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Characterizing the spiking dynamics of subthalamic nucleus neurons in Parkinson's disease using generalized linear models.

Eden UT, Gale JT, Amirnovin R, Eskandar EN - Front Integr Neurosci (2012)

Bottom Line: The model relates each neuron's spiking probability simultaneously to factors associated with movement planning and execution, directional selectivity, refractoriness, bursting, and oscillatory dynamics.The model indicated that while short-term history dependence related to refractoriness and bursting are most informative in predicting spiking activity, nearly all of the neurons analyzed have a structured pattern of long-term history dependence such that the spiking probability was reduced 20-30 ms and then increased 30-60 ms after a previous spike.This point process model provides a systematic framework for characterizing the dynamics of neuronal activity in STN.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, Boston University, Boston MA, USA.

ABSTRACT
Accurately describing the spiking patterns of neurons in the subthalamic nucleus (STN) of patients suffering from Parkinson's disease (PD) is important for understanding the pathogenesis of the disease and for achieving the maximum therapeutic benefit from deep brain stimulation (DBS). We analyze the spiking activity of 24 subthalamic neurons recorded in Parkinson's patients during a directed hand movement task by using a point process generalized linear model (GLM). The model relates each neuron's spiking probability simultaneously to factors associated with movement planning and execution, directional selectivity, refractoriness, bursting, and oscillatory dynamics. The model indicated that while short-term history dependence related to refractoriness and bursting are most informative in predicting spiking activity, nearly all of the neurons analyzed have a structured pattern of long-term history dependence such that the spiking probability was reduced 20-30 ms and then increased 30-60 ms after a previous spike. This suggests that the previously described oscillatory firing of neurons in the STN of Parkinson's patients during volitional movements is composed of a structured pattern of inhibition and excitation. This point process model provides a systematic framework for characterizing the dynamics of neuronal activity in STN.

No MeSH data available.


Related in: MedlinePlus

Visualizations of spiking activity. (A–C) Spike rasters for each movement direction, spike autocorrelation function, and spike power spectral density for one example neuron. (D) Mean and standard deviation of firing rate over trials for all neurons analyzed (blue dots). The red line indicates the expected relationship for a Poisson process.
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Figure 3: Visualizations of spiking activity. (A–C) Spike rasters for each movement direction, spike autocorrelation function, and spike power spectral density for one example neuron. (D) Mean and standard deviation of firing rate over trials for all neurons analyzed (blue dots). The red line indicates the expected relationship for a Poisson process.

Mentions: The firing properties of a single STN neuron during this reaching task are illustrated in Figures 3A–C. Figure 3A shows spike rasters of spiking relative to movement onset for trials in each direction. The neuron spikes with a high rate (over 100 Hz) for movements in each direction. By careful inspection, we observe a slight increase in spike rate at a time near movement onset (0 ms), but this increase is not dramatic and it is difficult to determine whether there is a significant difference for movements in each of the four directions. Figure 3B shows the autocorrelation function for this neuron's spiking. There is a clear negative correlation at lag 1 ms, which is expected because of the refractory period, and a clear positive correlation at lags 2–3 ms, suggesting a tendency to fire in rapid bursts. At longer lags, there appears to be some more subtle structure with slightly negative correlations between 20 and 30 ms and slightly positive correlations around 50 ms. This finding is corroborated by the spectral density estimator shown in Figure 3C, which identifies a large peak between 10 and 30 Hz, indicative of beta rhythm oscillatory activity.


Characterizing the spiking dynamics of subthalamic nucleus neurons in Parkinson's disease using generalized linear models.

Eden UT, Gale JT, Amirnovin R, Eskandar EN - Front Integr Neurosci (2012)

Visualizations of spiking activity. (A–C) Spike rasters for each movement direction, spike autocorrelation function, and spike power spectral density for one example neuron. (D) Mean and standard deviation of firing rate over trials for all neurons analyzed (blue dots). The red line indicates the expected relationship for a Poisson process.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3379030&req=5

Figure 3: Visualizations of spiking activity. (A–C) Spike rasters for each movement direction, spike autocorrelation function, and spike power spectral density for one example neuron. (D) Mean and standard deviation of firing rate over trials for all neurons analyzed (blue dots). The red line indicates the expected relationship for a Poisson process.
Mentions: The firing properties of a single STN neuron during this reaching task are illustrated in Figures 3A–C. Figure 3A shows spike rasters of spiking relative to movement onset for trials in each direction. The neuron spikes with a high rate (over 100 Hz) for movements in each direction. By careful inspection, we observe a slight increase in spike rate at a time near movement onset (0 ms), but this increase is not dramatic and it is difficult to determine whether there is a significant difference for movements in each of the four directions. Figure 3B shows the autocorrelation function for this neuron's spiking. There is a clear negative correlation at lag 1 ms, which is expected because of the refractory period, and a clear positive correlation at lags 2–3 ms, suggesting a tendency to fire in rapid bursts. At longer lags, there appears to be some more subtle structure with slightly negative correlations between 20 and 30 ms and slightly positive correlations around 50 ms. This finding is corroborated by the spectral density estimator shown in Figure 3C, which identifies a large peak between 10 and 30 Hz, indicative of beta rhythm oscillatory activity.

Bottom Line: The model relates each neuron's spiking probability simultaneously to factors associated with movement planning and execution, directional selectivity, refractoriness, bursting, and oscillatory dynamics.The model indicated that while short-term history dependence related to refractoriness and bursting are most informative in predicting spiking activity, nearly all of the neurons analyzed have a structured pattern of long-term history dependence such that the spiking probability was reduced 20-30 ms and then increased 30-60 ms after a previous spike.This point process model provides a systematic framework for characterizing the dynamics of neuronal activity in STN.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, Boston University, Boston MA, USA.

ABSTRACT
Accurately describing the spiking patterns of neurons in the subthalamic nucleus (STN) of patients suffering from Parkinson's disease (PD) is important for understanding the pathogenesis of the disease and for achieving the maximum therapeutic benefit from deep brain stimulation (DBS). We analyze the spiking activity of 24 subthalamic neurons recorded in Parkinson's patients during a directed hand movement task by using a point process generalized linear model (GLM). The model relates each neuron's spiking probability simultaneously to factors associated with movement planning and execution, directional selectivity, refractoriness, bursting, and oscillatory dynamics. The model indicated that while short-term history dependence related to refractoriness and bursting are most informative in predicting spiking activity, nearly all of the neurons analyzed have a structured pattern of long-term history dependence such that the spiking probability was reduced 20-30 ms and then increased 30-60 ms after a previous spike. This suggests that the previously described oscillatory firing of neurons in the STN of Parkinson's patients during volitional movements is composed of a structured pattern of inhibition and excitation. This point process model provides a systematic framework for characterizing the dynamics of neuronal activity in STN.

No MeSH data available.


Related in: MedlinePlus