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

High-Performance Magnetic-core Coils for Targeted Rodent Brain Stimulations.

BME frontiers·2023
Same author

Brain Connectivity Signature Extractions from TMS Invoked EEGs.

Sensors (Basel, Switzerland)·2023
Same author

Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques.

Sensors (Basel, Switzerland)·2023
Same author

Cross-Medium Photoacoustic Communications: Challenges, and State of the Art.

Sensors (Basel, Switzerland)·2022
Same author

Angle-tuned coils: attractive building blocks for TMS with improved depth-spread performance.

Journal of neural engineering·2022
Same author

Clinical characteristics and prognostic value of pre-retreatment plasma epstein-barr virus DNA in locoregional recurrent nasopharyngeal carcinoma.

Cancer medicine·2019

Related Experiment Video

Updated: Jun 4, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques.

Janerra D Allen1, Sravani Varanasi1, Fei Han2

  • 1Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
Summary

Schizophrenia patients show altered brain connectivity patterns, particularly in specific brain regions. Energy landscape analysis reveals these complex abnormalities, offering potential biomarkers for the disorder.

Keywords:
biomarkerenergy landscapefMRIfunctional connectivityschizophrenia

More Related Videos

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.0K
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

5.6K

Related Experiment Videos

Last Updated: Jun 4, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.0K
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

5.6K

Area of Science:

  • Neuroscience
  • Psychiatry
  • Medical Imaging

Background:

  • Brain connectivity is crucial for understanding brain function and neuropsychiatric disorders.
  • Schizophrenia is linked to impaired functional connectivity, but its complex patterns are difficult to characterize.

Purpose of the Study:

  • To investigate abnormal functional connectivity patterns in schizophrenia using energy landscape analysis.
  • To identify potential neuroimaging biomarkers for schizophrenia by analyzing brain connectivity.

Main Methods:

  • Resting-state functional magnetic resonance imaging (fMRI) data from 55 schizophrenia patients and 63 healthy controls.
  • Analysis of functional connectivity across 246 regions of interest (ROIs) using energy landscape (EL) analysis.
  • Comparison of clinical and demographic data, including BPRS, APTS, VPTS, working memory, and processing speed scores.

Main Results:

  • Significant differences in clinical measures (BPRS, APTS, VPTS, working memory, processing speed) between patients and controls.
  • Abnormal energy landscape patterns identified in schizophrenia patients between the right and left rostral lingual gyrus.
  • Aberrant connectivity patterns observed between the left lateral and orbital areas in 12/47 ROIs.

Conclusions:

  • Energy landscape analysis effectively captures functional brain complexity in schizophrenia.
  • The study highlights potential connectivity biomarkers for schizophrenia, linked to specific clinical features.
  • The proposed imaging analysis workflow shows promise for identifying novel biomarkers in schizophrenia research.