Maximum entropy model estimates functional connectivity
Cite this dataset
Lamberti, Martina (2021). Maximum entropy model estimates functional connectivity [Dataset]. Dryad. https://doi.org/10.5061/dryad.p5hqbzkqj
Tools to estimate brain connectivity have been useful to improve our understanding of brain functioning. Reduced models ofcultured neurons are often used to study the behavior of neuronal networks, including functional connectivity and how it mightbe affected by external stimuli. Cultured neurons tend to be active in ensembles, and when pairs of neurons show significantsynchronicity in their firing patterns they are said to be functionally connected. The most common methods to infer functionalconnections are based on pair-wise cross correlation between activity patterns of (small groups of) neurons. However, thesemethods are not designed to be used during external stimulation, and they are relatively insensitive to inhibitory connections.Maximum Entropy (MaxEnt) models may provide a conceptually different method to infer functional connectivity, with thepotential benefit to estimate functional connectivity in the presence of an external stimulus and to infer excitatory as well asinhibitory connections. These models do not use pairwise comparison, but are based on probability distributions of sets ofneurons that are synchronously active in discrete time bins. We investigate the ability of the MaxEnt models to infer functionalconnectivity, using electrophysiological recordings fromin vitroneuronal cultures on micro electrode arrays. We comparefunctional connectivity as inferred by MaxEnt models to that obtained by conditional firing probabilities (CFP), an establishedcross-correlation based method. We show that MaxEnt models provide connectivity estimates that correlate well with CFPoutcomes. In addition, stimulus-induced connectivity changes were detected by MaxEnt models, and were of the samemagnitude as those detected by CFP.
US Air Force for Scientific Research, Award: FA9550-19-1-0411