NDLI: Estimation and visualization of neuronal functional connectivity in motor tasks

Content Provider	IEEE Xplore Digital Library
Author	Lin Li Seth, S. Il Park Sanchez, J.C. Principe, J.C.
Copyright Year	2009
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.
Starting Page	2926
Ending Page	2929
File Size	685888
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5333991
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Brain modeling Data visualization Animal behavior Mouth Kinematics Explosions Displays USA Councils Sensitivity analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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