Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Quality of life domains revised by people with multiple sclerosis and healthcare professionals for adaptive measure development.

PloS one·2026
Same author

Consultation-Liaison psychiatry in a high-complexity university hospital in Turin, Italy: an observational study with a focus on delirium and predictive factors of clinical outcome.

BMJ open·2026
Same author

Assessing directional connections between symptoms, cognition, insight, and real-life functioning in schizophrenia: a partial ancestor graphs analysis.

Frontiers in psychiatry·2026
Same author

Preoperative Very-Low-Calorie Ketogenic Diet Versus Low-Calorie Diet in Bariatric Surgery: A Prospective Comparative Study.

Nutrients·2026
Same author

Post-COVID-19 physical and mental quality of life: a latent profile analysis and predictive factors.

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation·2026
Same author

Identification of a diagnosis-selective neurobiological substrate for bipolar disorder, major depressive disorder, and schizophrenia: a meta-analysis of 57,717 subjects.

Psychological medicine·2026

Related Experiment Video

Updated: Apr 6, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.2K

Electroencephalographic connectivity analysis in schizophrenia.

Elisa Carlino1, Monica Sigaudo2, Rosalba Rosato3

  • 1Department of Neuroscience, University of Turin Medical School, and National Institute of Neuroscience, Turin, Italy.

Neuroscience Letters
|August 5, 2015
PubMed
Summary

Schizophrenia patients show altered brain connectivity, particularly in temporal-parietal-occipital regions, using mutual information (MI) analysis of electroencephalogram (EEG) data. This neuroimaging technique reveals nonlinear EEG changes indicative of the disorder.

Keywords:
Electroencephalographic connectivityMutual informationSchizophrenia

More Related Videos

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

13.1K
Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

1.4K

Related Experiment Videos

Last Updated: Apr 6, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.2K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

13.1K
Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

1.4K

Area of Science:

  • Neuroscience
  • Psychiatry
  • Biomedical Engineering

Background:

  • Electroencephalogram (EEG) connectivity analysis is crucial for understanding brain function.
  • Schizophrenia is associated with complex alterations in neural network dynamics.
  • Mutual Information (MI) offers a robust method to assess both linear and nonlinear EEG components.

Purpose of the Study:

  • To investigate electroencephalogram (EEG) connectivity differences in schizophrenia patients compared to healthy controls.
  • To evaluate the utility of mutual information (MI) for detecting nonlinear EEG alterations in schizophrenia.
  • To explore the impact of eye conditions (open/closed) on brain connectivity patterns in schizophrenia.

Main Methods:

  • Recorded 19-lead EEGs from 17 stable schizophrenia patients and 17 healthy controls.
  • Utilized mutual information (MI) analysis to quantify EEG connectivity.
  • Compared connectivity patterns under closed eyes (CE) and open eyes (OE) conditions.

Main Results:

  • Schizophrenia patients exhibited higher MI values in temporal-parietal-occipital regions compared to controls.
  • Healthy controls showed increased frontal brain connectivity during the CE condition.
  • This frontal connectivity increase observed in controls was notably absent in schizophrenia patients.

Conclusions:

  • Mutual Information (MI) analysis can detect significant alterations in brain connectivity in schizophrenia patients.
  • Patients with schizophrenia display distinct patterns of EEG connectivity, particularly in posterior brain regions.
  • Findings suggest that altered nonlinear EEG dynamics are a potential neurophysiological marker in schizophrenia.