Evoked potentials reveal neural circuits engaged by human deep brain stimulation

Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics

Objective. Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson's disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics.Approach. Here, we develop a new robust adaptive closed-loop DBS method for regulating the Parkinsonian beta oscillatory dynamics of the cortex-BG-thalamus network. We first build an adaptive state-space model to quantify the dynamic, nonlinear, and non-stationary neural activity. We then construct an adaptive estimator to track the nonlinearity and non-stationarity in real time. We next design a robust controller to automatically determine the DBS frequency based on the estimated Parkinsonian neural state while reducing the system's sensitivity to high-frequency noise. We adopt and tune a biophysical cortex-BG-thalamus network model as an in-silico simulation testbed to generate nonlinear and non-stationary Parkinsonian neural dynamics for evaluating DBS methods.Main results. We find that under different nonlinear and non-stationary neural dynamics, our robust adaptive DBS method achieved accurate regulation of the BG Parkinsonian beta band oscillation power with small control error, bias, and deviation. Moreover, the accurate regulation generalizes across different therapeutic targets and consistently outperforms current on-off and LTI DBS methods.Significance. These results have implications for future designs of closed-loop DBS systems to treat PD.

Evoked resonant neural activity long-term dynamics can be reproduced by a computational model with vesicle depletion

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 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. Our results demonstrate that it is possible to reproduce the slow dynamics of ERNA (over hundreds of seconds) with a single neuronal population, and, crucially, with vesicle depletion as one of the key mechanisms behind the ERNA frequency decay in our model. We further validate the proposed model against experimental data from Parkinson's disease patients, where it captures the variations in ERNA frequency and amplitude in response to variable stimulation frequency, amplitude, and to stimulation pulse bursting. We provide a series of predictions from the model that could be the subject of future studies for further validation.

Unsupervised separation of nonlinearly mixed event-related potentials using manifold clustering and non-negative matrix factorization

Event-related potentials (ERPs) can quantify brain responses to reveal the neural mechanisms of sensory perception. However, ERPs often reflect nonlinear mixture responses to multiple sources of sensory stimuli, and an accurate separation of the response to each stimulus remains a challenge. This study aimed to separate the ERP into nonlinearly mixed source components specific to individual stimuli. We developed an unsupervised learning method based on clustering of manifold structures of mixture signals combined with channel optimization for signal source reconstruction using non-negative matrix factorization (NMF). Specifically, we first implemented manifold learning based on Local Tangent Space Alignment (LTSA) to extract the spatial manifold structure of multi-resolution sub-signals separated via wavelet packet transform. We then used fuzzy entropy to extract the dynamical process of the manifold structures and performed a k-means clustering to separate different sources. Lastly, we used NMF to obtain the optimal contributions of multiple channels to ensure accurate source reconstructions. We evaluated our developed approach using a simulated ERP dataset with known ground truth of two components of ERP mixture signals. Our results show that the correlation coefficient between the reconstructed source signal and the true source signal was 92.8 % and that the separation accuracy in ERP amplitude was 91.6 %. The results show that our unsupervised separation approach can accurately separate ERP signals from nonlinear mixture source components. The outcomes provide a promising way to isolate brain responses to multiple stimulus sources during multisensory perception.

Neural pathway activation in the subthalamic region depends on stimulation polarity

Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD); however, there is limited understanding of which subthalamic pathways are recruited in response to stimulation. Here, by focusing on the polarity of the stimulus waveform (cathodic vs. anodic), our goal was to elucidate biophysical mechanisms that underlie electrical stimulation in the human brain. In clinical studies, cathodic stimulation more easily triggers behavioral responses, but anodic DBS broadens the therapeutic window. This suggests that neural pathways involved respond preferentially depending on stimulus polarity. To experimentally compare the activation of therapeutically relevant pathways during cathodic and anodic subthalamic nucleus (STN) DBS, pathway activation was quantified by measuring evoked potentials resulting from antidromic or orthodromic activation in 15 PD patients undergoing DBS implantation. Cortical evoked potentials (cEP) were recorded using subdural electrocorticography, DBS local evoked potentials (DLEP) were recorded from non-stimulating contacts and EMG activity was recorded from arm and face muscles. We measured: 1) the amplitude of short-latency cEP, previously demonstrated to reflect activation of the cortico-STN hyperdirect pathway, 2) DLEP amplitude thought to reflect activation of STN-globus pallidus (GP) pathway, and 3) amplitudes of very short-latency cEP and motor evoked potentials (mEP) for activation of cortico-spinal/bulbar tract (CSBT). We constructed recruitment and strength-duration curves for each EP/pathway to compare the excitability for different stimulation polarities. We compared experimental data with the most advanced DBS computational models. Our results provide experimental evidence that subcortical cathodic and anodic stimulation activate the same pathways in the STN region and that cathodic stimulation is in general more efficient. However, relative efficiency varies for different pathways so that anodic stimulation is the least efficient in activating CSBT, more efficient in activating the HDP and as efficient as cathodic in activating STN-GP pathway. Our experiments confirm biophysical model predictions regarding neural activations in the central nervous system and provide evidence that stimulus polarity has differential effects on passing axons, terminal synapses, and local neurons. Comparison of experimental results with clinical DBS studies provides further evidence that the hyperdirect pathway may be involved in the therapeutic mechanisms of DBS.

Neural signatures of indirect pathway activity during subthalamic stimulation in Parkinson's disease

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) produces an electrophysiological signature called evoked resonant neural activity (ERNA); a high-frequency oscillation that has been linked to treatment efficacy. However, the single-neuron and synaptic bases of ERNA are unsubstantiated. This study proposes that ERNA is a subcortical neuronal circuit signature of DBS-mediated engagement of the basal ganglia indirect pathway network. In people with Parkinson's disease, we: (i) showed that each peak of the ERNA waveform is associated with temporally-locked neuronal inhibition in the STN; (ii) characterized the temporal dynamics of ERNA; (iii) identified a putative mesocircuit architecture, embedded with empirically-derived synaptic dynamics, that is necessary for the emergence of ERNA in silico; (iv) localized ERNA to the dorsal STN in electrophysiological and normative anatomical space; (v) used patient-wise hotspot locations to assess spatial relevance of ERNA with respect to DBS outcome; and (vi) characterized the local fiber activation profile associated with the derived group-level ERNA hotspot.

