Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Biological Causes of Schizophrenia

Biological Causes of Schizophrenia

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...

You might also read

Related Articles

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

Sort by

Same author

Regional, functional and transcriptomic decoding of multidimensional brain structure alterations in obsessive-compulsive disorder.

Nature communications·2026

Same author

Clinical Factors Associated With Non-Completion of Durvalumab Maintenance Therapy After Chemoradiotherapy in Patients With Locally Advanced Non-Small Cell Lung Cancer: A Single-Center Retrospective Study.

Thoracic cancer·2026

Same author

Multiagent First-Person Perspective Analysis for Leadership Assessment in Pediatric Emergency Simulations: A Feasibility Study.

Critical care explorations·2026

Same author

A case of dental implant treatment for occlusal reconstruction in a congenital acute myeloid leukemia survivor.

Journal of prosthodontic research·2026

Same author

Association between pretherapeutic M2BPGi levels and postoperative skeletal muscle depletion in patients with esophageal squamous cell carcinoma.

Esophagus : official journal of the Japan Esophageal Society·2026

Same author

Toward trustworthy clinical AI for obsessive-compulsive disorder: reliability, generalizability, and interpretability of a transformer model across the ENIGMA-OCD consortium.

medRxiv : the preprint server for health sciences·2026

Search research articles

Related Experiment Video

Updated: Oct 21, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Clustering of Multiple Psychiatric Disorders Using Functional Connectivity in the Data-Driven Brain Subnetwork.

Tomoki Tokuda^1,2, Okito Yamashita^1,3, Yuki Sakai¹

¹Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.

Frontiers in Psychiatry

|September 6, 2021

Summary

This study reveals a common brain circuit, the cerebellum-thalamus-pallidum-temporal network, linked across major depressive disorder, schizophrenia, bipolar disorder, and autism spectrum disorder using unsupervised learning. This finding supports a dimensional approach to psychiatric conditions.

Keywords:

biomarker clustering functional connectivity multiple clustering psychiatric disorder

More Related Videos

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

Related Experiment Videos

Last Updated: Oct 21, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

Area of Science:

Neuroscience
Psychiatry
Computational Biology

Background:

The dimensional approach offers a new paradigm for understanding psychiatric disorders.
Previous research often focused on distinct diagnostic categories.

Purpose of the Study:

To investigate common functional connectivity patterns across diverse psychiatric disorders.
To apply unsupervised learning to resting-state functional connectivity data without relying on diagnostic labels.

Main Methods:

Utilized a novel network-based multiple clustering method.
Analyzed resting-state functional connectivity data.
Identified subject clusters and associated brain subnetworks.

Main Results:

Discovered four distinct subject clusters corresponding to major depressive disorder (MDD), young healthy controls (HC), schizophrenia (SCZ)/bipolar disorder (BD), and autism spectrum disorder (ASD).
Identified the cerebellum-thalamus-pallidum-temporal circuit as a key subnetwork across these clusters.
Validated findings using independent datasets.

Conclusions:

The study provides the first cross-disorder analysis using unsupervised functional connectivity learning.
Results support the dimensional approach by identifying shared neural underpinnings.
The cerebellum-thalamus-pallidum-temporal circuit is implicated in multiple psychiatric conditions.