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

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

3D masked autoencoder with spatiotemporal transformer for modeling of 4D fMRI data.

Medical image analysis·2025

Same author

Combining static and dynamic brain network analysis with machine learning for enhanced diagnosis of major depressive disorder.

Psychiatry research. Neuroimaging·2025

Same author

Mapping dynamic spatial patterns of brain function with spatial-wise attention.

Journal of neural engineering·2024

Same author

Spatial-temporal convolutional attention for discovering and characterizing functional brain networks in task fMRI.

NeuroImage·2024

Same author

Functional brain network identification and fMRI augmentation using a VAE-GAN framework.

Computers in biology and medicine·2023

Same author

End-to-end Prediction of EGFR Mutation Status with Denseformer.

IEEE journal of biomedical and health informatics·2023

Same journal

Neuromelanin-sensitive MRI for identifying dopamine transporter imaging abnormality risk in idiopathic rapid eye movement sleep behavior disorder.

Brain imaging and behavior·2026

Same journal

Association between amygdala subregions and non-motor symptoms in Parkinson's disease: a fixel-based analysis.

Brain imaging and behavior·2026

Same journal

Normalization method for relative cerebral blood flow influences sex and cognitive status effects in nondemented older adults.

Brain imaging and behavior·2026

Same journal

Lack of association of the 5-HTTLPR polymorphism and brain serotonin transporter or receptor in healthy individuals: bayesian and frequentist meta-analyses.

Brain imaging and behavior·2026

Same journal

Prediction modeling in transdiagnostic risk: results from the PROCAN study.

Brain imaging and behavior·2026

Same journal

Impact of modifiable lifestyle factors on dementia subtypes and brain structural changes across KDIGO risk categories in the UK biobank.

Brain imaging and behavior·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 11, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Simultaneous spatial-temporal decomposition for connectome-scale brain networks by deep sparse recurrent

Qing Li^1,2, Qinglin Dong³, Fangfei Ge³

¹School of Artificial Intelligence, Beijing Normal University, Beijing, China.

Brain Imaging and Behavior

|March 23, 2021

Summary

This summary is machine-generated.

Researchers developed a deep sparse recurrent auto-encoder (DSRAE) to simultaneously model spatial and temporal brain network dynamics from functional Magnetic Resonance Imaging (fMRI) data, offering a unified approach for connectome analysis.

Keywords:

Deep sparse recurrent auto-encoder Spatial-temporal Task-based fMRI

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Related Experiment Videos

Last Updated: Nov 11, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Area of Science:

Neuroscience
Computational Neuroscience
Machine Learning

Background:

Understanding human brain activity requires analyzing spatial patterns and temporal dynamics of functional brain networks.
Existing models often focus on either spatial or temporal aspects of functional Magnetic Resonance Imaging (fMRI) data, lacking a unified simultaneous approach.
A simultaneous spatial-temporal model for connectome-scale brain networks remains a significant research gap.

Purpose of the Study:

To propose a novel deep sparse recurrent auto-encoder (DSRAE) for unsupervised learning of simultaneous spatial and temporal brain network patterns.
To address the limitations of current methods that typically model spatial or temporal dynamics separately.
To develop a unified model capable of extracting connectome-scale spatial-temporal networks from 4D fMRI data.

Main Methods:

Utilized a deep sparse recurrent auto-encoder (DSRAE) architecture.
Applied unsupervised learning to functional Magnetic Resonance Imaging (fMRI) data.
Leveraged sequential auto-encoder principles for brain decoding applications.

Main Results:

The proposed DSRAE effectively learned simultaneous spatial patterns and temporal fluctuations of brain networks.
Validation on the Human Connectome Project (HCP) fMRI dataset demonstrated promising results.
The model showed capability in extracting integrated spatial-temporal information from fMRI data.

Conclusions:

The DSRAE presents an early and effective unified model for simultaneous spatial-temporal analysis of brain networks.
This approach advances the understanding of connectome-scale brain network dynamics.
The method holds potential for future brain decoding and network analysis research using fMRI data.