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

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.

Dissecting deep brain stimulation evoked neural activity in the basal ganglia

Deep brain stimulation (DBS) is an established therapeutic tool for the treatment of Parkinson's disease (PD). The mechanisms of DBS for PD are likely rooted in modulation of the subthalamo-pallidal network. However, it can be difficult to electrophysiologically interrogate that network in human patients. The recent identification of large amplitude evoked potential (EP) oscillations from DBS in the subthalamic nucleus (STN) or globus pallidus internus (GPi) are providing new scientific opportunities to expand understanding of human basal ganglia network activity. In turn, the goal of this review is to provide a summary of DBS-induced EPs in the basal ganglia and attempt to explain various components of the EP waveforms from their likely network origins. Our analyses suggest that DBS-induced antidromic activation of globus pallidus externus (GPe) is a key driver of these oscillatory EPs, independent of stimulation location (i.e. STN or GPi). This suggests a potentially more important role for GPe in the mechanisms of DBS for PD than typically assumed. And from a practical perspective, DBS EPs are poised to become clinically useful electrophysiological biomarker signals for verification of DBS target engagement.

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

OBJECTIVE

Deep brain stimulation (DBS) is an effective treatment for movement disorders, including Parkinson disease and essential tremor. However, the underlying mechanisms of DBS remain elusive. Despite the capability of existing models in interpreting experimental data qualitatively, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated nuclei-including subthalamic nucleus (STN), substantia nigra pars reticulata (SNr), and ventral intermediate nucleus (Vim)-across different DBS frequencies.

MATERIALS AND METHODS

Both synthetic and experimental data were used in the model fitting; the synthetic data were generated by an established spiking neuron model that was reported in our previous work, and the experimental data were provided using single-unit microelectrode recordings (MERs) during DBS (microelectrode stimulation). Based on these data, we developed a novel mathematical model to represent the firing rate of neurons receiving DBS, including neurons in STN, SNr, and Vim-across different DBS frequencies. In our model, the DBS pulses were filtered through a synapse model and a nonlinear transfer function to formulate the firing rate variability. For each DBS-targeted nucleus, we fitted a single set of optimal model parameters consistent across varying DBS frequencies.

RESULTS

Our model accurately reproduced the firing rates observed and calculated from both synthetic and experimental data. The optimal model parameters were consistent across different DBS frequencies.

CONCLUSIONS

The result of our model fitting was in agreement with experimental single-unit MER data during DBS. Reproducing neuronal firing rates of different nuclei of the basal ganglia and thalamus during DBS can be helpful to further understand the mechanisms of DBS and to potentially optimize stimulation parameters based on their actual effects on neuronal activity.

Detection of evoked resonant neural activity in Parkinson's disease

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.Approach. Seven binary classifiers were trained to distinguish ERNA from the background neural activity using eight different time-domain signal features.Main 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. 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.

Single-pulse electrical stimulation artifact removal using the novel matching pursuit-based artifact reconstruction and removal method (MPARRM)

Objective.Single-pulse electrical stimulation (SPES) has been widely used to probe effective connectivity. However, analysis of the neural response is often confounded by stimulation artifacts. We developed a novel matching pursuit-based artifact reconstruction and removal method (MPARRM) capable of removing artifacts from stimulation-artifact-affected electrophysiological signals.Approach.To validate MPARRM across a wide range of potential stimulation artifact types, we performed a bench-top experiment in which we suspended electrodes in a saline solution to generate 110 types of real-world stimulation artifacts. We then added the generated stimulation artifacts to ground truth signals (stereoelectroencephalography signals from nine human subjects recorded during a receptive speech task), applied MPARRM to the combined signal, and compared the resultant denoised signal with the ground truth signal. We further applied MPARRM to artifact-affected neural signals recorded from the hippocampus while performing SPES on the ipsilateral basolateral amygdala in nine human subjects.Main results.MPARRM could remove stimulation artifacts without introducing spectral leakage or temporal spread. It accommodated variable stimulation parameters and recovered the early response to SPES within a wide range of frequency bands. Specifically, in the early response period (5-10 ms following stimulation onset), we found that the broadband gamma power (70-170 Hz) of the denoised signal was highly correlated with the ground truth signal (R=0.98±0.02, Pearson), and the broadband gamma activity of the denoised signal faithfully revealed the responses to the auditory stimuli within the ground truth signal with94%±1.47%sensitivity and99%±1.01%specificity. We further found that MPARRM could reveal the expected temporal progression of broadband gamma activity along the anterior-posterior axis of the hippocampus in response to the ipsilateral amygdala stimulation.Significance.MPARRM could faithfully remove SPES artifacts without confounding the electrophysiological signal components, especially during the early-response period. This method can facilitate the understanding of the neural response mechanisms of SPES.

Neuronal and synaptic adaptations underlying the benefits of deep brain stimulation for Parkinson's disease

Deep brain stimulation (DBS) is a well-established and effective treatment for patients with advanced Parkinson's disease (PD), yet its underlying mechanisms remain enigmatic. Optogenetics, primarily conducted in animal models, provides a unique approach that allows cell type- and projection-specific modulation that mirrors the frequency-dependent stimulus effects of DBS. Opto-DBS research in animal models plays a pivotal role in unraveling the neuronal and synaptic adaptations that contribute to the efficacy of DBS in PD treatment. DBS-induced neuronal responses rely on a complex interplay between the distributions of presynaptic inputs, frequency-dependent synaptic depression, and the intrinsic excitability of postsynaptic neurons. This orchestration leads to conversion of firing patterns, enabling both antidromic and orthodromic modulation of neural circuits. Understanding these mechanisms is vital for decoding position- and programming-dependent effects of DBS. Furthermore, patterned stimulation is emerging as a promising strategy yielding long-lasting therapeutic benefits. Research on the neuronal and synaptic adaptations to DBS may pave the way for the development of more enduring and precise modulation patterns. Advanced technologies, such as adaptive DBS or directional electrodes, can also be integrated for circuit-specific neuromodulation. These insights hold the potential to greatly improve the effectiveness of DBS and advance PD treatment to new levels.

