Patient-specific analysis of the volume of tissue activated during deep brain stimulation

Efficient, Robust, and Accurate CNN Predictor for Neuronal Activation in Directional Deep Brain Stimulation

The programming of clinical deep brain stimulation (DBS) systems involves numerous combinations of stimulation parameters, such as stimulus amplitude, pulse width, and frequency. As more complex electrode designs, such as directional electrodes, are introduced, the traditional trial-and-error approach to manual DBS programming becomes increasingly impractical. Visualization of the volume of tissue activated (VTA) can assist in selecting stimulation parameters by showing the direct effects of DBS on neural tissue. However, the standard method for VTA calculation, which involves modeling biological nerve fibers, is highly time-consuming and limits clinical applicability. In this study, we used finite element models (FEM) of implanted DBS systems to compute electric fields and obtained a large dataset of axonal responses under electrical stimulation using multicompartment cable models. We then trained a convolutional neural network (CNN) to replace the cable models. The CNN model's performance in calculating VTA was evaluated across various electrode configurations and stimulation parameters, and compared with existing activation function (AF) methods. The CNN model achieved a mean absolute error (MAE) of 0.032V in predicting nerve fiber activation thresholds, demonstrating greater stability and accuracy in VTA prediction compared to the AF method. Additionally, the CNN reduced computation time by five orders of magnitude compared to standard axonal modeling methods. We demonstrate that the CNN-based neural fiber predictor can quickly, accurately, and robustly predict neural activation responses to DBS, thereby improving the efficiency of DBS programming.

Evaluation of DBS computational modeling methodologies using in-vivo electrophysiology in Parkinson's disease

Deep brain stimulation (DBS) is an effective therapy for Parkinson's disease (PD) and other neuropsychiatric disorders, but its outcomes vary due to differences in patient selection, electrode placement, and programming. Optimizing DBS parameter settings requires postoperative adjustments through a trial-and-error process, which is complex and time-consuming. As such, researchers have been developing patient-specific computational models to help guide DBS programming. Despite growing interest in image-guided DBS technology, and recent adoption into clinical practice, the direct validation of the prediction accuracy remains limited. The objective of this study was to establish a comparative framework for validating the accuracy of various DBS computational modeling methodologies in predicting the activation of clinically relevant pathways using in vivo measurements from PD patients undergoing subthalamic (STN) DBS surgery. Our prior work assessed the accuracy of driving force (DF) models in native space by predicting activation of the corticospinal/bulbar tract (CSBT) and cortico-subthalamic hyperdirect pathway (HDP) using very short- (<2 ms) and short-latency (2-4 ms) cortical evoked potentials (cEPs). In this study, we extended our previous work by comparing the accuracy of five computational modeling variations for predicting the activation of HDP and CSBT based on three key factors: modeling method (DF vs. Volume of Tissue Activated [VTA]), imaging space (native vs. normative), and anatomical representation (pathway vs. volume). The model performances were quantified using the coefficient of determination (R2) between the cEP amplitudes and percent pathway activation or percent volume (structure) overlap. We compared model accuracy for 11 PD patients. The DF-Native-Pathway model was the most accurate method for quantitatively predicting experimental subcortical pathway activations. Additionally, our analysis showed that using normative brain space, instead of native (i.e., patient-specific) space, significantly diminished the accuracy of model predictions. Although the DF and VTA modeling methods exhibited comparable accuracy for the hyperdirect pathway, they diverged significantly in their predictions for the corticospinal tract. In conclusion, we believe that the choice of methodology should depend on the specific application and the required level of precision in clinical, surgical, or research settings. These findings offer valuable guidance for developing more accurate models, facilitating reliable DBS outcome predictions, and advancing both clinical practice and research.

Adult Neurogenesis in the Subventricular Zone of Patients with Huntington's and Parkinson's Diseases and following Long-Term Treatment with Deep Brain Stimulation

OBJECTIVE

Parkinson's and Huntington's diseases are characterized by progressive neuronal loss. Previous studies using human postmortem tissues have shown the impact of neurodegenerative disorders on adult neurogenesis. The extent to which adult neural stem cells are activated in the subventricular zone and whether therapeutic treatments such as deep brain stimulation promote adult neurogenesis remains unclear. The goal of the present study is to assess adult neural stem cells activation and neurogenesis in the subventricular zone of patients with Huntington's and Parkinson's diseases who were treated or not by deep brain stimulation.

METHODS

Postmortem brain samples from Huntington's and Parkinson's disease patients who had received or not long-term deep brain stimulation of the subthalamic nucleus were used.

RESULTS

Our results indicate a significant increase in the thickness of the subventricular zone and in the density of proliferating cells and activated stem cells in the brain of Huntington's disease subjects and Parkinson's disease patients treated with deep brain stimulation. We also observed an increase in the density of immature neurons in the brain of these patients.

INTERPRETATION

Overall, our data indicate that long-term deep brain stimulation of the subthalamic nucleus promotes cell proliferation and neurogenesis in the subventricular zone that are reduced in Parkinson's disease. Taken together, our results also provide a detailed characterization of the cellular composition of the adult human subventricular zone and caudate nucleus in normal condition and in Parkinson's and Huntington's diseases and demonstrate the plasticity of these regions in response to neurodegeneration. ANN NEUROL 2025;97:894-906.

The interplay between pulse width and activation depth in TENS: a computational study

Background

Transcutaneous electrical nerve stimulation (TENS) has been a commonly used modality to relieve aches and pain for over 40 years. Commercially available devices provide multiple therapy modes involving a different combination of frequency and pulse width with intensity. While frequency sets sensation, intensity helps determine tolerability, longer pulse width is reported to induce a feeling of deeper stimulation. In fact, longer pulse width has been empirically shown to deliver current into deeper tissues, but in context of other electrical stimulation modalities. The goal of this study was to unpack the relationship between pulse width and activation depth in TENS.

Methods

A highly realistic, anatomically-based, 3D finite element model of the forearm was used to simulate the electric field (E-field) distribution, as the pulse width is varied. A typical titration-guided mechanism was used to obtain the strength-duration (S-D) curves of a sensory McIntyre-Richardson-Grill (MRG) axonal model simulating the pain-transmitting A-delta fibers. The pulse widths tested ranged from 30 μs to 495 μs.

Results

As expected, shorter pulse widths required more current to achieve activation, resulting in a larger E-field. The S-D curve of the target median nerve indicates a rheobase of 1.75 mA and a chronaxie of 232 µs. When the applied currents are the same, shorter pulse widths result in a smaller volume of tissue activated (VTA) compared to the longer pulse widths. A 21 fold difference in VTA was found between the longest and shortest pulse widths considered. For the conditions tested in the study, an increase in pulse width resulted in an increase in activation depth, exhibiting a linear relationship.

Conclusion

Our findings highlight the impact of pulse width on activation depth. While choice of a given therapy mode is usually based on an ad-hoc desirable sensation basis, medical professionals may consider advocating a certain therapy mode based on the depth of the intended target nerve.

A Computational Study on the Activation of Neural Transmission in Deep Brain Stimulation

Deep brain stimulation (DBS) is an established treatment for neurodegenerative movement disorders such as Parkinson's disease that mitigates symptoms by overwriting pathological signals from the central nervous system to the motor system. Nearly all computational models of DBS, directly or indirectly, associate clinical improvements with the extent of fiber activation in the vicinity of the stimulating electrode. However, it is not clear how such activation modulates information transmission. Here, we use the exact cable equation for straight or curved axons and show that DBS segregates the signaling pathways into one of the three communicational modes: complete information blockage, uni-, and bi-directional transmission. Furthermore, all these modes respond to the stimulating pulse in an asynchronous but frequency-locked fashion. Asynchrony depends on the geometry of the axon, its placement and orientation, and the stimulation protocol. At the same time, the electrophysiology of the nerve determines frequency-locking. Such a trimodal response challenges the notion of activation as a binary state and studies that correlate it with the DBS outcome. Importantly, our work suggests that a mechanistic understanding of DBS action relies on distinguishing between these three modes of information transmission.

