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.

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.

Deep cerebellar stimulation reduces ataxic motor symptoms in the shaker rat.

OBJECTIVE: Degenerative cerebellar ataxias (DCAs) affect up to 1 in 5,000 people worldwide, leading to incoordination, tremor, and falls. Loss of Purkinje cells, nearly universal across DCAs, dysregulates the dentatothalamocortical network. To address the paucity of treatment strategies, we developed an electrical stimulation-based therapy for DCAs targeting the dorsal dentate nucleus. METHODS: We tested this therapeutic strategy in the Wistar Furth shaker rat model of Purkinje cell loss resulting in tremor and ataxia. We implanted shaker rats with stimulating electrodes targeted to the dorsal dentate nucleus and tested a spectrum of frequencies ranging from 4 to 180 Hz. RESULTS: Stimulation at 30 Hz most effectively reduced motor symptoms. Stimulation frequencies >100 Hz, commonly used for parkinsonism and essential tremor, worsened incoordination, and frequencies within the tremor physiologic range may worsen tremor. INTERPRETATION: Low-frequency deep cerebellar stimulation may provide a novel strategy for treating motor symptoms of degenerative cerebellar ataxias. Ann Neurol 2019;85:681-690.

Prospects for transcranial temporal interference stimulation in humans: A computational study.

Transcranial alternating current stimulation (tACS) is a noninvasive method used to modulate activity of superficial brain regions. Deeper and more steerable stimulation could potentially be achieved using transcranial temporal interference stimulation (tTIS): two high-frequency alternating fields interact to produce a wave with an envelope frequency in the range thought to modulate neural activity. Promising initial results have been reported for experiments with mice. In this study we aim to better understand the electric fields produced with tTIS and examine its prospects in humans through simulations with murine and human head models. A murine head finite element model was used to simulate previously published experiments of tTIS in mice. With a total current of 0.776 mA, tTIS electric field strengths up to 383 V/m were reached in the modeled mouse brain, affirming experimental results indicating that suprathreshold stimulation is possible in mice. Using a detailed anisotropic human head model, tTIS was simulated with systematically varied electrode configurations and input currents to investigate how these parameters influence the electric fields. An exhaustive search with 88 electrode locations covering the entire head (146M current patterns) was employed to optimize tTIS for target field strength and focality. In all analyses, we investigated maximal effects and effects along the predominant orientation of local neurons. Our results showed that it was possible to steer the peak tTIS field by manipulating the relative strength of the two input fields. Deep brain areas received field strengths similar to conventional tACS, but with less stimulation in superficial areas. Maximum field strengths in the human model were much lower than in the murine model, too low to expect direct stimulation effects. While field strengths from tACS were slightly higher, our results suggest that tTIS is capable of producing more focal fields and allows for better steerability. Finally, we present optimal four-electrode current patterns to maximize tTIS in regions of the pallidum (0.37 V/m), hippocampus (0.24 V/m) and motor cortex (0.57 V/m).

Correlation between cortical beta power and gait speed is suppressed in a parkinsonian model, but restored by therapeutic deep brain stimulation.

The motor cortex and subthalamic nucleus (STN) of patients with Parkinson's disease (PD) exhibit abnormally high levels of electrophysiological oscillations in the ~12-35 Hz beta-frequency range. Recent studies have shown that beta is partly carried forward to regulate future motor states in the healthy condition, suggesting that steady state beta power is lower when a sequence of movements occurs in a short period of time, such as during fast gait. However, whether this relationship between beta power and motor states persists upon parkinsonian onset or in response to effective therapy is unclear. Using a 6-hydroxy dopamine (6-OHDA) rat model of PD and a custom-built behavioral and neurophysiological recording system, we aimed to elucidate a better understanding of the mechanisms underlying cortical beta power and PD symptoms. In addition to elevated levels of beta oscillations, we show that parkinsonian onset was accompanied by a decoupling of movement intensity - quantified as gait speed - from cortical beta power. Although subthalamic deep brain stimulation (DBS) reduced general levels of beta oscillations in the cortex of all PD animals, the brain's capacity to regulate steady state levels of beta power as a function of movement intensity was only restored in animals with therapeutic DBS. We propose that, in addition to lowering general levels of cortical beta power, restoring the brain's ability to maintain this inverse relationship is critical for effective symptom suppression.

Hard real-time closed-loop electrophysiology with the Real-Time eXperiment Interface (RTXI).

