Estimation and visualization of neuronal functional connectivity in motor tasks

Lin Li, Sohan Seth, Il Park, Justin C Sanchez, Jose C Principe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In brain-machine interface (BMI) modeling, the firing patterns of hundreds of neurons are used to reconstruct a variety of kinematic variables. The large number of neurons produces an explosion in the number of free parameters, which affects model generalization. This paper proposes a model-free measure of pairwise neural dependence to rank the importance of neurons in neural to motor mapping. Compared to a model-dependent approach such as sensitivity analysis, sixty percent of the neurons with the strongest dependence coincide with the top 10 most sensitive neurons trained through the model. Using this data-driven approach that operates on the input data alone, it is possible to perform neuron selection in a more efficient way that is not subject to assumptions about decoding models. To further understand the functional dependencies that influence neural to motor mapping, we use an open source available graph visualization toolkit called Prefuse to visualize the neural dependency graph and quantify the functional connectivity in motor cortex. This tool when adapted to the analysis of neuronal recordings has the potential to easily display the relationships in data of large dimension.
Original languageEnglish
Title of host publicationEngineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
Pages2926-2929
Number of pages4
ISBN (Electronic) 978-1-4244-3296-7
DOIs
Publication statusPublished - 3 Sep 2009

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