Predictive modeling of sensory responses in deep brain stimulation

Introduction

Although stimulation-induced sensations are typically considered undesirable side effects in clinical DBS therapy, there are emerging scenarios, such as computer-brain interface applications, where these sensations may be intentionally created. The selection of stimulation parameters, whether to avoid or induce sensations, is a challenging task due to the vast parameter space involved. This study aims to streamline DBS parameter selection by employing a machine learning model to predict the occurrence and somatic location of paresthesias in response to thalamic DBS.

Methods

We used a dataset comprising 3,359 paresthetic sensations collected from 18 thalamic DBS leads from 10 individuals in two clinical centers. For each stimulation, we modeled the Volume of Tissue Activation (VTA). We then used the stimulation parameters and the VTA information to train a machine learning model to predict the occurrence of sensations and their corresponding somatic areas.

Results

Our results show fair to substantial agreement with ground truth in predicting the presence and somatic location of DBS-evoked paresthesias, with Kappa values ranging from 0.31 to 0.72. We observed comparable performance in predicting the presence of paresthesias for both seen and unseen cases (Kappa 0.72 vs. 0.60). However, Kappa agreement for predicting specific somatic locations was significantly lower for unseen cases (0.53 vs. 0.31).

Conclusion

The results suggest that machine learning can potentially be used to optimize DBS parameter selection, leading to faster and more efficient postoperative management. Outcome predictions may be used to guide clinical DBS programming or tuning of DBS based computer-brain interfaces.

Utilising activity patterns of a complex biophysical network model to optimise intra-striatal deep brain stimulation

A large-scale biophysical network model for the isolated striatal body is developed to optimise potential intrastriatal deep brain stimulation applied to, e.g. obsessive-compulsive disorder. The model is based on modified Hodgkin-Huxley equations with small-world connectivity, while the spatial information about the positions of the neurons is taken from a detailed human atlas. The model produces neuronal spatiotemporal activity patterns segregating healthy from pathological conditions. Three biomarkers were used for the optimisation of stimulation protocols regarding stimulation frequency, amplitude and localisation: the mean activity of the entire network, the frequency spectrum of the entire network (rhythmicity) and a combination of the above two. By minimising the deviation of the aforementioned biomarkers from the normal state, we compute the optimal deep brain stimulation parameters, regarding position, amplitude and frequency. Our results suggest that in the DBS optimisation process, there is a clear trade-off between frequency synchronisation and overall network activity, which has also been observed during in vivo studies.

Deep brain stimulation for the treatment of substance use disorders: a promising approach requiring caution

Substance use disorders are prevalent, causing extensive morbidity and mortality worldwide. Evidence-based treatments are of low to moderate effect size. Growth in the neurobiological understanding of addiction (e.g., craving) along with technological advancements in neuromodulation have enabled an evaluation of neurosurgical treatments for substance use disorders. Deep brain stimulation (DBS) involves surgical implantation of leads into brain targets and subcutaneous tunneling to connect the leads to a programmable implanted pulse generator (IPG) under the skin of the chest. DBS allows direct testing of neurobiologically-guided hypotheses regarding the etiology of substance use disorders in service of developing more effective treatments. Early studies, although with multiple limitations, have been promising. Still the authors express caution regarding implementation of DBS studies in this population and emphasize the importance of safeguards to ensure patient safety and meaningful study results. In this perspectives article, we review lessons learned through the years of planning an ongoing trial of DBS for methamphetamine use disorder.

Pulsatile electrical stimulation creates predictable, correctable disruptions in neural firing

Electrical stimulation is a key tool in neuroscience, both in brain mapping studies and in many therapeutic applications such as cochlear, vestibular, and retinal neural implants. Due to safety considerations, stimulation is restricted to short biphasic pulses. Despite decades of research and development, neural implants lead to varying restoration of function in patients. In this study, we use computational modeling to provide an explanation for how pulsatile stimulation affects axonal channels and therefore leads to variability in restoration of neural responses. The phenomenological explanation is transformed into equations that predict induced firing rate as a function of pulse rate, pulse amplitude, and spontaneous firing rate. We show that these equations predict simulated responses to pulsatile stimulation with a variety of parameters as well as several features of experimentally recorded primate vestibular afferent responses to pulsatile stimulation. We then discuss the implications of these effects for improving clinical stimulation paradigms and electrical stimulation-based experiments.

Evaluating functional connectivity differences between DBS ON/OFF states in essential tremor

Deep brain stimulation (DBS) targeting the ventral intermediate (Vim) nucleus of the thalamus is an effective treatment for essential tremor (ET). We studied 15 ​ET patients undergoing DBS to a major input/output tract of the Vim, the dentato-rubro-thalamic tract (DRTt), using resting state functional MRI (rsfMRI) to evaluate connectivity differences between DBS ON and OFF and elucidate significant regions most influential in impacting tremor control and/or concomitant gait ataxia. Anatomical/functional 1.5T MRIs were acquired and replicated for each DBS state. Tremor severity and gait ataxia severity were scored with DBS ON at optimal stimulation parameters and immediately upon DBS OFF. Whole brain analysis was performed using dual regression analysis followed by randomized permutation testing for multiple correction comparison. Regions of interest (ROI) analysis was also performed. All 15 patients had tremor improvement between DBS ON/OFF (p ​< ​0.001). Whole brain analysis revealed significant connectivity changes between states in the left pre-central gyrus and left supplemental motor area. Group analysis of ROIs revealed that, with threshold p ​< ​0.05, in DBS ON vs. OFF both tremor duration and tremor improvement were significantly correlated to changes in connectivity. A sub-group analysis of patients with greater ataxia had significantly decreased functional connectivity between multiple ROIs in the cortex and cerebellum when DBS was ON compared to OFF. Stimulation of the DRTt and concordant improvement of tremor resulted in connectivity changes seen in multiple regions outside the motor network; when combined with both structural and electrophysiologic connectivity, this may help to serve as a biomarker to improve DBS targeting and possibly predict outcome.

Short term cardiovascular symptoms improvement after deep brain stimulation in patients with Parkinson's disease: a systematic review

BACKGROUND

Autonomic dysfunction is common and disabling in Parkinson's disease (PD). The effects of deep brain stimulation (DBS) on the cardiovascular system in PD remain poorly understood. We aimed to assess the effect of DBS on cardiovascular symptoms and objective measures in PD patients.

METHODS

We conducted a systematic literature search in PubMed/MEDLINE.

RESULTS

36 out of 472 studies were included, mostly involving DBS of the subthalamic nucleus, and to a lesser extent the globus pallidus pars interna and pedunculopontine nucleus. Seventeen studies evaluated the effect of DBS on patient-reported or clinician-rated cardiovascular symptoms, showing an improvement in the first year after surgery but not with longer-term follow-up. DBS has no clear direct effects on blood pressure during an orthostatic challenge (n = 10 studies). DBS has inconsistent effects on heart rate variability (n = 10 studies).

CONCLUSION

Current evidence on the impact of DBS on cardiovascular functions in PD is inconclusive. DBS may offer short-term improvement of cardiovascular symptoms in PD, particularly orthostatic hypotension, which may be attributed to dopaminergic medication reduction after surgery. There is insufficient evidence to draw conclusions on the direct effect of DBS on blood pressure and heart rate variability.

