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

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...

You might also read

Related Articles

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

Sort by

Same author

Competing programs shape cortical sensorimotor-association axis development.

Nature·2026

Same author

Testing and tracking in the UK: A dynamic causal modelling study.

Wellcome open research·2026

Same author

PsiConnect: Multimodal Neuroimaging of Context-Dependent Brain and Behaviour Dynamics under Psilocybin.

Scientific data·2026

Same author

Predicting individual differences of fear and cognitive learning and extinction.

Nature communications·2026

Same author

Bed nucleus of the stria terminalis connectivity during food cue and taste processing under stress.

Nature communications·2026

Same author

The motion sensitivity and predictive utility of different estimates of interregional functional coupling in resting-state functional MRI.

Network neuroscience (Cambridge, Mass.)·2026

Search research articles

Related Experiment Video

Updated: Jun 5, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Spectral imprint of structural embedding in effective connectivity.

Matthew D Greaves^1,2, Leonardo Novelli^1,2, James C Pang^1,2

¹School of Psychological Sciences, Monash University, Clayton, Victoria, Australia.

Biorxiv : the Preprint Server for Biology

|July 16, 2025

Summary

This summary is machine-generated.

We introduce a new chromatic dynamic causal model (DCM) that links brain structure to neural fluctuations. This model reveals how brain connectivity varies across species and cortical hierarchy.

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

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

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

Published on: March 8, 2024

Related Experiment Videos

Last Updated: Jun 5, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

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

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

Published on: March 8, 2024

Area of Science:

Neuroscience
Computational Neuroscience
Network Science

Background:

Neural fluctuations display complex spectral profiles influenced by local dynamics and anatomical structure.
Current resting-state effective connectivity models often overlook structural embedding and assume uniform regional fluctuation timescales.

Purpose of the Study:

To introduce a chromatic dynamic causal model (DCM) integrating structural information into neural fluctuation analysis.
To investigate how structural valency influences the spectral properties of endogenous neural fluctuations.

Main Methods:

Developed a chromatic DCM where structural valency linearly maps to the spectral exponent of scale-free auto-spectra.
Utilized simulations to demonstrate the emergence of this mapping from structural embedding in a non-equilibrium system.
Validated the model's ability to recover parameters across various network sizes and noise levels, comparing it to standard spectral DCM.

Main Results:

Chromatic DCM reliably recovers ground-truth parameters, outperforming standard spectral DCM.
Analysis of empirical data showed that valency-exponent mappings differ across the cortical hierarchy.
These mapping parameters were found to be conserved across homologous brain networks in humans, macaques, marmosets, and mice.

Conclusions:

The chromatic DCM provides a generative framework for understanding structure-function coupling in the brain.
This model expands the available biophysical mechanisms for effective connectivity inference.
Findings highlight species- and hierarchy-specific variations in how brain structure shapes neural dynamics.