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Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

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Spontaneous Neural Dynamics and Multi-scale Network Organization.

Brett L Foster¹, Biyu J He², Christopher J Honey³

¹Department of Psychology, Stanford University CA, USA.

Frontiers in Systems Neuroscience

|February 24, 2016

Summary

This summary is machine-generated.

Spontaneous brain activity, once ignored, now reveals brain network structure. Invasive electrophysiology and neuroimaging show these intrinsic patterns reflect neural network architecture across scales.

Keywords:

brain networks connectivity electrocorticography (ECoG)neural dynamics resting-state fMRI

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Area of Science:

Neuroscience
Systems Neuroscience
Computational Neuroscience

Background:

Spontaneous neural activity was historically considered noise.
Recent studies link spontaneous activity to brain network structure.
Functional MRI identifies networks via spontaneous hemodynamics.

Purpose of the Study:

Identify neural signatures of spontaneous hemodynamics.
Elucidate the neural origin and temporal dynamics of spontaneous activity.
Survey methods for quantifying spontaneous neural activity.

Main Methods:

Analysis of spontaneous hemodynamics using functional MRI.
Electrophysiological measurements for neural signatures.
Amplitude- and phase-based analyses of invasive electrophysiology.

Main Results:

Spontaneous activity patterns closely correspond to structural network architecture.
Functional networks in the human brain are reliably identified.
Covariance structure of spontaneous neural activity reflects network properties.

Conclusions:

Spontaneous neural activity is a key indicator of brain network organization.
Invasive electrophysiology offers high spatiotemporal precision for studying these dynamics.
Findings in humans and animal models show conserved multi-scale properties.