Probabilistic stimulation mapping from intra-operative thalamic deep brain stimulation data in essential tremor

Deep brain stimulation (DBS) is a therapy for Parkinson's disease (PD) and essential tremor (ET). The mechanism of action of DBS is still incompletely understood. Retrospective group analysis of intra-operative data recorded from ET patients implanted in the ventral intermediate nucleus of the thalamus (Vim) is rare. Intra-operative stimulation tests generate rich data and their use in group analysis has not yet been explored.Objective.To implement, evaluate, and apply a group analysis workflow to generate probabilistic stimulation maps (PSMs) using intra-operative stimulation data from ET patients implanted in Vim.Approach.A group-specific anatomical template was constructed based on the magnetic resonance imaging scans of 6 ET patients and 13 PD patients. Intra-operative test data (total:n= 1821) from the 6 ET patients was analyzed: patient-specific electric field simulations together with tremor assessments obtained by a wrist-based acceleration sensor were transferred to this template. Occurrence and weighted mean maps were generated. Voxels associated with symptomatic response were identified through a linear mixed model approach to form a PSM. Improvements predicted by the PSM were compared to those clinically assessed. Finally, the PSM clusters were compared to those obtained in a multicenter study using data from chronic stimulation effects in ET.Main results.Regions responsible for improvement identified on the PSM were in the posterior sub-thalamic area (PSA) and at the border between the Vim and ventro-oral nucleus of the thalamus (VO). The comparison with literature revealed a center-to-center distance of less than 5 mm and an overlap score (Dice) of 0.4 between the significant clusters. Our workflow and intra-operative test data from 6 ET-Vim patients identified effective stimulation areas in PSA and around Vim and VO, affirming existing medical literature.Significance.This study supports the potential of probabilistic analysis of intra-operative stimulation test data to reveal DBS's action mechanisms and to assist surgical planning.

Electrocardiographic approach strategies in patients with Parkinson disease treated with deep brain stimulation

Deep brain stimulation (DBS) is an interdisciplinary and reversible therapy that uses high-frequency electrical stimulation to correct aberrant neural pathways in motor and cognitive neurological disorders. However, the high frequency of the waves used in DBS can interfere with electrical recording devices (e.g., electrocardiogram, electroencephalogram, cardiac monitor), creating artifacts that hinder their interpretation. The compatibility of DBS with these devices varies and depends on factors such as the underlying disease and the configuration of the neurostimulator. In emergencies where obtaining an electrocardiogram is crucial, the need for more consensus on reducing electrical artifacts in patients with DBS becomes a significant challenge. Various strategies have been proposed to attenuate the artifact generated by DBS, such as changing the DBS configuration from monopolar to bipolar, temporarily deactivating DBS during electrocardiographic recording, applying frequency filters both lower and higher than those used by DBS, and using non-standard leads. However, the inexperience of medical personnel, variability in DBS models, or the lack of a controller at the time of approach limit the application of these strategies. Current evidence on their reproducibility and efficacy is limited. Due to the growing elderly population and the rising utilization of DBS, it is imperative to create electrocardiographic methods that are easily accessible and reproducible for general physicians and emergency services.

Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review

Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson's disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.

Model-Based Analysis of Pathway Recruitment During Subthalamic Deep Brain Stimulation

BACKGROUND

Subthalamic deep brain stimulation (DBS) is an established clinical therapy, but an anatomically clear definition of the underlying neural target(s) of the stimulation remains elusive. Patient-specific models of DBS are commonly used tools in the search for stimulation targets, and recent iterations of those models are focused on characterizing the brain connections that are activated by DBS.

OBJECTIVE

The goal of this study was to quantify axonal pathway activation in the subthalamic region from DBS at different electrode locations and stimulation settings.

MATERIALS AND METHODS

We used an anatomically and electrically detailed computational model of subthalamic DBS to generate recruitment curves for eight different axonal pathways of interest, at three generalized DBS electrode locations in the subthalamic nucleus (STN) (ie, central STN, dorsal STN, posterior STN). These simulations were performed with three levels of DBS electrode localization uncertainty (ie, 0.5 mm, 1.0 mm, 1.5 mm).

RESULTS

The recruitment curves highlight the diversity of pathways that are theoretically activated with subthalamic DBS, in addition to the dependence of the stimulation location and parameter settings on the pathway activation estimates. The three generalized DBS locations exhibited distinct pathway recruitment curve profiles, suggesting that each stimulation location would have a different effect on network activity patterns. We also found that the use of anodic stimuli could help limit activation of the internal capsule relative to other pathways. However, incorporating realistic levels of DBS electrode localization uncertainty in the models substantially limits their predictive capabilities.

CONCLUSIONS

Subtle differences in stimulation location and/or parameter settings can impact the collection of pathways that are activated during subthalamic DBS.

Biophysical Principles and Computational Modeling of Deep Brain Stimulation

BACKGROUND

Deep brain stimulation (DBS) has revolutionized the treatment of neurological disorders, yet the mechanisms of DBS are still under investigation. Computational models are important in silico tools for elucidating these underlying principles and potentially for personalizing DBS therapy to individual patients. The basic principles underlying neurostimulation computational models, however, are not well known in the clinical neuromodulation community.

OBJECTIVE

In this study, we present a tutorial on the derivation of computational models of DBS and outline the biophysical contributions of electrodes, stimulation parameters, and tissue substrates to the effects of DBS.

RESULTS

Given that many aspects of DBS are difficult to characterize experimentally, computational models have played an important role in understanding how material, size, shape, and contact segmentation influence device biocompatibility, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Neural activation is dictated by stimulation parameters including frequency, current vs voltage control, amplitude, pulse width, polarity configurations, and waveform. These parameters also affect the potential for tissue damage, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Activation of the neural substrate also is influenced by the encapsulation layer surrounding the electrode, the conductivity of the surrounding tissue, and the size and orientation of white matter fibers. These properties modulate the effects of the electric field and determine the ultimate therapeutic response.

CONCLUSION

This article describes biophysical principles that are useful for understanding the mechanisms of neurostimulation.

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.

Disbalanced recruitment of crossed and uncrossed cerebello-thalamic pathways during deep brain stimulation is predictive of delayed therapy escape in essential tremor

BACKGROUND

Thalamic deep brain stimulation (DBS) is an efficacious treatment for drug-resistant essential tremor (ET) and the dentato-rubro-thalamic tract (DRT) constitutes an important target structure. However, up to 40% of patients habituate and lose treatment efficacy over time, frequently accompanied by a stimulation-induced cerebellar syndrome. The phenomenon termed delayed therapy escape (DTE) is insufficiently understood. Our previous work showed that DTE clinically is pronounced on the non-dominant side and suggested that differential involvement of crossed versus uncrossed DRT (DRTx/DRTu) might play a role in DTE development.

METHODS

We retrospectively enrolled right-handed patients under bilateral thalamic DBS >12 months for ET from a cross-sectional study. They were characterized with the Fahn-Tolosa-Marin Tremor Rating Scale (FTMTRS) and Scale for the Assessment and Rating of Ataxia (SARA) scores at different timepoints. Normative fiber tractographic evaluations of crossed and uncrossed cerebellothalamic pathways and volume of activated tissue (VAT) studies together with [18F]Fluorodeoxyglucose positron emission tomography were applied.

RESULTS

A total of 29 patients met the inclusion criteria. Favoring DRTu over DRTx in the non-dominant VAT was associated with DTE (R2 = 0.4463, p < 0.01) and ataxia (R2 = 0.2319, p < 0.01). Moreover, increasing VAT size on the right (non-dominant) side was associated at trend level with more asymmetric glucose metabolism shifting towards the right (dominant) dentate nucleus.

CONCLUSION

Our results suggest that a disbalanced recruitment of DRTu in the non-dominant VAT induces detrimental stimulation effects on the dominant cerebellar outflow (together with contralateral stimulation) leading to DTE and thus hampering the overall treatment efficacy.

Spatial and amplitude dynamics of neurostimulation: Insights from the acute intrahippocampal kainate seizure mouse model

OBJECTIVE

Neurostimulation is an emerging treatment for patients with drug-resistant epilepsy, which is used to suppress, prevent, and terminate seizure activity. Unfortunately, after implantation and despite best clinical practice, most patients continue to have persistent seizures even after years of empirical optimization. The objective of this study is to determine optimal spatial and amplitude properties of neurostimulation in inhibiting epileptiform activity in an acute hippocampal seizure model.

METHODS

We performed high-throughput testing of high-frequency focal brain stimulation in the acute intrahippocampal kainic acid mouse model of status epilepticus. We evaluated combinations of six anatomic targets and three stimulus amplitudes.

