Circadian changes in aperiodic activity are correlated with seizure reduction in patients with mesial temporal lobe epilepsy treated with responsive neurostimulation.

OBJECTIVES: Responsive neurostimulation (RNS) is an established therapy for drug-resistant epilepsy that delivers direct electrical brain stimulation in response to detected epileptiform activity. However, despite an overall reduction in seizure frequency, clinical outcomes are variable, and few patients become seizure-free. The aim of this retrospective study was to evaluate aperiodic electrophysiological activity, associated with excitation/inhibition balance, as a novel electrographic biomarker of seizure reduction to aid early prognostication of the clinical response to RNS. METHODS: We identified patients with intractable mesial temporal lobe epilepsy who were implanted with the RNS System between 2015 and 2021 at the University of Utah. We parameterized the neural power spectra from intracranial RNS System recordings during the first 3 months following implantation into aperiodic and periodic components. We then correlated circadian changes in aperiodic and periodic parameters of baseline neural recordings with seizure reduction at the most recent follow-up. RESULTS: Seizure reduction was correlated significantly with a patient's average change in the day/night aperiodic exponent (r = .50, p = .016, n = 23 patients) and oscillatory alpha power (r = .45, p = .042, n = 23 patients) across patients for baseline neural recordings. The aperiodic exponent reached its maximum during nighttime hours (12 a.m. to 6 a.m.) for most responders (i.e., patients with at least a 50% reduction in seizures). SIGNIFICANCE: These findings suggest that circadian modulation of baseline broadband activity is a biomarker of response to RNS early during therapy. This marker has the potential to identify patients who are likely to respond to mesial temporal RNS. Furthermore, we propose that less day/night modulation of the aperiodic exponent may be related to dysfunction in excitation/inhibition balance and its interconnected role in epilepsy, sleep, and memory.

Closed-loop stimulation in periods with less epileptiform activity drives improved epilepsy outcomes.

In patients with drug-resistant epilepsy, electrical stimulation of the brain in response to epileptiform activity can make seizures less frequent and debilitating. This therapy, known as closed-loop responsive neurostimulation (RNS), aims to directly halt seizure activity via targeted stimulation of a burgeoning seizure. Rather than immediately stopping seizures as they start, many RNS implants produce slower, long-lasting changes in brain dynamics that better predict clinical outcomes. Here we hypothesize that stimulation during brain states with less epileptiform activity drives long-term changes that restore healthy brain networks. To test this, we quantified stimulation episodes during low- and high-risk brain states-that is, stimulation during periods with a lower or higher risk of generating epileptiform activity-in a cohort of 40 patients treated with RNS. More frequent stimulation in tonic low-risk states and out of rhythmic high-risk states predicted seizure reduction. Additionally, stimulation events were more likely to be phase-locked to prolonged episodes of abnormal activity for intermediate and poor responders when compared to super-responders, consistent with the hypothesis that improved outcomes are driven by stimulation during low-risk states. These results support the hypothesis that stimulation during low-risk periods might underlie the mechanisms of RNS, suggesting a relationship between temporal patterns of neuromodulation and plasticity that facilitates long-term seizure reduction.

A novel naïve Bayes approach to identifying grooming behaviors in the force-plate actometric platform.

BACKGROUND: Self-grooming behavior in rodents serves as a valuable behavioral index for investigating stereotyped and perseverative responses. Most current grooming analyses rely on video observation, which lacks standardization, efficiency, and quantitative information about force. To address these limitations, we developed an automated paradigm to analyze grooming using a force-plate actometer. NEW METHOD: Grooming behavior is quantified by calculating ratios of relevant movement power spectral bands. These ratios are input into a naïve Bayes classifier, trained with manual video observations. The effectiveness of this method was tested using CIN-d mice, an animal model developed through early-life depletion of striatal cholinergic interneurons (CIN-d) and featuring prolonged grooming responses to acute stressors. Behavioral monitoring was simultaneously conducted on the force-place actometer and by video recording. RESULTS: The naïve Bayes approach achieved 93.7% accurate classification and an area under the receiver operating characteristic curve of 0.894. We confirmed that male CIN-d mice displayed significantly longer grooming durations than controls. However, this elevation was not correlated with increases in grooming force. Notably, the dopaminergic antagonist haloperidol reduced grooming force and duration. COMPARISON WITH EXISTING METHODS: In contrast to observation-based approaches, our method affords rapid, unbiased, and automated assessment of grooming duration, frequency, and force. CONCLUSIONS: Our novel approach enables fast and accurate automated detection of grooming behaviors. This method holds promise for high-throughput assessments of grooming stereotypies in animal models of neuropsychiatric disorders.

