Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Altered brain criticality in schizophrenia: new insights from magnetoencephalography

Alamian, Golnoush, Lajnef, Tarek, Pascarella, Annalisa, Lina, Jean-Marc, Knight, Laura, Walters, James ORCID:, Singh, Krish D. ORCID: and Jerbi, Karim 2022. Altered brain criticality in schizophrenia: new insights from magnetoencephalography. Frontiers in Neural Circuits 16 , 630621. 10.3389/fncir.2022.630621

[thumbnail of Singh. Altered Brain Criticality in Schizophrenia. pub.pdf] PDF - Published Version
Available under License Creative Commons Attribution.

Download (5MB)


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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information: This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY)
Publisher: Frontiers Media
ISSN: 1662-5110
Date of First Compliant Deposit: 29 March 2022
Date of Acceptance: 3 March 2022
Last Modified: 06 May 2023 01:40

Citation Data

Cited 3 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics