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

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials.

ArXivĀ·2025
Same author

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials.

ArXivĀ·2025
Same author

Correction: DiffBP: generative diffusion of 3D molecules for target protein binding.

Chemical scienceĀ·2025
Same author

Learning to Discover Regulatory Elements for Gene Expression Prediction.

ArXivĀ·2025
Same author

Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models.

ArXivĀ·2024
Same author

Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.

Nature methodsĀ·2024

Related Experiment Video

Updated: Apr 26, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

1.0K

High-resolution prediction of mouse brain connectivity using gene expression patterns.

Ahmed Fakhry1, Shuiwang Ji1

  • 1Department of Computer Science, Old Dominion University, 4700 Elkhorn Avenue, Suite 3300, Norfolk, VA 23529-0162, USA.

Methods (San Diego, Calif.)
|August 12, 2014
PubMed
Summary

Gene expression patterns accurately predict brain wiring in mice at a fine voxel level. A small set of key genes, primarily in neurons, drives this connectivity prediction, offering insights into brain structure and function.

Keywords:
Brain connectivityCorrelationGene expression patternsPrediction

More Related Videos

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.6K
Microdissection of Mouse Brain into Functionally and Anatomically Different Regions
08:06

Microdissection of Mouse Brain into Functionally and Anatomically Different Regions

Published on: February 15, 2021

53.2K

Related Experiment Videos

Last Updated: Apr 26, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

1.0K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.6K
Microdissection of Mouse Brain into Functionally and Anatomically Different Regions
08:06

Microdissection of Mouse Brain into Functionally and Anatomically Different Regions

Published on: February 15, 2021

53.2K

Area of Science:

  • Neuroscience
  • Genomics
  • Computational Biology

Background:

  • Brain function arises from complex genetic mechanisms.
  • Spatiotemporal gene expression patterns have been shown to predict brain connectivity across species.
  • Previous studies indicated gene expression predicts connectivity at coarse regional levels in mammals.

Purpose of the Study:

  • To perform the first high-resolution, large-scale integrative analysis of the transcriptome and connectome in a mammalian brain at a fine voxel level.
  • To predict voxel-level brain connectivity using gene expression data from the adult mouse brain.
  • To identify key genes involved in predicting brain wiring.

Main Methods:

  • Utilized the Allen Brain Atlas data for integrative analysis.
  • Employed regularized models to predict voxel-level brain connectivity from gene expression.
  • Performed correlative studies to elucidate the transcriptome-connectome relationship.

Main Results:

  • Gene expression was found to be predictive of connectivity at the voxel-level with 93% accuracy.
  • Identified a small set of genes crucial for connectivity prediction, achieving over 80% accuracy using only these genes.
  • Discovered that these important genes are enriched in neurons and involved in connectivity-related functions.

Conclusions:

  • High-resolution gene expression data can accurately predict mammalian brain connectivity at a fine voxel level.
  • A specific subset of genes plays a critical role in determining brain wiring.
  • This research provides a deeper understanding of the relationship between the transcriptome and connectome.