Modeling deep brain stimulation evoked responses with phase oscillator networks

Realmuto, Jonathan; Vidmark, Jessica; Sanger, Terence D.

doi:10.1109/ner52421.2023.10123849

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Furthermore, they are not detected when stimulating and recording in the ventral intermediate nucleus of the thalamus of essential tremor patients [ 14 ]. However, similar DBS-induced EP waveforms have been recorded in the GPi of PD patients [ 12 , 13 ], as well as in the STN of dystonia patients [ 19 , 20 ]. These coupled observations suggests that these EP signals are explicitly associated with the subthalamo-pallidal circuit, but they are not necessarily associated with PD.…”

Section: Introductionmentioning

confidence: 75%

“…Schmidt et al. [ 13 ] referred to these signals as a “DBS Local Evoked Potentials”, Realmuto et al [ 19 ] used the term “Evoked Oscillatory Neural Responses,” while others have opted for the more general terms “Evoked Compound Activity” and “Evoked Compound Action Potentials” [18] , [34] . Moving forward, we propose that the research community should simply adopt the ERNA acronym for consistency and clarity, while acknowledging a more general definition of “Evoked Recurrent Neural Activity”.…”

Section: Standardizing Terminologymentioning

confidence: 99%

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Dissecting deep brain stimulation evoked neural activity in the basal ganglia

Noor,

Steina,

McIntyre

2024

Neurotherapeutics

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Section: Introductionmentioning

confidence: 75%

Section: Standardizing Terminologymentioning

confidence: 99%

Dissecting deep brain stimulation evoked neural activity in the basal ganglia

Noor,

Steina,

McIntyre

2024

Neurotherapeutics

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“…A recent modelling study was able to replicate some of the key ERNA characteristics over a time period of approximately 200ms using a phase oscillator network [50]. The Kuramoto network included delays between distinct sub-populations of oscillators, which would likely be a fundamental reason as to why the network was able to reproduce these evoked oscillatory responses.…”

Section: Hypothesised Mechanisms and Models Of Ernamentioning

confidence: 99%

Evoked Resonant Neural Activity Long-Term Dynamics can be Reproduced by a Computational Model with Vesicle Depletion

Sermon,

Wiest,

Tan

et al. 2024

Preprint

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Subthalamic deep brain stimulation (DBS) robustly generates high-frequency oscillations known as evoked resonant neural activity (ERNA). Recently the importance of ERNA has been demonstrated through its ability to predict the optimal DBS contact in the subthalamic nucleus in patients with Parkinson’s disease. However, the underlying mechanisms of ERNA are not well understood, and previous modelling efforts have not managed to reproduce the wealth of published data describing the dynamics of ERNA. Here, we therefore aim to present a minimal model capable of reproducing the characteristics of the slow ERNA dynamics published to date. We make biophysically-motivated modifications to the Kuramoto model and fit its parameters to the slow dynamics of ERNA obtained from data. We further validate the model against experimental data from Parkinson’s disease patients by simulating variable stimulation and medication states, as well as the response of individual neurons. Our results demonstrate that it is possible to reproduce the slow dynamics of ERNA with a single neuronal population, and, crucially, with vesicle depletion as the key mechanism behind the ERNA frequency decay. We provide a series of predictions from the model that could be the subject of future studies for further validation.Author SummaryERNA is a high amplitude response to stimulation of deep brain structures, with a frequency over twice that of the frequency of stimulation. While the underlying mechanisms of ERNA are still unclear, recent findings have demonstrated its importance as the best indicator of which stimulation contact to select for DBS therapy in patients with Parkinson’s disease. Previous modelling studies of ERNA focus on the immediate responses to stimulation (<200ms) and rely on interconnected neural structures and delays. Our work shows that the long-term (on the scale of one or more seconds) ERNA response to continuous stimulation can be modelled using a single neural structure. The proposed model also captures the long-term frequency and amplitude characteristics of ERNA with variable stimulation and medication paradigms. The key features of the model, in particular the depletion of vesicles carrying neurotransmitters between neurons by high-frequency stimulation, may provide insights into the underlying mechanisms of ERNA and inform future investigations into this neural response.

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An algorithm for automated detection of evoked potentials from polarity reversed electrical stimulations

Vidmark

Sanger

2023

Preprint

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BackgroundIn neurophysiological research involving neural responses to electrical stimuli, each recording must be searched for evoked potentials (EPs) prior to further analysis. Conducting this process manually is time consuming for the researcher and also inserts bias. However, automated detection methods often struggle to distinguish between artifacts and neural responses, which can have highly complex and varying shapes.MethodsWe have developed a novel algorithm for automated detection of polarity reversed EPs (ADPREP), which uses the knowledge that reversing the polarity of a pair of stimulating contacts reverses the sign of the stimulus artifact, but not that of the neural response. Hence, our algorithm searches for any positive correlation between the recordings from two polarity reversed stimulations, after removing any stimulus decay artifacts. If the peak-to-peak amplitude in a positively correlated region surpasses a user-defined threshold, the recording is labeled as an EP; otherwise, it is not. Neural recordings from deep brain nuclei and cortical regions during deep brain stimulation (DBS) in 28 pediatric patients with dystonia were used to test and prove the validity of the method.ResultsThe ADPREP algorithm is able to distinguish EPs of varying shapes and sizes with a high level of accuracy, as early as 0.35 ms after stimulation, despite large stimulus artifacts. The algorithm has proven useful in initial tests with DBS data in hundreds of stimulation/recording combinations within the basal ganglia and thalamic nuclei as well as from these deep brain nuclei to cortex, at stimulation frequencies up to 250 Hz.ConclusionOur automated EP detection algorithm can accurately detect DBS EPs in deep brain nuclei and cortex, and has promising applications in other stimulation and recording modalities that allow for polarity reversal of the recorded stimulus artifact. The algorithm successfully labels EPs of varying shapes and sizes, as early as a fraction of a millisecond after stimulation, in a range of stimulus frequencies and stimulation-recording pairs – even under large stimulus decay artifacts and in same-lead stimulation and recordings. As such, it is a great method to improve efficacy and minimize human bias, setting up for more reliable conclusions to be drawn from the data.

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Modeling deep brain stimulation evoked responses with phase oscillator networks

Cited by 3 publications

References 18 publications

Dissecting deep brain stimulation evoked neural activity in the basal ganglia

Dissecting deep brain stimulation evoked neural activity in the basal ganglia

Evoked Resonant Neural Activity Long-Term Dynamics can be Reproduced by a Computational Model with Vesicle Depletion

An algorithm for automated detection of evoked potentials from polarity reversed electrical stimulations

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