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Modeling and interpreting mesoscale network dynamics.

Ankit N Khambhati1, Ann E Sizemore2, Richard F Betzel2

  • 1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA 19104, USA.

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|June 25, 2017
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Summary
This summary is machine-generated.

New brain imaging data allows for dynamic graph modeling of neural circuits. This approach helps understand brain dynamics, cognition, and disease by analyzing connectivity and activity patterns.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • High temporal resolution neural data from advanced brain imaging techniques are now abundant.
  • Understanding brain circuit dynamics and their role in cognition and disease requires robust computational models.

Purpose of the Study:

  • To review recent advances in modeling approaches for dynamic neural structures.
  • To explore methods for analyzing dynamic connectivity and activity patterns in the brain.
  • To discuss statistical testing and interpretation of dynamic graph architectures.

Main Methods:

  • Review of computational and mathematical modeling approaches for dynamic brain networks.
  • Focus on models representing temporally-evolving interconnected brain structures as dynamic graphs.
  • Examination of methods for modeling dynamic connectivity, activity, and activity-on-connectivity.

Main Results:

  • Dynamic graph models offer a framework to capture the brain's evolving structure and function.
  • Recent modeling efforts focus on dynamic connectivity, activity patterns, and their interplay.
  • Considerations for statistical testing, including parametric and non-parametric methods, are reviewed.

Conclusions:

  • Dynamic graph approaches are crucial for mechanistic understanding of brain function and dysfunction.
  • Accurate interpretation of dynamic graph architecture is essential for advancing the field.
  • Future directions emphasize method development for analyzing complex neural dynamics.