Abstract
Deep brain stimulation (DBS) is a widely used clinical therapy that modulates neuronal firing in subcortical structures, eliciting downstream network effects. Its effectiveness is determined by electrode geometry and location as well as adjustable stimulation parameters including pulse width, interstimulus interval, frequency, and amplitude. These parameters are often determined empirically during clinical or intraoperative programming and can be altered to an almost unlimited number of combinations. Conventional high-frequency stimulation uses a continuous high-frequency square-wave pulse (typically 130–160 Hz), but other stimulation patterns may prove efficacious, such as continuous or bursting theta-frequencies, variable frequencies, and coordinated reset stimulation. Here we summarize the current landscape and potential clinical applications for novel stimulation patterns.
Introduction
Deep brain stimulation (DBS) uses implantable depth electrodes to modulate neuronal firing in subcortical structures, eliciting downstream effects in human brain circuits (Figure 1). Intraoperative placement is followed by device programming where parameters such as pulse width, interstimulus interval (ISI), frequency, and amplitude are titrated to improve pathologic symptoms and avoid adverse side effects. Current DBS applications target motor symptoms of Parkinson’s disease (PD), essential tremor (ET), and various forms of dystonia as well as neuropsychiatric symptoms of treatment-resistant obsessive-compulsive disorder (OCD), Tourette syndrome (TS), and treatment-resistant depression (TRD) with continuous high-frequency stimulation (HFS; typically, 130–160 Hz) (Figure 2). Although its exact mechanism of action is unknown, numerous theories exist. Some studies suggest that HFS may exert its effects via desynchronization or reorganization of pathologic network oscillations (Wilson and Moehlis, 2015; ). Similarly, Rosenbaum et al. (2014) theorized that HFS works through short-term depression and decoupling of specific circuits. Other groups have theorized that HFS directly inhibits neural activity (; ), while some suggest the opposite, that it acts through direct excitation of neural activity (; ). Another theory is that HFS introduces an “information lesion,” producing similar effects to neural ablation that is also used to treat the same disease processes (e.g., PD, OCD, etc.) (; ; ), however, to date, there is no single accepted theory on the mechanism of action of HFS and further work is required to elucidate its mechanism (; ).
FIGURE 1
FIGURE 2
While the benefits of HFS are well-established for motor outcomes, its impact on cognitive control is less clear. reviewed studies of how DBS impacts various aspects of cognitive function and found heterogenous results within and between neurocognitive metrics based on sample size, DBS target, and disease pathology. General trends suggested declines in verbal fluency, assessed by various measures such as “phonemic fluency” (ability to recall words starting with a specific letter) and “semantic fluency” (ability to recall words related to a certain category of knowledge). These declines occurred regardless of whether the DBS target was the subthalamic nucleus (STN), globus pallidus interna (GPi), or various thalamic nuclei (; ; ). Other global metrics such as mini-mental state examination (MMSE) or measures of executive function such as Wisconsin Card Sorting Test (WCST) have yielded mixed results (). In contrast, STN low-frequency stimulation (LFS) in the theta-range has been shown to improve VF in PD patients (). Negative impacts of HFS on verbal fluency and other aspects of cognitive function challenge the notion of expanding DBS or other neuromodulation therapies for cognitive dysfunction in patients with movement disorders and other complex neuropsychiatric diseases.
These relative shortcomings of HFS, as well as a need to avoid sensorimotor side effects, have prompted investigation of non-continuous, or “patterned,” stimulation paradigms such as theta burst stimulation (TBS) (Titiz et al., 2017; ), paired pulse stimulation (; ), variable frequency stimulation (VFS) (; Zhang et al., 2019), interleaved stimulation (ILS) (; ), burst cycling stimulation (Velasco et al., 2007; ), coordinated reset stimulation (CR-DBS) (), temporally optimized stimulation (; ), and adaptive, or “closed-loop” stimulation (aDBS/CL-DBS) (; ) as alternatives. These paradigms were derived either from more physiologic patterns of neuronal firing or feedback-based systems that were computationally designed to better disrupt pathologic circuits. In this review, we provide an overview of alternative stimulation patterns and their potential applications.
