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

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

Neural Regulation

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

Network Function of a Circuit

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

You might also read

Related Articles

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

Sort by

Same author

Brain Aging in Specific Phobia: An ENIGMA-Anxiety Mega-Analysis.

Human brain mapping·2026

Same author

Brain-structural differences underlying dialect competence in the bilingual network.

Scientific reports·2026

Same author

Decomposing neuroanatomical heterogeneity in depression: insights from an ENIGMA major depressive disorder working group study in 5146 individuals.

Translational psychiatry·2026

Same author

Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.

JMIR mental health·2026

Same author

In-patient treatment and structural dysconnectivity in major depression - A longitudinal connectome study.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology·2026

Same author

Distinguishing Chronic and Non-Chronic Depression: A Clinical Profile and Symptom Networks Approach.

Psychotherapy and psychosomatics·2026

Same journal

Computing inspired by the brain: a journey from algorithms to organoids.

Nature computational science·2026

Same journal

Systematic bottom-up coarse-graining of hydrated excess proton transport across scales.

Nature computational science·2026

Same journal

Gaining biological insights through supervised data visualization.

Nature computational science·2026

Same journal

The inequalities of GPU access.

Nature computational science·2026

Same journal

Social technologies need societal alignment.

Nature computational science·2026

Same journal

The Quantum Optimization Benchmarking Library.

Nature computational science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 1, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

deepmriprep: voxel-based morphometry preprocessing via deep neural networks.

Lukas Fisch¹, Nils R Winter², Janik Goltermann^2,3

¹Institute for Translational Psychiatry, University of Münster, Münster, Germany. l.fisch@uni-muenster.de.

Nature Computational Science

|January 30, 2026

Summary

This summary is machine-generated.

DeepMRIPrep, a novel neural network pipeline, accelerates voxel-based morphometry (VBM) preprocessing for magnetic resonance imaging. This tool significantly enhances processing speed while maintaining high accuracy in brain tissue analysis.

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Related Experiment Videos

Last Updated: Feb 1, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Neuroimaging
Computational Neuroscience

Background:

Voxel-based morphometry (VBM) is crucial for analyzing neuroimaging data.
Current VBM preprocessing methods can be computationally intensive.

Purpose of the Study:

Introduce deepMRIPrep, a deep learning-based pipeline for VBM preprocessing.
Evaluate deepMRIPrep's speed and accuracy compared to existing tools.

Main Methods:

Developed deepMRIPrep using neural networks for T1-weighted MRI preprocessing.
Leveraged graphics processing units (GPUs) for accelerated computation.
Compared deepMRIPrep against CAT12 on over 100 datasets.

Main Results:

deepMRIPrep demonstrated a 37x speed increase over CAT12.
Achieved comparable accuracy in tissue segmentation and image registration.
Tissue segmentation maps showed >95% agreement with ground-truth data.
Nonlinear registration produced comparable deformation fields to CAT12.

Conclusions:

deepMRIPrep offers a highly efficient and accurate alternative for VBM preprocessing.
Its speed facilitates the analysis of large neuroimaging datasets.
Potential for real-time applications in neuroimaging research.