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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...

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Updated: May 1, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation.

Yijun Liu¹, Jian Li^2,3, Jessica L Wisnowski^4,5

¹Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.

Human Brain Mapping

|March 6, 2026

Summary

This summary is machine-generated.

Untamed, a new framework, uses tensor decomposition and graph embedding for brain parcellation. It creates spatially coherent regions aligned with functional networks, improving analysis of neuroimaging data.

Keywords:

cortical parcellation graph representation learning resting‐state fMRI temporal synchronization tensor decomposition

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Area of Science:

Neuroscience
Neuroimaging
Computational Biology

Background:

Cortical parcellation is crucial for interpreting neuroimaging data.
Optimal spatial features for parcellation from resting-state fMRI (rsfMRI) are unclear.
Existing methods like Independent Component Analysis (ICA) have limitations.

Purpose of the Study:

To introduce Untamed, a novel framework for cortical parcellation.
To integrate unconstrained tensor decomposition and graph node embedding.
To generate spatially coherent cortical regions aligned with functional networks.

Main Methods:

Utilized unconstrained tensor decomposition (NASCAR) for functional network identification.
Employed graph node embedding for generating cortical parcellations.
Developed an automated pipeline for rapid adaptation and custom parcellation generation.

Main Results:

Untamed produces near-homogeneous, spatially coherent regions.
Regions align well with large-scale functional networks.
Demonstrated improved or comparable performance in functional connectivity homogeneity and task contrast alignment.

Conclusions:

Untamed offers an effective approach to cortical parcellation using rsfMRI data.
The framework avoids strong assumptions of methods like ICA.
Publicly available atlases and code facilitate further research in brain mapping.