Understanding how neurons act in concert requires observation of the collective activity of large, spatially distributed neuronal aggregates. Largely motivated by the rapid advances in microfabrication technology, high-density implantable electronic interfaces are now enabling the acquisition of large volumes of physiological and behavioral data, triggering concomitant neurobiological discoveries. Nevertheless, advances in the fabrication of high-density microelectrode arrays (MEAs) were not associated with quantum advances in array processing and data analysis techniques in order to unveil the affluent information content in the recorded neural data. As the number of recording channels on a single microprobe device becomes astoundingly large, no discipline is more challenged than signal processing and data mining in accommodating these new advances within the emerging neural engineering arena. There is an intrinsic need to design new algorithms and software tools to optimize array processing and information retrieval from multiple spike train neural data to answer several persistent neuroscience questions. The fundamental objective of this research is to explore and develop an integrated array processing and data mining framework with companion software tools to extract the useful information from large-scale neuronal ensemble recordings through the following aims: 1. Develop scalable and adaptive array processing algorithms for processing high-density microelectrode array recordings in short and long-term experimental setups; 2. Develop data analysis and clustering techniques for mining functional interdependency among neural ensembles from the recorded mixtures; 3. Develop an open source software package that integrates the array processing algorithms developed under aim 1 with the data clustering algorithms developed under aim 2 and disseminate the package to the community; 4. Test and demonstrate the efficiency of these techniques and the integrity of the developed software on simulated and experimental data shared by investigators in the field. Upon completion of the proposed research activity, we anticipate to provide numerous users in the neuroscience community with novel tools for processing and analyzing their data with increased accuracy, maximized efficiency and sustained reliability in their behavioral experiments.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Exploratory/Developmental Grants Phase II (R33)
Project #
5R33NS054148-05
Application #
8004067
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Liu, Yuan
Project Start
2007-01-15
Project End
2012-12-31
Budget Start
2011-01-01
Budget End
2012-12-31
Support Year
5
Fiscal Year
2011
Total Cost
$311,568
Indirect Cost
Name
Michigan State University
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
193247145
City
East Lansing
State
MI
Country
United States
Zip Code
48824
Mohebi, Ali; Oweiss, Karim G (2014) A fully automated rodent conditioning protocol for sensorimotor integration and cognitive control experiments. J Vis Exp :
Daly, John; Liu, Jianbo; Aghagolzadeh, Mehdi et al. (2012) Optimal space-time precoding of artificial sensory feedback through mutichannel microstimulation in bi-directional brain-machine interfaces. J Neural Eng 9:065004
Kwon, Ki Yong; Eldawlatly, Seif; Oweiss, Karim (2012) NeuroQuest: a comprehensive analysis tool for extracellular neural ensemble recordings. J Neurosci Methods 204:189-201
Eldawlatly, Seif; Oweiss, Karim G (2011) Millisecond-timescale local network coding in the rat primary somatosensory cortex. PLoS One 6:e21649
Liu, Jianbo; Khalil, Hassan K; Oweiss, Karim G (2011) Neural feedback for instantaneous spatiotemporal modulation of afferent pathways in bi-directional brain-machine interfaces. IEEE Trans Neural Syst Rehabil Eng 19:521-33
Liu, Jianbo; Khalil, Hassan K; Oweiss, Karim G (2011) Model-based analysis and control of a network of basal ganglia spiking neurons in the normal and parkinsonian states. J Neural Eng 8:045002
Grosse-Wentrup, Moritz; Mattia, Donatella; Oweiss, Karim (2011) Using brain-computer interfaces to induce neural plasticity and restore function. J Neural Eng 8:025004
Aghagolzadeh, Mehdi; Eldawlatly, Seif; Oweiss, Karim (2010) Synergistic Coding by Cortical Neural Ensembles. IEEE Trans Inf Theory 56:875-899
Eldawlatly, Seif; Zhou, Yang; Jin, Rong et al. (2010) On the use of dynamic Bayesian networks in reconstructing functional neuronal networks from spike train ensembles. Neural Comput 22:158-89
Eldawlatly, Seif; Oweiss, Karim (2010) Causal neuronal networks provide functional signatures of stimulus encoding. Conf Proc IEEE Eng Med Biol Soc 2010:5460-3

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