Neuro-PC: Causal Functional Connectivity from Neural Dynamics

Functional connectome extends the anatomical connectome by capturing the relations between neurons according to their activity and interactions. When these relations are causal, the functional connectome maps how neural activity flows within neural circuits and provides the possibility for inference of functional neural pathways, such as sensory-motor-behavioral pathways. While there exist various information approaches for non-causal estimations of the functional connectome, approaches that characterize the causal functional connectivity - the causal relationships between neuronal time series, are scarce. In this work, we develop the Neuro-PC algorithm which is a novel methodology for inferring the causal functional connectivity between neurons from multi-dimensional time series, such as neuronal recordings. The core of our methodology relies on a novel adaptation of the PC algorithm, a state-of-the-art method for statistical causal inference, to the multi-dimensional time-series of neural dynamics. We validate the performance of the method on network motifs with various interactions between their neurons simulated using continuous-time artificial network of neurons. We then consider the application of the method to obtain the causal functional connectome for recent multi-array electrophysiological recordings from the mouse visual cortex in the presence of different stimuli. We show how features of the mapping can be used for quantification of the similarities between neural responses subject to different stimuli.