Related Experiment Video
Updated: May 27, 2026
10:44
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
Dynamical stability of intrinsic connectivity networks
Michael A Ferguson1, Jeffrey S Anderson
1Department of Bioengineering, University of Utah, 72 S Central Campus Drive, Rm 2750, Salt Lake City, UT 84112, USA.
Neuroimage
|November 8, 2011
Summary
Functional connectivity MRI reveals brain activity converges to two main states, high or low default mode network activity. This simulation models brain dynamics and network interactions.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- Functional connectivity MRI (fcMRI) measures correlations in brain activity between regions.
- Understanding brain dynamics requires modeling interactions beyond static correlations.
Purpose of the Study:
- To model brain activity dynamics using functional connectivity data.
- To investigate convergence to stable brain states.
Main Methods:
- Utilized a large resting-state fcMRI dataset (n=961).
- Calculated high-resolution functional connectivity maps.
- Approximated weighted connection strength using Cholesky decomposition.
- Simulated brain activity dynamics with weighted connectivity.
Main Results:
- Simulations converged to two inverted states: high or low default mode network activity.
- Metastable intermediate states reflected combinations of functional networks.
- Convergence speed varied based on initial conditions, slowest at the default mode network borders.
Conclusions:
- Brain activity exhibits metastable dynamics converging to distinct network states.
- Weighted functional connectivity can predict emergent brain activity patterns.
- fcMRI data can inform computational models of brain function.
More Related Videos
Related Concept Videos
01:14Stability of structures
In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
01:23Stability of Equilibrium Configuration
Understanding the stability of equilibrium configurations is a fundamental part of mechanical engineering. In any system, there are three distinct types of equilibrium: stable, neutral, and unstable.
A stable equilibrium occurs when a system tends to return to its original position when given a small displacement, and the potential energy is at its minimum. An example of a stable equilibrium is when a cantilever beam is fixed at one end and a weight is attached to the other end. If the weight...
A stable equilibrium occurs when a system tends to return to its original position when given a small displacement, and the potential energy is at its minimum. An example of a stable equilibrium is when a cantilever beam is fixed at one end and a weight is attached to the other end. If the weight...
01:25Multimachine Stability
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
02:17Microtubule Instability
Microtubules are hollow cylindrical filaments having a diameter of approximately 25 nm and a length that varies from 200 nm to 25 μm. GTP-bound tubulin subunits form αβ-heterodimers for microtubule assembly. These core building blocks interact longitudinally, polymerizing into protofilaments. The protofilaments then interact with one another through lateral bonding forces to form stable cylindrical microtubules. These cylindrical filaments are dynamic as they undergo repeated assembly and...
01:24BIBO stability of continuous and discrete -time systems
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
01:28Stability
The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
The stability of an LTI system is determined by the roots of its characteristic equation, known as poles. A system is stable if it produces a bounded...
The stability of an LTI system is determined by the roots of its characteristic equation, known as poles. A system is stable if it produces a bounded...