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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

State-dependent intrinsic predictability of cortical network dynamics.

Leila Fakhraei¹, Shree Hari Gautam¹, Woodrow L Shew¹

¹Department of Physics, University of Arkansas, Fayetteville, Arkansas, United States of America.

|May 5, 2017

Summary

Cortical circuit dynamics are predictable based on their recent history. Predictability depends on the brain state, synaptic inhibition, and prediction timescale, with near-future predictions better in synchronous states and distant-future predictions better in asynchronous states.

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Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Cortical circuit dynamics are constantly changing.
Temporal continuity in neural activity is crucial for brain function but not fully understood.
Predicting future brain states from past activity is a key challenge.

Purpose of the Study:

To investigate the temporal continuity and predictability of cortical population dynamics.
To explore how factors like cortical state and synaptic inhibition influence this predictability.
To compare findings in vivo with a computational network model.

Main Methods:

Recorded multisite local field potentials from rat somatosensory cortex.
Developed and utilized a computational network model of spiking neurons.
Manipulated synaptic inhibition to alter cortical states (asynchronous to synchronous).
Assessed prediction accuracy over different future timescales (milliseconds to seconds).

Main Results:

Cortical dynamics exhibit intrinsic predictability based on recent history.
Predictability is modulated by cortical state, synaptic inhibition, and prediction timescale.
Near-future (10-100 ms) predictability increases with synchrony.
Distant-future (>1 s) predictability is higher in asynchronous states.
Computational model results corroborated experimental findings.

Conclusions:

Network dynamics exhibit determinism and predictability that are state-dependent.
The timescale of neural dynamics significantly impacts their predictability.
A continuum of cortical states, including criticality, influences information processing.