GSC-ABTA: A group-level brain sources connectivity framework based on adaptive block tensor analysis

Background

This study presents a group analysis method for identifying shared brain connectivity patterns using tensor analysis. The method’s efficacy is evaluated through a validation framework, considering various scenarios of group brain data generation and diverse control parameters.

Methodology

The proposed group estimation method for source-level brain connectivity begins by modeling the activity of brain and noise sources using a quasi-real six-layer head model to solve the direct problem. Pseudo-EEG data are then generated at the group level for three scenarios: Volume Conduction Effect (VC), Inter-Trial Variability (ITV), and Time Varying Connectivity (TV). The Group-Level Source Connectivity based on Adaptive Block Tensor Analysis (GSC-ABTA) is used to solve the inverse problem and estimate group-level source activity. This method allows for trial-dependent streaming updates in the group estimation of brain sources. Finally, a tensorial multivariate autoregressive model is developed in an adaptive format, taking into account a forgetting parameter for determining the contribution of observations in estimating effective brain connectivity coefficients at the group level. Statistical analysis was performed for six control parameters (including data length, signal-to-noise ratio, density, percentage of real connections added to the model, model order, and the number of trials) and compared with tensorial and non-tensorial methods in the three proposed scenarios. Additionally, the framework was validated with real data.

Results

The proposed method outperforms other methods in the VC scenario for all control parameters and in the ITV and TV scenarios for most control parameters. These findings underscore the importance of adaptive updating in extracting the activity of the sources for group investigation, facilitating the group extraction of brain connectivity coefficients on a more generalizable scale.