A Connectionist Approach to Mapping the Human Connectome Permits Simulations of Neural Activity Within an Artificial Brain

Abstract

Data-driven models drawn from statistical correlations between brain activity and behavior are used to inform theory-driven models, such as those described by computational models, which provide a mechanistic account of these correlations. This article introduces a novel multivariate approach for bootstrapping neurologically-plausible computational models that accurately encodes cortical effective connectivity from resting state functional neuroimaging data (rs-fMRI). We show that a network modularity algorithm finds comparable resting state networks within connectivity matrices produced by our approach and by the benchmark method. Unlike existing methods, however, ours permits simulation of brain activation that is a direct reflection of this cortical connectivity. Cross-validation of our model suggests that neural activity in some regions may be more consistent between individuals, providing novel insight into brain function. We suggest this method to make an important contribution toward modeling macro-scale human brain activity, and it has the potential to advance our understanding of complex neurological disorders and the development of neural connectivity.