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Brain science is an evolving research area inviting great enthusiasm with itspotential for providing insights and thereby, preventing, and treating multipleneuronal disorders affecting millions of patients. Discovery of relationships,such as brain connectivity, is a major goal in basic, translational, andclinical science. Algorithms for causal discovery are used in diverse fieldsfor tackling problems similar to the task of reconstruction of neuronal brainconnectivity. Our aim is to understand the strengths and limitations of thesemethods, measure performance and its determinants, and provide insights toenhance their performance and applicability. We performed extensive empiricaltesting and benchmarking of reconstruction performance of severalstate-of-the-art algorithms along with several ensemble techniques used tocombine them. Our experiments used a clear and broadly relevant gold standardbased on calcium fluorescence time series recordings of thousands of neuronssampled from a previously validated realistic, neuronal model. Correlation,entropy-based measures, Cross-Correlation for short time lags, and GeneralizedTransfer Entropy had the best performances with area under ROC curve (AUC) inthe range of 0.7-0.8 even for smaller sample sizes of n = 100 to 1,000 andconverged quickly (at less than n = 1,000). Ensembles of best-performingmethods using random forests and neural networks generated AUC of ~0.9 with n =10,000. Several important insights regarding parameter choice and sample sizewere gained for guiding the experimental design of studies. Our data are alsosupportive of the feasibility of reliably reconstructing complex neuronalconnectivity using existing techniques.

Evaluation and Ensembling of Methods for Reverse Engineering of Brain Connectivity from Imaging Data