Neurophysiological mechanisms of deep brain stimulation across spatiotemporal resolutions

Deep brain stimulation is a neuromodulatory treatment for managing the symptoms of Parkinson's disease and other neurological and psychiatric disorders. Electrodes are chronically implanted in disease-relevant brain regions and pulsatile electrical stimulation delivery is intended to restore neurocircuit function. However, the widespread interest in the application and expansion of this clinical therapy has preceded an overarching understanding of the neurocircuit alterations invoked by deep brain stimulation. Over the years, various forms of neurophysiological evidence have emerged which demonstrate changes to brain activity across spatiotemporal resolutions; from single neuron, to local field potential, to brain-wide cortical network effects. Though fruitful, such studies have often led to debate about a singular putative mechanism. In this Update we aim to produce an integrative account of complementary instead of mutually exclusive neurophysiological effects to derive a generalizable concept of the mechanisms of deep brain stimulation. In particular, we offer a critical review of the most common historical competing theories, an updated discussion on recent literature from animal and human neurophysiological studies, and a synthesis of synaptic and network effects of deep brain stimulation across scales of observation, including micro-, meso- and macroscale circuit alterations.

Temporally non-regular patterns of deep brain stimulation (DBS) enhance assessment of evoked potentials while maintaining motor symptom management in Parkinson's disease (PD)

BACKGROUND

Traditional deep brain stimulation (DBS) at fixed regular frequencies (>100 Hz) is effective in treating motor symptoms of Parkinson's disease (PD). Temporally non-regular patterns of DBS are a new parameter space that may help increase efficacy and efficiency.

OBJECTIVE

To compare the effects of temporally non-regular patterns of DBS to traditional regularly-spaced pulses.

METHODS

We simultaneously recorded local field potentials (LFP) and monitored motor symptoms (tremor and bradykinesia) in persons with PD during DBS in subthalamic nucleus (STN). We quantified both oscillatory activity and DBS local evoked potentials (DLEPs) from the LFP.

RESULTS

Temporally non-regular patterns were as effective as traditional pulse patterns in modulating motor symptoms, oscillatory activity, and DLEPs. Moreover, one of our novel patterns enabled recording of longer duration DLEPs during clinically effective stimulation.

CONCLUSIONS

Stimulation gaps of 50 ms can be used to increase efficiency and to enable regular assessment of long-duration DLEPs while maintaining effective symptom management. This may be a promising paradigm for closed-loop DBS with biomarker assessment during the gaps.

Low-frequency deep brain stimulation reveals resonant beta-band evoked oscillations in the pallidum of Parkinson's Disease patients

Introduction

Evidence suggests that spontaneous beta band (11-35 Hz) oscillations in the basal ganglia thalamocortical (BGTC) circuit are linked to Parkinson's disease (PD) pathophysiology. Previous studies on neural responses in the motor cortex evoked by electrical stimulation in the subthalamic nucleus have suggested that circuit resonance may underlie the generation of spontaneous and stimulation-evoked beta oscillations in PD. Whether these stimulation-evoked, resonant oscillations are present across PD patients in the internal segment of the globus pallidus (GPi), a primary output nucleus in the BGTC circuit, is yet to be determined.

Methods

We characterized spontaneous and stimulation-evoked local field potentials (LFPs) in the GPi of four PD patients (five hemispheres) using deep brain stimulation (DBS) leads externalized after DBS implantation surgery.

Results

Our analyses show that low-frequency (2-4 Hz) stimulation in the GPi evoked long-latency (>50 ms) beta-band neural responses in the GPi in 4/5 hemispheres. We demonstrated that neural sources generating both stimulation-evoked and spontaneous beta oscillations were correlated in their frequency content and spatial localization.

Discussion

Our results support the hypothesis that the same neuronal population and resonance phenomenon in the BGTC circuit generates both spontaneous and evoked pallidal beta oscillations. These data also support the development of closed-loop control systems that modulate the GPi spontaneous oscillations across PD patients using beta band stimulation-evoked responses.

Synaptic network structure shapes cortically evoked spatio-temporal responses of STN and GPe neurons in a computational model

Introduction

The basal ganglia (BG) are involved in motor control and play an essential role in movement disorders such as hemiballismus, dystonia, and Parkinson's disease. Neurons in the motor part of the BG respond to passive movement or stimulation of different body parts and to stimulation of corresponding cortical regions. Experimental evidence suggests that the BG are organized somatotopically, i.e., specific areas of the body are associated with specific regions in the BG nuclei. Signals related to the same body part that propagate along different pathways converge onto the same BG neurons, leading to characteristic shapes of cortically evoked responses. This suggests the existence of functional channels that allow for the processing of different motor commands or information related to different body parts in parallel. Neurological disorders such as Parkinson's disease are associated with pathological activity in the BG and impaired synaptic connectivity, together with reorganization of somatotopic maps. One hypothesis is that motor symptoms are, at least partly, caused by an impairment of network structure perturbing the organization of functional channels.

Methods

We developed a computational model of the STN-GPe circuit, a central part of the BG. By removing individual synaptic connections, we analyzed the contribution of signals propagating along different pathways to cortically evoked responses. We studied how evoked responses are affected by systematic changes in the network structure. To quantify the BG's organization in the form of functional channels, we suggested a two-site stimulation protocol.

Results

Our model reproduced the cortically evoked responses of STN and GPe neurons and the contributions of different pathways suggested by experimental studies. Cortical stimulation evokes spatio-temporal response patterns that are linked to the underlying synaptic network structure. Our two-site stimulation protocol yielded an approximate functional channel width.

Discussion/conclusion

The presented results provide insight into the organization of BG synaptic connectivity, which is important for the development of computational models. The synaptic network structure strongly affects the processing of cortical signals and may impact the generation of pathological rhythms. Our work may motivate further experiments to analyze the network structure of BG nuclei and their organization in functional channels.

Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives

Parkinson's disease (PD) is a neurodegenerative disease with a heavy burden on patients, families, and society. Deep brain stimulation (DBS) can improve the symptoms of PD patients for whom medication is insufficient. However, current open-loop uninterrupted conventional DBS (cDBS) has inherent limitations, such as adverse effects, rapid battery consumption, and a need for frequent parameter adjustment. To overcome these shortcomings, adaptive DBS (aDBS) was proposed to provide responsive optimized stimulation for PD. This topic has attracted scientific interest, and a growing body of preclinical and clinical evidence has shown its benefits. However, both achievements and challenges have emerged in this novel field. To date, only limited reviews comprehensively analyzed the full framework and procedures for aDBS implementation. Herein, we review current preclinical and clinical data on aDBS for PD to discuss the full procedures for its achievement and to provide future perspectives on this treatment.

Insights and opportunities for deep brain stimulation as a brain circuit intervention

Deep brain stimulation (DBS) is an effective treatment and has provided unique insights into the dynamic circuit architecture of brain disorders. This Review illustrates our current understanding of the pathophysiology of movement disorders and their underlying brain circuits that are modulated with DBS. It proposes principles of pathological network synchronization patterns like beta activity (13-35 Hz) in Parkinson's disease. We describe alterations from microscale including local synaptic activity via modulation of mesoscale hypersynchronization to changes in whole-brain macroscale connectivity. Finally, an outlook on advances for clinical innovations in next-generation neurotechnology is provided: from preoperative connectomic targeting to feedback controlled closed-loop adaptive DBS as individualized network-specific brain circuit interventions.

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

Evoked resonant neural activity (ERNA) is induced by subthalamic deep brain stimulation (DBS) and was recently suggested as a marker of lead placement and contact selection in Parkinson's disease. Yet, its underlying mechanisms and how it is modulated by stimulation parameters are unclear. Here, we recorded local field potentials from 27 Parkinson's disease patients, while leads were externalised to scrutinise the ERNA. First, we show that ERNA in the time series waveform and spectrogram likely represent the same activity, which was contested before. Second, our results show that the ERNA has fast and slow dynamics during stimulation, consistent with the synaptic failure hypothesis. Third, we show that ERNA parameters are modulated by different DBS frequencies, intensities, medication states and stimulation modes (continuous DBS vs. adaptive DBS). These results suggest the ERNA might prove useful as a predictor of the best DBS frequency and lowest effective intensity in addition to contact selection. Changes with levodopa and DBS mode suggest that the ERNA may indicate the state of the cortico-basal ganglia circuit making it a putative biomarker to track clinical state in adaptive DBS.

Evolving characterization of the human hyperdirect pathway

The hyperdirect pathway (HDP) represents the main glutamatergic input to the subthalamic nucleus (STN), through which the motor and prefrontal cerebral cortex can modulate basal ganglia activity. Further, direct activation of the motor HDP is thought to be an important component of therapeutic deep brain stimulation (DBS), mediating the disruption of pathological oscillations. Alternatively, unintended recruitment of the prefrontal HDP may partly explain some cognitive side effects of DBS therapy. Previous work describing the HDP has focused on non-human primate (NHP) histological pathway tracings, diffusion-weighted MRI analysis of human white matter, and electrophysiology studies involving paired cortical recordings with DBS. However, none of these approaches alone yields a complete understanding of the complexities of the HDP. As such, we propose that generative modeling methods hold promise to bridge anatomy and physiology results, from both NHPs and humans, into a more detailed representation of the human HDP. Nonetheless, numerous features of the HDP remain to be experimentally described before model-based methods can simulate corticosubthalamic activity with a high degree of scientific detail. Therefore, the goals of this review are to examine the experimental evidence for HDP projections from across the primate neocortex and discuss new data which are required to improve the utility of anatomical and biophysical models of the human corticosubthalamic system.

Subthalamic Nucleus Stimulation-Induced Local Field Potential Changes in Dystonia

BACKGROUND

Subthalamic nucleus (STN) stimulation is an effective treatment for Parkinson's disease and induced local field potential (LFP) changes that have been linked with clinical improvement. STN stimulation has also been used in dystonia although the internal globus pallidus is the standard target where theta power has been suggested as a physiomarker for adaptive stimulation.

OBJECTIVE

We aimed to explore if enhanced theta power was also present in STN and if stimulation-induced spectral changes that were previously reported for Parkinson's disease would occur in dystonia.

METHODS

We recorded LFPs from 7 patients (12 hemispheres) with isolated craniocervical dystonia whose electrodes were placed such that inferior, middle, and superior contacts covered STN, zona incerta, and thalamus.

RESULTS

We did not observe prominent theta power in STN at rest. STN stimulation induced similar spectral changes in dystonia as in Parkinson's disease, such as broadband power suppression, evoked resonant neural activity (ERNA), finely-tuned gamma oscillations, and an increase in aperiodic exponents in STN-LFPs. Both power suppression and ERNA localize to STN. Based on this, single-pulse STN stimulation elicits evoked neural activities with largest amplitudes in STN, which are relayed to the zona incerta and thalamus with changing characteristics as the distance from STN increases.

CONCLUSIONS

Our results show that STN stimulation-induced spectral changes are a nondisease-specific response to high-frequency stimulation, which can serve as placement markers for STN. This broadens the scope of STN stimulation and makes it an option for other disorders with excessive oscillatory peaks in STN. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Classification of electrically-evoked potentials in the parkinsonian subthalamic nucleus region

Electrically evoked compound action potentials (ECAPs) generated in the subthalamic nucleus (STN) contain features that may be useful for titrating deep brain stimulation (DBS) therapy for Parkinson's disease. Delivering a strong therapeutic effect with DBS therapies, however, relies on selectively targeting neural pathways to avoid inducing side effects. In this study, we investigated the spatiotemporal features of ECAPs in and around the STN across parameter sweeps of stimulation current amplitude, pulse width, and electrode configuration, and used a linear classifier of ECAP responses to predict electrode location. Four non-human primates were implanted unilaterally with either a directional (n = 3) or non-directional (n = 1) DBS lead targeting the sensorimotor STN. ECAP responses were characterized by primary features (within 1.6 ms after a stimulus pulse) and secondary features (between 1.6 and 7.4 ms after a stimulus pulse). Using these features, a linear classifier was able to accurately differentiate electrodes within the STN versus dorsal to the STN in all four subjects. ECAP responses varied systematically with recording and stimulating electrode locations, which provides a subject-specific neuroanatomical basis for selecting electrode configurations in the treatment of Parkinson's disease with DBS therapy.