Methods
We conducted PubMed searches in November 2022 (TBS, paired pulse, VFS, CR-DBS, aDBS) and March 2023 (ILS, burst cycling, temporally optimized stimulation) to review the existing literature on alternative patterns of DBS. Search terms included: TBS {[(“Deep Brain Stimulation”[Mesh]) OR (“Deep Brain Stimulation”)] AND [(“theta burst stimulation”) OR (“intermittent theta burst”) OR (“intermittent theta burst stimulation”) OR (“continuous theta burst stimulation”) OR (“continuous theta burst”) OR (“iTBS”) OR (“cTBS”) OR (“theta burst”)]}; paired pulse {[(“Deep Brain Stimulation”[Mesh]) OR (“Deep Brain Stimulation”)] AND [(“paired pulse stimulation”) OR (“paired pulse”)]}; VFS {[(“Deep Brain Stimulation”[Mesh]) OR (“Deep Brain Stimulation”)] AND [(“variable frequency stimulation”) OR (“variable frequency”)]}; ILS {[(“Deep Brain Stimulation”[Mesh]) OR (“Deep Brain Stimulation”)] AND [(“interleaving”) OR (“interleaved stimulation”)]}; burst cycling {[(“Deep Brain Stimulation”[Mesh]) OR (“Deep Brain Stimulation”)] AND [(“cycling”) OR (“cyclical stimulation”) OR (“burst cycling”) OR (“cycling stimulation”)]}; CR-DBS {[(“Deep Brain Stimulation”[Mesh]) OR (“Deep Brain Stimulation”)] AND [(“coordinated reset stimulation”) OR (“coordinated reset”) OR (“CR-DBS”)]}; temporally optimized stimulation {[(“Deep Brain Stimulation”[Mesh]) OR (“Deep Brain Stimulation”)] AND [(“temporally optimized stimulation”) OR (“temporally optimized patterned stimulation”)]}; aDBS {[(“Deep Brain Stimulation”[Mesh]) OR (“Deep Brain Stimulation”)] AND [(“closed loop stimulation”) OR (“closed loop”) OR (“closed-loop”) OR (“adaptive stimulation”) OR (“adaptive DBS”) OR (“aDBS”) OR (“adaptive deep brain stimulation”) OR (“closed-loop stimulation”)]}.
Studies meeting these search criteria with original clinical data on human subjects undergoing DBS were included. Exclusion criteria included animal studies, computational models, reviews, non-DBS studies (i.e., transcranial magnetic stimulation, responsive neurostimulation), DBS studies using only HFS and not the alternative pattern of interest, and non-English manuscripts. Abstracts and full texts were manually screened by a single author using the inclusion and exclusion criteria mentioned above. Our goals were to qualitatively review the current literature on alternative neuromodulation techniques, and therefore we did not utilize Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Results
Literature search
Our literature search of TBS yielded 29 studies, six of which were included (; Titiz et al., 2017; ; ; ; Sáenz-Farret et al., 2021). Of the excluded studies, 11 were reviews, seven were non-human animal studies, three were TMS studies without DBS, and two were editorials or commentaries with no original clinical data. The paired pulse search yielded 28 studies, of which three were included (; ; ). Of the excluded studies, nine were non-human animal studies, six did not use paired pulse stimulation, five were reviews, and five were TMS studies without DBS. The search for VFS resulted in 12 studies, five of which were included (, , ; Zhang et al., 2019; ). Of the excluded studies, two used stimulation modalities other than DBS (e.g., TMS), two were protocols for upcoming studies with no original clinical data, one was a review, one was a non-human