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

A 3D Brain Geometry Toolkit for Multisite Neuroimaging Analysis.

bioRxiv : the preprint server for biology·2026
Same author

Brain mechanisms for processing systematic sound-to-meaning mappings for concrete and abstract concepts.

Cortex; a journal devoted to the study of the nervous system and behavior·2026
Same author

Feasibility study: Use of an optical scanning system to obtain 3D body surface images suitable for total skin electron therapy treatment dosimetry.

Journal of applied clinical medical physics·2026
Same author

From body hulls to musculoskeletal models: Personalized inertial parameter estimation.

PloS one·2026
Same author

Authors' reply to 'Comments on dose-dependent impairment of brain functional and microstructural connectivity during leukaemia chemotherapy'.

British journal of haematology·2026
Same author

Shape features of white matter tracts associated with post-surgical speech production outcomes.

Brain and language·2026

Related Experiment Video

Updated: Feb 17, 2026

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
11:50

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

4.6K

APPROXIMATING PRINCIPAL GENETIC COMPONENTS OF SUBCORTICAL SHAPE.

Boris A Gutman1, Fabrizio Pizzagalli1, Neda Jahanshad1

  • 1Imaging Genetics Center, University of Southern California, Los Angeles, CA, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|December 5, 2017
PubMed
Summary

Researchers identified key genetic components influencing brain structure by analyzing neuroimaging data. These components link to specific genes, offering insights into brain development and Alzheimer's Disease (AD).

Keywords:
Alzheimer’s diseasebrain imaginggenome-wide association studyimaging geneticssubcortical shape

More Related Videos

Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

10.3K
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

16.3K

Related Experiment Videos

Last Updated: Feb 17, 2026

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
11:50

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

4.6K
Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

10.3K
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

16.3K

Area of Science:

  • Neurogenetics
  • Quantitative Genetics
  • Brain Imaging Genetics

Background:

  • Understanding the genetic basis of brain structure is crucial for neuroscience.
  • Complex neuroimaging phenotypes are influenced by intricate genetic architectures.
  • Discovering optimal genetic representations is key to brain research.

Purpose of the Study:

  • To develop a strategy for representing the genetic structure of complex neuroimaging phenotypes.
  • To decompose the genetic covariance matrix into heritable and independent components.
  • To identify genetic components associated with brain structure and neurological disorders.

Main Methods:

  • Decomposition of the genetic covariance matrix using eigenvectors.
  • Analysis of 520 twin pairs from the QTIM dataset.
  • Estimation of 500 principal genetic components from 54,000 vertex-wise shape features of subcortical regions.
  • Association analysis with Alzheimer's Disease (AD) dataset.

Main Results:

  • Principal genetic components effectively approximate the genetic covariance structure.
  • The identified genetic components maintain desired properties in practice.
  • Specific genetic components showed significant association with CLU and PICALM genes in an AD dataset.
  • These genes were not associated with other neuroimaging measures.

Conclusions:

  • The proposed method provides an effective way to represent the genetic architecture of brain imaging phenotypes.
  • This approach can uncover novel genetic associations relevant to brain disorders like Alzheimer's Disease.
  • The findings highlight the importance of specific genetic components and their link to CLU and PICALM genes.