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Dietary Connections

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In biological systems, most metabolic pathways are interconnected. The cellular respiration processes that convert glucose to ATP—such as glycolysis, pyruvate oxidation, and the citric acid cycle—tie into those that break down other organic compounds. As a result, various foods—from apples to cheese to guacamole—end up as ATP. In addition to carbohydrates, food also contains proteins and lipids—such as cholesterol and fats. All of these organic compounds are used...
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Connective tissues perform a broad range of functions in the body. Their primary function is to connect and link different tissues in the body and act as packaging material between tissues. The areolar tissue, a connective tissue prototype, commonly cements various tissue types in diverse body organs. In contrast, adipose tissue cushions internal organs while insulating the body from heat loss.
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Loose Connective Tissue01:26

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Connective tissues are one of the four main tissue types in humans that are extensively present in the body. They are characterized by cells embedded in an extracellular matrix (ECM) composed of a ground substance and three main types of protein fibers— collagen, elastic, and reticular fibers. The ground substance of connective tissues can range from a watery and jelly-like consistency to mineralized and hard. The wide variety of cells in the connective tissues include fibroblasts,...
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The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
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During early development, the embryo forms two types of connective tissues— the mesenchyme and mucoid connective tissue.
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Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
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Detecting connectivity in EEG: A comparative study of data-driven effective connectivity measures.

Hanieh Bakhshayesh1, Sean P Fitzgibbon2, Azin S Janani1

  • 1College of Science and Engineering, Flinders University, Adelaide, Australia; Medical Device Research Institute, Flinders University, Adelaide, Australia.

Computers in Biology and Medicine
|August 20, 2019
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Summary

This study compared various effective connectivity measures for detecting causal effects in interacting systems. Conditional Granger causality demonstrated the best overall performance and computational efficiency across diverse scenarios.

Keywords:
Biomedical signal processingConnectivityEEG

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Area of Science:

  • Neuroscience
  • Complex Systems
  • Data Analysis

Background:

  • Effective connectivity analysis is crucial for understanding interactions in complex systems.
  • Numerous methods exist to estimate causal relationships from time-series data.
  • A comprehensive comparison of these methods is lacking.

Purpose of the Study:

  • To conduct the first extensive comparison of diverse effective connectivity measures.
  • To evaluate their ability to detect causal effects in simulated interacting systems.
  • To identify the most reliable and computationally efficient measures.

Main Methods:

  • Compared information theoretic, model-based (time/frequency domains), and phase-based measures.
  • Tested performance on simulated data from coupled Hénon maps, MVAR models, and simulated EEG.
  • Assessed measures based on statistical significance and computational cost.

Main Results:

  • No single measure was consistently superior across all simulations.
  • Model-based measures excelled when data matched their assumptions (e.g., MVAR).
  • Frequency domain measures were effective for data with clear frequency bands.
  • Information theoretic measures performed well in other scenarios.
  • Conditional Granger causality showed the best balance of performance and low computational cost.

Conclusions:

  • The choice of effective connectivity measure depends on the data characteristics and research question.
  • Conditional Granger causality is a robust and efficient option for many applications.
  • Partial and multivariate Granger causality are effective but computationally intensive.
  • Copula Granger causality is reliable but also computationally demanding with large datasets.