RESULTS

We found that the spike-suppressive effects of high-frequency neurostimulation are highly dependent on the stimulation amplitude and location, with higher amplitude stimulation being significantly more effective. Epileptiform spiking activity was significantly reduced with ipsilateral 250 μA stimulation of the CA1 and CA3 hippocampal regions with 21.5% and 22.2% reductions, respectively. In contrast, we found that spiking frequency and amplitude significantly increased with stimulation of the ventral hippocampal commissure. We further found spatial differences with broader effects from CA1 versus CA3 stimulation.

SIGNIFICANCE

These findings demonstrate that the effects of therapeutic neurostimulation in an acute hippocampal seizure model are highly dependent on the location of stimulation and stimulus amplitude. We provide a platform to optimize the anti-seizure effects of neurostimulation, and demonstrate that an exploration of the large electrical parameter and location space can improve current modalities for treating epilepsy.

PLAIN LANGUAGE SUMMARY

In this study, we tested how electrical pulses in the brain can help control seizures in mice. We found that the electrode's placement and the stimulation amplitude had a large effect on outcomes. Some brain regions, notably nearby CA1 and CA3, responded positively with reduced seizure-like activities, while others showed increased activity. These findings emphasize that choosing the right spot for the electrode and adjusting the strength of electrical pulses are both crucial when considering neurostimulation treatments for epilepsy.

Visualization of volume of tissue activated modeling in a clinical planning system for deep brain stimulation

BACKGROUND

Pathway activating models try to describe stimulation spread in deep brain stimulation (DBS). Volume of tissue activated (VTA) models are simplified model variants allowing faster and easier computation. Our study aimed to investigate, how VTA visualization can be integrated into a clinical workflow applying directional electrodes using a standard clinical DBS planning system.

METHODS

Twelve patients underwent DBS, using directional electrodes for bilateral subthalamic nucleus (STN) stimulation in Parkinson's disease. Preoperative 3T magnetic resonance imaging was used for automatic visualization of the STN outline, as well as for fiber tractography. Intraoperative computed tomography was used for automatic lead detection. The Guide XT software, closely integrated into the DBS planning software environment, was used for VTA calculation and visualization.

RESULTS

VTA visualization was possible in all cases. The percentage of VTA covering the STN volume ranged from 25% to 100% (mean: 60±25%) on the left side and from 0% to 98% (51±30%) on the right side. The mean coordinate of all VTA centers was: 12.6±1.2 mm lateral, 2.1±1.2 mm posterior, and 2.3±1.4 mm inferior in relation to the midcommissural point. Stimulation effects can be compared to the VTA visualization in relation to surrounding structures, potentially facilitating programming, which might be especially beneficial in case of suboptimal lead placement.

CONCLUSIONS

VTA visualization in a clinical planning system allows an intuitive adjustment of the stimulation parameters, supports programming, and enhances understanding of effects and side effects of DBS.

Towards an optimised deep brain stimulation using a large-scale computational network and realistic volume conductor model

Objective. Constructing a theoretical framework to improve deep brain stimulation (DBS) based on the neuronal spatiotemporal patterns of the stimulation-affected areas constitutes a primary target.Approach. We develop a large-scale biophysical network, paired with a realistic volume conductor model, to estimate theoretically efficacious stimulation protocols. Based on previously published anatomically defined structural connectivity, a biophysical basal ganglia-thalamo-cortical neuronal network is constructed using Hodgkin-Huxley dynamics. We define a new biomarker describing the thalamic spatiotemporal activity as a ratio of spiking vs. burst firing. The per cent activation of the different pathways is adapted in the simulation to minimise the differences of the biomarker with respect to its value under healthy conditions.Main results.This neuronal network reproduces spatiotemporal patterns that emerge in Parkinson's disease. Simulations of the fibre per cent activation for the defined biomarker propose desensitisation of pallido-thalamic synaptic efficacy, induced by high-frequency signals, as one possible crucial mechanism for DBS action. Based on this activation, we define both an optimal electrode position and stimulation protocol using pathway activation modelling.Significance. A key advantage of this research is that it combines different approaches, i.e. the spatiotemporal pattern with the electric field and axonal response modelling, to compute the optimal DBS protocol. By correlating the inherent network dynamics with the activation of white matter fibres, we obtain new insights into the DBS therapeutic action.

The Use of Image Guided Programming to Improve Deep Brain Stimulation Workflows with Directional Leads in Parkinson's Disease

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a preferred treatment for parkinsonian patients with severe motor fluctuations. Proper targeting of the STN sensorimotor segment appears to be a crucial factor for success of the procedure. The recent introduction of directional leads theoretically increases stimulation specificity in this challenging area but also requires more precise stimulation parameters.

OBJECTIVE

We investigated whether commercially available software for image guided programming (IGP) could maximize the benefits of DBS by informing the clinical standard care (CSC) and improving programming workflows.

METHODS

We prospectively analyzed 32 consecutive parkinsonian patients implanted with bilateral directional leads in the STN. Double blind stimulation parameters determined by CSC and IGP were assessed and compared at three months post-surgery. IGP was used to adjust stimulation parameters if further clinical refinement was required. Overall clinical efficacy was evaluated one-year post-surgery.

RESULTS

We observed 78% concordance between the two electrode levels selected by the blinded IGP prediction and CSC assessments. In 64% of cases requiring refinement, IGP improved clinical efficacy or reduced mild side effects, predominantly by facilitating the use of directional stimulation (93% of refinements).

CONCLUSIONS

The use of image guided programming saves time and assists clinical refinement, which may be beneficial to the clinical standard care for STN-DBS and further improve the outcomes of DBS for PD patients.

Deep brain stimulation of the subthalamic nucleus in severe Parkinson's disease: relationships between dual-contact topographic setting and 1-year worsening of speech and gait

BACKGROUND

Subthalamic nucleus (STN) deep brain stimulation (DBS) alleviates severe motor fluctuations and dyskinesia in Parkinson's disease, but may result in speech and gait disorders. Among the suspected or demonstrated causes of these adverse effects, we focused on the topography of contact balance (CB; individual, right and left relative dual positions), a scantly studied topic, analyzing the relationships between symmetric or non-symmetric settings, and the worsening of these signs.

METHOD

An observational monocentric study was conducted on a series of 92 patients after ethical approval. CB was specified by longitudinal and transversal positions and relation to the STN (CB sub-aspects) and totalized at the patient level (patient CB). CB was deemed symmetric when the two contacts were at the same locations relative to the STN. CB was deemed asymmetric when at least one sub-aspect differed in the patient CB. Baseline and 1-year characteristics were routinely collected: (i) general, namely, Unified Parkinson's Disease Rating Scores (UPDRS), II, III motor and IV, daily levodopa equivalent doses, and Parkinson's Disease Questionnaire of Quality of Life (PDQ39) scores; (ii) specific, namely scores for speech (II-5 and III-18) and axial signs (II-14, III-28, III-29, and III-30). Only significant correlations were considered (p < 0.05).

RESULTS

Baseline characteristics were comparable (symmetric versus asymmetric). CB settings were related to deteriorations of speech and axial signs: communication PDQ39 and UPDRS speech and gait scores worsened exclusively with symmetric settings; the most influential CB sub-aspect was symmetric longitudinal position.

CONCLUSION

Our findings suggest that avoiding symmetric CB settings, whether by electrode positioning or shaping of electric fields, could reduce worsening of speech and gait.

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.

Deep Brain Stimulation Lead Localization Variability Comparing Intraoperative MRI Versus Postoperative Computed Tomography

BACKGROUND AND OBJECTIVES

Commercially available lead localization software for deep brain stimulation (DBS) often relies on postoperative computed tomography (CT) scans to define electrode positions. When cases are performed with intraoperative MRI, another imaging set exists with which to perform these localizations. To compare DBS localization error between postoperative CT scans and intraoperative MRI.

METHODS

A retrospective cohort of patients who underwent MRI-guided placement of DBS electrodes using the ClearPoint platform was identified. Using Brainlab Elements, postoperative CT scans were coregistered to intraoperative magnetic resonance images visualizing the ClearPoint guidance sheaths and ceramic stylets. DBS electrodes were identified in CT scans using Brainlab's lead localization tool. Trajectory and vector errors were quantified between scans for each lead in each patient.

RESULTS

Eighty patients with a total of 157 implanted DBS electrodes were included. We observed mean trajectory and vector errors of 0.78 ± 0.44 mm (range 0.1-2.0 mm) and 1.57 ± 0.79 mm (range 0.2-4.2 mm), respectively, between postoperative CT and intraoperative MRI. There were 7 patients with CT scans collected at multiple time points. Trajectory error increased by 0.15 ± 0.42 mm ( P = .31), and vector error increased by 0.22 ± 0.53 mm ( P = .13) in the later scans. Across all scans, there was no significant association between trajectory ( P = .053) or vector ( P = .98) error and the date of CT acquisition. DBS electrodes targeting the subthalamic nucleus had significantly greater trajectory errors ( P = .02) than those targeting the globus pallidus pars internus nucleus.

CONCLUSION

Commercially available software produced largely concordant lead localizations when comparing intraoperative MRIs with postoperative CT scans, with trajectory errors on average <1 mm. CT scans tend to be more comparable with intraoperative MRI in the immediate postoperative period, with increased time intervals associated with a greater magnitude of error between modalities.

Impedance Rhythms in Human Limbic System

The impedance is a fundamental electrical property of brain tissue, playing a crucial role in shaping the characteristics of local field potentials, the extent of ephaptic coupling, and the volume of tissue activated by externally applied electrical brain stimulation. We tracked brain impedance, sleep-wake behavioral state, and epileptiform activity in five people with epilepsy living in their natural environment using an investigational device. The study identified impedance oscillations that span hours to weeks in the amygdala, hippocampus, and anterior nucleus thalamus. The impedance in these limbic brain regions exhibit multiscale cycles with ultradian (∼1.5-1.7 h), circadian (∼21.6-26.4 h), and infradian (∼20-33 d) periods. The ultradian and circadian period cycles are driven by sleep-wake state transitions between wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Limbic brain tissue impedance reaches a minimum value in NREM sleep, intermediate values in REM sleep, and rises through the day during wakefulness, reaching a maximum in the early evening before sleep onset. Infradian (∼20-33 d) impedance cycles were not associated with a distinct behavioral correlate. Brain tissue impedance is known to strongly depend on the extracellular space (ECS) volume, and the findings reported here are consistent with sleep-wake-dependent ECS volume changes recently observed in the rodent cortex related to the brain glymphatic system. We hypothesize that human limbic brain ECS changes during sleep-wake state transitions underlie the observed multiscale impedance cycles. Impedance is a simple electrophysiological biomarker that could prove useful for tracking ECS dynamics in human health, disease, and therapy.SIGNIFICANCE STATEMENT The electrical impedance in limbic brain structures (amygdala, hippocampus, anterior nucleus thalamus) is shown to exhibit oscillations over multiple timescales. We observe that impedance oscillations with ultradian and circadian periodicities are associated with transitions between wakefulness, NREM, and REM sleep states. There are also impedance oscillations spanning multiple weeks that do not have a clear behavioral correlate and whose origin remains unclear. These multiscale impedance oscillations will have an impact on extracellular ionic currents that give rise to local field potentials, ephaptic coupling, and the tissue activated by electrical brain stimulation. The approach for measuring tissue impedance using perturbational electrical currents is an established engineering technique that may be useful for tracking ECS volume.

Quantitative analysis of the optogenetic excitability of CA1 neurons

Introduction

Optogenetics has emerged as a promising technique for modulating neuronal activity and holds potential for the treatment of neurological disorders such as temporal lobe epilepsy (TLE). However, clinical translation still faces many challenges. This in-silico study aims to enhance the understanding of optogenetic excitability in CA1 cells and to identify strategies for improving stimulation protocols.

Methods

Employing state-of-the-art computational models coupled with Monte Carlo simulated light propagation, the optogenetic excitability of four CA1 cells, two pyramidal and two interneurons, expressing ChR2(H134R) is investigated.

Results and discussion

The results demonstrate that confining the opsin to specific neuronal membrane compartments significantly improves excitability. An improvement is also achieved by focusing the light beam on the most excitable cell region. Moreover, the perpendicular orientation of the optical fiber relative to the somato-dendritic axis yields superior results. Inter-cell variability is observed, highlighting the importance of considering neuron degeneracy when designing optogenetic tools. Opsin confinement to the basal dendrites of the pyramidal cells renders the neuron the most excitable. A global sensitivity analysis identified opsin location and expression level as having the greatest impact on simulation outcomes. The error reduction of simulation outcome due to coupling of neuron modeling with light propagation is shown. The results promote spatial confinement and increased opsin expression levels as important improvement strategies. On the other hand, uncertainties in these parameters limit precise determination of the irradiance thresholds. This study provides valuable insights on optogenetic excitability of CA1 cells useful for the development of improved optogenetic stimulation protocols for, for instance, TLE treatment.

A structural magnetic resonance imaging review of clinical motor outcomes from deep brain stimulation in movement disorders

Patients with movement disorders treated by deep brain stimulation do not always achieve successful therapeutic alleviation of motor symptoms, even in cases where surgery is without complications. Magnetic resonance imaging (MRI) offers methods to investigate structural brain-related factors that may be predictive of clinical motor outcomes. This review aimed to identify features which have been associated with variability in clinical post-operative motor outcomes in patients with Parkinson's disease, dystonia, and essential tremor from structural MRI modalities. We performed a literature search for articles published between 1 January 2000 and 1 April 2022 and identified 5197 articles. Following screening through our inclusion criteria, we identified 60 total studies (39 = Parkinson's disease, 11 = dystonia syndromes and 10 = essential tremor). The review captured a range of structural MRI methods and analysis techniques used to identify factors related to clinical post-operative motor outcomes from deep brain stimulation. Morphometric markers, including volume and cortical thickness were commonly identified in studies focused on patients with Parkinson's disease and dystonia syndromes. Reduced metrics in basal ganglia, sensorimotor and frontal regions showed frequent associations with reduced motor outcomes. Increased structural connectivity to subcortical nuclei, sensorimotor and frontal regions was also associated with greater motor outcomes. In patients with tremor, increased structural connectivity to the cerebellum and cortical motor regions showed high prevalence across studies for greater clinical motor outcomes. In addition, we highlight conceptual issues for studies assessing clinical response with structural MRI and discuss future approaches towards optimizing individualized therapeutic benefits. Although quantitative MRI markers are in their infancy for clinical purposes in movement disorder treatments, structural features obtained from MRI offer the powerful potential to identify candidates who are more likely to benefit from deep brain stimulation and provide insight into the complexity of disorder pathophysiology.

Modeling the Impact of Electrode/Tissue Geometry on Electrical Stimulation in Stereo-EEG

PURPOSE

Electrical stimulation through depth electrodes is used to map function and seizure onset during stereoelectroencephalography in patients undergoing evaluation for epilepsy surgery. Factors such as electrode design, location, and orientation are expected to impact effects of electrical stimulation.

METHODS

We developed a steady-state finite element model of brain tissue including five layers (skull through white matter) and an implanted electrode to explore the impact of electrode design and placement on the activation of brain tissue by electrical stimulation. We calculated electric potentials, current densities, and volume of tissue activated ( Volact ) in response to constant current bipolar stimulation. We modeled two depth electrode designs (3.5- and 4.43-mm intercontact spacing) and varied electrode location and orientation.

RESULTS

The electrode with greater intercontact spacing produced 8% to 23% larger Volact (1% to 16% considering only gray matter). Vertical displacement of the electrodes by half intercontact space increased Volact for upward displacement (6% to 83% for all brain tissue or -5% to 96% gray matter only) and decreased Volact (1% to 16% or 24% to 49% gray matter only) for downward displacement. Rotating the electrode in the tissue by 30° to 60° with respect to the vertical axis increased Volact by 30% to 90% (20%-48% gray matter only).