The ability to experimentally perturb biological systems has traditionally been limited to static pre-programmed or operator-controlled protocols. In contrast, real-time control allows dynamic probing of biological systems with perturbations that are computed on-the-fly during experimentation. Real-time control applications for biological research are available; however, these systems are costly and often restrict the flexibility and customization of experimental protocols. The Real-Time eXperiment Interface (RTXI) is an open source software platform for achieving hard real-time data acquisition and closed-loop control in biological experiments while retaining the flexibility needed for experimental settings. RTXI has enabled users to implement complex custom closed-loop protocols in single cell, cell network, animal, and human electrophysiology studies. RTXI is also used as a free and open source, customizable electrophysiology platform in open-loop studies requiring online data acquisition, processing, and visualization. RTXI is easy to install, can be used with an extensive range of external experimentation and data acquisition hardware, and includes standard modules for implementing common electrophysiology protocols.

Deep brain stimulation exacerbates hypokinetic dysarthria in a rat model of Parkinson's disease.

Motor symptoms of Parkinson's disease (PD) follow the degeneration of dopaminergic neurons in the substantia nigra pars compacta. Deep brain stimulation (DBS) treats some parkinsonian symptoms, such as tremor, rigidity, and bradykinesia, but may worsen certain medial motor symptoms, including hypokinetic dysarthria. The mechanisms by which DBS exacerbates dysarthria while improving other symptoms are unclear and difficult to study in human patients. This study proposes an animal model of DBS-exacerbated dysarthria. We use the unilateral, 6-hydroxydopamine (6-OHDA) rat model of PD to test the hypothesis that DBS exacerbates quantifiable aspects of vocalization. Mating calls were recorded from sexually experienced male rats under healthy and parkinsonian conditions and during DBS of the subthalamic nucleus. Relative to healthy rats, parkinsonian animals made fewer calls with shorter and less complex vocalizations. In the parkinsonian rats, putatively therapeutic DBS further reduced call frequency, duration, and complexity. The individual utterances of parkinsonian rats spanned a greater bandwidth than those of healthy rats, potentially reducing the effectiveness of the vocal signal. This utterance bandwidth was further increased by DBS. We propose that the parkinsonism-associated changes in call frequency, duration, complexity, and dynamic range combine to constitute a rat analog of parkinsonian dysarthria. Because DBS exacerbates the parkinsonism-associated changes in each of these metrics, the subthalamic stimulated 6-OHDA rat is a good model of DBS-induced hypokinetic dysarthria in PD. This model will help researchers examine how DBS alleviates many motor symptoms of PD while exacerbating parkinsonian speech deficits that can greatly diminish patient quality of life.

Computational Field Shaping for Deep Brain Stimulation With Thousands of Contacts in a Novel Electrode Geometry.

OBJECTIVE: Deep brain stimulation (DBS) alleviates symptoms associated with some neurological disorders by stimulating specific deep brain targets. However, incomplete stimulation of the target region can provide suboptimal therapy, and spread of stimulation to tissue outside the target can generate side-effects. Existing DBS electrodes generate stimulation profiles that are roughly spherical, neither matching nor enabling the mapping of therapeutic targets. We present a novel electrode design and will perform computational modeling of the new design to investigate its use as a next generation DBS electrode. MATERIALS AND METHODS: Computational simulations of a finite element model are performed for both the novel electrode and for a commercially available DBS electrode. RESULTS: Computational modeling results show that this new electrode design is able to steer stimulation radially around the device, creating voltage distributions that may more closely match deep brain targets. CONCLUSION: The ability to better match the anatomy and compensate for targeting errors during implantation will enable strict localization of the generated stimulation fields to within target tissues, potentially providing more complete symptom alleviation while reducing the occurrence of side-effects.

Deep brain stimulation of the subthalamic nucleus reestablishes neuronal information transmission in the 6-OHDA rat model of parkinsonism.

Pathophysiological activity of basal ganglia neurons accompanies the motor symptoms of Parkinson's disease. High-frequency (>90 Hz) deep brain stimulation (DBS) reduces parkinsonian symptoms, but the mechanisms remain unclear. We hypothesize that parkinsonism-associated electrophysiological changes constitute an increase in neuronal firing pattern disorder and a concomitant decrease in information transmission through the ventral basal ganglia, and that effective DBS alleviates symptoms by decreasing neuronal disorder while simultaneously increasing information transfer through the same regions. We tested these hypotheses in the freely behaving, 6-hydroxydopamine-lesioned rat model of hemiparkinsonism. Following the onset of parkinsonism, mean neuronal firing rates were unchanged, despite a significant increase in firing pattern disorder (i.e., neuronal entropy), in both the globus pallidus and substantia nigra pars reticulata. This increase in neuronal entropy was reversed by symptom-alleviating DBS. Whereas increases in signal entropy are most commonly indicative of similar increases in information transmission, directed information through both regions was substantially reduced (>70%) following the onset of parkinsonism. Again, this decrease in information transmission was partially reversed by DBS. Together, these results suggest that the parkinsonian basal ganglia are rife with entropic activity and incapable of functional information transmission. Furthermore, they indicate that symptom-alleviating DBS works by lowering the entropic noise floor, enabling more information-rich signal propagation. In this view, the symptoms of parkinsonism may be more a default mode, normally overridden by healthy basal ganglia information. When that information is abolished by parkinsonian pathophysiology, hypokinetic symptoms emerge.

