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Related Concept Videos

State Space Representation01:27

State Space Representation

431
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
431
State Space to Transfer Function01:21

State Space to Transfer Function

466
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
466

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Related Experiment Video

Updated: Dec 11, 2025

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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A state-space model for dynamic functional connectivity.

Sourish Chakravarty1,2,3, Zachary D Threlkeld4, Yelena G Bodien3

  • 1Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA.

Conference Record. Asilomar Conference on Signals, Systems & Computers
|August 18, 2020
PubMed
Summary
This summary is machine-generated.

We introduce a novel statistical model to improve dynamic functional connectivity (DFC) analysis. This method enhances the estimation of brain region correlations, aiding in the exploration of disorders of consciousness biomarkers.

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Area of Science:

  • Neuroscience
  • Statistics
  • Biomarker Discovery

Background:

  • Dynamic functional connectivity (DFC) measures correlated neural activity over time.
  • Traditional DFC methods using sliding windows have statistical limitations.
  • Resting-state functional magnetic resonance imaging (fMRI) is commonly used to study brain connectivity.

Purpose of the Study:

  • To propose a novel multivariate stochastic volatility model for more accurate DFC estimation.
  • To address the statistical limitations of conventional DFC analysis.
  • To advance the principled use of DFC in exploring biomarkers for disorders of consciousness (DoC).

Main Methods:

  • Developed a state-space model where correlation dynamics are governed by a latent process.
  • Employed a sequential Bayesian estimation framework.
  • Applied the model to simulated data and resting-state fMRI data from a patient with DoC.

Main Results:

  • The proposed model provides improved estimation of correlation trajectories.
  • Demonstrated the framework's utility on simulated and real-world fMRI data.
  • Successfully estimated dynamic correlation patterns in a patient with DoC.

Conclusions:

  • The multivariate stochastic volatility model offers an advanced approach to DFC analysis.
  • This framework has significant potential for identifying biomarkers in disorders of consciousness.
  • Advances the field of neuroimaging analysis for neurological disorders.