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Related Concept Videos

Distributed Loads01:19

Distributed Loads

Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
Combining Functions01:16

Combining Functions

Functions can be combined to form new mathematical models that describe interactions between variables. These combinations are fundamental in understanding relationships between changing quantities and are commonly encountered in scientific and engineering contexts. The combination methods—addition, subtraction, multiplication, division, and composition—each have unique implications for the resulting function’s domain and behavior.When combining functions through arithmetic operations, such...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Probability Distributions01:32

Probability Distributions

The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Distributed processing; distributed functions?

Peter T Fox1, Karl J Friston

  • 1Research Imaging Institute and Department of Radiology, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, USA. Fox@uthscsa.edu

Neuroimage
|January 17, 2012
PubMed
Summary
This summary is machine-generated.

Neuroimaging has advanced our understanding of brain architecture but still struggles to map brain function to structure. Emerging research in functional connectivity and distributed brain responses offers new insights into this challenge.

Related Experiment Videos

Last Updated: May 25, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Neuroimaging

Background:

  • Over twenty years of human brain mapping using neuroimaging have yielded significant insights.
  • Key concepts in functional neuroimaging include functional segregation and integration.
  • A persistent challenge is the principled mapping of brain function to structure for cognitive neuroscience.

Purpose of the Study:

  • To review the progress and challenges in human brain mapping.
  • To explore structure-function relationships in the context of functional segregation and integration.
  • To highlight emerging approaches for mapping brain function onto structure.

Main Methods:

  • Review of neuroimaging literature focusing on structure-function relationships.
  • Analysis of functional segregation and integration principles.
  • Examination of functional connectivity and distributed brain responses.
  • Consideration of large-scale meta-analyses.

Main Results:

  • Despite advances, a clear method for mapping brain function to structure remains elusive.
  • Growing appreciation for functional integration in distributed neural processing.
  • Emerging interest in data-driven cognitive ontologies aligned with functional anatomy.

Conclusions:

  • Neuroimaging has provided valuable insights but faces fundamental challenges in structure-function mapping.
  • Functional connectivity and distributed responses are key areas for future research.
  • Large-scale data analyses are crucial for uncovering broad structure-function mappings.