A novel naïve Bayes approach to identifying grooming behaviors in the force-plate actometric platform.

Background: Self-grooming behavior in rodents serves as a valuable model for investigating stereotyped and perseverative responses. Most current grooming analyses primarily rely on video observation, which lacks standardization, efficiency, and quantitative information about force. To address these limitations, we developed an automated paradigm to analyze grooming using a force-plate actometer. New Method: Grooming behavior is quantified by calculating ratios of relevant movement power spectral bands. These ratios are then input into a naïve Bayes classifier, trained with manual video observations. To validate the effectiveness of this method, we applied it to the behavioral analysis of the early-life striatal cholinergic interneuron depletion (CIN-d) mouse, a model of tic pathophysiology recently developed in our laboratory, which exhibits prolonged grooming responses to acute stressors. Behavioral monitoring was simultaneously conducted on the force-place actometer and by video recording. Results: The naïve Bayes approach achieved 93.7% accurate classification and an area under the receiver operating characteristic curve of 0.894. We confirmed that male CIN-d mice displayed significantly longer grooming durations compared to controls. However, this elevation was not correlated with increases in grooming force. Notably, haloperidol, a benchmark therapy for tic disorders, reduced both grooming force and duration. Comparison with Existing Methods: In contrast to observation-based approaches, our method affords rapid, unbiased, and automated assessment of grooming duration, frequency, and force. Conclusions: Our novel approach enables fast and accurate automated detection of grooming behaviors. This method holds promise for high-throughput assessments of grooming stereotypies in animal models of tic disorders and other psychiatric conditions.

Chronic intracranial recordings after resection for epilepsy reveal a "running down" of epileptiform activity.

We describe an electrical "running down" phenomenon and also a consistent spectral change (in the aperiodic component of the power spectrum) derived from chronic interictal electrocorticography (ECoG) after surgery in a patient with drug-resistant epilepsy. These data were recorded using a closed-loop neurostimulation system that was implanted after resection. The patient has been seizure-free for 2.5 years since resection without requiring the neurostimulator to be turned on to stimulate. Concurrently, there was an exponential decrease in the number of epileptiform electrographic detections recorded by the device, particularly over the first 26 weeks, indicative of an electrical running down phenomenon as the brain adapted to an extended period of seizure freedom. We also find that the aperiodic exponent of the power spectrum gradually decreases over time. The aperiodic component of intracranial ECoG may represent a novel marker of epileptogenicity, independent of seizures.

Subsets of cortico-cortical evoked potentials propagate as traveling waves.

Emerging evidence suggests that the temporal dynamics of cortico-cortical evoked potentials (CCEPs) may be used to characterize the patterns of information flow between and within brain networks. At present, however, the spatiotemporal dynamics of CCEP propagation cortically and subcortically are incompletely understood. We hypothesized that CCEPs propagate as an evoked traveling wave emanating from the site of stimulation. To elicit CCEPs, we applied single-pulse stimulation to stereoelectroencephalography (SEEG) electrodes implanted in 21 adult patients with intractable epilepsy. For each robust CCEP, we measured the timing of the maximal descent in evoked local field potentials and broadband high-gamma power (70-150 Hz) envelopes relative to the distance between the recording and stimulation contacts using three different metrics (i.e., Euclidean distance, path length, geodesic distance), representing direct, subcortical, and transcortical propagation, respectively. Many evoked responses to single-pulse electrical stimulation appear to propagate as traveling waves (~17-30%), even in the sparsely sampled, three-dimensional SEEG space. These results provide new insights into the spatiotemporal dynamics of CCEP propagation.

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.

Corrigendum: LeGUI: A Fast and Accurate Graphical User Interface for Automated Detection and Anatomical Localization of Intracranial Electrodes.

[This corrects the article DOI: 10.3389/fnins.2021.769872.].

Probabilistic comparison of gray and white matter coverage between depth and surface intracranial electrodes in epilepsy.

