuals using the Sternberg's paradigm with increasing memory load (three, five, and seven words). The stimuli were four-letter words. Artifact-free 5-s EEG segments during retention period were analyzed. The most significant finding was the increase in FD with the increase in memory load in temporal regions T3 and T4, and in parietal region Pz, while decrease in FD with increase in memory load was registered in frontal midline region Fz. Results point to increase in frontal midline (Fz) theta spectral power, decrease in alpha spectral power in parietal region-Pz, and increase in beta spectral power in T3 and T4 region with increase in memory load. Decrease in theta coherence within right hemisphere due to memory load was obtained. Alpha coherence increased in posterior regions with anterior decrease. Beta coherence increased in fronto-temporal regions. Source localization delineated theta activity increase in frontal midline region, alpha decrease in superior parietal region, and beta increase in superior temporal gyrus with increase in memory load. In conclusion, FD as a nonlinear measure may serve as a sensitive index for quantifying dynamical changes in EEG signals during AVSTM tasks.

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Brain Dynamics Explained by Means of Spectral-Structural Neuronal Networks

Agop

Gavriluţ

Crumpei

et al. 2020

12th Chaotic Modeling and Simulation International Conference

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Starting from the morphological-functional assumption of the fractal brain, a mathematical model is given by activating brain's non-differentiable dynamics through the determinism-nondeterminism inference of the responsible mechanisms. The postulation of a scale covariance principle in Schrödinger's type representation of the brain geodesics implies the spectral functionality of the brain dynamics through mechanisms of tunelling, percolation etc., while in the hydrodynamical type representation, it implies their structural functionality through mechanisms of wave schock, solitons type etc. For external constraints proportional with the states density, the fluctuations of the brain stationary dynamics activate both the spectral neuronal networks and the structural ones through a mapping principle of two distinct classes of cnoidal oscillation modes. The spectral-structural compatibility of the neuronal networks generates the communication codes of algebraic type, while the same compatibility on the solitonic component induces a strange topology (the direct product of the spectral topology and the structural one) that is responsible of the quadruple law (for instance, the nucleotide base from the human DNA structure). Implications in the elucidation of some neuropsychological mechanisms (memory's location and functioning, dementia etc.) are also presented.

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Brain activity of women is more fractal than men

Cited by 23 publications

References 39 publications

Sex Differences in the Complexity of Healthy Older Adults’ Magnetoencephalograms

Sex Differences in the Complexity of Healthy Older Adults’ Magnetoencephalograms

Memory load effect in auditory–verbal short-term memory task: EEG fractal and spectral analysis

Brain Dynamics Explained by Means of Spectral-Structural Neuronal Networks

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