Spike phase locking in CA1 pyramidal neurons depends on background conductance and firing rate.

Oscillatory activity in neuronal networks correlates with different behavioral states throughout the nervous system, and the frequency-response characteristics of individual neurons are believed to be critical for network oscillations. Recent in vivo studies suggest that neurons experience periods of high membrane conductance, and that action potentials are often driven by membrane potential fluctuations in the living animal. To investigate the frequency-response characteristics of CA1 pyramidal neurons in the presence of high conductance and voltage fluctuations, we performed dynamic-clamp experiments in rat hippocampal brain slices. We drove neurons with noisy stimuli that included a sinusoidal component ranging, in different trials, from 0.1 to 500 Hz. In subsequent data analysis, we determined action potential phase-locking profiles with respect to background conductance, average firing rate, and frequency of the sinusoidal component. We found that background conductance and firing rate qualitatively change the phase-locking profiles of CA1 pyramidal neurons versus frequency. In particular, higher average spiking rates promoted bandpass profiles, and the high-conductance state promoted phase-locking at frequencies well above what would be predicted from changes in the membrane time constant. Mechanistically, spike rate adaptation and frequency resonance in the spike-generating mechanism are implicated in shaping the different phase-locking profiles. Our results demonstrate that CA1 pyramidal cells can actively change their synchronization properties in response to global changes in activity associated with different behavioral states.

Stimulus features underlying reduced tremor suppression with temporally patterned deep brain stimulation.

Deep brain stimulation (DBS) provides dramatic tremor relief when delivered at high-stimulation frequencies (more than ∼100 Hz), but its mechanisms of action are not well-understood. Previous studies indicate that high-frequency stimulation is less effective when the stimulation train is temporally irregular. The purpose of this study was to determine the specific characteristics of temporally irregular stimulus trains that reduce their effectiveness: long pauses, bursts, or irregularity per se. We isolated these characteristics in stimulus trains and conducted intraoperative measurements of postural tremor in eight volunteers. Tremor varied significantly across stimulus conditions (P < 0.015), and stimulus trains with pauses were significantly less effective than stimulus trains without (P < 0.002). There were no significant differences in tremor between trains with or without bursts or between trains that were irregular or periodic. Thus the decreased effectiveness of temporally irregular DBS trains is due to long pauses in the stimulus trains, not the degree of temporal irregularity alone. We also conducted computer simulations of neuronal responses to the experimental stimulus trains using a biophysical model of the thalamic network. Trains that suppressed tremor in volunteers also suppressed fluctuations in thalamic transmembrane potential at the frequency associated with cerebellar burst-driver inputs. Clinical and computational findings indicate that DBS suppresses tremor by masking burst-driver inputs to the thalamus and that pauses in stimulation prevent such masking. Although stimulation of other anatomic targets may provide tremor suppression, we propose that the most relevant neuronal targets for effective tremor suppression are the afferent cerebellar fibers that terminate in the thalamus.

Stochastic Resonance Governs Memory Consolidation Accuracy in a Neural Network Model.

The formation and recollection of memories is a multi-step neural process subject to errors. We propose a computational model of memory nodes receiving input from a colored tic-tac-toe board. We report memory errors during consolidation and reconsolidation when different noise levels are introduced into the model. The model is based on Hebbian plasticity and attempts to store the color and position of an X or O from the board. Memory nodes simulating neurons use an integrate-and-fire model to represent the correct or incorrect storage of the board information by scaling synaptic weights. We explored how baseline firing rate, which we considered analogous to noise in storing memory, impacted the creation of correct and incorrect memories. We found that a higher firing rate was associated with fewer accurate memories. Interestingly, the ideal amount of noise for correct memory storage was nonzero. This phenomenon is known as stochastic resonance, wherein random noise enhances processing. We also examined how many times our model could reactivate a memory before making an error. We found an exponentially decaying response, with a low firing rate yielding more stable memories. Even though our model incorporates only two memory nodes, it provides a basis for examining the consolidation and retrieval of memory storage based on the unique visual input of a tic-tac-toe board. Further work may incorporate different inputs, more nodes, and increased network complexity. Clinical Relevance- This model enables investigation of how the human cortex may utilize and exploit noise during information processing.