In this study, we quantified the coverage of gray and white matter during intracranial electroencephalography in a cohort of epilepsy patients with surface and depth electrodes. We included 65 patients with strip electrodes (n = 12), strip and grid electrodes (n = 24), strip, grid, and depth electrodes (n = 7), or depth electrodes only (n = 22). Patient-specific imaging was used to generate probabilistic gray and white matter maps and atlas segmentations. Gray and white matter coverage was quantified using spherical volumes centered on electrode centroids, with radii ranging from 1 to 15 mm, along with detailed finite element models of local electric fields. Gray matter coverage was highly dependent on the chosen radius of influence (RoI). Using a 2.5 mm RoI, depth electrodes covered more gray matter than surface electrodes; however, surface electrodes covered more gray matter at RoI larger than 4 mm. White matter coverage and amygdala and hippocampal coverage was greatest for depth electrodes at all RoIs. This study provides the first probabilistic analysis to quantify coverage for different intracranial recording configurations. Depth electrodes offer increased coverage of gray matter over other recording strategies if the desired signals are local, while subdural grids and strips sample more gray matter if the desired signals are diffuse.

LeGUI: A Fast and Accurate Graphical User Interface for Automated Detection and Anatomical Localization of Intracranial Electrodes.

Accurate anatomical localization of intracranial electrodes is important for identifying the seizure foci in patients with epilepsy and for interpreting effects from cognitive studies employing intracranial electroencephalography. Localization is typically performed by coregistering postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI). Electrodes are then detected in the CT, and the corresponding brain region is identified using the MRI. Many existing software packages for electrode localization chain together separate preexisting programs or rely on command line instructions to perform the various localization steps, making them difficult to install and operate for a typical user. Further, many packages provide solutions for some, but not all, of the steps needed for confident localization. We have developed software, Locate electrodes Graphical User Interface (LeGUI), that consists of a single interface to perform all steps needed to localize both surface and depth/penetrating intracranial electrodes, including coregistration of the CT to MRI, normalization of the MRI to the Montreal Neurological Institute template, automated electrode detection for multiple types of electrodes, electrode spacing correction and projection to the brain surface, electrode labeling, and anatomical targeting. The software is written in MATLAB, core image processing is performed using the Statistical Parametric Mapping toolbox, and standalone executable binaries are available for Windows, Mac, and Linux platforms. LeGUI was tested and validated on 51 datasets from two universities. The total user and computational time required to process a single dataset was approximately 1 h. Automatic electrode detection correctly identified 4362 of 4695 surface and depth electrodes with only 71 false positives. Anatomical targeting was verified by comparing electrode locations from LeGUI to locations that were assigned by an experienced neuroanatomist. LeGUI showed a 94% match with the 482 neuroanatomist-assigned locations. LeGUI combines all the features needed for fast and accurate anatomical localization of intracranial electrodes into a single interface, making it a valuable tool for intracranial electrophysiology research.

Computational investigation of the impact of deep brain stimulation contact size and shape on neural selectivity.

Background.Understanding neural selectivity is essential for optimizing medical applications of deep brain stimulation (DBS). We previously showed that modulation of the DBS waveform can induce changes in orientation-based selectivity, and that lengthening of DBS pulses or directional segmentation can reduce preferential selectivity for large axons. In this work, we sought to investigate a simple, but important question from a generalized perspective: how do the size and shape of the contact influence neural selectivity?Methods.We created multicompartment neuron models for several axon diameters and used finite element modeling with standard-sized cylindrical leads to determine the effects on changing contact size and shape on axon activation profiles and volumes of tissue activated. Contacts ranged in size from 0.04 to 16 mm2, compared with a standard size of 6 mm2.Results.We found that changes in contact size are predicted to induce substantial changes in orientation-based selectivity in the context of a cylindrical lead, and changes in contact width or height can alter this selectivity. Smaller contact sizes were more effective in constraining neural activation to small, nearby axons. However, micro-scale contacts enable only limited spread of neural activation before exceeding standard charge density limitations; further, energetic efficiency is optimized by somewhat larger contacts.Interpretations.Small-scale contacts may be optimal for constraining stimulation in nearby grey matter and avoiding orientation-selective activation. However, given charge density limitations and energy inefficiency of micro-scale contacts, we predict that contacts sized similarly to or slightly smaller than segmented clinical leads may optimize energy efficiency while avoiding charge density limitations.

Structural connectivity predicts clinical outcomes of deep brain stimulation for Tourette syndrome.