Electrophysiologic Evidence That Directional Deep Brain Stimulation Activates Distinct Neural Circuits in Patients With Parkinson Disease

OBJECTIVES

Deep brain stimulation (DBS) delivered via multicontact leads implanted in the basal ganglia is an established therapy to treat Parkinson disease (PD). However, the different neural circuits that can be modulated through stimulation on different DBS contacts are poorly understood. Evidence shows that electrically stimulating the subthalamic nucleus (STN) causes a therapeutic effect through antidromic activation of the hyperdirect pathway-a monosynaptic connection from the cortex to the STN. Recent studies suggest that stimulating the substantia nigra pars reticulata (SNr) may improve gait. The advent of directional DBS leads now provides a spatially precise means to probe these neural circuits and better understand how DBS affects distinct neural networks.

MATERIALS AND METHODS

We measured cortical evoked potentials (EPs) using electroencephalography (EEG) in response to low-frequency DBS using the different directional DBS contacts in eight patients with PD.

RESULTS

A short-latency EP at 3 milliseconds originating from the primary motor cortex appeared largest in amplitude when stimulating DBS contacts closest to the dorsolateral STN (p < 0.001). A long-latency EP at 10 milliseconds originating from the premotor cortex appeared strongest for DBS contacts closest to the SNr (p < 0.0001).

CONCLUSIONS

Our results show that at the individual patient level, electrical stimulation of different nuclei produces distinct EP signatures. Our approach could be used to identify the functional location of each DBS contact and thus help patient-specific DBS programming.

CLINICAL TRIAL REGISTRATION

The ClinicalTrials.gov registration number for the study is NCT04658641.

Globus pallidus internus deep brain stimulation evokes resonant neural activity in Parkinson's disease

Globus pallidus internus deep brain stimulation is an established therapy for patients with medication-refractory Parkinson's disease. Clinical outcomes are highly dependent on applying stimulation to precise locations in the brain. However, robust neurophysiological markers are needed to determine the optimal electrode location and to guide postoperative stimulation parameter selection. In this study, we evaluated evoked resonant neural activity in the pallidum as a potential intraoperative marker to optimize targeting and stimulation parameter selection to improve outcomes of deep brain stimulation for Parkinson's disease. Intraoperative local field potential recordings were acquired in 22 patients with Parkinson's disease undergoing globus pallidus internus deep brain stimulation implantation (N = 27 hemispheres). A control group of patients undergoing implantation in the subthalamic nucleus (N = 4 hemispheres) for Parkinson's disease or the thalamus for essential tremor (N = 9 patients) were included for comparison. High-frequency (135 Hz) stimulation was delivered from each electrode contact sequentially while recording the evoked response from the other contacts. Low-frequency stimulation (10 Hz) was also applied as a comparison. Evoked resonant neural activity features, including amplitude, frequency and localization were measured and analysed for correlation with empirically derived postoperative therapeutic stimulation parameters. Pallidal evoked resonant neural activity elicited by stimulation in the globus pallidus internus or externus was detected in 26 of 27 hemispheres and varied across hemispheres and across stimulating contacts within individual hemispheres. Bursts of high-frequency stimulation elicited evoked resonant neural activity with similar amplitudes (P = 0.9) but a higher frequency (P = 0.009) and a higher number of peaks (P = 0.004) than low-frequency stimulation. We identified a 'hotspot' in the postero-dorsal pallidum where stimulation elicited higher evoked resonant neural activity amplitudes (P < 0.001). In 69.6% of hemispheres, the contact that elicited the maximum amplitude intraoperatively matched the contact empirically selected for chronic therapeutic stimulation by an expert clinician after 4 months of programming sessions. Pallidal and subthalamic nucleus evoked resonant neural activity were similar except for lower pallidal amplitudes. No evoked resonant neural activity was detected in the essential tremor control group. Given its spatial topography and correlation with postoperative stimulation parameters empirically selected by expert clinicians, pallidal evoked resonant neural activity shows promise as a potential marker to guide intraoperative targeting and to assist the clinician with postoperative stimulation programming. Importantly, evoked resonant neural activity may also have the potential to guide directional and closed-loop deep brain stimulation programming for Parkinson's disease.

A convergent subcortical signature to explain the common efficacy of subthalamic and pallidal deep brain stimulation

This scientific commentary refers to 'Globus pallidus internus deep brain stimulation evokes resonant neural activity in Parkinson's disease', by Johnson et al. (https://doi.org/10.1093/braincomms/fcad025).

Deep brain stimulation on the external segment of the globus pallidus improves the electrical activity of internal segment of globus pallidus in a rat model of Parkinson's disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by the progressive degeneration of neurons in the substantia nigra pars compacta. Deep brain stimulation (DBS) is an effective treatment for PD cardinal motor symptoms. DBS of GPe has been recognized as an effective treatment option for motor symptoms of PD, but the mechanism is still essentially unknown. To investigate the impact of DBS in the external segment of globus pallidus (GPe) on the pathway of the basal ganglia (BG), we recorded the electrical activities of single neurons and local field potential (LFP) of the internal segment of globus pallidus (GPi). The results showed that the firing rate of GPi neurons in the 6-OHDA lesioned rats returned to the normal level after GPe-DBS for two weeks. Moreover, the CV value of GPi neurons is significantly lower than that in the PD group. The different frequency bands of GPi LFP in PD rats have improved correspondingly. These findings indicate that the improvement of the electrical activity of GPi by GPe-DBS in PD rats may be an important electrophysiological mechanism for treating PD.

