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

Biological Causes of Schizophrenia01:29

Biological Causes of Schizophrenia

54
Schizophrenia, a severe psychiatric disorder, arises from a complex interplay of biological factors, including genetic predisposition, structural brain abnormalities, neurotransmitter dysregulation, and developmental irregularities. These factors collectively contribute to the onset and progression of the disorder, which typically manifests in late adolescence or early adulthood.
Genetic Factors in Schizophrenia
The genetic basis of schizophrenia is strongly supported by family and twin...
54

You might also read

Related Articles

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

Sort by
Same author

Converting negative symptom dimension scores across SANS and PANSS.

Schizophrenia research·2026
Same author

Decomposing neuroanatomical heterogeneity in depression: insights from an ENIGMA major depressive disorder working group study in 5146 individuals.

Translational psychiatry·2026
Same author

A lipidomic based metabolic age score for monitoring the effects of lifestyle and diet on metabolic disease risk.

Research square·2026
Same author

Mediation Analysis Between Brain Age, Disease-Modifying Factors, and Disability and Cognitive Performance in Multiple Sclerosis.

Neurology·2026
Same author

AI-associated delusions: terminology.

The lancet. Psychiatry·2026
Same author

Using AI to Detect Psychosis Relapse: Scoping Review.

JMIR mental health·2026

Related Experiment Video

Updated: Jun 21, 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

Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference

Yuchao Jiang1,2, Cheng Luo3,4,5, Jijun Wang6

  • 1Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.

Nature Communications
|July 16, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning identified two distinct brain structure subtypes in schizophrenia patients. These neurostructural subtypes reveal different patterns of gray matter change, aiding in a biologically-based understanding of mental disorders.

More Related Videos

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra
05:14

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Published on: September 8, 2021

3.3K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.1K

Related Experiment Videos

Last Updated: Jun 21, 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
Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra
05:14

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Published on: September 8, 2021

3.3K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.1K

Area of Science:

  • Neuroimaging
  • Psychiatric Genetics
  • Computational Psychiatry

Background:

  • Psychiatric conditions lack precise biological definitions.
  • Subtyping mental disorders can improve treatment efficacy.
  • Neuroimaging offers insights into brain structure alterations in schizophrenia.

Purpose of the Study:

  • To identify distinct neurostructural subtypes in schizophrenia using machine learning.
  • To map the trajectory of gray matter changes in schizophrenia subtypes.
  • To explore a biologically-based taxonomy for psychiatric disorders.

Main Methods:

  • Analysis of cross-sectional brain images from 4,222 individuals with schizophrenia and 7,038 healthy controls.
  • Utilized the Subtype and Stage Inference (SuStaIn) algorithm for subgroup identification.
  • Pooled data from 41 international cohorts (ENIGMA, non-ENIGMA, public datasets).

Main Results:

  • Identified two reproducible neurostructural subtypes of schizophrenia.
  • Subtype 1: Early cortical gray matter loss with striatal enlargement.
  • Subtype 2: Early subcortical gray matter loss (hippocampus, striatum).

Conclusions:

  • Machine learning can define biologically distinct subtypes of schizophrenia.
  • These subtypes exhibit unique gray matter change trajectories.
  • An imaging-based taxonomy may refine psychiatric disorder constructs.