Deep brain stimulation may be an effective therapy for select cases of severe, treatment-refractory Tourette syndrome; however, patient responses are variable, and there are no reliable methods to predict clinical outcomes. The objectives of this retrospective study were to identify the stimulation-dependent structural networks associated with improvements in tics and comorbid obsessive-compulsive behaviour, compare the networks across surgical targets, and determine if connectivity could be used to predict clinical outcomes. Volumes of tissue activated for a large multisite cohort of patients (n = 66) implanted bilaterally in globus pallidus internus (n = 34) or centromedial thalamus (n = 32) were used to generate probabilistic tractography to form a normative structural connectome. The tractography maps were used to identify networks that were correlated with improvement in tics or comorbid obsessive-compulsive behaviour and to predict clinical outcomes across the cohort. The correlated networks were then used to generate 'reverse' tractography to parcellate the total volume of stimulation across all patients to identify local regions to target or avoid. The results showed that for globus pallidus internus, connectivity to limbic networks, associative networks, caudate, thalamus, and cerebellum was positively correlated with improvement in tics; the model predicted clinical improvement scores (P = 0.003) and was robust to cross-validation. Regions near the anteromedial pallidum exhibited higher connectivity to the positively correlated networks than posteroventral pallidum, and volume of tissue activated overlap with this map was significantly correlated with tic improvement (P < 0.017). For centromedial thalamus, connectivity to sensorimotor networks, parietal-temporal-occipital networks, putamen, and cerebellum was positively correlated with tic improvement; the model predicted clinical improvement scores (P = 0.012) and was robust to cross-validation. Regions in the anterior/lateral centromedial thalamus exhibited higher connectivity to the positively correlated networks, but volume of tissue activated overlap with this map did not predict improvement (P > 0.23). For obsessive-compulsive behaviour, both targets showed that connectivity to the prefrontal cortex, orbitofrontal cortex, and cingulate cortex was positively correlated with improvement; however, only the centromedial thalamus maps predicted clinical outcomes across the cohort (P = 0.034), but the model was not robust to cross-validation. Collectively, the results demonstrate that the structural connectivity of the site of stimulation are likely important for mediating symptom improvement, and the networks involved in tic improvement may differ across surgical targets. These networks provide important insight on potential mechanisms and could be used to guide lead placement and stimulation parameter selection, as well as refine targets for neuromodulation therapies for Tourette syndrome.

Neural selectivity, efficiency, and dose equivalence in deep brain stimulation through pulse width tuning and segmented electrodes.

BACKGROUND: Achieving deep brain stimulation (DBS) dose equivalence is challenging, especially with pulse width tuning and directional contacts. Further, the precise effects of pulse width tuning are unknown, and recent reports of the effects of pulse width tuning on neural selectivity are at odds with classic biophysical studies. METHODS: We created multicompartment neuron models for two axon diameters and used finite element modeling to determine extracellular influence from standard and segmented electrodes. We analyzed axon activation profiles and calculated volumes of tissue activated. RESULTS: We find that long pulse widths focus the stimulation effect on small, nearby fibers, suppressing distant white matter tract activation (responsible for some DBS side effects) and improving battery utilization when equivalent activation is maintained for small axons. Directional leads enable similar benefits to a greater degree. Reexamining previous reports of short pulse stimulation reducing side effects, we explore a possible alternate explanation: non-dose equivalent stimulation may have resulted in reduced spread of neural activation. Finally, using internal capsule avoidance as an example in the context of subthalamic stimulation, we present a patient-specific model to show how long pulse widths could help increase the biophysical therapeutic window. DISCUSSION: We find agreement with classic studies and predict that long pulse widths may focus the stimulation effect on small, nearby fibers and improve power consumption. While future pre-clinical and clinical work is necessary regarding pulse width tuning, it is clear that future studies must ensure dose equivalence, noting that energy- and charge-equivalent amplitudes do not result in equivalent spread of neural activation when changing pulse width.

Activation robustness with directional leads and multi-lead configurations in deep brain stimulation.