The impact of pulse timing on cortical and subthalamic nucleus deep brain stimulation evoked potentials

The impact of pulse timing is an important factor in our understanding of how to effectively modulate the basal ganglia thalamocortical (BGTC) circuit. Single pulse low-frequency DBS-evoked potentials generated through electrical stimulation of the subthalamic nucleus (STN) provide insight into circuit activation, but how the long-latency components change as a function of pulse timing is not well-understood. We investigated how timing between stimulation pulses delivered in the STN region influence the neural activity in the STN and cortex. DBS leads implanted in the STN of five patients with Parkinson's disease were temporarily externalized, allowing for the delivery of paired pulses with inter-pulse intervals (IPIs) ranging from 0.2 to 10 ms. Neural activation was measured through local field potential (LFP) recordings from the DBS lead and scalp EEG. DBS-evoked potentials were computed using contacts positioned in dorsolateral STN as determined through co-registered post-operative imaging. We quantified the degree to which distinct IPIs influenced the amplitude of evoked responses across frequencies and time using the wavelet transform and power spectral density curves. The beta frequency content of the DBS evoked responses in the STN and scalp EEG increased as a function of pulse-interval timing. Pulse intervals <1.0 ms apart were associated with minimal to no change in the evoked response. IPIs from 1.5 to 3.0 ms yielded a significant increase in the evoked response, while those >4 ms produced modest, but non-significant growth. Beta frequency activity in the scalp EEG and STN LFP response was maximal when IPIs were between 1.5 and 4.0 ms. These results demonstrate that long-latency components of DBS-evoked responses are pre-dominantly in the beta frequency range and that pulse interval timing impacts the level of BGTC circuit activation.

Persistent synaptic inhibition of the subthalamic nucleus by high frequency stimulation

BACKGROUND

Deep brain stimulation (DBS) provides symptomatic relief in a growing number of neurological indications, but local synaptic dynamics in response to electrical stimulation that may relate to its mechanism of action have not been fully characterized.

OBJECTIVE

The objectives of this study were to (1) study local synaptic dynamics during high frequency extracellular stimulation of the subthalamic nucleus (STN), and (2) compare STN synaptic dynamics with those of the neighboring substantia nigra pars reticulata (SNr).

METHODS

Two microelectrodes were advanced into the STN and SNr of patients undergoing DBS surgery for Parkinson's disease (PD). Neuronal firing and evoked field potentials (fEPs) were recorded with one microelectrode during stimulation from an adjacent microelectrode.

RESULTS

Inhibitory fEPs could be discerned within the STN and their amplitudes predicted bidirectional effects on neuronal firing (p = .013). There were no differences between STN and SNr inhibitory fEP dynamics at low stimulation frequencies (p > .999). However, inhibitory neuronal responses were sustained over time in STN during high frequency stimulation but not in SNr (p < .001) where depression of inhibitory input was coupled with a return of neuronal firing (p = .003).

INTERPRETATION

Persistent inhibitory input to the STN suggests a local synaptic mechanism for the suppression of subthalamic firing during high frequency stimulation. Moreover, differences in the resiliency versus vulnerability of inhibitory inputs to the STN and SNr suggest a projection source- and frequency-specificity for this mechanism. The feasibility of targeting electrophysiologically-identified neural structures may provide insight into how DBS achieves frequency-specific modulation of neuronal projections.

Meta-Bayesian Optimization for Deep Brain Stimulation

Deep brain stimulation (DBS) is becoming a fundamental tool for the treatment and study of neurological and psychiatric diseases and disorders. Recently developed DBS devices and electrodes have allowed for more flexible and precise stimulation. Densely packed stimulation contacts can be independently stimulated to shape the electric field, targeting pathways of interest, and avoiding those that may cause side-effects. However, this flexibility comes at a cost. Each additional stimulation setting causes an exponential increase in the number of potential stimulation settings. Recent works have addressed this problem using Bayesian optimization. However, this approach has a limited ability to learn from multiple subjects to improve performance. In this study we extend a recently developed meta-Bayesian optimization algorithm to the DBS domain. We evaluated this approach compared to classical Bayesian optimization and a random search using data collected from a nonhuman primate during stimulation of the subthalamic nucleus while recording evoked potentials in the motor cortex and locally within the subthalamic nucleus. On the task of finding the stimulation setting that maximized the evoked potential across a distribution of generated objective functions, meta-Bayesian optimization significantly outperformed the other approaches with a cumulative reward of 8.93±0.70, compared to 7.17±1.64 for Bayesian optimization (p < 10^-9) and 6.89±1.56 for the random search (p < 10^-9). Moreover, the algorithm outperformed Bayesian optimization when tested on an objective function not used during training. These results demonstrate that meta-Bayesian optimization can take advantage of the structure underlying a distribution of objective function and learn an optimal search strategy that can generalize beyond the objective functions that were not part of the training data. Clinical Relevance - This extends a meta-Bayesian optimization approach for optimizing DBS stimulation settings that outperforms state-of-art algorithms by 24.6%.

Initial Clinical Outcome With Bilateral, Dual-Target Deep Brain Stimulation Trial in Parkinson Disease Using Summit RC + S

BACKGROUND

Deep brain stimulation (DBS) is an effective therapy in advanced Parkinson disease (PD). Although both subthalamic nucleus (STN) and globus pallidus (GP) DBS show equivalent efficacy in PD, combined stimulation may demonstrate synergism.

OBJECTIVE

To evaluate the clinical benefit of stimulating a combination of STN and GP DBS leads and to demonstrate biomarker discovery for adaptive DBS therapy in an observational study.

METHODS

We performed a pilot trial (n = 3) of implanting bilateral STN and GP DBS leads, connected to a bidirectional implantable pulse generator (Medtronic Summit RC + S; NCT03815656, IDE No. G180280). Initial 1-year outcome in 3 patients included Unified PD Rating Scale on and off medications, medication dosage, Hauser diary, and recorded beta frequency spectral power.