Estimating Neuronal Information: Logarithmic Binning of Neuronal Inter-Spike Intervals.

Neurons communicate via the relative timing of all-or-none biophysical signals called spikes. For statistical analysis, the time between spikes can be accumulated into inter-spike interval histograms. Information theoretic measures have been estimated from these histograms to assess how information varies across organisms, neural systems, and disease conditions. Because neurons are computational units that, to the extent they process time, work not by discrete clock ticks but by the exponential decays of numerous intrinsic variables, we propose that neuronal information measures scale more naturally with the logarithm of time. For the types of inter-spike interval distributions that best describe neuronal activity, the logarithm of time enables fewer bins to capture the salient features of the distributions. Thus, discretizing the logarithm of inter-spike intervals, as compared to the inter-spike intervals themselves, yields histograms that enable more accurate entropy and information estimates for fewer bins and less data. Additionally, as distribution parameters vary, the entropy and information calculated from the logarithm of the inter-spike intervals are substantially better behaved, e.g., entropy is independent of mean rate, and information is equally affected by rate gains and divisions. Thus, when compiling neuronal data for subsequent information analysis, the logarithm of the inter-spike intervals is preferred, over the untransformed inter-spike intervals, because it yields better information estimates and is likely more similar to the construction used by nature herself.

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.

Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation.

Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications.

Deep brain stimulation alleviates parkinsonian bradykinesia by regularizing pallidal activity.

Deep brain stimulation (DBS) of the basal ganglia can alleviate the motor symptoms of Parkinson's disease although the therapeutic mechanisms are unclear. We hypothesize that DBS relieves symptoms by minimizing pathologically disordered neuronal activity in the basal ganglia. In human participants with parkinsonism and clinically effective deep brain leads, regular (i.e., periodic) high-frequency stimulation was replaced with irregular (i.e., aperiodic) stimulation at the same mean frequency (130 Hz). Bradykinesia, a symptomatic slowness of movement, was quantified via an objective finger tapping protocol in the absence and presence of regular and irregular DBS. Regular DBS relieved bradykinesia more effectively than irregular DBS. A computational model of the relevant neural structures revealed that output from the globus pallidus internus was more disordered and thalamic neurons made more transmission errors in the parkinsonian condition compared with the healthy condition. Clinically therapeutic, regular DBS reduced firing pattern disorder in the computational basal ganglia and minimized model thalamic transmission errors, consistent with symptom alleviation by clinical DBS. However, nontherapeutic, irregular DBS neither reduced disorder in the computational basal ganglia nor lowered model thalamic transmission errors. Thus we show that clinically useful DBS alleviates motor symptoms by regularizing basal ganglia activity and thereby improving thalamic relay fidelity. This work demonstrates that high-frequency stimulation alone is insufficient to alleviate motor symptoms: DBS must be highly regular. Descriptive models of pathophysiology that ignore the fine temporal resolution of neuronal spiking in favor of average neural activity cannot explain the mechanisms of DBS-induced symptom alleviation.

Parkinsonism and subthalamic deep brain stimulation dysregulate behavioral motivation in a rodent model.

BACKGROUND: Apathy and impulsivity constitute opposite poles of a behavioral motivation spectrum often disrupted by both the symptoms and therapies for Parkinson's Disease (PD). Upwards of 70% of PD patients experience symptoms of apathy, frequently unresolved or worsened by deep brain stimulation (DBS) of the subthalamic nucleus (STN). Worse, more than half of patients receiving DBS for PD experience new-onset impulse control disorders of varying severity following therapy initiation. While these symptoms and side-effects have been widely reported in clinical studies, they are largely unexplored in animal models. METHODS: We applied high-frequency DBS in a 6-OHDA hemiparkinsonian rat model. We trained rats on a series of go/stop and go/no-go behavioral paradigms and examined how parkinsonism and DBS modulated task responses. RESULTS: STN DBS in healthy rodents drove impulsive behavior in the form of stop and no-go task failure, impulsive reward seeking, and noninstructed task attempts. While trained rats without DBS only tended to fail stop and no-go cues very shortly after the cue, DBS led to failures at significantly later time points. Hemiparkinsonism slowed response times and reduced response rates, not alleviated by effective DBS. INTERPRETATIONS: PD interrupts neural signaling responsible for healthy action selection, not restored by DBS. PD may be associated with a dearth of action commands, manifesting as apathy. Conversely, effective DBS may bias the system toward the impulsive end of the behavioral motivation spectrum without restoring behaviorally reasonable actions, mis-weighting reward-based action selection and manifesting as impulsivity, aided by DBS interfering with stop signaling.

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.

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.