Estimation and Visualization of Neuronal Functional Connectivity in Motor Tasks

Estimation and Visualization of Neuronal Functional Connectivity in Motor Tasks. Li, L., Seth, S., Park, I., Sanchez, J. C., & Príncipe, J. C. 2009.
doi abstract bibtex

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.

@CONFERENCE{Li2009,
  author = {Lin Li and Sohan Seth and Il Park and Justin C. Sanchez and Jos\'e
	C. Pr\'incipe},
  title = {Estimation and Visualization of Neuronal Functional Connectivity
	in Motor Tasks},
  booktitle = {Annual International Conference of the IEEE Engineering in Medicine
	and Biology Society (EMBS)},
  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.},
  doi = {10.1109/IEMBS.2009.5333991},
  owner = {memming},
  timestamp = {2009.12.15}
}

Downloads: 0

{"_id":"5bksqRBJMRT39yHKt","bibbaseid":"li-seth-park-sanchez-principe-estimationandvisualizationofneuronalfunctionalconnectivityinmotortasks-2009","downloads":0,"creationDate":"2016-10-24T21:01:39.576Z","title":"Estimation and Visualization of Neuronal Functional Connectivity in Motor Tasks","author_short":["Li, L.","Seth, S.","Park, I.","Sanchez, J. C.","Príncipe, J. C."],"year":2009,"bibtype":"conference","biburl":"https://raw.githubusercontent.com/catniplab/catniplab.github.io/master/catniplab.bib","bibdata":{"bibtype":"conference","type":"conference","author":[{"firstnames":["Lin"],"propositions":[],"lastnames":["Li"],"suffixes":[]},{"firstnames":["Sohan"],"propositions":[],"lastnames":["Seth"],"suffixes":[]},{"firstnames":["Il"],"propositions":[],"lastnames":["Park"],"suffixes":[]},{"firstnames":["Justin","C."],"propositions":[],"lastnames":["Sanchez"],"suffixes":[]},{"firstnames":["José","C."],"propositions":[],"lastnames":["Príncipe"],"suffixes":[]}],"title":"Estimation and Visualization of Neuronal Functional Connectivity in Motor Tasks","booktitle":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS)","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.","doi":"10.1109/IEMBS.2009.5333991","owner":"memming","timestamp":"2009.12.15","bibtex":"@CONFERENCE{Li2009,\n author = {Lin Li and Sohan Seth and Il Park and Justin C. Sanchez and Jos\\'e\n\tC. Pr\\'incipe},\n title = {Estimation and Visualization of Neuronal Functional Connectivity\n\tin Motor Tasks},\n booktitle = {Annual International Conference of the IEEE Engineering in Medicine\n\tand Biology Society (EMBS)},\n year = {2009},\n abstract = {In brain-machine interface (BMI) modeling, the firing patterns of\n\thundreds of neurons are used to reconstruct a variety of kinematic\n\tvariables. The large number of neurons produces an explosion in the\n\tnumber of free parameters, which affects model generalization. This\n\tpaper proposes a model-free measure of pairwise neural dependence\n\tto rank the importance of neurons in neural to motor mapping. Compared\n\tto a model-dependent approach such as sensitivity analysis, sixty\n\tpercent of the neurons with the strongest dependence coincide with\n\tthe top 10 most sensitive neurons trained through the model. Using\n\tthis data-driven approach that operates on the input data alone,\n\tit is possible to perform neuron selection in a more efficient way\n\tthat is not subject to assumptions about decoding models. To further\n\tunderstand the functional dependencies that influence neural to motor\n\tmapping, we use an open source available graph visualization toolkit\n\tcalled Prefuse to visualize the neural dependency graph and quantify\n\tthe functional connectivity in motor cortex. This tool when adapted\n\tto the analysis of neuronal recordings has the potential to easily\n\tdisplay the relationships in data of large dimension.},\n doi = {10.1109/IEMBS.2009.5333991},\n owner = {memming},\n timestamp = {2009.12.15}\n}\n\n","author_short":["Li, L.","Seth, S.","Park, I.","Sanchez, J. C.","Príncipe, J. C."],"key":"Li2009","id":"Li2009","bibbaseid":"li-seth-park-sanchez-principe-estimationandvisualizationofneuronalfunctionalconnectivityinmotortasks-2009","role":"author","urls":{},"downloads":0,"html":""},"search_terms":["estimation","visualization","neuronal","functional","connectivity","motor","tasks","li","seth","park","sanchez","príncipe"],"keywords":[],"authorIDs":[],"dataSources":["MSNTC753AtSzTtTJ8"]}