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Published in final edited form as: Curr Opin Biomed Eng. 2018 Sep 5;8:1–6. doi: 10.1016/j.cobme.2018.08.007

Temporal Pattern of Electrical Stimulation is a New Dimension of Therapeutic Innovation

Warren M Grill 1
PMCID: PMC6426311  NIHMSID: NIHMS1505917  PMID: 30906909

Abstract

Artificial activation of the nervous system requires selection of appropriate stimulation parameters including stimulation amplitude, stimulation pulse duration, and stimulation pulse repetition rate. The temporal pattern of stimulation, i.e., the timing between stimulation pulses, is a novel dimension of stimulation parameter tuning. The effects evoked by artificial activation of the nervous system are dependent on the pattern of stimulation, and different patterns of stimulation, even when delivered at the same average rate, evoke different functional effects, different changes in synaptic plasticity, and even different patterns of gene expression. Non-regular temporal patterns of stimulation offer the opportunity to improve the efficacy and efficiency of therapeutic stimulation as well as to manipulate other processes in the nervous system. The potential design space for sequences of varying interpulse intervals is exceedingly large and sound approaches to design stimulation patterns are required as an empirical approach is not practical.

Introduction

Application of electrical stimulation to the nervous system reliably evokes precisely timed action potentials and can be used for the treatment of neurological diseases, injuries or disorders. Applications for therapy or functional restoration require the appropriate adjustment of stimulation parameters (the “dose”) to achieve the desired effect(s) while minimizing potential side effect(s). This challenge is typically described as one of selectivity – activating the targeted neurons to achieve therapeutic effects while minimizing activation of non-targeted neurons (or spillover) that mediate side effects.

Stimulation parameters typically includes the selection of the active electrode contacts (or electrode geometry), stimulation pulse amplitude (current or voltage), stimulation pulse duration (pulsewidth), and stimulation pulse repetition rate (frequency). There are a number of studies informing the effects of stimulation parameter selection based on the biophysical principles underlying neural stimulation, as well as empirical studies that measure the effects of parametric variation on symptoms and side effects [Kuncel & Grill 2004, Merrill et al. 2005]. Here we introduce and review a new dimension of stimulation parameter adjustment, the temporal pattern of stimulation. The temporal pattern of stimulation strongly effects the responses evoked by artificial stimulation of the nervous system, and this dimension can be exploited to increase the efficiency and efficacy of stimulation-based therapies.

Pattern Matters

An important question in fundamental neurophysiology is whether the temporal pattern and precise timing of spiking activity is an important element of the neural code or rather if neural coding is simply by the number of spikes per unit time (i.e., average firing rate). Several recent studies support the importance of precise temporal patterns in both sensory perception and motor control. For example, the precise timing of spikes, as opposed to simply the average firing rate, plays a key role in both auditory and tactile perception [Saal et al. 2016]. Measurements with vibrotactile stimuli indicated that frequency perception was influenced by the temporal structure of neural activity [Birznieks & Vickery 2017]. In a somewhat surprising result, the perceived frequency was not dependent on the number of spikes within a burst or the spike rate, but rather was coded by the absence of spikes – the duration of silent gap between successive bursts of spikes was most strongly correlated with the perceived frequency. In addition to a demonstrated role in sensory coding and perception, the precise temporal pattern of activity plays a key role in motor control, as well. For example, shuffling or jittering the timing of spikes in motor neurons reduced the information content of the spike train and the precise timing of spikes in motor neurons predicted respiratory air pressure, while the shuffled patterns did not [•Srivastava et al. 2017].

