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Edge-based network analysis reveals frequency-specific network dynamics in aberrant anxiogenic processing in rats.

Yin-Shing Lam1,2, Xiu-Xiu Liu1,2, Ya Ke1,2

  • 1School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong.

Network Neuroscience (Cambridge, Mass.)
|January 6, 2023
PubMed
Summary

Edge-based network analysis revealed abnormal brain network signaling in a rat model of prodromal Parkinson's disease, linking altered information flow to increased anxiety behaviors. This method helps understand network dysregulation in neuropsychiatric conditions.

Keywords:
AnxietyEdge-based network analysisPhase locking valuePhase transfer entropyTheta oscillation

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Psychiatry

Background:

  • Brain network interactions are crucial for understanding organizational principles and dysregulations in neuropsychiatric diseases.
  • Edge-based network analysis offers a novel approach to investigate complex network dynamics.

Purpose of the Study:

  • To explore the applicability of edge-based network analysis in identifying network mechanisms of aberrant anxiogenic processing.
  • To investigate how a dorsomedial striatum-tied associative network (DSAN) mediates context-based anxiogenic behavior in a rat model of prodromal Parkinson's disease.

Main Methods:

  • Utilized a rat model with dopamine depletion in the dorsomedial striatum.
  • Examined changes in bottom-up signaling and theta frequency gradients within the DSAN.
  • Employed an edge-based approach correlating phase transfer entropy (informational flow) with functional connectivity.

Main Results:

  • Observed exaggerated bottom-up signaling (posterior parietal-hippocampal-retrosplenial to anterior prefrontal-cingulate-amygdala regions) following dopamine depletion.
  • Identified a theta frequency gradient specific to this network.
  • Correlated alterations in informational flow-connectivity motifs with abnormal bottom-up signaling and increased anxiety behavior.

Conclusions:

  • Edge-based network analysis effectively reveals concurrent informational processing and functional organization dynamics in brain networks.
  • This approach can unveil network abnormalities and their impact on behavioral outcomes.
  • The findings provide insights into the network basis of neuropsychiatric conditions like anxiety and Parkinson's disease.