Altered Brain Criticality in Schizophrenia: New Insights From Magnetoencephalography

Abstract

Schizophrenia has a complex etiology and symptomatology that is difficult to untangle. After decades of research, important advancements toward a central biomarker are still lacking. One of the missing pieces is a better understanding of how non-linear neural dynamics are altered in this patient population. In this study, the resting-state neuromagnetic signals of schizophrenia patients and healthy controls were analyzed in the framework of criticality. When biological systems like the brain are in a state of criticality, they are thought to be functioning at maximum efficiency (e.g., optimal communication and storage of information) and with maximum adaptability to incoming information. Here, we assessed the self-similarity and multifractality of resting-state brain signals recorded with magnetoencephalography in patients with schizophrenia patients and in matched controls. Schizophrenia patients had similar, although attenuated, patterns of self-similarity and multifractality values. Statistical tests showed that patients had higher values of self-similarity than controls in fronto-temporal regions, indicative of more regularity and memory in the signal. In contrast, patients had less multifractality than controls in the parietal and occipital regions, indicative of less diverse singularities and reduced variability in the signal. In addition, supervised machine-learning, based on logistic regression, successfully discriminated the two groups using measures of self-similarity and multifractality as features. Our results provide new insights into the baseline cognitive functioning of schizophrenia patients by identifying key alterations of criticality properties in their resting-state brain data.

Quasicriticality explains variability of human neural dynamics across life span

“…Broadly speaking, these works have used distance from the critical point, variously assessed by the branching ratio, the quality of the power laws/avalanche shape collapse, the extent of multifractality, or the degree to which the exponent relation is satisfied as the relevant variables. For example, this approach has been taken with respect to sleep and sleep deprivation (Meisel et al, 2013 ; Priesemann et al, 2014 ), epilepsy (Meisel et al, 2012 , 2015 ; Arviv et al, 2016 ; Hagemann et al, 2021 ), hypoxia (Roberts et al, 2014 ), stroke (Rocha et al, 2022 ), schizophrenia (Alamian et al, 2022 ), and Alzheimer's disease (Jiang et al, 2018 ), to name a few. For overviews, see Massobrio et al ( 2015 ), Zimmern ( 2020 ), and Fekete et al ( 2021 ).…”

Fractal Structure of Brain Electrical Activity of Patients With Mental Disorders

“…In ( Alamian et al, 2022 ), using the wavelet leaders-based multifractal analysis ( Wendt and Abry, 2007 ), an increase in the multifractality of the neuromagnetic (MEG) signal was shown in patients with schizophrenia in the temporal, parietal, and occipital areas compared to healthy controls. In ( Racz et al, 2021 ), a greater degree of multifractality was found in patients with schizophrenia compared to the control group values in delta band (0.5–4 Hz) neural activity.…”

“…Despite the huge number of works devoted to the study of the nonlinear dynamics of the bioelectrical activity of the brain in various pathologies ( Slezin et al, 2007 ; Suckling et al, 2008 ; Mukli et al, 2018 ; Racz et al, 2020 ; Lee et al, 2021 ; Racz et al, 2021 ; Alamian et al, 2022 ), the identification of neurophysiological markers of these pathologies remains an extremely urgent task. This is especially true for diseases associated with cognitive impairment such as Alzheimer’s disease, schizophrenia, epilepsy, and the goal of such work is not only to obtain new theoretical data and understanding of pathophysiology, but also to use these data to improve clinical diagnosis, assess the severity or progression of the disease.…”

Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia

“…In the prediction models, the features in the DMN have opposite contributions to SAPS and SANS, and using a multivariate regression model, we further confirmed that SANS and SAPS have opposite effects on brain networks. Previous studies found that primary motor and cerebellar connectivity have opposite predictions on positive and negative symptoms 22,23 , and self-similarity and multifractality of resting-state brain signals with opposite distribution patterns have the same associations with negative and positive symptoms 24 . Here, we provided the first direct evidence that positive and negative symptoms are opposite on the brain while excluding their interaction, and DMN, negatively correlated with other systems, may underlie the positive symptoms 69,71 and negative symptoms 74,75,76 , highly consistent with Ref.…”

“…Opposing predictions regarding positive and negative symptoms were found for primary motor and cerebellar connectivity 22,23 . Crucially, both the self-similarity and the multifractality of resting-state brain signals were associated with increased negative and positive symptoms, but they had opposite distribution patterns across the brain 24 . Thus, positive and negative symptoms may have opposite effects on the dominant components of the brain FC network.…”