As in these fundamental studies of neural coding, a number of studies employing artificial electrical stimulation also support that the temporal pattern of neural activation is important. For example, random patterns of deep brain stimulation (DBS) to treat movement disorders, even when delivered at an average rate sufficient to relieve symptoms, were ineffective at treating tremor [Birdno et al. 2008, Birdno et al. 2012] and bradykinesia [Montgomery & Gale, 2006, Dorval et al. 2010, Brocker et al. 2013]. Similarly, ON/OFF cycling patterns of DBS were less effective at treating tremor [Kuncel et al. 2012] and bradykinesia [Montgomery 2005] than continuous DBS. While it was well established that the symptom relieving effects of DBS were strongly dependent on the pulse repetition frequency [Birdno & Grill 2008], these findings demonstrated that effects were also strongly dependent on the temporal pattern of stimulation.

Similarly, the pattern of DBS of the central thalamus in mice influenced the degree of arousal, as assessed by motor activity, with differential effects between fixed frequency, random patterns of stimulation, and chaotic patterns of stimulation all of which shared the same average frequency [Quinkert and Pfaff 2012]. Potentially important for therapeutic translation of these findings, these effects were also observed in a model of traumatic brain injury [Tabansky et al. 2014], although with somewhat different effects of specific patterns than observed in the arousal studies on healthy animals.

Pattern as a Probe for Mechanism

The finding that the effects of DBS were dependent on the pattern of stimulation motivated the design and application of specific patterns to probe the underlying mechanisms of action of DBS. Although DBS is an established therapy for the treatment of movement disorders, debate remains over the mechanisms by which high frequency stimulation treats symptoms. One hypothesis – termed the informational lesion – posits that the important effect of DBS is to regularize the activity of stimulated neurons [Grill et al. 2004] and thereby “jam” the transmission of activity through the stimulated region [Benabid et al. 2005]. The striking parallel between the frequency-dependent effects of DBS on the activity of model neurons and the clinical effects on symptoms provided strong correlative evidence for this hypothesis [Grill et al. 2004]. The use of random temporal patterns of DBS enabled a direct test of this hypothesis. Specifically, as described above, random patterns of DBS were not effective at treating tremor [Birdno et al. 2008, Birdno et al. 2012] or bradykinesia [Montgomery & Gale, 2006, Dorval et al. 2010], and these findings reinforced the importance of regularization of neuronal activity in the efficacy of DBS.

Subsequent studies used specific patterns to determine what features of the random patterns were indeed responsible for the failure to relieve symptoms. In the case of thalamic DBS for tremor, short pauses in stimulation (i.e., long interpulse intervals in the random patterns) enabled bursting activity to propagate through the stimulated thalamic nucleus (i.e., relief from “jamming”) [Birdno et al. 2012, •Swan et al. 2016]. Similarly, in subthalamic nucleus DBS for PD, random patterns of stimulation were ineffective at suppressing thalamic burst activity [Dorval et al. 2010]

Patterns to Improve Functional Stimulation

“Attention to stimulation pattern might allow effective symptom control at a lower frequency. … the development of alternative stimulation patterns could lead to increased battery life and possibly to reduced side effects, as well as to improvements in therapeutic efficacy.” [Hess et al. 2013]

The finding that the effects of electrical stimulation were dependent on the temporal pattern of stimulation, in addition to the frequency of stimulation, inspired the design and testing of novel temporal patterns of stimulation to improve efficacy and efficiency. For example, in the application of DBS to treat movement disorders, all patients do not receive optimal symptom relief with current therapy [Kleiner-Fisman et al. 2006], and optimized patterns of DBS could improve clinical efficacy [Hess et al. 2013]. As well, new patterns that reduce the energy required for therapeutic effects will prolong the lifetime of battery-powered implanted pulse generators, which have a median lifetime of less than 4 years [Bin-Mahfoodh et al. 2003]. When the batteries are depleted the devices require surgical replacement, which is expensive and carries associated risks of infection [Pepper et al. 2013]. For rechargeable pulse generators, a reduction in stimulation energy will enable smaller devices and less frequent recharging.