animal study, and one did not utilize VFS. For ILS, our search yielded 27 results, of which 16 were included (Wojtecki et al., 2011; ; ; ; ; ; Zhang et al., 2016, 2018; ; Shu et al., 2018; ; ; , ; ; Zafar et al., 2021). Of the excluded studies, five were reviews, three did not use ILS, one was in silico (i.e., no human subjects), one was a video, and one was not in English. For burst cycling, the search found 32 studies, of which 14 were included (; Velasco et al., 2007; Tai et al., 2011; ; ; ; ; ; ; ; Vázquez-Barrón et al., 2021; Wong et al., 2021; ; ). Of the excluded studies, five did not report clinical outcomes (e.g., imaging study with fMRI), four did not use burst cycling, four did not use DBS, three were reviews, one was a conceptual study with no human subjects, and one combined VNS and DBS. For CR-DBS, our search found 32 studies, but only one was included (). Of the excluded studies, 21 tested computational or theoretical models of coordinated reset, six were non-human animal studies, two were reviews, and two used stimulation modalities other than DBS (e.g., TMS). For temporally optimized stimulation, our search yielded 93 results, of which two were included (; ). Of those excluded, 25 were reviews, 21 did not utilize temporally optimized stimulation, 15 did not use DBS, 13 were animal studies, nine did not provide clinical outcomes, seven were computational models, and one was theoretical/conceptual with no human subjects. Finally, our literature search of aDBS yielded 576 studies, of which 31 were included (, ,; Rosa et al., 2015, 2017; ; ; ; , , ,; Tinkhauser et al., 2017; , ; Swann et al., 2018; Velisar et al., 2019; ; ; ; ; ; ; ; ; ; ; Sasaki et al., 2021; Scangos et al., 2021; ; Sarikhani et al., 2022). Of the excluded studies, 180 were reviews, 139 did not use closed-loop systems, 112 tested purely theoretical or computational models of closed-loop stimulation, 51 were non-human animal studies, seven explored stimulation modalities other than DBS (e.g., TMS), and 56 were excluded for various other reasons (e.g., editorials, commentaries, study protocols, non-English language studies). Results from the literature search are summarized in Table 1.
TABLE 1
| Author (Year) | N | Diagnosis | Pattern of stimulation |
| 4 | Epilepsy | TBS | |
| Titiz et al. (2017) | 13 | Epilepsy | TBS |
| 4 | Epilepsy | TBS | |
| 17 | PD | TBS | |
| 7 | PD | TBS | |
| Sáenz-Farret et al. (2021) | 10 | PD, ET, Dystonia | TBS |
| 5 | PD, Epilepsy | Paired pulse | |
| 5 | ET | Paired pulse | |
| 17 | PD, ET | Paired pulse | |
| 6 | PD | CR-DBS | |
| 1 | PD | VFS | |
| 1 | PD | VFS | |
| 4 | PD | VFS | |
| Zhang et al. (2019) | 1 | PD | VFS |
| 1 | PD | VFS | |
| Wojtecki et al. (2011) | 1 | PD | ILS |
| 1 | PD, ET | ILS | |
| 4 | Dystonia | ILS | |
| 3 | PD | ILS | |
| 10 | ET | ILS | |
| 9 | PD | ILS | |
| Zhang et al. (2016) | 12 | PD | ILS |
| 50 | PD, ET, Dystonia | ILS | |
| Shu et al. (2018) | 1 | Meige syndrome | ILS |
| Zhang et al. (2018) | 1 | Dystonia | ILS |
| 20 | PD | ILS | |
| 76 | PD | ILS | |
| 17 | PD | ILS | |
| 20 | PD | ILS | |
| 1 | PD | ILS | |
| Zafar et al. (2021) | 19 | PD | ILS |
| 7 | PD | Burst cycling | |
| Velasco et al. (2007) | 22 | Epilepsy | Burst cycling |
| Tai et al. (2011) | 1 | Dystonia | Burst cycling |
| 10 | ET | Burst cycling | |
