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

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

You might also read

Related Articles

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

Sort by

Same author

Quantitative anatomy and biophysical modeling of ascending neuromodulatory systems in the developing rat neocortex.

PLoS computational biology·2026

Same author

Modeling and simulation of neocortical micro- and mesocircuitry (Part I, anatomy).

eLife·2026

Same author

Modeling and simulation of neocortical micro- and mesocircuitry (Part II, Physiology and experimentation).

eLife·2026

Same author

Enhancement of brain atlases with laminar coordinate systems: Flatmaps and barrel column annotations.

Imaging neuroscience (Cambridge, Mass.)·2025

Same author

Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region.

PLoS biology·2024

Same author

Synthesis of geometrically realistic and watertight neuronal ultrastructure manifolds for in silico modeling.

Briefings in bioinformatics·2024

Same journal

Mammalian Respiratory Chain Complex Assemblies and Their Links to Mitochondria Stress-Induced Human Diseases.

Advances in experimental medicine and biology·2026

Same journal

Enzyme Assemblies in Nucleotide Metabolism: Structure, Regulation, and Disease Implications.

Advances in experimental medicine and biology·2026

Same journal

The Pyruvate Dehydrogenase Complex: A 90-Year-Old Enigma Shaping the Future of Structural Enzymology.

Advances in experimental medicine and biology·2026

Same journal

Regulation of the Anti-termination RNA Transcription Complex by Lon-Mediated Lambda N Degradation.

Advances in experimental medicine and biology·2026

Same journal

PCNA Macromolecular Complexes: PCNA Serves as a Molecular Hub Regulating Multiple Cellular Processes Inside and Outside of the Nucleus.

Advances in experimental medicine and biology·2026

Same journal

Dynamic Assemblies in Genome Maintenance.

Advances in experimental medicine and biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 25, 2025

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

Computational Concepts for Reconstructing and Simulating Brain Tissue.

Felix Schürmann¹, Jean-Denis Courcol², Srikanth Ramaswamy²

¹Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland. felix.schuermann@epfl.ch.

Advances in Experimental Medicine and Biology

|April 26, 2022

Summary

This summary is machine-generated.

Researchers are creating detailed brain tissue models, or digital twins, using computational analysis and data. These models aid scientific investigation by simulating brain functions and connections.

Keywords:

Biophysically realistic neural networks Brain tissue modeling Computational brain science Data-driven simulation Digital Twin Multi-modal data integration

More Related Videos

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Related Experiment Videos

Last Updated: Sep 25, 2025

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Area of Science:

Neuroscience
Computational Biology
Biophysics

Background:

Biophysically detailed brain tissue models can be derived by analyzing multi-modal and multi-scale brain organization.
The development of these models, or digital twins, is facilitated by increased quantitative data and computational advancements.
The EPFL Blue Brain Project has pioneered a data-driven approach to reconstructing and simulating brain tissue.

Purpose of the Study:

To present the computational science concepts underpinning the data-driven reconstruction and simulation of brain tissue.
To illustrate the application of these concepts to neocortical microcircuitry and other brain regions.
To discuss strategies for model validation and refinement.

Main Methods:

Knowledge graph-based data organization and dataset release management.
Algorithmic innovations for parameter optimization in electrical neuron models.
Exploiting spatial constraints for predicting synaptic connections.
Developing efficient simulation strategies and addressing schemes for in silico experimentation.

Main Results:

Demonstration of a data-driven framework for building and simulating brain tissue models.
Successful application and extension of the methodology beyond initial neocortical focus.
Identification of key algorithmic and computational strategies for model development and execution.

Conclusions:

The data-driven approach provides a robust foundation for creating and validating biophysically detailed brain models.
Systematic validation and complementary strategies are crucial for model fidelity and refinement.
These advanced computational models offer powerful tools for diverse scientific investigations into brain function.