Closing the loop of DBS using the beta oscillations in cortex

Liu, Chen; Zhao, Ge; Meng, Zihan; Zhou, Changsong; Zhu, Xiaodong; Zhang, Wei; Wang, Jiang; Li, Yong; Wu, Hao; Fietkiewicz, Chris; Loparo, Kenneth A.

doi:10.1007/s11571-021-09690-1

Cited by 10 publications

(19 citation statements)

References 41 publications

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“…More recent computational studies on adaptive techniques have focused on stimulation within the STN that desynchronizes the rhythmic bursting dynamics found in the STN-GPe loop [ 13 , 24 , 25 , 26 , 27 , 28 , 29 , 31 , 32 , 33 , 34 , 35 , 36 ]. In this work, we demonstrate that the two proposed aDBS mechanisms are able to deburst the parkinsonian state and improve the TC error index while stimulation is applied.…”

Section: Discussionmentioning

confidence: 99%

“…However, this method lacks an adaptive mechanism because the stimulation remains on the entire time once it is turned on. The primary recent computational studies in aDBS have focused on desynchronizing the STN neurons in parkinsonian networks [ 13 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Based on work conducted by Popovych and Tass in 2019, they concluded that using the LFP as a signal to control aDBS can be more efficient in suppressing abnormal synchronization than continuous stimulation [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Stimulations in a Biophysical Network Model of Parkinson’s Disease

Stojsavljevic

Guo

Macaluso

2023

IJMS

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Deep brain stimulation (DBS)—through a surgically implanted electrode to the subthalamic nucleus (STN)—has become a widely used therapeutic option for the treatment of Parkinson’s disease and other neurological disorders. The standard conventional high-frequency stimulation (HF) that is currently used has several drawbacks. To overcome the limitations of HF, researchers have been developing closed-loop and demand-controlled, adaptive stimulation protocols wherein the amount of current that is delivered is turned on and off in real-time in accordance with a biophysical signal. Computational modeling of DBS in neural network models is an increasingly important tool in the development of new protocols that aid researchers in animal and clinical studies. In this computational study, we seek to implement a novel technique of DBS where we stimulate the STN in an adaptive fashion using the interspike time of the neurons to control stimulation. Our results show that our protocol eliminates bursts in the synchronized bursting neuronal activity of the STN, which is hypothesized to cause the failure of thalamocortical neurons (TC) to respond properly to excitatory cortical inputs. Further, we are able to significantly decrease the TC relay errors, representing potential therapeutics for Parkinson’s disease.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Stimulations in a Biophysical Network Model of Parkinson’s Disease

Stojsavljevic

Guo

Macaluso

2023

IJMS

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“…Using results of the present study, we, in our future works, will develop firing rate network models to detect the mechanisms underlying the interacting neuronal circuits influenced by DBS. Neuronal network models can be implemented in closed-loop control systems to automatically adjust DBS frequencies based on the needs of individual patients 66, 68 .…”

Section: Discussionmentioning

confidence: 99%

Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

Tian

Murphy

Steiner

et al. 2022

Preprint

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Parkinson's disease (PD) is the second most common neurodegenerative disorder symptom, and is closely related to the dysfunction of the basal ganglia-thalamocortical network. Deep Brain Stimulation (DBS) is an effective treatment for suppressing PD motor symptoms; however, the underlying mechanisms of DBS remain elusive. A recent study demonstrated that different substructures of the basal ganglia and thalamus respond differently to various frequencies of DBS. Despite the capability of existing models in qualitatively interpreting experimental data, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated substructures - including subthalamic nucleus (STN), substantia nigra pars reticulate (SNr) and ventralis intermedius (Vim) - across different DBS frequencies. Here, we developed a novel mathematical model to represent the firing rate of nuclei receiving DBS (microstimulation), including STN, SNr and Vim - across different DBS frequencies. In our model, the DBS pulses are filtered through a synapse model and a transfer function to formulate the firing rate variability. To consistently fit the model in varying frequencies of DBS, we developed a novel parameter optimization method based on the concatenated data from all DBS frequencies. Our model accurately reproduces the firing rates observed and calculated from both experimental and synthetic data. The optimal model parameters are consistent across different DBS frequencies, and this consistency conforms to the relatively static synaptic structures in short durations of DBS. Our model can be potentially implemented in navigating the DBS parameter space and improves DBS clinical effects.

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“…[20][21][22] Many studies used the beta band (12-35 Hz) signals as the biomarker. [16,[23][24][25] However, electrophysiological investigations have shown that the pathological oscillations in the 4-8 Hz are also associated with tremor symptoms. [26,27] Thus, the closed-loop DBS only using the beta band (12-35 Hz) signals as the biomarker is insufficient.…”

Section: Introductionmentioning

confidence: 99%

Parkinsonian oscillations and their suppression by closed-loop deep brain stimulation based on fuzzy concept

Wei¹,

Bai²,

Wang³

et al. 2022

Chinese Phys. B

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This paper provides an adaptive closed-loop strategy for suppressing the pathological oscillations of the basal ganglia based on a variable universe fuzzy algorithm. The pathological basal ganglia oscillations in the theta (4-9Hz) and beta (12-35Hz) frequency bands have been demonstrated to be associated with the tremor and rigidity/bradykinesia symptoms in Parkinson's disease (PD). Although the clinical application of open-loop deep brain stimulation (DBS) is effective, the stimulation waveform with the fixed parameters cannot be self-adjusted as the disease progresses, and thus the stimulation effects go poor. To deal with this difficult problem, a variable universe fuzzy closed-loop strategy is proposed to modulate different PD states. This paper establishes a cortico-basal ganglia-thalamocortical network model to simulate pathological oscillations and test the control effect. The results suggest that the proposed closed-loop control strategy can accommodate the variation of brain states and symptoms, which may become an alternative method to administrate the symptoms in PD.

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Closing the loop of DBS using the beta oscillations in cortex

Cited by 10 publications

References 41 publications

Adaptive Stimulations in a Biophysical Network Model of Parkinson’s Disease

Adaptive Stimulations in a Biophysical Network Model of Parkinson’s Disease

Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

Parkinsonian oscillations and their suppression by closed-loop deep brain stimulation based on fuzzy concept

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