| 2 | Epilepsy | Burst cycling | |
| 1 | Epilepsy | Burst cycling | |
| 3 | Chronic Pain | Burst cycling | |
| 23 | Epilepsy | Burst cycling | |
| 3 | PD | Burst cycling | |
| Wong et al. (2021) | 10 | PD | Burst cycling |
| 6 | PD, Progressive Supranuclear Palsy | Burst cycling | |
| Vázquez-Barrón et al. (2021) | 6 | Epilepsy | Burst cycling |
| 20 | Epilepsy | Burst cycling | |
| 1 | Spinocerebellar Ataxia | Burst cycling | |
| 26 | PD | TOPS | |
| 8 | PD | TOPS | |
| 8 | PD | aDBS | |
| Rosa et al. (2015) | 1 | PD | aDBS |
| 4 | PD | aDBS | |
| 5 | PD | aDBS | |
| 10 | PD | aDBS | |
| 9 | ET, Dystonia | aDBS | |
| Tinkhauser et al. (2017) | 13* | PD | aDBS |
| 1 | ET | aDBS | |
| Rosa et al. (2017) | 10 | PD | aDBS |
| 1 | PD | aDBS | |
| 13 | PD | aDBS | |
| Swann et al. (2018) | 2 | PD | aDBS |
| 13 | PD, Dystonia | aDBS | |
| Velisar et al. (2019) | 13 | PD | aDBS |
| 3 | ET | aDBS | |
| 1 | PD | aDBS | |
| 7 | Dystonia | aDBS | |
| 13 | PD | aDBS | |
| 3 | ET | aDBS | |
| 3 | ET | aDBS | |
| 2 | ET | aDBS | |
| Sasaki et al. (2021) | 12 | PD | aDBS |
| 16 | PD | aDBS | |
| 5 | PD | aDBS | |
| 5 | PD | aDBS | |
| Scangos et al. (2021) | 1 | TRD | aDBS |
| 1 | Dystonia | aDBS | |
| 3 | PD | aDBS | |
| 1 | PD | aDBS | |
| Sarikhani et al. (2022) | 15 | ET, PD | aDBS |
| 4 | TS | aDBS |
Literature review of alternative patterns of stimulation.
*Some patients included in other Little et al. studies.
Theta burst stimulation
TBS began in the context of transcranial magnetic stimulation, most notably when Huang et al. applied repetitive TMS (rTMS) to modulate motor networks. TBS can be delivered in a continuous or intermittent fashion (cTBS or iTBS), consisting of repeated trains of 5 Hz bursts, each consisting of three pulses at 50 Hz. In cTBS, these bursts occur regularly at 5 Hz intervals, whereas iTBS consists of repeated trains of 5 Hz bursts for 2 s followed by an 8-s pause interval (Figure 2) ().
Since then, TBS has been applied successfully in several neurologic disorders and has been shown to modulate neuronal activity and associated cognitive functions. Our group found that subcortical iTBS can evoke theta oscillatory activity, known to be important in cognitive domains such as decision making and memory (Zavala et al., 2017; ; Vivekananda et al., 2021; Zeng et al., 2021; ), in connected dorsolateral prefrontal cortex (). Others expanded on this and found that cortical TBS evokes frequency-specific oscillations (Solomon et al., 2021), such that 5 Hz TBS maximally increases 5 Hz power. Behavioral modulation with TBS is evident with studies showing improvements in visual-spatial memory with fornix stimulation () and declarative memory with entorhinal stimulation (Titiz et al., 2017). Titiz et al. additionally states that delivering stimulation through microwires as opposed to large DBS stimulation electrodes can yield improved outcomes in various cognitive functions such as face categorization and learning rate during reinforcement learning. found differential effects on which components of memory were recalled by identifying and stimulating network nodes that were involved in these processes using theta coherence as a marker. For example, they were able to specifically impair ability to recall spatial details of a memory while sparing recall of temporal details.
