Comparing brain connectivity metrics: a didactic tutorial with a toy model and experimental data.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Data Analysis

Background:

• Resting-state connectivity analysis is crucial for understanding brain function.
• Phase locking value (PLV) and convergent cross-mapping (CCM) are two distinct methods for assessing brain connectivity.
• PLV assumes oscillatory coupling, while CCM is a model-free approach detecting causal influences.

Purpose of the Study:

• To compare the efficacy of PLV and CCM in computing resting-state brain networks.
• To evaluate these methods on both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data.
• To determine the suitability of each method for different applications, considering computational cost and accuracy.

Main Methods:

• A toy dataset was used to test PLV and CCM under controlled synchronization conditions.
• Experimental resting-state EEG and fMRI datasets were analyzed using both PLV and CCM.
• Network characteristics derived from both methods were compared for similarity and trends.

Main Results:

• Both PLV and CCM yielded comparable resting-state brain network results for EEG and fMRI datasets.
• Network characteristics computed by PLV and CCM showed significant similarity across both neuroimaging modalities.
• CCM demonstrated superior ability in identifying one-way or weak connections but at a higher computational cost.

Conclusions:

• PLV serves as a computationally efficient and accurate approximation for CCM in resting-state network analysis.
• PLV is a suitable metric for real-time (online) brain network identification due to its speed and reliability.
• The findings support the use of PLV as a practical tool in electrophysiological and fMRI research for network analysis.