Evoked resonant neural activity in subthalamic local field potentials reflects basal ganglia network dynamics

Wiest, Christoph; He, Shenghong; Duchet, Benoit; Pogosyan, Alek; Benjaber, Moaad; Denison, Timothy; Hasegawa, Harutomo; Ashkan, Keyoumars; Baig, Fahd; Bertaina, Ilaria; Morgante, Francesca; Pereira, Erlick; Torrecillos, Flavie; Tan, Huiling

doi:10.1016/j.nbd.2023.106019

Cited by 16 publications

(34 citation statements)

References 32 publications

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“…Figure 2(c) shows the ERNA distribution according to the Hjorth complexity and frequency. Interestingly, the ERNA frequency estimated by Hjorth parameter lies between 250 and 450 Hz, which is consistent with previous publications [15,30]. This result suggests that Hjorth frequency could be a useful surrogate of ERNA frequency when peak detection fails.…”

Section: Discussionsupporting

confidence: 91%

“…Previous studies have demonstrated the potential of beta [7,8], high-frequency oscillations [9,10] and gamma-band activity [11], but these spontaneously occurring signals are of small amplitude and are challenging to measure reliably. A recently discovered neuronal signal called evoked resonant neural activity (ERNA) has generated interest among researchers due to its robustness [12][13][14][15]. ERNA is a large neural signal, elicited by DBS stimulation pulses, which resembles a damped sinusoidal waveform (figure 1(b)).…”

Section: Introductionmentioning

confidence: 99%

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Detection of evoked resonant neural activity in Parkinson’s disease

Lee,

Ward,

Petoe

et al. 2024

J. Neural Eng.

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Objective: This study investigated a machine-learning approach to detect the presence of evoked resonant neural activity (ERNA) recorded during deep brain stimulation (DBS) of the subthalamic nucleus (STN) in people with Parkinson’s disease (PD). Methods: Seven binary classifiers were trained to distinguish ERNA from the background neural activity using eight different time-domain signal features. Results: Nested cross-validation revealed a strong classification performance of 99.1% accuracy, with 99.6% specificity and 98.7% sensitivity to detect ERNA. Using a semi-simulated ERNA dataset, the results show that a signal-to-noise ratio of 15 dB is required to maintain a 90% classifier sensitivity.Conclusion: ERNA detection is feasible with an appropriate combination of signal processing, feature extraction and classifier. Future work should consider reducing the computational complexity for use in real-time applications.Significance: The presence of ERNA can be used to indicate the location of a DBS electrode array during implantation surgery. The confidence score of the detector could be useful for assisting clinicians to adjust the position of the DBS electrode array inside/outside the STN.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Detection of evoked resonant neural activity in Parkinson’s disease

Lee,

Ward,

Petoe

et al. 2024

J. Neural Eng.

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“…We performed a statistical analysis to determine whether the frequency response differed significantly between medication states, taking the same approach as in the experimental study [6]. We used a non-parametric cluster-based permutation test with 1,000 permutations at each time point across the 15 different simulations for each medication state.…”

Section: Statistics For Erna Amplitude On and Off Medicationmentioning

confidence: 99%

“…ERNA is a series of high-frequency oscillations (HFOs, >200Hz) of high-amplitude in response to 70-180Hz stimulation, despite there being very little to no high frequency activity at the stimulation site pre-stimulation [1] (also see 4 in Fig 2A). ERNA has been observed in different neural structures, but so far only in the pallidal [2,3] and subthalamic regions [1,[4][5][6]. Furthermore, ERNA has also been characterised as a robust response across different devices, patient groups and stimulation conditions [1,[4][5][6].…”

Section: Introductionmentioning

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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“…from the thalamo-cortical loop in the case of DBS of the subthalamic nucleus). Our models were also not designed to reproduce the dynamic temporal activity that can be evoked by stimulation (such as the evoked resonant neural activity or ERNA in the case of DBS of the subthalamic nucleus [58][59][60]). These limitations should be carefully considered before translational clinical testing.…”

Section: Limitationsmentioning

confidence: 99%

How to entrain a selected neuronal rhythm but not others: open-loop dithered brain stimulation for selective entrainment

et al. 2023

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Objective. While brain stimulation therapies such as deep brain stimulation for Parkinson’s disease (PD) can be effective, they have yet to reach their full potential across neurological disorders. Entraining neuronal rhythms using rhythmic brain stimulation has been suggested as a new therapeutic mechanism to restore neurotypical behaviour in conditions such as chronic pain, depression, and Alzheimer’s disease. However, theoretical and experimental evidence indicate that brain stimulation can also entrain neuronal rhythms at sub- and super-harmonics, far from the stimulation frequency. Crucially, these counterintuitive effects could be harmful to patients, for example by triggering debilitating involuntary movements in PD. We therefore seek a principled approach to selectively promote rhythms close to the stimulation frequency, while avoiding potential harmful effects by preventing entrainment at sub- and super-harmonics. Approach. Our open-loop approach to selective entrainment, dithered stimulation, consists in adding white noise to the stimulation period. Main results. We theoretically establish the ability of dithered stimulation to selectively entrain a given brain rhythm, and verify its efficacy in simulations of uncoupled neural oscillators, and networks of coupled neural oscillators. Furthermore, we show that dithered stimulation can be implemented in neurostimulators with limited capabilities by toggling within a finite set of stimulation frequencies. Significance. Likely implementable across a variety of existing brain stimulation devices, dithering-based selective entrainment has potential to enable new brain stimulation therapies, as well as new neuroscientific research exploiting its ability to modulate higher-order entrainment.

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Evoked resonant neural activity in subthalamic local field potentials reflects basal ganglia network dynamics

Cited by 16 publications

References 32 publications

Detection of evoked resonant neural activity in Parkinson’s disease

Detection of evoked resonant neural activity in Parkinson’s disease

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

How to entrain a selected neuronal rhythm but not others: open-loop dithered brain stimulation for selective entrainment

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