Paired-pulse stimulation
Paired stimulus pulses have been used for decades to investigate neural refractoriness, augmentation, and plasticity (Zucker and Regehr, 2002; ). These paradigms typically consist of a “conditioning” pulse followed by a “test” pulse separated by a specific ISI (Figure 2). The goal is to record short-term changes in neural activities that propagate through the engaged network. At certain ISIs, paired pulses likely increase presynaptic influx of calcium ions (Ca2+), and, in turn, enhance secretory exocytosis of neurotransmitters into the synaptic cleft (Zucker and Regehr, 2002). Paired pulses can be studied rapidly and elicit diverse neural responses, such that they are a versatile tool to study network dynamics and mechanisms of action in various circuits of interest (), including the STN, GPi (; Yamawaki et al., 2012; ; ), and the ventrolateral thalamus (; ).
demonstrated that paired-pulse stimulation is feasible in humans in both PD and drug-resistant epilepsy (n = 4), using externalized DBS leads and custom external pulse generators. investigated the mechanism of action of DBS using paired pulses in PD (n = 8) and essential tremor (ET; n = 6). The authors validated the neural origin of short- and long-latency tissue responses and suggested that paired DBS pulses increase local tissue electrophysiologic synchrony. Specifically, they observed that certain properties (e.g., long latency, amplitude) of later oscillatory response [i.e., evoked resonant neural activity (ERNA)] mirrored properties consistent with orthodromic synaptic activity and vesicle release. Moreover, ERNA was faster at specific ISIs (∼5–10 ms), which corresponded with the timing of therapeutic stimulation frequencies (100–200 Hz), suggesting that the timing of prior effective stimulation may facilitate recruitment of subsequent responses within the same local circuit. Thus, the authors concluded that ERNA evokes short-term facilitation/plasticity in the STN-GPi circuit in various movement disorders and showed a positive correlation with both clinical efficacy and resting beta power. investigated the effects of pulse timing on DBS evoked potentials within the basal ganglia-thalamocortical (BGTC) circuit using a wide range of ISIs in a paired pulse stimulation paradigm in patients with PD (n = 5). They demonstrated that ISIs with frequencies > 250 Hz significantly impacted evoked potentials recorded from the STN (via DBS leads) and motor cortex (via scalp EEG). Specifically, ISIs from 1.0 to 3.0 ms produced enhanced activation (i.e., greater wavelet amplitude), while ISIs outside of this range yielded no significant changes.
evaluated the effects of pulse-to-pulse changes in DBS frequency in ET (n = 5) using biphasic paired pulses (ISI 0.3–7.7 ms). They applied monopolar stimulation for 40–60 s in blinded subjects and found that tremor suppression decreased as “IPIdiff” increased. In other words, as the ISI increased and/or the time between each pair of pulses increased, paired pulse stimulation became less effective than continuous HFS for treatment of ET [for more on “IPIdiff,” see ]. Furthermore, continuous HFS at 130 Hz with regular temporal spacing was more effective at reducing tremor than paired pulse stimulation with irregular ISIs at the same overall rate (130 pulses per second). This and similar studies suggest that DBS is dependent not only on the average frequency but also on the temporal spacing of DBS pulses ().
Variable frequency stimulation
While HFS DBS effectively treats PD motor symptoms (i.e., bradykinesia, rigidity, and tremor) (; Xie et al., 2016), it is less beneficial for levodopa-unresponsive elements of gait dysfunction, freezing of gait (FOG), postural instability, and speech disorders (e.g., dysarthria, hypophonia) (; Schlenstedt et al., 2017). Moreover, HFS may exacerbate existing symptoms or cause side effects such as decreased verbal fluency (). Studies investigating LFS (<100 Hz) provide evidence for greater improvements in speech, dysphagia, gait dysfunction, and FOG versus HFS (Yu et al., 2020; ; ). Additionally, a double-blind study found that LFS was associated with improved speech intelligibility, prosody, and both semantic and phonemic verbal fluency (; ). One meta-analysis reported that while HFS did have more pronounced effects on tremor reduction, LFS was significantly more effective in treatment of gait dysfunction, FOG, and akinesia (Su et al., 2018). However, some studies show that the benefits of LFS versus HFS decrease with long-term use and may depend on PD phenotype (Vallabhajosula et al., 2015; Xie et al., 2018).