CONCLUSIONS

Location and orientation of depth electrodes with respect to surrounding brain tissue have a large impact on the amount of tissue activated during electrical stimulation mapping in stereoelectroencephalography. Electrode design has an impact, although modest for commonly used designs. Individualization of stimulation intensity at each location remains critical, especially for avoiding false-negative results.

Spatial and Amplitude Dynamics of Neurostimulation: Insights from the Acute Intrahippocampal Kainate Seizure Mouse Model

Objective

Neurostimulation is an emerging treatment for patients with medically refractory epilepsy, which is used to suppress, prevent, and terminate seizure activity. Unfortunately, after implantation and despite best clinical practice, most patients continue to have persistent seizures even after years of empirical optimization. The objective of this study is to determine optimal spatial and amplitude properties of neurostimulation in inhibiting epileptiform activity in an acute hippocampal seizure model.

Methods

We performed high-throughput testing of high-frequency focal brain stimulation in the acute intrahippocampal kainic acid mouse model of temporal lobe epilepsy. We evaluated combinations of six anatomic targets and three stimulus amplitudes.

Results

We found that the spike-suppressive effects of high-frequency neurostimulation are highly dependent on the stimulation amplitude and location, with higher amplitude stimulation being significantly more effective. Epileptiform spiking activity was significantly reduced with ipsilateral 250 μA stimulation of the CA1 and CA3 hippocampal regions with 21.5% and 22.2% reductions, respectively. In contrast, we found that spiking frequency and amplitude significantly increased with stimulation of the ventral hippocampal commissure. We further found spatial differences with broader effects from CA1 versus CA3 stimulation.

Significance

These findings demonstrate that the effects of therapeutic neurostimulation in an acute hippocampal seizure model are highly dependent on the location of stimulation and stimulus amplitude. We provide a platform to optimize the anti-seizure effects of neurostimulation, and demonstrate that an exploration of the large electrical parameter and location space can improve current modalities for treating epilepsy.

Key Points

Evaluated spatial and temporal parameters of neurostimulation in a mouse model of acute seizuresBrief bursts of high-frequency (100 Hz) stimulation effectively interrupted epileptiform activity.The suppressive effect was highly dependent on stimulation amplitude and was maximal at the ipsilateral CA1 and CA3 regions.Pro-excitatory effects were identified with high-amplitude high-frequency stimulation at the ventral hippocampal commissure and contralateral CA1.

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.

Experimental Verification for Numerical Simulation of Thalamic Stimulation-Evoked Calcium-Sensitive Fluorescence and Electrophysiology with Self-Assembled Multifunctional Optrode

Owing to its capacity to eliminate a long-standing methodological limitation, fiber photometry can assist research gaining novel insight into neural systems. Fiber photometry can reveal artifact-free neural activity under deep brain stimulation (DBS). Although evoking neural potential with DBS is an effective method for mediating neural activity and neural function, the relationship between DBS-evoked neural Ca²⁺ change and DBS-evoked neural electrophysiology remains unknown. Therefore, in this study, a self-assembled optrode was demonstrated as a DBS stimulator and an optical biosensor capable of concurrently recording Ca²⁺ fluorescence and electrophysiological signals. Before the in vivo experiment, the volume of tissue activated (VTA) was estimated, and the simulated Ca²⁺ signals were presented using Monte Carlo (MC) simulation to approach the realistic in vivo environment. When VTA and the simulated Ca²⁺ signals were combined, the distribution of simulated Ca²⁺ fluorescence signals matched the VTA region. In addition, the in vivo experiment revealed a correlation between the local field potential (LFP) and the Ca²⁺ fluorescence signal in the evoked region, revealing the relationship between electrophysiology and the performance of neural Ca²⁺ concentration behavior. Concurrent with the VTA volume, simulated Ca²⁺ intensity, and the in vivo experiment, these data suggested that the behavior of neural electrophysiology was consistent with the phenomenon of Ca²⁺ influx to neurons.

Understanding mental health through computers: An introduction to computational psychiatry

Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.

Decoupling of interacting neuronal populations by time-shifted stimulation through spike-timing-dependent plasticity

The synaptic organization of the brain is constantly modified by activity-dependent synaptic plasticity. In several neurological disorders, abnormal neuronal activity and pathological synaptic connectivity may significantly impair normal brain function. Reorganization of neuronal circuits by therapeutic stimulation has the potential to restore normal brain dynamics. Increasing evidence suggests that the temporal stimulation pattern crucially determines the long-lasting therapeutic effects of stimulation. Here, we tested whether a specific pattern of brain stimulation can enable the suppression of pathologically strong inter-population synaptic connectivity through spike-timing-dependent plasticity (STDP). More specifically, we tested how introducing a time shift between stimuli delivered to two interacting populations of neurons can effectively decouple them. To that end, we first used a tractable model, i.e., two bidirectionally coupled leaky integrate-and-fire (LIF) neurons, to theoretically analyze the optimal range of stimulation frequency and time shift for decoupling. We then extended our results to two reciprocally connected neuronal populations (modules) where inter-population delayed connections were modified by STDP. As predicted by the theoretical results, appropriately time-shifted stimulation causes a decoupling of the two-module system through STDP, i.e., by unlearning pathologically strong synaptic interactions between the two populations. Based on the overall topology of the connections, the decoupling of the two modules, in turn, causes a desynchronization of the populations that outlasts the cessation of stimulation. Decoupling effects of the time-shifted stimulation can be realized by time-shifted burst stimulation as well as time-shifted continuous simulation. Our results provide insight into the further optimization of a variety of multichannel stimulation protocols aiming at a therapeutic reshaping of diseased brain networks.

Artificial neural network-based rapid predictor of biological nerve fiber activation for DBS applications

Objective.Computational models are powerful tools that can enable the optimization of deep brain stimulation (DBS). To enhance the clinical practicality of these models, their computational expense and required technical expertise must be minimized. An important aspect of DBS models is the prediction of neural activation in response to electrical stimulation. Existing rapid predictors of activation simplify implementation and reduce prediction runtime, but at the expense of accuracy. We sought to address this issue by leveraging the speed and generalization abilities of artificial neural networks (ANNs) to create a novel predictor of neural fiber activation in response to DBS.Approach.We developed six variations of an ANN-based predictor to predict the response of individual, myelinated axons to extracellular electrical stimulation. ANNs were trained using datasets generated from a finite-element model of an implanted DBS system together with multi-compartment cable models of axons. We evaluated the ANN-based predictors using three white matter pathways derived from group-averaged connectome data within a patient-specific tissue conductivity field, comparing both predicted stimulus activation thresholds and pathway recruitment across a clinically relevant range of stimulus amplitudes and pulse widths.Main results.The top-performing ANN could predict the thresholds of axons with a mean absolute error (MAE) of 0.037 V, and pathway recruitment with an MAE of 0.079%, across all parameters. The ANNs reduced the time required to predict the thresholds of 288 axons by four to five orders of magnitude when compared to multi-compartment cable models.Significance.We demonstrated that ANNs can be fast, accurate, and robust predictors of neural activation in response to DBS.

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.

Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications

Neurological and psychiatric disorders typically result from dysfunction across multiple neural circuits. Most of these disorders lack a satisfactory neuromodulation treatment. However, deep brain stimulation (DBS) has been successful in a limited number of disorders; DBS typically targets one or two brain areas with single contacts on relatively large electrodes, allowing for only coarse modulation of circuit function. Because of the dysfunction in distributed neural circuits - each requiring fine, tailored modulation - that characterizes most neuropsychiatric disorders, this approach holds limited promise. To develop the next generation of neuromodulation therapies, we will have to achieve fine-grained, closed-loop control over multiple neural circuits. Recent work has demonstrated spatial and frequency selectivity using microstimulation with many small, closely-spaced contacts, mimicking endogenous neural dynamics. Using custom electrode design and stimulation parameters, it should be possible to achieve bidirectional control over behavioral outcomes, such as increasing or decreasing arousal during central thalamic stimulation. Here, we discuss one possible approach, which we term microscale multicircuit brain stimulation (MMBS). We discuss how machine learning leverages behavioral and neural data to find optimal stimulation parameters across multiple contacts, to drive the brain towards desired states associated with behavioral goals. We expound a mathematical framework for MMBS, where behavioral and neural responses adjust the model in real-time, allowing us to adjust stimulation in real-time. These technologies will be critical to the development of the next generation of neurostimulation therapies, which will allow us to treat problems like disorders of consciousness and cognition.

Brain metabolic changes and clinical response to superolateral medial forebrain bundle deep brain stimulation for treatment-resistant depression

Deep brain stimulation (DBS) to the superolateral branch of the medial forebrain bundle is an efficacious therapy for treatment-resistant depression, providing rapid antidepressant effects. In this study, we use 18F-fluorodeoxyglucose-positron emission tomography (PET) to identify brain metabolic changes over 12 months post-DBS implantation in ten of our patients, compared to baseline. The primary outcome measure was a 50% reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score, which was interpreted as a response. Deterministic fiber tracking was used to individually map the target area; probabilistic tractography was used to identify modulated fiber tracts modeled using the cathodal contacts. Eight of the ten patients included in this study were responders. PET imaging revealed significant decreases in bilateral caudate, mediodorsal thalamus, and dorsal anterior cingulate cortex metabolism that was evident at 6 months and continued to 12 months post surgery. At 12 months post-surgery, significant left ventral prefrontal cortical metabolic decreases were also observed. Right caudate metabolic decrease at 12 months was significantly correlated with mean MADRS reduction. Probabilistic tractography modeling revealed that such metabolic changes lay along cortico-limbic nodes structurally connected to the DBS target site. Such observed metabolic changes following DBS correlated with clinical response provide insights into how future studies can elaborate such data to create biomarkers to predict response, the development of which likely will require multimodal imaging analysis.

Utility of Postoperative Imaging Software for Deep Brain Stimulation Targeting in Patients with Movement Disorders

OBJECTIVE

The objective of this study was to evaluate the accuracy of the SureTune3 postoperative imaging software in determining the location of a deep brain stimulation (DBS) electrode based on clinical outcomes and the adverse effects (AEs) observed.

METHODS

Twenty-six consecutive patients with Parkinson disease (n = 17), essential tremor (n = 8), and dystonia (n = 1) who underwent bilateral DBS surgery (52 electrodes) were included in this study. Presurgical assessments were performed in all patients prior to surgery and at 3 and 6 months after surgery, using quality-of-life and clinical scales in each case. The SureTune3 software was used to evaluate the anatomical positioning of the DBS electrodes.

RESULTS

Following DBS surgery, motor and quality-of-life improvement was observed in all patients. Different AEs were detected in 12 patients, in 10 of whom (83.3%) SureTune3 related the symptoms to the positioning of an electrode. A clinical association was observed with SureTune3 for 48 of 52 (92.3%) electrodes, whereas no association was found between the AEs or clinical outcomes and the SureTune3 reconstructions for 4 of 52 electrodes (7.7%) from 4 different patients. In 2 patients, the contact chosen was modified based on the SureTune3 data, and in 2 cases, the software helped determine that second electrode replacement surgery was necessary.

CONCLUSIONS

The anatomical position of electrodes analyzed with SureTune3 software was strongly correlated with both the AEs and clinical outcomes. Thus, SureTune3 may be useful in clinical practice, and it could help improve stimulation parameters and influence decisions to undertake electrode replacement surgery.

Subthalamic high-beta oscillation informs the outcome of deep brain stimulation in patients with Parkinson's disease

Background

The therapeutic effect of deep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson's disease (PD) is related to the modulation of pathological neural activities, particularly the synchronization in the β band (13–35 Hz). However, whether the local β activity in the STN region can directly predict the stimulation outcome remains unclear.

Objective

We tested the hypothesis that low-β (13–20 Hz) and/or high-β (20–35 Hz) band activities recorded from the STN region can predict DBS efficacy.

Methods

Local field potentials (LFPs) were recorded in 26 patients undergoing deep brain stimulation surgery in the subthalamic nucleus area. Recordings were made after the implantation of the DBS electrode prior to its connection to a stimulator. The maximum normalized powers in the theta (4–7 Hz), alpha (7–13 Hz), low-β (13–20 Hz), high-β (20–35 Hz), and low-γ (40–55 Hz) subbands in the postoperatively recorded LFP were correlated with the stimulation-induced improvement in contralateral tremor or bradykinesia–rigidity. The distance between the contact selected for stimulation and the contact with the maximum subband power was correlated with the stimulation efficacy. Following the identification of the potential predictors by the significant correlations, a multiple regression analysis was performed to evaluate their effect on the outcome.

Results

The maximum high-β power was positively correlated with bradykinesia–rigidity improvement (rs = 0.549, p &lt; 0.0001). The distance to the contact with maximum high-β power was negatively correlated with bradykinesia–rigidity improvement (rs = −0.452, p &lt; 0.001). No significant correlation was observed with low-β power. The maximum high-β power and the distance to the contact with maximum high-β power were both significant predictors for bradykinesia–rigidity improvement in the multiple regression analysis, explaining 37.4% of the variance altogether. Tremor improvement was not significantly correlated with any frequency.

Conclusion

High-β oscillations, but not low-β oscillations, recorded from the STN region with the DBS lead can inform stimulation-induced improvement in contralateral bradykinesia–rigidity in patients with PD. High-β oscillations can help refine electrode targeting and inform contact selection for DBS therapy.

Quantitative susceptibility atlas construction in Montreal Neurological Institute space: towards histological-consistent iron-rich deep brain nucleus subregion identification

Iron-rich deep brain nuclei (DBN) of the human brain are involved in various motoric, emotional and cognitive brain functions. The abnormal iron alterations in the DBN are closely associated with multiple neurological and psychiatric diseases. Quantitative susceptibility mapping (QSM) provides the spatial distribution of the magnetic susceptibility of human brain tissues. Compared to traditional structural imaging, QSM provides superiority for imaging the iron-rich DBN owing to the susceptibility difference existing between brain tissues. In this study, we constructed a Montreal Neurological Institute (MNI) space unbiased QSM human brain atlas via group-wise registration from 100 healthy subjects aged 19-29 years. The atlas construction process was guided by hybrid images that were fused from multi-modal magnetic resonance images (MRI). We named it as Multi-modal-fused magnetic Susceptibility (MuSus-100) atlas. The high-quality susceptibility atlas provides extraordinary image contrast between iron-rich DBN with their surroundings. Parcellation maps of DBN and their subregions that are highly related to neurological and psychiatric pathology were then manually labeled based on the atlas set with the assistance of an image border-enhancement process. Especially, the bilateral thalamus was delineated into 64 detailed subregions referring to the Schaltenbrand-Wahren stereotactic atlas. To our best knowledge, the histological-consistent thalamic nucleus parcellation map is well defined for the first time in the MNI space. Compared with existing atlases that emphasizing DBN parcellation, the newly proposed atlas outperforms on the task of atlas-guided individual brain image DBN segmentation both in accuracy and robustness. Moreover, we applied the proposed DBN parcellation map to conduct detailed identification of the pathology-related iron content alterations in subcortical nuclei for Parkinson's Disease (PD) patients. We envision that the MuSus-100 atlas can play a crucial role in improving the accuracy of DBN segmentation for the research of neurological and psychiatric disease progress and also be helpful for target planning in deep brain stimulation surgery.

