Evidence map›Paper›PMID 35418839›Full record

ArticleFrontiers in neural circuits2022

Altered Brain Criticality in Schizophrenia: New Insights From Magnetoencephalography.

Golnoush Alamian, Tarek Lajnef, Annalisa Pascarella, Jean-Marc Lina, Laura Knight, James Walters, Krish D Singh, Karim Jerbi

Abstract read
In one paragraph

Article in Frontiers in neural circuits, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Critical Dynamics in the Association Cortex Predict Higher Intelligence in Typically Developing Children.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  3. Article
  4. Article
  5. Review
  6. Structural Modularity Tunes Mesoscale Criticality in Biological Neuronal Networks.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023
    Article
  7. Article
  8. Article
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Golnoush AlamianCoCo Lab, Department of Psychology, Université de Montréal, Montréal, QC, Canada.
Tarek LajnefCoCo Lab, Department of Psychology, Université de Montréal, Montréal, QC, Canada.
Annalisa PascarellaInstitute for Applied Mathematics Mauro Picone, National Research Council, Roma, Italy.
Jean-Marc LinaDepartment of Electrical Engineering, École de Technologie Supérieure, Montréal, QC, Canada.
Laura KnightCUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom.
James WaltersDivision of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom.
Krish D SinghCUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom.
Karim JerbiCoCo Lab, Department of Psychology, Université de Montréal, Montréal, QC, Canada.

Funding

Medical Research Council MR/L010305/1
6 · The paper itself

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.

Indexed as

MagnetoencephalographySchizophreniaBrainBrain MappingCognitionHumansMagnetic Resonance Imagingcomplexitycriticalitymachine-learningmagnetoencephalographymultifractal analysisresting-statescale-free dynamics

Identifiers

PMID35418839
PMCPMC8995790

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.