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

The overlapping genetic architecture of psychiatric disorders and cortical brain structure.

Nature. Mental health·2026
Same author

Prefrontal Circuitry Abnormalities and Cognitive Impairment in Adolescents with Early- Onset Psychosis.

Research square·2026
Same author

Association of Fetal Gene Regulatory Gene Deletions With Poor Cognition in Schizophrenia and Community-Based Samples.

The American journal of psychiatry·2026
Same author

Author Correction: Cerebellar aging is spatially heterogeneous and supports cognitive resilience in later life.

Nature neuroscience·2026
Same author

An open, fully-processed data resource for studying mood and sleep variability in the developing brain.

Aperture neuro·2026
Same author

Neuroimaging Summary Scores Predict Trajectories of Psychotic-Like Experiences in Youth.

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

A neuroimaging meta-analysis on social impression formation of stable characteristics.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

An expanded cortical map of von Economo neurons in the human medial prefrontal cortex.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

For better and worse: neural self-partner overlap during social feedback is associated with relationship satisfaction and depressive symptoms.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

Regions in the human inferior temporal gyrus are engaged in numerosity processing across visual stimulus categories.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

Differentiation of cortical areas: effects of free energy minimization with broken symmetry.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

Prior exposure to speech rapidly modulates cortical processing of high-level linguistic structure.

Cerebral cortex (New York, N.Y. : 1991)·2026
See all related articles

Related Experiment Video

Updated: Dec 23, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.2K

Genetic Contributions to Multivariate Data-Driven Brain Networks Constructed via Source-Based Morphometry.

Amanda L Rodrigue1, Aaron F Alexander-Bloch2, Emma E M Knowles1

  • 1Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.

Cerebral Cortex (New York, N.Y. : 1991)
|April 23, 2020
PubMed
Summary
This summary is machine-generated.

This study reveals that brain network variations are heritable and genetically influenced. These findings offer new insights into the genetic basis of brain structure and its links to neurological disorders like schizophrenia.

Keywords:
genome-wide association analysis (GWAS)genome-wide complex trait analysis (GCTA)linkage disequilibrium score regression (LDSC)source-based morphometry (SBM)structural magnetic resonance imaging (sMRI)

More Related Videos

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

16.0K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.8K

Related Experiment Videos

Last Updated: Dec 23, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.2K
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

16.0K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.8K

Area of Science:

  • Neurogenetics
  • Brain Imaging
  • Quantitative Genetics

Background:

  • Identifying genetic influences on brain structure is challenging using traditional methods.
  • Predetermined brain parcellations may not accurately reflect functional neuroanatomy or regional covariance.
  • Network-based phenotypes offer a data-driven approach to analyze neuroanatomical variation.

Purpose of the Study:

  • To investigate the genetic architecture of neuroanatomical variation using network-based phenotypes.
  • To explore the heritability and genetic overlap of brain networks.
  • To identify genetic loci associated with neuroanatomical networks and their relationship with psychiatric disorders.

Main Methods:

  • Source-based morphometry (SBM) was used to derive network-based phenotypes from UK Biobank neuroimaging data (~20,000 individuals).
  • Genome-wide association (GWA) studies and genetic correlation analyses were performed on SBM networks.
  • Heritability and genetic overlap between networks were assessed.

Main Results:

  • Anatomical SBM networks were found to be heritable.
  • Significant functional genetic overlap was observed between different SBM networks.
  • Twenty-seven unique genetic loci were identified that contribute to SBM networks.
  • Complex patterns of pleiotropy and polygenicity were evident, similar to other complex traits.
  • A genetic overlap was found between a default mode network and schizophrenia.

Conclusions:

  • Network-based phenotypes derived from SBM are a valuable tool for studying the genetic basis of neuroanatomy.
  • The findings highlight the polygenic and pleiotropic nature of genetic influences on brain structure.
  • The identified genetic overlap between brain networks and schizophrenia provides insights into the neurobiological underpinnings of the disorder.