OBJECTIVE: Clinical outcomes from deep brain stimulation (DBS) can be highly variable, and two critical factors underlying this variability are the location and type of stimulation. In this study we quantified how robustly DBS activates a target region when taking into account a range of different lead designs and realistic variations in placement. The objective of the study is to assess the likelihood of achieving target activation. APPROACH: We performed finite element computational modeling and established a metric of performance robustness to evaluate the ability of directional and multi-lead configurations to activate target fiber pathways while taking into account location variability. A more robust lead configuration produces less variability in activation across all stimulation locations around the target. MAIN RESULTS: Directional leads demonstrated higher overall performance robustness compared to axisymmetric leads, primarily 1-2 mm outside of the target. Multi-lead configurations demonstrated higher levels of robustness compared to any single lead due to distribution of electrodes in a broader region around the target. SIGNIFICANCE: Robustness measures can be used to evaluate the performance of existing DBS lead designs and aid in the development of novel lead designs to better accommodate known variability in lead location and orientation. This type of analysis may also be useful to understand how DBS clinical outcome variability is influenced by lead location among groups of patients.

The µDBS: Multiresolution, Directional Deep Brain Stimulation for Improved Targeting of Small Diameter Fibers.

Directional deep brain stimulation (DBS) leads have recently been approved and used in patients, and growing evidence suggests that directional contacts can increase the therapeutic window by redirecting stimulation to the target region while avoiding side-effect-inducing regions. We outline the design, fabrication, and testing of a novel directional DBS lead, the µDBS, which utilizes microscale contacts to increase the spatial resolution of stimulation steering and improve the selectivity in targeting small diameter fibers. We outline the steps of fabrication of the µDBS, from an integrated circuit design to post-processing and validation testing. We tested the onboard digital circuitry for programming fidelity, characterized impedance for a variety of electrode sizes, and demonstrated functionality in a saline bath. In a computational experiment, we determined that reduced electrode sizes focus the stimulation effect on small, nearby fibers. Smaller electrode sizes allow for a relative decrease in small-diameter axon thresholds compared to thresholds of large-diameter fibers, demonstrating a focusing of the stimulation effect within small, and possibly therapeutic, fibers. This principle of selectivity could be useful in further widening the window of therapy. The µDBS offers a unique, multiresolution design in which any combination of microscale contacts can be used together to function as electrodes of various shapes and sizes. Multiscale electrodes could be useful in selective neural targeting for established neurological targets and in exploring novel treatment targets for new neurological indications.

Evaluation of methodologies for computing the deep brain stimulation volume of tissue activated.

OBJECTIVE: Computational models are a popular tool for predicting the effects of deep brain stimulation (DBS) on neural tissue. One commonly used model, the volume of tissue activated (VTA), is computed using multiple methodologies. We quantified differences in the VTAs generated by five methodologies: the traditional axon model method, the electric field norm, and three activating function based approaches-the activating function at each grid point in the tangential direction (AF-Tan) or in the maximally activating direction (AF-3D), and the maximum activating function along the entire length of a tangential fiber (AF-Max). APPROACH: We computed the VTA using each method across multiple stimulation settings. The resulting volumes were compared for similarity, and the methodologies were analyzed for their differences in behavior. MAIN RESULTS: Activation threshold values for both the electric field norm and the activating function varied with regards to electrode configuration, pulse width, and frequency. All methods produced highly similar volumes for monopolar stimulation. For bipolar electrode configurations, only the maximum activating function along the tangential axon method, AF-Max, produced similar volumes to those produced by the axon model method. Further analysis revealed that both of these methods are biased by their exclusive use of tangential fiber orientations. In contrast, the activating function in the maximally activating direction method, AF-3D, produces a VTA that is free of axon orientation and projection bias. SIGNIFICANCE: Simulating tangentially oriented axons, the standard approach of computing the VTA, is too computationally expensive for widespread implementation and yields results biased by the assumption of tangential fiber orientation. In this work, we show that a computationally efficient method based on the activating function, AF-Max, reliably reproduces the VTAs generated by direct axon modeling. Further, we propose another method, AF-3D as a potentially superior model for representing generic neural tissue activation.

Anodic stimulation misunderstood: preferential activation of fiber orientations with anodic waveforms in deep brain stimulation.

