Computational Methods for Unraveling Temporal Brain Connectivity Data.

Brain science is a frontier research area with great promise for understanding, preventing, and treating multiple diseases affecting millions of patients. Its key task of reconstructing neuronal brain connectivity poses unique Big Data Analysis challenges distinct from those in clinical or "-omics" domains. Our goal is to understand the strengths and limitations of reconstruction algorithms, measure performance and its determinants, and ultimately enhance performance and applicability. We devised a set of experiments in a well-controlled setting using an established gold-standard based on calcium fluorescence time series recordings of thousands of neurons sampled from a previously validated neuronal model of complex time-varying causal neuronal connections. Following empirical testing of several state-of-the-art reconstruction algorithms, and using the best-performing algorithms, we constructed features of a classifier and predicted the presence or absence of connections using meta-learning. This approach combines information-theoretic, feature construction, and pattern recognition meta-learning methods to considerably improve the Area under ROC curve performance. Our data are very promising toward the feasibility of reliably reconstructing complex neuronal connectivity.