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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Statistical models of complex brain networks: a maximum entropy approach.

Vito Dichio1, Fabrizio De Vico Fallani1

  • 1Sorbonne Universite, Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hopital de la Pitie Salpêtriere, F-75013 Paris, France.

Reports on Progress in Physics. Physical Society (Great Britain)
|July 12, 2023
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Summary

Understanding brain complexity requires analyzing its intricate network structure. This study uses statistical models to uncover local connection mechanisms, aiding in characterizing brain networks and identifying disease biomarkers.

Keywords:
brain networkscomplex systemsexponential random graph modelinferencemaximum entropy principlestatistical modeling

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Area of Science:

  • Neuroscience
  • Network Science
  • Statistical Modeling

Background:

  • The brain's complexity arises from interconnected neural networks, crucial for cognitive functions.
  • Analyzing brain networks is challenging due to inherent variability from underlying stochastic processes.
  • Understanding brain network structure is vital for both healthy function and neurological diseases.

Purpose of the Study:

  • To develop formal methods for characterizing brain network properties despite intrinsic variability.
  • To identify local connection mechanisms driving observed global network structures.
  • To explore applications in identifying biomarkers for neurological diseases like stroke.

Main Methods:

  • Focus on maximum entropy models, specifically exponential random graph models (ERGMs).
  • Utilize tools from network science and statistics to analyze network variability.
  • Review efforts in characterizing human brain network organization.

Main Results:

  • ERGMs offer a parsimonious approach to understanding local connection mechanisms.
  • Progress has been made in identifying organizational properties of human brain networks.
  • Potential for identifying predictive biomarkers for neurological conditions.

Conclusions:

  • Statistical graph modeling provides essential tools for network neuroscience.
  • Emerging methods coupled with improved data acquisition can refine probabilistic descriptions of complex brain systems.
  • This approach enhances understanding of brain function and disease states.