Multifractal and Entropy-Based Analysis of Delta Band Neural Activity Reveals Altered Functional Connectivity Dynamics in Schizophrenia

Abstract

Dynamic functional connectivity (DFC) was established in the past decade as a potent approach to reveal non-trivial, time-varying properties of neural interactionssuch 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 longrange 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.

Decreased connection density and modularity of functional brain networks during n‐back working memory paradigm

“…Additionally, FC has been shown previously to vary not only in response to stimuli (Sakoglu et al., 2010) but also to fluctuate even in the resting state (Chang & Glover, 2010; Hutchison et al., 2013). Furthermore, dynamic graph theoretical analysis (Dimitriadis et al., 2010) has been successfully applied to reveal nontrivial features of dynamic FC such as its (multi)fractality (Racz, Mukli, et al., 2018; Racz, Stylianou, et al., 2018) or (spatially varying) information content (Racz et al., 2019, 2020). Since fractal aspects of brain dynamics have been shown to correlate with cognitive stimulation (He, 2011; He et al., 2010), the question of how these novel features of dynamic FC could be utilized in characterizing the functional organization of the brain during various WM paradigms appears important to pursue.…”

Scale-Free Coupled Dynamics in Brain Networks Captured by Bivariate Focus-Based Multifractal Analysis

“…Additionally, the ubiquitous presence of scale-free dynamics in the resting-state brain ( Werner, 2010 ; Fraiman and Chialvo, 2012 ), – especially in the electroencephalogram (EEG) ( Lutzenberger et al, 1992 ; Preißl et al, 1997 ; Gong et al, 2003 ; Stam and de Bruin, 2004 ; Racz et al, 2018b ) – encouraged the investigation of power-law scaling in time-varying network properties. Utilizing a combination of dynamic graph theoretical analysis and multifractal time series analysis, we recently revealed that both global ( Racz et al,2018a,b ) and local ( Racz et al, 2019 ) properties of functional brain networks fluctuate according to a multifractal pattern, which may also be affected in pathological conditions ( Racz et al, 2020 ). However, a different aspect of connectivity dynamics, namely the scale-free nature of the inter-regional coupling itself, remained inaccessible to these approaches, which mainly utilized a sliding window technique.…”

“…Therefore, it is indispensable to verify the presence of true bivariate scale-free coupling by carrying out appropriate statistical tests of power-law cross-coherence ( Kristoufek, 2014 ) and cross-correlation ( Wendt et al, 2009 ; Podobnik et al, 2011 ; Blythe et al, 2016 ). Although true multifractality can be confirmed with statistical certainty by extending the testing framework applied for univariate analytical tools ( Kantelhardt et al, 2002 ; Clauset et al, 2009 ; Roux et al, 2009 ; Racz et al, 2019 , 2020 ), these methods do not provide much insight into the generating mechanism of bivariate multifractality. Depending on the mechanism, bivariate multifractality could be considered as a consequence of independent univariate dynamics ( Wendt et al, 2009 ; Jaffard et al, 2019a ).…”

Separating scale‐free and oscillatory components of neural activity in schizophrenia

“…In other words, the scaling property itself becomes a local instead of a global feature, in which case the process is referred to as multifractal (instead of monofractal) whose scaling can only be properly characterized using a set of exponents (Kantelhardt, 2009). Alterations in the multifractal properties of neural activity were reported in many physiological and pathological conditions such as healthy aging (Mukli et al., 2018), epilepsy (Dutta et al., 2014), Alzheimer's disease (Ni et al., 2016), and also schizophrenia (Racz et al., 2020; Slezin et al., 2007). In the current work, we implicitly treated neurophysiological signals as monofractals and thus only analyzed their global scale‐free properties, as our aim was to compare the contribution of the fractal and oscillatory components to BLP estimates.…”