Patterns were developed that treat the symptoms of Parkinson’s disease (PD) more effectively than high frequency unpatterned (i.e., fixed interpulse interval) DBS. Burst patterns of stimulation produced greater reductions in bradykinesia (i.e., improvements in finger tapping speed and regularity) in persons with PD, and importantly, with the two most effective patterns, these improvements were seen consistently in 9/10 participants [Brocker et al. 2013]. Further, the ability of particular patterns to improve bradykinesia in humans with PD was correlated with their ability to suppress beta-band oscillatory activity in a computational model of the effects of DBS in the basal ganglia. Beta-band power reflects the enhanced synchronized oscillatory activity present in PD [Sharott et al. 2014], and beta-band power is also suppressed (transiently) by dopaminergic medication [Kuhn et al. 2006] and conventional DBS [Kuhn et al. 2008]. Similarly, burst stimulation produced greater improvements in reach and grasp function in a non-human primate model of PD than regular stimulation at the same frequency [Baker et al. 2011].

In another complementary approach, Tass and colleagues developed a novel temporal pattern of DBS, termed coordinated reset [Tass 2003]. Coordinated reset (CR) DBS delivers multiple burst stimuli, each at different times with respect to one another and at different locations (i.e., through different contacts of the DBS lead), and can disrupt synchronized oscillatory neural activity [Popovych and Tass 2012]. Further, disrupting pathological synchronized bursting activity is intended to exploit activity-dependent plasticity to produce lasting changes (“anti-kindling”) within interconnected circuits and thereby produce protracted changes in symptoms.

Following extensive model-based development and analysis [Popovych and Tass 2012], several recent studies assessed the effects of CR DBS in vivo. In the first of these studies in a non-human primate model of PD, Tass et al. (2012) demonstrated first that 30 min of CR DBS of the STN produced a much longer carry-over effect in reducing akinesia than convention high frequency STN DBS. Even more profound, 5 days of 30 min of CR DBS produced extended reductions in akinesia for several weeks, something not seen with conventional DBS. In a recent follow on study, also in a nonhuman primate model of PD, •Wang et al. (2016) found that 2 h/day of CR STN DBS produced effects on motor symptoms that were quite similar to those produced by conventional high frequency DBS, i.e., a lack of protracted carry-over effect on symptoms. However, extending CR DBS to 4 h/day generated cumulative benefit across days, and these improvements were sustained for several weeks beyond end of therapy. Similar results were reported in persons with PD, where 4 h/day of CR DBS across three days produced cumulative benefit (i.e., carry over) on motor symptoms [Adamchic et al. 2014]. Notably, these changes in symptoms were correlated with changes in beta-band power, a reflection of the enhanced synchronized oscillatory activity present in PD (see above). Collectively, these data suggest that CR DBS produces reductions in motor symptoms that outlast the period of stimulation, supporting the idea that such a pattern can produce plastic changes in the brain. However, the optimal spatiotemporal pattern of CR DBS is unclear, as are the underlying mechanisms of action.

The imperative to improve the energy efficiency of stimulation has driven interest in patterns of stimulation that are effective at relieving symptoms at a lower average frequency than the high frequencies (typically 130 – 185 Hz) required to relieve symptoms with conventional fixed interpulse interval stimulation [Birdno & Grill 2008]. Again, burst stimulation, in this case with bursts delivered at specific times, with respect to the phase of the mechanical tremor oscillation, was highly effective at treating tremor, and did so with substantially less energy than required for conventional high-frequency DBS [••Cagnan et al. 2017]. In a second open-loop approach, model-based optimization with computational evolution–an approach that mimics biological evolution–was used to design a more efficient temporal pattern of DBS. The resulting pattern treated bradykinesia as effectively as conventional high-frequency DBS, but operated at an average frequency of 45 Hz, approximately 1/3 of the typically programmed clinical frequency, and this reduced frequency was predicted to lead to a more than two-fold increase in pulse generator battery life [••Brocker et al. 2017].