Patient-specific structural connectivity informs outcomes of responsive neurostimulation for temporal lobe epilepsy

OBJECTIVE

Responsive neurostimulation is an effective therapy for patients with refractory mesial temporal lobe epilepsy. However, clinical outcomes are variable, few patients become seizure-free, and the optimal stimulation location is currently undefined. The aim of this study was to quantify responsive neurostimulation in the mesial temporal lobe, identify stimulation-dependent networks associated with seizure reduction, and determine if stimulation location or stimulation-dependent networks inform outcomes.

METHODS

We modeled patient-specific volumes of tissue activated and created probabilistic stimulation maps of local regions of stimulation across a retrospective cohort of 22 patients with mesial temporal lobe epilepsy. We then mapped the network stimulation effects by seeding tractography from the volume of tissue activated with both patient-specific and normative diffusion-weighted imaging. We identified networks associated with seizure reduction across patients using the patient-specific tractography maps and then predicted seizure reduction across the cohort.

RESULTS

Patient-specific stimulation-dependent connectivity was correlated with responsive neurostimulation effectiveness after cross-validation (p = .03); however, normative connectivity derived from healthy subjects was not (p = .44). Increased connectivity from the volume of tissue activated to the medial prefrontal cortex, cingulate cortex, and precuneus was associated with greater seizure reduction.

SIGNIFICANCE

Overall, our results suggest that the therapeutic effect of responsive neurostimulation may be mediated by specific networks connected to the volume of tissue activated. In addition, patient-specific tractography was required to identify structural networks correlated with outcomes. It is therefore likely that altered connectivity in patients with epilepsy may be associated with the therapeutic effect and that utilizing patient-specific imaging could be important for future studies. The structural networks identified here may be utilized to target stimulation in the mesial temporal lobe and to improve seizure reduction for patients treated with responsive neurostimulation.

New Targets and New Technologies in the Treatment of Parkinson's Disease: A Narrative Review

Parkinson's disease (PD) is a progressive neurodegenerative disease, whose main neuropathological finding is pars compacta degeneration due to the accumulation of Lewy bodies and Lewy neurites, and subsequent dopamine depletion. This leads to an increase in the activity of the subthalamic nucleus (STN) and the internal globus pallidus (GPi). Understanding functional anatomy is the key to understanding and developing new targets and new technologies that could potentially improve motor and non-motor symptoms in PD. Currently, the classical targets are insufficient to improve the entire wide spectrum of symptoms in PD (especially non-dopaminergic ones) and none are free of the side effects which are not only associated with the procedure, but with the targets themselves. The objective of this narrative review is to show new targets in DBS surgery as well as new technologies that are under study and have shown promising results to date. The aim is to give an overview of these new targets, as well as their limitations, and describe the current studies in this research field in order to review ongoing research that will probably become effective and routine treatments for PD in the near future.

A Control-Theoretical System for Modulating Hippocampal Gamma Oscillations using Stimulation of the Posterior Cingulate Cortex

Closed-loop stimulation for targeted modulation of brain signals has emerged as a promising strategy for episodic memory restoration. In parallel, closed-loop neuromodulation strategies have been applied to treat brain conditions including drug-resistant depression, Parkinson’s Disease, and epilepsy. In this study, we seek to apply control theoretical principles to achieve closed loop modulation of hippocampal oscillatory activity. We focus on hippocampal gamma power, a signal with an established association for episodic memory processing, which may be a promising ‘biomarker’ for the modulation of memory performance. To develop a closed-loop stimulation paradigm that effectively modulates hippocampal gamma power, we use a novel data-set in which open-loop stimulation was applied to the posterior cingulate cortex and hippocampal gamma power was recorded during the encoding of episodic memories. The dataset was used to design and evaluate a linear quadratic integral (LQI) servo-controller in order to determine its viability for in-vivo use. In our simulation framework, we demonstrate that applying an LQI servo controller based on an autoregressive with exogenous input (ARX) plant model achieves effective control of hippocampal gamma power in 15 out of 17 experimental subjects. We demonstrate that we are able to modulate gamma power using stimulation thresholds that are physiologically safe and on time scales that are reasonable for application in a clinical system. We outline further experimentation to test our proposed system and compare our findings to emerging closed-loop neuromodulation strategies.

Computation of Activating Fields for Approximation of the Orientation-Specific Neural Response to Electrical Stimulation

Computational methods of determining the response of neural tissue to electrical stimulation have demonstrated value for the development of novel devices and the programming of neuromodulation therapies. Detailed biophysical models are excessively computationally intensive for many applications; simple metrics to approximate activation can speed up progress in this area. The activating function provides such a useful metric. However, this measure, defined for a specific axon orientation, is not immediately applicable to computed electric fields to assess their effects. We demonstrate a method for computation of the activating function generalized to a field in order to allow rapid computation of the effects of stimulation on neural tissue while preserving information on axon orientation. Clinical Relevance- This demonstrates a useful method of approximating the effect of electrical stimulation on nervous tissue for the development of devices and the optimization of parameters for electrical neuromodulation.

Deep brain stimulation of the "medial forebrain bundle": sustained efficacy of antidepressant effect over years

Deep brain stimulation (DBS) to the superolateral branch of the medial forebrain bundle (MFB) has emerged as a quite efficacious therapy for treatment resistant depression (TRD), leading to rapid antidepressant effects. In this study, we complete our assessment of our first 10 enrolled patients throughout one year post-implantation, showing sustained antidepressant effect up to 5 years. The primary outcome measure was a 50% reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score, which was interpreted as a response. Deterministic fiber tracking was used to individually map the target area. An insertional effect was seen during the 4-week sham stimulation phase (29% mean MADRS reduction, p = 0.02). However, after 2 weeks of initiating stimulation, five patients met response criteria (47% mean MADRS reduction, p < 0.001). One patient withdrew from study participation at 6 weeks. Twelve weeks after initiating stimulation, six of nine remaining patients had a >50% decrease in MADRS scores relative to baseline (52% mean MADRS reduction, p = 0.001); these same six patients continued to meet response criteria at 52 weeks (63% overall mean MADRS reduction, p < 0.001). Four of five patients who achieved the 5-year time point analysis continued to be responders (81% mean MADRS reduction, p < 0.001). Evaluation of modulated fiber tracts reveals significant common prefrontal/orbitofrontal connectivity to the target region in all responders. Key points learned from this study that we can incorporate in future protocols to better elucidate the effect of this therapy are a longer blinded sham stimulation phase and use of scheduled discontinuation concomitant with functional imaging.

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.

The Accuracy of Imaging Guided Targeting with Microelectrode Recoding in Subthalamic Nucleus for Parkinson's Disease: A Single-Center Experience

Background:

Accurate electrode targeting was essential for the efficacy of deep brain stimulation (DBS). There is ongoing debate about the necessary of microelectrode recording (MER) in subthalamic nucleus (STN)-DBS surgery for accurate targeting.

Objective:

This study aimed to analyze the accuracy of imaging-guided awake DBS with MER in STN for Parkinson’s disease in a single center.

Methods:

The authors performed a retrospective analysis of 161 Parkinson’s disease patients undergoing STN-DBS at our center from March 2013 to June 2021. The implantation was performed by preoperative magnetic resonance imaging (MRI)-based direct targeting with intraoperative MER and macrostimulation testing. 285 electrode tracks with preoperative and postoperative coordinates were included to calculate the placement error in STN targeting.

Results:

85.9% of electrodes guided by preoperative MRI were implanted without intraoperative adjustment. 31 (10.2%) and 12 (3.9%) electrodes underwent intraoperative adjustment due to MER and intraoperative testing, respectively. We found 86.2% (245/285) of electrodes with trajectory error ≤2 mm. The MER physiological signals length < 4 mm and ≥4 mm group showed trajectory error > 2 mm in 38.0% and 8.8% of electrodes, respectively. Compared to non-adjustment electrodes, the final positioning of MER-adjusted electrodes deviated from the center of STN.

Conclusion:

The preoperative MRI guided STN targeting results in approximately 14% cases that require electrode repositioning. MER physiological signals length < 4 mm at first penetration implied deviation off planned target. MER combined with intraoperative awake testing served to rescue such deviation based on MRI alone.

Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization

Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.