Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

The Resting Membrane Potential01:21

The Resting Membrane Potential

143.4K
Overview
143.4K
Resting Membrane Potential01:24

Resting Membrane Potential

22.2K
The relative difference in electrical charge, or voltage, between the inside and the outside of a cell membrane, is called the membrane potential. It is generated by differences in permeability of the membrane to various ions and the concentrations of these ions across the membrane.
The Inside of a Neuron is More Negative
The membrane potential of a cell can be measured by inserting a microelectrode into a cell and comparing the charge to a reference electrode in the extracellular fluid. The...
22.2K
Resting Potential Decay01:15

Resting Potential Decay

6.5K
The resting membrane potential of a neuron (-70mV) is sustained due to the selective ion permeability of the membrane. At the resting potential, the membrane is slightly permeable to ions like sodium (Na+) and chloride (Cl−) and highly permeable to potassium ions (K+). Differences in the ions' concentration inside the cell compared to the outside are maintained by membrane transport proteins like channels and pumps.
At rest, the K+ is the main ion that moves across the membrane...
6.5K
pH Scale02:41

pH Scale

80.5K
Hydronium and hydroxide ions are present both in pure water and in all aqueous solutions, and their concentrations are inversely proportional as determined by the ion product of water (Kw). The concentrations of these ions in a solution are often critical determinants of the solution’s properties and the chemical behaviors of its other solutes. Two different solutions can differ in their hydronium or hydroxide ion concentrations by a million, billion, or even trillion times. A common means of...
80.5K
Pressure Variation in a Fluid at Rest01:11

Pressure Variation in a Fluid at Rest

857
In a fluid at rest, the pressure at any point beneath the fluid surface depends solely on the depth, not on the container's shape or size. This principle, known as hydrostatic pressure, arises because, in stationary fluids, there is no acceleration, meaning the forces within the fluid balance out. Only vertical forces, caused by the weight of the fluid above, contribute to pressure changes with depth.
When measuring pressure at two different levels within the fluid, the difference in...
857
Scaling01:26

Scaling

601
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
601

You might also read

Related Articles

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

Sort by
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

Could agentic AI topple grant-funding systems?

Nature·2026
Same author

Hierarchical Bayesian inference for community detection and connectivity of functional brain networks.

IEEE transactions on medical imaging·2026
Same author

Early detection of dementia with default-mode network effective connectivity.

Nature. Mental health·2026
Same author

Shape matters: Predicting Huntington's disease using progression modelling.

Computer methods and programs in biomedicine·2026
Same journal

Cortical similarity networks in the rat brain: Postnatal development and sensitivity to early life stress.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Increased sensitivity in identifying language-related functional connectivity using jackknife resampling analyses.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Phase-dependent stimulation response is shaped by the brain's dynamic functional connectivity.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Restoring oscillatory dynamics in Alzheimer's disease: A laminar whole-brain model of serotonergic psychedelic effects.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Distributed cortical network dynamics of binocular convergent eye movements in humans.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

High-resolution Bayesian Virtual Epileptic Patient using neural field models.

Network neuroscience (Cambridge, Mass.)·2026
See all related articles

Related Experiment Video

Updated: Feb 15, 2026

Real-Time fMRI Brain Mapping in Animals
04:05

Real-Time fMRI Brain Mapping in Animals

Published on: September 24, 2020

4.1K

Large-scale DCMs for resting-state fMRI.

Adeel Razi1,2,3, Mohamed L Seghier1,4, Yuan Zhou1,5

  • 1The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.

Network Neuroscience (Cambridge, Mass.)
|February 6, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces spectral dynamic causal modeling (DCM) for identifying directed brain networks from resting-state fMRI data, offering a more neurobiologically plausible alternative to functional connectivity methods.

Keywords:
Bayesian inferenceDynamic causal modelingEffective connectivityFunctional connectivityGraph theoryLarge-scale networksResting statefMRI

More Related Videos

fMRI Validation of fNIRS Measurements During a Naturalistic Task
10:36

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

21.6K
A Free-breathing fMRI Method to Study Human Olfactory Function
10:42

A Free-breathing fMRI Method to Study Human Olfactory Function

Published on: July 30, 2017

10.1K

Related Experiment Videos

Last Updated: Feb 15, 2026

Real-Time fMRI Brain Mapping in Animals
04:05

Real-Time fMRI Brain Mapping in Animals

Published on: September 24, 2020

4.1K
fMRI Validation of fNIRS Measurements During a Naturalistic Task
10:36

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

21.6K
A Free-breathing fMRI Method to Study Human Olfactory Function
10:42

A Free-breathing fMRI Method to Study Human Olfactory Function

Published on: July 30, 2017

10.1K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • Resting-state functional MRI (fMRI) commonly uses symmetric correlation-based functional connectivity.
  • Identifying directed, large-scale brain networks requires more sophisticated methods than simple correlations.

Purpose of the Study:

  • To develop and validate a method for identifying directed, effective connectivity in large-scale brain networks using resting-state fMRI.
  • To contrast this directed approach with traditional symmetric functional connectivity analyses.

Main Methods:

  • Spectral dynamic causal modeling (DCM) was employed to invert large graphs of brain regions.
  • The method identifies directed, weighted graphs representing excitatory and inhibitory neuronal coupling.
  • Functional connectivity modes were used to constrain effective connectivity, enhancing model inversion efficiency.

Main Results:

  • Spectral DCM successfully estimated directed connectivity in large brain graphs.
  • Estimates from spectral DCM correlated strongly with those from stochastic DCM.
  • The approach avoids arbitrary thresholding common in functional connectivity analyses.

Conclusions:

  • Spectral DCM provides a neurobiologically plausible and efficient method for directed graph analysis of resting-state fMRI.
  • This directed graph approach is well-suited for investigating brain network alterations in various disorders.