TY - GEN
T1 - Using point process models to describe rhythmic spiking in the subthalamic nucleus of Parkinson's patients
AU - Eden, Uri T.
AU - Amirnovin, Ramin
AU - Eskandar, Emad N.
PY - 2011
Y1 - 2011
N2 - Neurological disease is often associated with changes in firing activity in specific brain areas. Accurate statistical models of neural spiking can provide insight into the mechanisms by which the disease develops and clinical symptoms manifest. Point process theory provides a powerful framework for constructing, fitting, and evaluating the quality of neural spiking models. We illustrate an application of point process modeling to the problem of characterizing abnormal oscillatory firing patterns of neurons in the subthalamic nucleus (STN) of patients with Parkinson's disease (PD). We characterize the firing properties of these neurons by constructing conditional intensity models using spline basis functions that relate the spiking of each neuron to movement variables and the neuron's past firing history, both at short and long time scales. By calculating maximum likelihood estimators for all of the parameters and their significance levels, we are able to describe the relative propensity of aberrant STN spiking in terms of factors associated with voluntary movements, with intrinsic properties of the neurons, and factors that may be related to dysregulated network dynamics.
AB - Neurological disease is often associated with changes in firing activity in specific brain areas. Accurate statistical models of neural spiking can provide insight into the mechanisms by which the disease develops and clinical symptoms manifest. Point process theory provides a powerful framework for constructing, fitting, and evaluating the quality of neural spiking models. We illustrate an application of point process modeling to the problem of characterizing abnormal oscillatory firing patterns of neurons in the subthalamic nucleus (STN) of patients with Parkinson's disease (PD). We characterize the firing properties of these neurons by constructing conditional intensity models using spline basis functions that relate the spiking of each neuron to movement variables and the neuron's past firing history, both at short and long time scales. By calculating maximum likelihood estimators for all of the parameters and their significance levels, we are able to describe the relative propensity of aberrant STN spiking in terms of factors associated with voluntary movements, with intrinsic properties of the neurons, and factors that may be related to dysregulated network dynamics.
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U2 - 10.1109/IEMBS.2011.6090173
DO - 10.1109/IEMBS.2011.6090173
M3 - Conference contribution
C2 - 22254421
AN - SCOPUS:84861952972
SN - 9781424441211
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 757
EP - 760
BT - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Y2 - 30 August 2011 through 3 September 2011
ER -