OBJECTIVE: During deep brain stimulation (DBS), it is well understood that extracellular cathodic stimulation can cause activation of passing axons. Activation can be predicted from the second derivative of the electric potential along an axon, which depends on axonal orientation with respect to the stimulation source. We hypothesize that fiber orientation influences activation thresholds and that fiber orientations can be selectively targeted with DBS waveforms. APPROACH: We used bioelectric field and multicompartment NEURON models to explore preferential activation based on fiber orientation during monopolar or bipolar stimulation. Preferential fiber orientation was extracted from the principal eigenvectors and eigenvalues of the Hessian matrix of the electric potential. We tested cathodic, anodic, and charge-balanced pulses to target neurons based on fiber orientation in general and clinical scenarios. MAIN RESULTS: Axons passing the DBS lead have positive second derivatives around a cathode, whereas orthogonal axons have positive second derivatives around an anode, as indicated by the Hessian. Multicompartment NEURON models confirm that passing fibers are activated by cathodic stimulation, and orthogonal fibers are activated by anodic stimulation. Additionally, orthogonal axons have lower thresholds compared to passing axons. In a clinical scenario, fiber pathways associated with therapeutic benefit can be targeted with anodic stimulation at 50% lower stimulation amplitudes. SIGNIFICANCE: Fiber orientations can be selectively targeted with simple changes to the stimulus waveform. Anodic stimulation preferentially activates orthogonal fibers, approaching or leaving the electrode, at lower thresholds for similar therapeutic benefit in DBS with decreased power consumption.

Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes.

OBJECTIVE: Deep brain stimulation (DBS) is a growing treatment option for movement and psychiatric disorders. As DBS technology moves toward directional leads with increased numbers of smaller electrode contacts, trial-and-error methods of manual DBS programming are becoming too time-consuming for clinical feasibility. We propose an algorithm to automate DBS programming in near real-time for a wide range of DBS lead designs. APPROACH: Magnetic resonance imaging and diffusion tensor imaging are used to build finite element models that include anisotropic conductivity. The algorithm maximizes activation of target tissue and utilizes the Hessian matrix of the electric potential to approximate activation of neurons in all directions. We demonstrate our algorithm's ability in an example programming case that targets the subthalamic nucleus (STN) for the treatment of Parkinson's disease for three lead designs: the Medtronic 3389 (four cylindrical contacts), the direct STNAcute (two cylindrical contacts, six directional contacts), and the Medtronic-Sapiens lead (40 directional contacts). MAIN RESULTS: The optimization algorithm returns patient-specific contact configurations in near real-time-less than 10 s for even the most complex leads. When the lead was placed centrally in the target STN, the directional leads were able to activate over 50% of the region, whereas the Medtronic 3389 could activate only 40%. When the lead was placed 2 mm lateral to the target, the directional leads performed as well as they did in the central position, but the Medtronic 3389 activated only 2.9% of the STN. SIGNIFICANCE: This DBS programming algorithm can be applied to cylindrical electrodes as well as novel directional leads that are too complex with modern technology to be manually programmed. This algorithm may reduce clinical programming time and encourage the use of directional leads, since they activate a larger volume of the target area than cylindrical electrodes in central and off-target lead placements.

Subthalamic deep brain stimulation reduces pathological information transmission to the thalamus in a rat model of parkinsonism.

The degeneration of dopaminergic neurons in the substantia nigra pars compacta leads to parkinsonian motor symptoms via changes in electrophysiological activity throughout the basal ganglia. High-frequency deep brain stimulation (DBS) partially treats these symptoms, but the mechanisms are unclear. We hypothesize that motor symptoms of Parkinson's disease (PD) are associated with increased information transmission from basal ganglia output neurons to motor thalamus input neurons and that therapeutic DBS of the subthalamic nucleus (STN) treats these symptoms by reducing this extraneous information transmission. We tested these hypotheses in a unilateral, 6-hydroxydopamine-lesioned rodent model of hemiparkinsonism. Information transfer between basal ganglia output neurons and motor thalamus input neurons increased in both the orthodromic and antidromic directions with hemiparkinsonian (hPD) onset, and these changes were reversed by behaviorally therapeutic STN-DBS. Omnidirectional information increases in the parkinsonian state underscore the detrimental nature of that pathological information and suggest a loss of information channel independence. Therapeutic STN-DBS reduced that pathological information, suggesting an effective increase in the number of independent information channels. We interpret these data with a model in which pathological information and fewer information channels diminishes the scope of possible motor activities, driving parkinsonian symptoms. In this model, STN-DBS restores information-channel independence by eliminating or masking the parkinsonism-associated information, and thus enlarges the scope of possible motor activities, alleviating parkinsonian symptoms.