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Related Concept Videos

Entropy02:39

Entropy

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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Standard Entropy Change for a Reaction03:00

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Certain organic substances change color in dilute solution when the hydronium ion concentration reaches a particular value. For example, phenolphthalein is a colorless substance in any aqueous solution with a hydronium ion concentration greater than 5.0 × 10−9 M (pH < 8.3). In more basic solutions where the hydronium ion concentration is less than 5.0 × 10−9 M (pH > 8.3), it is red or pink. Substances such as phenolphthalein, which can be used to determine the pH of a solution, are...
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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
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Related Experiment Video

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An Integrated Approach for Microprotein Identification and Sequence Analysis
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Multi-Indicator Entropy Hub Score: A quantitative approach to hub analysis in brain networks.

Hongzhou Wu1, Jinming Xiao2, Elijah Agoalikum3

  • 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, No.2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu 610072, China.

Neuroimage
|February 13, 2026
PubMed
Summary

A new Multi-Indicator Entropy Hub Score (MIEHS) quantifies brain network hubs using multiple metrics. This approach reveals how hub alterations in networks like the default mode network correlate with cognitive function and clinical conditions.

Keywords:
Brain networksConnector hubsGraph property analysisProvincial hubsResting-state fMRI

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • The human brain relies on dynamic interactions within modular networks, with connector and provincial hubs crucial for information integration.
  • Previous hub identification methods often used single metrics, neglecting multi-metric integration and quantitative node contributions.
  • Establishing robust clinical biomarkers for brain hubs remains a challenge.

Purpose of the Study:

  • To introduce and validate the Multi-Indicator Entropy Hub Score (MIEHS) for quantifying brain network hub properties.
  • To investigate the localization and functional roles of connector and provincial hubs.
  • To explore the association between hub alterations and cognitive functions in clinical populations.

Main Methods:

  • Developed the Multi-Indicator Entropy Hub Score (MIEHS) by integrating six graph-theoretical metrics.
  • Validated MIEHS on benchmark networks, simulated data, and resting-state fMRI data (Midnight Scan Club).
  • Analyzed hub distribution, functional roles using gradient mapping, and associations with cognitive measures in clinical datasets (UCLA) using Partial Least Squares analysis.

Main Results:

  • MIEHS reliably identifies hubs across different network types and datasets.
  • High-scoring connector hubs were found in the attention network, bridging unimodal and transmodal regions, while provincial hubs concentrated in the default mode network, supporting intra-network communication.
  • Hub alterations in default mode, salience, limbic, and dorsal attention networks were significantly associated with cognitive flexibility, abstract reasoning, and verbal expression in clinical populations (ADHD, BD, SCHZ).

Conclusions:

  • MIEHS offers a robust and versatile framework for mapping brain network organization and characterizing functional reconfiguration.
  • The findings highlight the distinct roles of connector and provincial hubs in information processing.
  • Hub alterations represent potential biomarkers for cognitive deficits in neurological and psychiatric disorders.