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Source-reconstruction of the sensorimotor network from resting-state macaque electrocorticography
R Hindriks1, C Micheli2, C A Bosman3
1Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra (UPF), Spain; Department of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Neuroimage
|June 11, 2018
Summary
This study uses electrocorticography (ECoG) and spatial ICA to map brain networks, revealing detailed functional connectivity of resting-state cortical rhythms. The findings highlight ECoG
Area of Science:
- Neuroscience
- Computational Neuroscience
- Electrophysiology
Background:
- Resting-state networks (RSNs) are crucial for understanding intrinsic brain activity.
- The electrophysiological basis of RSNs is not well understood.
- Electrocorticography (ECoG) offers high spatial resolution for studying cortical rhythms.
Purpose of the Study:
- To investigate the electrophysiological underpinnings of RSNs using ECoG.
- To develop and validate a source-space spatial independent component analysis (spatial ICA) method for ECoG data.
- To assess functional connectivity within RSNs using amplitude correlations and phase-locking.
Main Methods:
- Applied source-space spatial ICA to ECoG recordings of resting-state cortical rhythms.
- Utilized band-limited amplitude envelope correlations and oscillatory phase-locking for network analysis.
- Simulated rhythmic cortical generators to evaluate the accuracy of connectivity measures.
Main Results:
- Spatial ICA successfully identified generators of resting-state cortical rhythms in ECoG data.
- Reconstruction of oscillatory phase-locking was more challenging than amplitude correlations, especially at low signal-to-noise ratios.
- The method decomposed the sensorimotor network into three distinct cortical generators in a macaque model.
- Identified significant and reproducible amplitude correlations and phase-locking with non-zero lags between generators.
Conclusions:
- Source-projected ECoG combined with spatial ICA provides high spatial detail of resting-state cortical dynamics.
- The methodology is effective for studying functional connectivity in RSNs.
- Findings support wider application of this technique in both resting-state and event-related analyses in humans and macaques.
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