Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural...

Area of Science:

• Neuroscience
• Computational Biology
• Brain Imaging

Background:

• Functional connectivity (FC) patterns are often studied using computational models based on neuronal dynamics and brain anatomy.
• It is unclear if group-averaged data can accurately predict individual FC patterns.

Purpose of the Study:

• To assess the predictive power of computational models for individual FC patterns.
• To compare the efficacy of individual versus group-averaged structural connectivity (SC) in predicting empirical FC.
• To evaluate different metrics for estimating FC and their suitability for individual predictions.

Main Methods:

• Employed the Jansen and Rit neural mass model coupled with individual structural connectivity (SC).
• Correlated simulated FC with individual magnetoencephalography-derived empirical FC.
• Utilized phase-based (phase lag index (PLI), phase locking value (PLV)) and amplitude-based (amplitude envelope correlation (AEC)) metrics for FC estimation.

Main Results:

• Amplitude envelope correlation (AEC) showed a better fit between simulated and empirical FC compared to phase-based metrics.
• Individual SC led to higher correlations between simulated and empirical FC than group-averaged SC.
• SC from other participants yielded similar prediction correlations as using a participant's own SC.

Conclusions:

• FC simulations benefit from using individual SC over group-averaged SC for the Jansen and Rit model.
• This approach enhances understanding of mechanisms driving individual functional network trajectories.
• The findings highlight the importance of personalized data in computational neuroscience models.