RESULTS

Combined DBS improved PD symptom control, allowing >80% levodopa medication reduction. There was a greater decrease in off-medication motor Unified PD Rating Scale with multiple electrodes activated (mean difference from off stimulation off medications -18.2, range -25.5 to -12.5) than either STN (-12.8, range -20.5 to 0) or GP alone (-9, range -11.5 to -4.5). Combined DBS resulted in a greater reduction of beta oscillations in STN in 5/6 hemispheres than either site alone. Adverse events occurred in 2 patients, including a small cortical hemorrhage and seizure at 24 hours postoperatively, which resolved spontaneously, and extension wire scarring requiring revision at 2 months postoperatively.

CONCLUSION

Patients with PD preferred combined DBS stimulation in this preliminary cohort. Future studies will address efficacy of adaptive DBS as we further define biomarkers and control policy.

Can brain signals and anatomy refine contact choice for deep brain stimulation in Parkinson's disease?

INTRODUCTION

Selecting the ideal contact to apply subthalamic nucleus deep brain stimulation (STN-DBS) in Parkinson's disease is time-consuming and reliant on clinical expertise. The aim of this cohort study was to assess whether neuronal signals (beta oscillations and evoked resonant neural activity (ERNA)), and the anatomical location of electrodes, can predict the contacts selected by long-term, expert-clinician programming of STN-DBS.

METHODS

We evaluated 92 hemispheres of 47 patients with Parkinson's disease receiving chronic monopolar and bipolar STN-DBS. At each contact, beta oscillations and ERNA were recorded intraoperatively, and anatomical locations were assessed. How these factors, alone and in combination, predicted the contacts clinically selected for chronic deep brain stimulation at 6 months postoperatively was evaluated using a simple-ranking method and machine learning algorithms.

RESULTS

The probability that each factor individually predicted the clinician-chosen contact was as follows: ERNA 80%, anatomy 67%, beta oscillations 50%. ERNA performed significantly better than anatomy and beta oscillations. Combining neuronal signal and anatomical data did not improve predictive performance.

CONCLUSION

This work supports the development of probability-based algorithms using neuronal signals and anatomical data to assist programming of deep brain stimulation.

Imaging versus electrographic connectivity in human mood-related fronto-temporal networks

BACKGROUND

The efficacy of psychiatric DBS is thought to be driven by the connectivity of stimulation targets with mood-relevant fronto-temporal networks, which is typically evaluated using diffusion-weighted tractography.

OBJECTIVE

Leverage intracranial electrophysiology recordings to better predict the circuit-wide effects of neuromodulation to white matter targets. We hypothesize strong convergence between tractography-predicted structural connectivity and stimulation-induced electrophysiological responses.

METHODS

Evoked potentials were elicited by single-pulse stimulation to two common DBS targets for treatment-resistant depression - the subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VCVS) - in two patients undergoing DBS with stereo-electroencephalographic (sEEG) monitoring. Evoked potentials were compared with predicted structural connectivity between DBS leads and sEEG contacts using probabilistic, patient-specific diffusion-weighted tractography.

RESULTS

Evoked potentials and tractography showed strong convergence in both patients in orbitofrontal, ventromedial prefrontal, and lateral prefrontal cortices for both SCC and VCVS stimulation targets. Low convergence was found in anterior cingulate (ACC), where tractography predicted structural connectivity from SCC targets but produced no evoked potentials during SCC stimulation. Further, tractography predicted no connectivity to ACC from VCVS targets, but VCVS stimulation produced robust evoked potentials.

CONCLUSION

The two connectivity methods showed significant convergence, but important differences emerged with respect to the ability of tractography to predict electrophysiological connectivity between SCC and VCVS to regions of the mood-related network. This multimodal approach raises intriguing implications for the use of tractography in surgical targeting and provides new data to enhance our understanding of the network-wide effects of neuromodulation.

The cortical evoked potential corresponds with deep brain stimulation efficacy in rats

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) antidromically activates the motor cortex (M1), and this cortical activation appears to play a role in the treatment of hypokinetic motor behaviors (Gradinaru V, Mogri M, Thompson KR, Henderson JM, Deisseroth K. Science 324: 354-359, 2009; Yu C, Cassar IR, Sambangi J, Grill WM. J Neurosci 40: 4323-4334, 2020). The synchronous antidromic activation takes the form of a short-latency cortical evoked potential (cEP) in electrocorticography (ECoG) recordings of M1. We assessed the utility of the cEP as a biomarker for STN DBS in unilateral 6-hydroxydopamine-lesioned female Sprague Dawley rats, with stimulating electrodes implanted in the STN and the ECoG recorded above M1. We quantified the correlations of the cEP magnitude and latency with changes in motor behavior from DBS and compared them to the correlation between motor behaviors and several commonly used spectral-based biomarkers. The cEP features correlated strongly with motor behaviors and were highly consistent across animals, whereas the spectral biomarkers correlated weakly with motor behaviors and were highly variable across animals. The cEP may thus be a useful biomarker for assessing the therapeutic efficacy of DBS parameters, as its features strongly correlate with motor behavior, it is consistent across time and subjects, it can be recorded under anesthesia, and it is simple to quantify with a large signal-to-noise ratio, enabling rapid, real-time evaluation. Additionally, our work provides further evidence that antidromic cortical activation mediates changes in motor behavior from STN DBS and that the dependence of DBS efficacy on stimulation frequency may be related to antidromic spike failure.NEW & NOTEWORTHY We characterize a new potential biomarker for deep brain stimulation (DBS), the cortical evoked potential (cEP), and demonstrate that it exhibits a robust correlation with motor behaviors as a function of stimulation frequency. The cEP may thus be a useful clinical biomarker for changes in motor behavior. This work also provides insight into the cortical mechanisms of DBS, suggesting that motor behaviors are strongly affected by the rate of antidromic spike failure during DBS.