In addition to DBS, novel temporal patterns of stimulation have also been introduced to increase the efficacy of spinal cord stimulation (SCS) for chronic pain. Burst spinal cord stimulation is constituted of bursts of 5 pulses at 500 Hz, delivered at 40 Hz, rather than the continuous 50 – 150 Hz stimulation that is typically used [De Ridder et al. 2010]. Early clinical results indicate that burst SCS may be more effective at treating leg and back pain than traditional SCS, and this appears to be mediated by engagement of the medial pain pathways signaling the affective rather than discriminative aspects of pain [De Ridder et al. 2013].

A second novel approach to treating chronic pain with SCS is the use of kHz frequency (KHF) pulse trains (i.e., stimulation frequencies of 1–10 kHz) compared to frequencies of 50–150 Hz for conventional SCS. Early clinical results suggest that KHF-SCS may be more effective than conventional SCS, and produce pain relief without generating the paresthesias associated with conventional SCS [Kapural et al., 2015]. KHF stimulation is best-known for its ability to block axonal conduction [Bhadra and Kilgore 2014]. However, in vivo rat experiments revealed that conduction block is an unlikely outcome from KHF-SCS at amplitudes that reduce behavioral sensitivity [Crosby et al. 2017], and the underlying mechanisms remain unclear. KHF-SCS has been applied in patients at frequencies ranging from 1–10 kHz [Perruchoud et al., 2013; Kapural et al., 2015; Smith et al., 2015], but there is no consensus or rationale for the choice of kHz frequency.

Prosthetic Sensation

A variant of the temporal pattern of stimulation is patterned stimulation intensity. The intensity (amplitude or duration) of each stimulation pulse is modulated over the course of the stimulation pattern to produce different patterns of activity in different populations of nerve fibers. This patterned stimulation produced more natural sensations in prosthetic restoration of touch [Tan et al. 2014] and was applied to spinal cord stimulation for the treatment of pain, as well [Tan et al. 2016]. Further, burst patterns and amplitude-modulated patterns of transcutaneous stimulation made it more likely that participants would embody a rubber hand as their own [Mulvey et al. 2015].

A Design Challenge

Non-regular temporal patterns of stimulation clearly offer the opportunity to improve the efficacy and efficiency of therapies employing artificial activation of the nervous system for treatment or functional restoration. However, the potential design space for arbitrary sequences of varying interpulse intervals is enormous, and, in most instances, it is not clear how patterns of stimulation should be selected for therapeutic advantage. For example, for a temporal pattern that is 200 ms in length (appended tip to tail to create longer epochs of stimulation) and split into 1 ms bins that either can have a pulse or not have a pulse, this creates 1050 different possible patterns – clearly not amendable to empirical evaluation and demonstrating that alternative approaches are required.

Approaches to design of temporal patterns of stimulation have fallen into two broad categories: biomimetic and optimal design. The biomimetic approach attempts to use artificial patterns of stimulation – either embodying characteristic features of physiological activity or patterned based on specific recordings of activity – to replicate biological patterns of activity. McGee and Grill [2016] tested a broad range of patterns of sensory pudendal nerve stimulation inspired by patterns of activity observed in primary afferent firing, including accelerating and decelerating ramps and burst patterns, as well as random frequency controls. Although evoked bladder pressure was indeed dependent on the temporal pattern of stimulation, no pattern evoked larger pressures than constant 33 Hz stimulation. In contrast, burst patterns of stimulation applied intraurethrally led to higher voiding efficiencies than either low frequency or high frequency stimulation [Bruns et al. 2009]. In another application of electrical stimulation to treat inadequate bladder emptying, electrical stimulation of the external urethral sphincter, with a pattern derived from motor unit recordings during normal bladder emptying, proved very effective at increasing voiding [•Langdale and Grill 2016]. Finally, a temporal pattern of stimulation of the nucleus of the solitary tract (the first synaptic relay for taste) that mimicked activity in response to quinine produced an aversive response, while a randomized pattern of stimulation did not [Di Lorenzo et al. 2003].

