المؤلفون: Frigyes Samuel Racz, Orestis Stylianou, Peter Mukli, Andras Eke

المصدر: Frontiers in Systems Neuroscience, Vol 14 (2020)

مصطلحات موضوعية: dynamic functional connectivity, multifractal analysis, information-theoretical entropy, machine learning, schizophrenia, electroencephalography, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

الوصف: Dynamic functional connectivity (DFC) was established in the past decade as a potent approach to reveal non-trivial, time-varying properties of neural interactions – such as their multifractality or information content –, that otherwise remain hidden from conventional static methods. Several neuropsychiatric disorders were shown to be associated with altered DFC, with schizophrenia (SZ) being one of the most intensely studied among such conditions. Here we analyzed resting-state electroencephalography recordings of 14 SZ patients and 14 age- and gender-matched healthy controls (HC). We reconstructed dynamic functional networks from delta band (0.5–4 Hz) neural activity and captured their spatiotemporal dynamics in various global network topological measures. The acquired network measure time series were made subject to dynamic analyses including multifractal analysis and entropy estimation. Besides group-level comparisons, we built a classifier to explore the potential of DFC features in classifying individual cases. We found stronger delta-band connectivity, as well as increased variance of DFC in SZ patients. Surrogate data testing verified the true multifractal nature of DFC in SZ, with patients expressing stronger long-range autocorrelation and degree of multifractality when compared to controls. Entropy analysis indicated reduced temporal complexity of DFC in SZ. When using these indices as features, an overall cross-validation accuracy surpassing 89% could be achieved in classifying individual cases. Our results imply that dynamic features of DFC such as its multifractal properties and entropy are potent markers of altered neural dynamics in SZ and carry significant potential not only in better understanding its pathophysiology but also in improving its diagnosis. The proposed framework is readily applicable for neuropsychiatric disorders other than schizophrenia.

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/article/10.3389/fnsys.2020.00049/fullTest; https://doaj.org/toc/1662-5137Test

الوصول الحر: https://doaj.org/article/258c0ad111614af9a2068e961abceec6Test

المؤلفون: Frigyes Samuel Racz, Akos Czoch, Zalan Kaposzta, Orestis Stylianou, Peter Mukli, Andras Eke

المصدر: Frontiers in Physiology, Vol 13 (2022)

مصطلحات موضوعية: fractal connectivity, scale-free, bivariate, multiple-resampling, spectral analysis, electroencephalography, Physiology, QP1-981

الوصف: Investigating scale-free (i.e., fractal) functional connectivity in the brain has recently attracted increasing attention. Although numerous methods have been developed to assess the fractal nature of functional coupling, these typically ignore that neurophysiological signals are assemblies of broadband, arrhythmic activities as well as oscillatory activities at characteristic frequencies such as the alpha waves. While contribution of such rhythmic components may bias estimates of fractal connectivity, they are also likely to represent neural activity and coupling emerging from distinct mechanisms. Irregular-resampling auto-spectral analysis (IRASA) was recently introduced as a tool to separate fractal and oscillatory components in the power spectrum of neurophysiological signals by statistically summarizing the power spectra obtained when resampling the original signal by several non-integer factors. Here we introduce multiple-resampling cross-spectral analysis (MRCSA) as an extension of IRASA from the univariate to the bivariate case, namely, to separate the fractal component of the cross-spectrum between two simultaneously recorded neural signals by applying the same principle. MRCSA does not only provide a theoretically unbiased estimate of the fractal cross-spectrum (and thus its spectral exponent) but also allows for computing the proportion of scale-free coupling between brain regions. As a demonstration, we apply MRCSA to human electroencephalographic recordings obtained in a word generation paradigm. We show that the cross-spectral exponent as well as the proportion of fractal coupling increases almost uniformly over the cortex during the rest-task transition, likely reflecting neural desynchronization. Our results indicate that MRCSA can be a valuable tool for scale-free connectivity studies in characterizing various cognitive states, while it also can be generalized to other applications outside the field of neuroscience.

