Toward a multisubject analysis of neural connectivity

Hyperscanning fNIRS data analysis using multiregression dynamic models: an illustration in a violin duo

Introduction

Interpersonal neural synchronization (INS) demands a greater understanding of a brain's influence on others. Therefore, brain synchronization is an even more complex system than intrasubject brain connectivity and must be investigated. There is a need to develop novel methods for statistical inference in this context.

Methods

In this study, motivated by the analysis of fNIRS hyperscanning data, which measure the activity of multiple brains simultaneously, we propose a two-step network estimation: Tabu search local method and global maximization in the selected subgroup [partial conditional directed acyclic graph (DAG) + multiregression dynamic model]. We illustrate this approach in a dataset of two individuals who are playing the violin together.

Results

This study contributes new tools to the social neuroscience field, which may provide new perspectives about intersubject interactions. Our proposed approach estimates the best probabilistic network representation, in addition to providing access to the time-varying parameters, which may be helpful in understanding the brain-to-brain association of these two players.

Discussion

The illustration of the violin duo highlights the time-evolving changes in the brain activation of an individual influencing the other one through a data-driven analysis. We confirmed that one player was leading the other given the ROI causal relation toward the other player.

A group analysis using the Multiregression Dynamic Models for fMRI networked time series

Connectivity studies of the brain are usually based on functional Magnetic Resonance Imaging (fMRI) experiments involving many subjects. These studies need to take into account not only the interaction between areas of a single brain but also the differences amongst those subjects. In this paper we develop a methodology called the group-structure (GS) approach that models possible heterogeneity between subjects and searches for distinct homogeneous sub-groups according to some measure that reflects the connectivity maps. We suggest a GS method that uses a novel distance based on a model selection measure, the Bayes factor. We then develop a new class of Multiregression Dynamic Models to estimate individual networks whilst acknowledging a GS type dependence structure across subjects. We compare the efficacy of this methodology to three other methods, virtual-typical-subject (VTS), individual-structure (IS) and common-structure (CS), used to infer a group network using both synthetic and real fMRI data. We find that the GS approach provides results that are both more consistent with the data and more flexible in their interpretative power than its competitors. In addition, we present two methods, the Individual Estimation of Multiple Networks (IEMN) and the Marginal Estimation of Multiple Networks (MEMN), generated from the GS approach and used to estimate all types of networks informed by an experiment —individual, homogeneous subgroups and group networks. These methods are then compared both from a theoretical perspective and in practice using real fMRI data.

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The group-structure (GS) approach models possible heterogeneity between subjects.

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The search for distinct homogeneous groups is based on the connectivity maps.

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All four methods for group network were compared using synthetic and real fMRI data

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IEMN and MEMN estimate individual, homogeneous subgroups and group networks.