Subthalamic deep brain stimulation of an anatomically detailed model of the human hyperdirect pathway

The motor hyperdirect pathway (HDP) is considered a key target in the treatment of Parkinson's disease with subthalamic deep brain stimulation (DBS). This hypothesis is partially derived from the association of HDP activation with evoked potentials (EPs) generated in the motor cortex and subthalamic nucleus (STN) after a DBS pulse. However, the biophysical details of how and when DBS-induced action potentials (APs) in HDP neurons reach their terminations in the cortex or STN remain unclear. Therefore, we used an anatomically detailed representation of the motor HDP, as well as the internal capsule (IC), in a model of human subthalamic DBS to explore AP activation and transmission in the HDP and IC. Our results show that small diameter HDP axons exhibited AP initiation in their subthalamic terminal arbor, which resulted in relatively long transmission latencies to cortex (∼3.5-8 ms). Alternatively, large diameter HDP axons were most likely to be directly activated in the capsular region, which resulted in short transmission times to the cortex (∼1-3 ms). However, those large diameter HDP antidromic APs would be indistinguishable from any other IC axons that were also activated by the stimulus. Conversely, DBS-induced APs in both small and large diameter HDP axons reached their synaptic boutons in the STN with similar timings, but both spanned a wide temporal range (∼0.5-5 ms). We also found that using anodic or bipolar stimulation helped to bias activation of the HDP over the IC. These computational results provide useful information for linking HDP activation with EP recordings in clinical experiments.NEW & NOTEWORTHY We used biophysical models to study pathway recruitment and conduction latencies of the hyperdirect pathway (HDP) in response to subthalamic deep brain stimulation (DBS). The model system allowed us to assess the influence of increased anatomical realism on pathway activity and the possibility of identifying HDP activity in evoked potentials (EPs) recorded in either the subthalamic nucleus (STN) or cortex. The model predicts that HDP activation is accentuated by complex axonal branching in the STN.

Comparative efficacy of surgical approaches to disease modification in Parkinson disease

Parkinson’s disease (PD) may optimally be treated with a disease-modifying therapy to slow progression. We compare data underlying surgical approaches proposed to impart disease modification in PD: (1) cell transplantation therapy with stem cell-derived dopaminergic neurons to replace damaged cells; (2) clinical trials of growth factors to promote survival of existing dopaminergic neurons; (3) subthalamic nucleus deep brain stimulation early in the course of PD; and (4) abdominal vagotomy to lower risk of potential disease spread from gut to brain. Though targeted to engage potential mechanisms of PD these surgical approaches remain experimental, indicating the difficulty in translating therapeutic concepts into clinical practice. The choice of outcome measures to assess disease modification separate from the symptomatic benefit will be critical to evaluate the effect of the disease-modifying intervention on long-term disease burden, including imaging studies and clinical rating scales, i.e., Unified Parkinson Disease Rating Scale. Therapeutic interventions will require long follow-up times (i.e., 5–10 years) to analyze disease modification compared to symptomatic treatments. The promise of invasive, surgical treatments to achieve disease modification through mechanistic approaches has been constrained by the reality of translating these concepts into effective clinical trials.

Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson's disease

Objective.Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD) but its success depends on a time-consuming process of trial-and-error to identify the optimal stimulation settings for each individual patient. Data-driven optimization algorithms have been proposed to efficiently find the stimulation setting that maximizes a quantitative biomarker of symptom relief. However, these algorithms cannot efficiently take into account stimulation settings that may control symptoms but also cause side effects. Here we demonstrate how multi-objective data-driven optimization can be used to find the optimal trade-off between maximizing symptom relief and minimizing side effects.Approach.Cortical and motor evoked potential data collected from PD patients during intraoperative stimulation of the subthalamic nucleus were used to construct a framework for designing and prototyping data-driven multi-objective optimization algorithms. Using this framework, we explored how these techniques can be applied clinically, and characterized the design features critical for solving this optimization problem. Our two optimization objectives were to maximize cortical evoked potentials, a putative biomarker of therapeutic benefit, and to minimize motor potentials, a biomarker of motor side effects.Main Results.Using thisin silicodesign framework, we demonstrated how the optimal trade-off between two objectives can substantially reduce the stimulation parameter space by 61 ± 19%. The best algorithm for identifying the optimal trade-off between the two objectives was a Bayesian optimization approach with an area under the receiver operating characteristic curve of up to 0.94 ± 0.02, which was possible with the use of a surrogate model and a well-tuned acquisition function to efficiently select which stimulation settings to sample.Significance.These findings show that multi-objective optimization is a promising approach for identifying the optimal trade-off between symptom relief and side effects in DBS. Moreover, these approaches can be readily extended to newly discovered biomarkers, adapted to DBS for disorders beyond PD, and can scale with the development of more complex DBS devices.

Electroceutically induced subthalamic high-frequency oscillations and evoked compound activity may explain the mechanism of therapeutic stimulation in Parkinson's disease

Despite having remarkable utility in treating movement disorders, the lack of understanding of the underlying mechanisms of high-frequency deep brain stimulation (DBS) is a main challenge in choosing personalized stimulation parameters. Here we investigate the modulations in local field potentials induced by electrical stimulation of the subthalamic nucleus (STN) at therapeutic and non-therapeutic frequencies in Parkinson's disease patients undergoing DBS surgery. We find that therapeutic high-frequency stimulation (130-180 Hz) induces high-frequency oscillations (~300 Hz, HFO) similar to those observed with pharmacological treatment. Along with HFOs, we also observed evoked compound activity (ECA) after each stimulation pulse. While ECA was observed in both therapeutic and non-therapeutic (20 Hz) stimulation, the HFOs were induced only with therapeutic frequencies, and the associated ECA were significantly more resonant. The relative degree of enhancement in the HFO power was related to the interaction of stimulation pulse with the phase of ECA. We propose that high-frequency STN-DBS tunes the neural oscillations to their healthy/treated state, similar to pharmacological treatment, and the stimulation frequency to maximize these oscillations can be inferred from the phase of ECA waveforms of individual subjects. The induced HFOs can, therefore, be utilized as a marker of successful re-calibration of the dysfunctional circuit generating PD symptoms.