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fphys.2022.817239/fullTest; https://doaj.org/toc/1664-042XTest

الوصول الحر: https://doaj.org/article/508ea27bf302467ba8443f83a8e1edf7Test

المؤلفون: Orestis Stylianou, Frigyes Samuel Racz, Keumbi Kim, Zalan Kaposzta, Akos Czoch, Andriy Yabluchanskiy, Andras Eke, Peter Mukli

المصدر: Frontiers in Human Neuroscience, Vol 15 (2021)

مصطلحات موضوعية: multifractal, functional connectivity, brain networks, electroencephalography, visual pattern recognition, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

الوصف: The human brain consists of anatomically distant neuronal assemblies that are interconnected via a myriad of synapses. This anatomical network provides the neurophysiological wiring framework for functional connectivity (FC), which is essential for higher-order brain functions. While several studies have explored the scale-specific FC, the scale-free (i.e., multifractal) aspect of brain connectivity remains largely neglected. Here we examined the brain reorganization during a visual pattern recognition paradigm, using bivariate focus-based multifractal (BFMF) analysis. For this study, 58 young, healthy volunteers were recruited. Before the task, 3-3 min of resting EEG was recorded in eyes-closed (EC) and eyes-open (EO) states, respectively. The subsequent part of the measurement protocol consisted of 30 visual pattern recognition trials of 3 difficulty levels graded as Easy, Medium, and Hard. Multifractal FC was estimated with BFMF analysis of preprocessed EEG signals yielding two generalized Hurst exponent-based multifractal connectivity endpoint parameters, H(2) and ΔH15; with the former indicating the long-term cross-correlation between two brain regions, while the latter captures the degree of multifractality of their functional coupling. Accordingly, H(2) and ΔH15 networks were constructed for every participant and state, and they were characterized by their weighted local and global node degrees. Then, we investigated the between- and within-state variability of multifractal FC, as well as the relationship between global node degree and task performance captured in average success rate and reaction time. Multifractal FC increased when visual pattern recognition was administered with no differences regarding difficulty level. The observed regional heterogeneity was greater for ΔH15 networks compared to H(2) networks. These results show that reorganization of scale-free coupled dynamics takes place during visual pattern recognition independent of difficulty level. Additionally, the observed regional variability illustrates that multifractal FC is region-specific both during rest and task. Our findings indicate that investigating multifractal FC under various conditions – such as mental workload in healthy and potentially in diseased populations – is a promising direction for future research.

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fnhum.2021.740225/fullTest; https://doaj.org/toc/1662-5161Test

الوصول الحر: https://doaj.org/article/da386808bccd49779bbaaaecf89aec1bTest

المؤلفون: Frigyes Samuel Racz, Kinga Farkas, Orestis Stylianou, Zalan Kaposzta, Akos Czoch, Peter Mukli, Gabor Csukly, Andras Eke

المصدر: Brain and Behavior, Vol 11, Iss 5, Pp n/a-n/a (2021)

مصطلحات موضوعية: electroencephalography, fractal analysis, power spectral density, scale‐free neural activity, schizophrenia, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

الوصف: Abstract Introduction Alterations in narrow‐band spectral power of electroencephalography (EEG) recordings are commonly reported in patients with schizophrenia (SZ). It is well established however that electrophysiological signals comprise a broadband scale‐free (or fractal) component generated by mechanisms different from those producing oscillatory neural activity. Despite this known feature, it has not yet been investigated if spectral abnormalities found in SZ could be attributed to scale‐free or oscillatory brain function. Methods In this study, we analyzed resting‐state EEG recordings of 14 SZ patients and 14 healthy controls. Scale‐free and oscillatory components of the power spectral density (PSD) were separated, and band‐limited power (BLP) of the original (mixed) PSD, as well as its fractal and oscillatory components, was estimated in five frequency bands. The scaling property of the fractal component was characterized by its spectral exponent in two distinct frequency ranges (1–13 and 13–30 Hz). Results Analysis of the mixed PSD revealed a decrease of BLP in the delta band in SZ over the central regions; however, this difference could be attributed almost exclusively to a shift of power toward higher frequencies in the fractal component. Broadband neural activity expressed a true bimodal nature in all except frontal regions. Furthermore, both low‐ and high‐range spectral exponents exhibited a characteristic topology over the cortex in both groups. Conclusion Our results imply strong functional significance of scale‐free neural activity in SZ and suggest that abnormalities in PSD may emerge from alterations of the fractal and not only the oscillatory components of neural activity.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2162-3279Test

