Modeling Instantaneous Firing Rate of Deep Brain Stimulation Target Neuronal Ensembles in the Basal Ganglia and Thalamus

Tian, Yupeng; Murphy, Matthew J. H.; Steiner, Leon Amadeus; Kalia, Suneil K.; Hodaie, Mojgan; Lozano, Andrés M.; Hutchison, William D.; Popović, Miloš R.; Milosevic, Luka; Lankarany, Milad

doi:10.1016/j.neurom.2023.03.012

Cited by 3 publications

(25 citation statements)

References 65 publications

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“…In this work, we developed a mean-field firing rate network model that is consistent with the first principles, and also responds effectively to abrupt local input changes, i.e., the injection of DBS pulses to a local group of Vim neurons. We modeled the immediate impact of DBS pulses to the local Vim neural group as inducing synaptic release, and the synapses were characterized with the Tsodyks & Markram (TM) model 50 ; such modeling of DBS effects is consistent with previously established works 9,22,91 . Our whole model consists of the local neural group directly receiving DBS, and its interactions with the external excitatory and inhibitory neural groups; the three neural groups are mutually recurrent (Figure 1).…”

Section: Relationship With Other Network Modelsmentioning

confidence: 70%

“…We showed that modeling solely the excitatory effect of DBS is an insufficient approach in capturing the dynamics during high frequency Vim-DBS 22 . The network model emphasizing the dominant excitatory recurrent connections is known as a "Hebbian assembly" model 28 , which downplays the role of inhibitory nuclei 29,28,30 .…”

Section: Introductionmentioning

confidence: 99%

“…This transition is likely due to a combination of factors such as axonal conduction failure and the time-course of synaptic depression 20,9,21 . Recently, a firing rate model representing neural activities of a single population of neurons was developed to replicate instantaneous firing rates of different nuclei of the basal ganglia and thalamus in response to various frequencies of DBS 22 . The model appropriately fitted the firing rate response data during low frequency Vim-DBS, but it deviated from observations during high frequency Vim-DBS 22 .…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a firing rate model representing neural activities of a single population of neurons was developed to replicate instantaneous firing rates of different nuclei of the basal ganglia and thalamus in response to various frequencies of DBS 22 . The model appropriately fitted the firing rate response data during low frequency Vim-DBS, but it deviated from observations during high frequency Vim-DBS 22 . An improved network modeling approach that incorporates the synaptic contributions of recurrent inhibitory and excitatory axonal processes can be more informative in capturing the neural response dynamics to varying frequencies of DBS.…”

Section: Introductionmentioning

confidence: 99%

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Uncovering network mechanism underlying thalamic deep brain stimulation

Tian,

Bello,

Crompton

et al. 2023

Preprint

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Thalamic ventral intermediate nucleus (Vim) is the primary surgical target of deep brain stimulation (DBS) for reducing symptoms of essential tremor. In-vivo single unit recordings of patients with essential tremor revealed that low frequency Vim-DBS (≤50Hz) induces periodic excitatory responses and high-frequency Vim-DBS (≥100Hz) induces a transient excitatory response lasting for ≤600ms followed by a suppressed steady-state. Yet, the neural mechanisms that generate Vim firing rate in response to different DBS frequencies are not fully uncovered. Previously developed models of Vim neurons could not capture the full dynamics of Vim-DBS despite incorporating the dynamics of short-term synaptic plasticity. In this work, we developed a network rate model and a novel parameter optimization method to accurately track the instantaneous firing rate of Vim neurons in response to various DBS frequencies, ranging in low- and high-frequency (5 to 200Hz) Vim-DBS. We showed that the firing rate dynamics during high frequency Vim-DBS are best characterized when incorporating an inhibitory population into the network model. Further, we discovered that the Vim firing rate in response to varying frequencies of DBS pulses can be explained by abalanced amplificationmechanism, in which strong excitation (Vim) is stabilized by equally strong feedback inhibition. As a further validation of this work, we demonstrated similar behavior in a detailed biophysical model consisting of spiking neural networks in which the Vim neural-network can implement balanced amplification and explain in-vivo human Vim-DBS observations.Graphical abstract

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Section: Relationship With Other Network Modelsmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Uncovering network mechanism underlying thalamic deep brain stimulation

Tian,

Bello,

Crompton

et al. 2023

Preprint

Self Cite

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“…ζ(𝑥(𝑡)) is the standardization of 𝑥(𝑡); 𝑥̅ (𝑡) and 𝑠𝑑[𝑥(𝑡)] are the mean and standard deviation of 𝑥(𝑡), respectively. The polynomial order is 25 and {𝜑 0 , 𝜑 showed that the consistent model parameters fitted based on concatenated DBS frequencies in a certain range -in this case, [10,200] Hz -can be consistently applied to unobserved frequencies (e.g., 25 Hz and 180 Hz) in the same range [31]. Thus, we implement the polynomial estimated EMG as the biomarker to control the DBS frequencies in the range [10,200] Hz.…”

Section: Biomarker Identificationmentioning

confidence: 99%

Model-Based Closed-Loop Control of Thalamic Deep Brain Stimulation

Tian,

Saradhi,

Bello

et al. 2023

Preprint

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Closed-loop control of deep brain stimulation (DBS) is crucial for effective and automatic treatments of various neurological disorders like Parkinson’s disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists’ expertise and patients’ experience. The continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system utilizes a feedback biomarker/signal to track worsening (or improving) patient’s symptoms and offers several advantages compared to open-loop DBS. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying the DBS or symptoms, for example how DBS modulates dynamics of synaptic plasticity. In this work, we proposed a computational framework for development of a model-based DBS controller where a biophysically-reasonable model can describe the relationship between DBS and neural activity, and a polynomial-based approximation can estimate the relationship between the neural and behavioral activity. A controller is utilized in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. These DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given EMG recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed a biophysically-reasonable simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By utilizing a PID controller, we showed that a closed-loop system can track EMG signals and adjusts the stimulation frequency of Vim-DBS so that the power of EMG in [2, 200] Hz reaches a desired target. We demonstrated that our model-based closed-loop control system of Vim-DBS finds an appropriate DBS frequency that aligns well with clinical studies. Our model-based closed-loop system is adaptable to different control targets, highlighting its potential usability for different diseases and personalized systems.

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Model-based closed-loop control of thalamic deep brain stimulation

Tian,

Saradhi,

Bello

et al. 2024

Front. Netw. Physiol.

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Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson’s disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists’ expertise and patients’ experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients’ symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity.Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim–DBS are linked to symptomatic changes in EMG signals. By using a proportional–integral–derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim–DBS so that the power of EMG reaches a desired control target.Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

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Modeling Instantaneous Firing Rate of Deep Brain Stimulation Target Neuronal Ensembles in the Basal Ganglia and Thalamus

Cited by 3 publications

References 65 publications

Uncovering network mechanism underlying thalamic deep brain stimulation

Uncovering network mechanism underlying thalamic deep brain stimulation

Model-Based Closed-Loop Control of Thalamic Deep Brain Stimulation

Model-based closed-loop control of thalamic deep brain stimulation

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