Extensions of multivariate dynamical systems to simultaneously explain neural and behavioral data

To examine how the brain produces behavior, new statistical methods have linked neurophysiological measures directly to mechanisms of cognitive models, modeling both modalities simultaneously. However, current simultaneous modeling efforts are largely based on either correlational methods or on functions that map one stream of data to the other. Such frameworks are limited in their ability to infer causality between brain activity and behavior. We investigate one causal framework for explaining how behavior can be viewed as an emergent property of brain dynamics. Our proposed framework can be considered an extension of multivariate dynamical systems (MDS; Ryali et al. Neuroimage, 54(2), 807–823, 2011), as it is constructed with temporal dynamics and brain functional connectivities. To test the MDS framework, we formulate a concrete model, demonstrate that it generates reasonable predictions about both behavioral and fMRI data, and conduct a parameter recovery study. Specifically, we develop a generative model of perceptual decision-making in a visual motion-direction discrimination task. Two simulation studies under different experimental protocols illustrate that the MDS model can capture key characteristics of both behavioral and neural measures that typically occur in experimental data. We also examine whether or not such a complex system can be inferred from experimental data by evaluating whether current algorithms for fitting models to data can recover sensible parameter estimates. Our parameter recovery study suggests that the MDS parameters can be recovered using likelihood-free estimation techniques. Together, these results suggest that our MDS-based framework shows great promise for developing fully integrative models of brain-behavior relationships.