الوصول الحر: https://doaj.org/article/3b6156fd9b5340f486f1a98c5c7a7cb2Test

المؤلفون: Orestis Stylianou, Frigyes Samuel Racz, Andras Eke, Peter Mukli

المصدر: Frontiers in Physiology, Vol 11 (2021)

مصطلحات موضوعية: scale-free, bivariate, multifractal, functional connectivity, network physiology, electroencephalography, Physiology, QP1-981

الوصف: While most connectivity studies investigate functional connectivity (FC) in a scale-dependent manner, coupled neural processes may also exhibit broadband dynamics, manifesting as power-law scaling of their measures of interdependence. Here we introduce the bivariate focus-based multifractal (BFMF) analysis as a robust tool for capturing such scale-free relations and use resting-state electroencephalography (EEG) recordings of 12 subjects to demonstrate its performance in reconstructing physiological networks. BFMF was employed to characterize broadband FC between 62 cortical regions in a pairwise manner, with all investigated connections being tested for true bivariate multifractality. EEG channels were also grouped to represent the activity of six resting-state networks (RSNs) in the brain, thus allowing for the analysis of within- and between- RSNs connectivity, separately. Most connections featured true bivariate multifractality, which could be attributed to the genuine scale-free coupling of neural dynamics. Bivariate multifractality showed a characteristic topology over the cortex that was highly concordant among subjects. Long-term autocorrelation was higher in within-RSNs, while the degree of multifractality was generally found stronger in between-RSNs connections. These results offer statistical evidence of the bivariate multifractal nature of functional coupling in the brain and validate BFMF as a robust method to capture such scale-independent coupled dynamics.

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fphys.2020.615961/fullTest; https://doaj.org/toc/1664-042XTest

الوصول الحر: https://doaj.org/article/d8c0e7b766d841a785999fcba1b6b074Test

المؤلفون: Zalan Kaposzta, Orestis Stylianou, Peter Mukli, Andras Eke, Frigyes Samuel Racz

المصدر: Brain and Behavior, Vol 11, Iss 1, Pp n/a-n/a (2021)

مصطلحات موضوعية: brain, cognition, electroencephalography, functional connectivity, working memory, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

الوصف: Abstract Introduction Investigating how the brain adapts to increased mental workload through large‐scale functional reorganization appears as an important research question. Functional connectivity (FC) aims at capturing how disparate regions of the brain dynamically interact, while graph theory provides tools for the topological characterization of the reconstructed functional networks. Although numerous studies investigated how FC is altered in response to increased working memory (WM) demand, current results are still contradictory as few studies confirmed the robustness of these findings in a low‐density setting. Methods In this study, we utilized the n‐back WM paradigm, in which subjects were presented stimuli (single digits) sequentially, and their task was to decide for each given stimulus if it matched the one presented n‐times earlier. Electroencephalography recordings were performed under a control (0‐back) and two task conditions of varying difficulty (2‐ and 3‐back). We captured the characteristic connectivity patterns for each difficulty level by performing FC analysis and described the reconstructed functional networks with various graph theoretical measures. Results We found a substantial decrease in FC when transitioning from the 0‐ to the 2‐ or 3‐back conditions, however, no differences relating to task difficulty were identified. The observed changes in brain network topology could be attributed to the dissociation of two (frontal and occipitotemporal) functional modules that were only present during the control condition. Furthermore, behavioral and performance measures showed both positive and negative correlations to connectivity indices, although only in the higher frequency bands. Conclusion The marked decrease in FC may be due to temporarily abandoned connections that are redundant or irrelevant in solving the specific task. Our results indicate that FC analysis is a robust tool for investigating the response of the brain to increased cognitive workload.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2162-3279Test

الوصول الحر: https://doaj.org/article/6be91ea3ba214fd68499835a417082b3Test