A sophisticated and elegant biomimetic approach was developed by Weber and colleagues – termed “replay stimulation” – to provide artificial sensory reception. The concept was to first record the spatiotemporal pattern of activity in a population of primary sensory neurons evoked by a mechanical stimulus and then use that activity as a template for subsequent “replay” stimulation. This approach was used successfully in the somatosensory system [Weber et al. 2011] where it is otherwise unclear what the appropriate pattern of stimulation should be to evoke a particular sensation.

The second broad approach, optimal design, relies on a model of the effects of stimulation on the system of interest coupled to an algorithm for engineering optimization (e.g., gradient descent). In one successful example, a genetic algorithm – an optimization technique inspired by biological evolution, where the temporal pattern was the “organism” to be optimized – was coupled to models of the effects of DBS in the basal ganglia or the effect of SCS on the neural network in the dorsal horn of the spinal cord [••Cassar et al. 2017]. The standard genetic algorithm performed quite poorly, whereas a genetic algorithm modified to account for the nuances of temporal patterns of neural stimulation converged to patterns with fitness that exceed conventional fixed frequency controls. The utility of this approach was demonstrated by Brocker et al. [••2017], who employed a genetic algorithm to design a temporal pattern of DBS that treated bradykinesia as effectively as constant 130 Hz DBS, but did so with an average pulse rate of only 45 Hz, resulting in a substantial energy savings.

Conclusion

The temporal pattern of stimulation clearly influences the responses evoked by artificial activation of the nervous system. Temporal pattern is thus an important parameter that can be exploited, as a tool to understand the relationship between the patterns of neural activity and function – both normal neural coding as well as changes that occur in disease or injury – and as a novel way to improve the efficacy and efficiency of therapeutic stimulation. This new dimension of stimulation parameter is in its infancy, and in addition to control of the functional effects of stimulation, as described herein, the temporal pattern of stimulation can be exploited to manipulate other processes in the nervous system. The changes in synaptic strength that result from repeated stimulation, i.e., neural plasticity, are dependent on the pattern of stimulation [Jung et al. 2016]. As well, patterns of activity-dependent gene expression are also sensitive to the temporal pattern of activity. The same number of stimuli delivered in two different temporal patterns resulted in differential expression of activity-dependent genes [•Lee et al. 2017]. As well, the temporal pattern of stimulation can also influence non-neural cells. Gamma frequency (40 Hz) stimulation caused activation of microglia and reductions in amyloid beta in a mouse model of Alzheimer’s disease, while random patterns of stimulation, with an average frequency of 40 Hz, did not [••Iaccarino et al. 2016], further emphasizing the importance of the temporal pattern of stimulation. A clear challenge to exploit these myriad opportunities is developing sound approaches to design a priori stimulation patterns for intended effects, rather than relying an empirical approach, which is unlikely to be effective in such a vast design space.

Highlights.

  • The effects evoked by artificial activation of the nervous system are dependent on the temporal pattern of stimulation

  • The temporal pattern of stimulation can be manipulated to increase the efficacy and efficiency of therapeutic stimulation.

  • The potential design space for patters of stimulation is vast, and model-based engineering design is one successful approach to determine optimal patterns

  • Other processes in the nervous system, including plasticity and gene expression, are also dependent on the temporal pattern of activity

ACKNOWLEDGMENTS

This work is supported by NIH grant R37 NS040894.

Footnotes

CONFLICT OF INTEREST Warren M. Grill is a co-inventor on patents on non-regular patterns of stimulation that are owned by Duke University, and he receives royalties from the licensing of these patents. He also serves as Director, Chief Scientific Officer and holds equity in Deep Brain Innovations, LLC, which is commercializing temporallyoptimized patterns of deep brain stimulation.

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