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

145
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...
145
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

3.5K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Successful initiation of catatonia treatment with oral diazepam in bipolar disorder: A case report.

Dusunen adam : Bakirkoy Ruh ve Sinir Hastaliklari Hastanesi yayin organi·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

Individualized cortical gradient and network topology reveal symptom-linked disruptions and neurobiological subtypes in schizophrenia.

medRxiv : the preprint server for health sciences·2026
Same author

Functional connectivity gradients of the insula in major depressive disorder.

Frontiers in psychiatry·2026
Same author

Delirious Mania with Mild Encephalitis and a Reversible Splenial Lesion Successfully Treated with Electroconvulsive Therapy: A Case Report.

Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology·2026
Same author

Extrapyramidal symptom severity across machine learning-derived neuroanatomical subtypes of schizophrenia.

Psychiatry research. Neuroimaging·2026

Related Experiment Video

Updated: Sep 9, 2025

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.8K

Neuroanatomical subtyping for schizophrenia with machine learning.

Ibrahim Sungur1, Simay Selek2, Kaan Keskin1

  • 1Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey; SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey.

Psychiatry Research. Neuroimaging
|September 4, 2025
PubMed
Summary

Machine learning identified two distinct neuroanatomical subtypes in schizophrenia patients. These subtypes, characterized by different grey matter volumes, show potential for personalized treatment strategies.

Keywords:
Machine learningNeuroimagingSchizophreniaStructural magnetic resonance imagingSubtypes

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
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

15.8K

Related Experiment Videos

Last Updated: Sep 9, 2025

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
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

15.8K

Area of Science:

  • Neuroimaging
  • Psychiatry
  • Computational Neuroscience

Background:

  • Schizophrenia exhibits significant heterogeneity in clinical and neurobiological presentations.
  • Understanding this heterogeneity is crucial for developing targeted treatments.

Purpose of the Study:

  • To identify neuroanatomical subtypes of schizophrenia using a data-driven machine learning approach.
  • To characterize the distinct structural brain patterns associated with these subtypes.

Main Methods:

  • Analysis of structural MRI data from 222 participants (136 schizophrenia patients, 86 controls).
  • Application of HYDRA (Heterogeneity Through Discriminative Analysis), a semi-supervised machine learning algorithm.
  • Voxel-based morphometry (VBM) used for subtype comparison with healthy controls.

Main Results:

  • Two distinct neuroanatomical subtypes of schizophrenia were identified.
  • Subtype 1: Widespread lower grey matter volumes in cortical regions (insula, cingulate, frontal, temporal).
  • Subtype 2: Increased subcortical volumes (pallidum, thalamus, hippocampus) relative to controls and Subtype 1.

Conclusions:

  • Machine learning can effectively reveal structural heterogeneity in schizophrenia.
  • Identified subtypes offer a refined framework for neuroanatomical subtyping.
  • Distinct subtypes may facilitate personalized treatment and improve research precision.