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State-independent and state-dependent patterns in the rat default mode network.

Wei Jing1, Yang Xia2, Min Li2

  • 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, China; Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan 4030030, China.

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Summary

Dynamic functional connectivity (FC) in the rat default mode network (DMN) fluctuates across vigilance states. These dynamic patterns offer insights into brain organization supporting cognition and behavior.

Keywords:
Default mode networkDynamic functional connectivityElectrophysiological signalsFactor analysisVigilance state

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Area of Science:

  • Neuroscience
  • Computational Neuroscience

Background:

  • Resting-state functional connectivity (FC) studies traditionally assume stable brain network interactions.
  • Recent evidence suggests dynamic FC fluctuations, particularly in higher-order networks like the default mode network (DMN).
  • The neural underpinnings of dynamic FC remain largely unexplored.

Purpose of the Study:

  • To investigate the neural basis of dynamic FC within the rat DMN across different vigilance states.
  • To identify underlying spatial and temporal patterns of DMN functional organization.

Main Methods:

  • Electrophysiological signals were recorded from DMN regions in freely behaving rats.
  • Dynamic FC was quantified using the phase locking value (PLV) with sliding time windows.
  • Factor analysis was employed to uncover hidden patterns within the DMN.

Main Results:

  • Distinct spatial FC patterns within the DMN were identified based on temporal dynamics.
  • Some identified patterns exhibited vigilance state-dependency, while others were state-independent.
  • Temporal contributions of these patterns varied over time and differed across vigilance states (quiet wakefulness, slow-wave sleep, REM sleep).

Conclusions:

  • Dynamic spatial patterns with fluctuating temporal contributions provide a flexible framework for information integration.
  • These findings reveal novel insights into the dynamic functional organization of the rat DMN.
  • The study highlights the importance of considering dynamic FC for understanding brain function across different behavioral states.