To address these issues, researchers have experimented with VFS, with the rationale that combined elements of HFS and LFS might more optimally improve both appendicular and axial symptoms of PD. The VFS paradigm alternates between high (>100 Hz) and low (<100 Hz) frequencies within a stimulation cycle (<60 s) (Figure 3). Three studies from a single group (n = 6) demonstrated the feasibility of VFS in the context of bilateral STN DBS for PD. They delivered VFS, cycling between HFS and LFS patterns with a variable duty cycle (≤300 s; PINS Medical, Beijing), in patients who had previously undergone HFS DBS optimization without resolution of axial motor symptoms or with development of side effects. Their VFS paradigm involved 10–50 s trains of alternating HFS and LFS (e.g., 130 Hz for 30 s and 60 Hz for 20 s per 50 s cycle). VFS increased gait speed, mitigated FOG, and improved bradykinesia, while tremor and rigidity either remained stable or further improved compared to HFS alone (, , ). In two cases, HFS-associated decline in verbal fluency and dysarthria resolved with VFS without diminishing HFS-related improvement in primary appendicular motor symptoms (; Zhang et al., 2019). An additional case report showed improvement in freezing and limb dyskinesia in a single patient with bilateral STN and GPi leads programmed for VFS, which did not occur with HFS alone (). In ET, HFS was superior to LFS for tremor reduction but worsened verbal fluency, while LFS was more effective at enhancing verbal fluency compared to DBS-OFF and HFS (p = 0.0119), but had no significant incremental effects on tremor () and in some cases actually exacerbated the tremor (). Thus, while VFS appears promising in PD, its potential seems less promising for ET with the frequency range and duty cycles previously applied in PD patients. Larger, prospective randomized controlled trials of VFS versus HFS for PD are currently underway (; ).
FIGURE 3
Interleaved stimulation
Standard DBS contact configurations are either monopolar, where the contact serves as the cathode, or bipolar, where two different contacts serve as an anode and cathode, respectively. The shape or coverage of the generated stimulation field can be modified by alternating between these two settings. Suboptimal position of an electrode can result in inadequate coverage of the target with overlap into anatomic regions that cause side effects. If adjusting between monopolar, double monopolar, bipolar, and double bipolar stimulation or changing stimulation parameters fails to achieve therapeutic effect, ILS can be used to contour the stimulation field and avoid unwanted overlap with non-target regions. ILS is a novel strategy that is supported in newer- generation DBS electrodes and involves rapidly alternating between two different stimulation settings using two different contacts on the same lead. Contacts can be programmed to deliver different amplitudes and pulse widths, however, the combined frequency is set to a maximum of 250 Hz per device by manufacturers to avoid potentially harmful charge delivery. ILS has been successfully used in PD to improve rigidity, bradykinesia, tremor (Wojtecki et al., 2011; ; ; Zhang et al., 2016; ; ), and gait (e.g., FOG) symptoms (Zhang et al., 2016; ; Zafar et al., 2021), as well as to decrease unwanted side effects such as dysarthria (Wojtecki et al., 2011; ; Zhang et al., 2016), dyskinesias (; Zhang et al., 2016; ; ; ; ), and diplopia or paresthesias ().
Baumann et al. described a case of concomitant PD and ET where initially one set of stimulation parameters relieved PD but not ET symptoms and another set of parameters did the opposite. However, ILS using alternating unipolar pulses with different amplitudes at opposite poles of the STN and ventrolateral anterior thalamic region relieved both PD and ET symptoms (). Barbe et al. reported the successful use of ILS in several ET patients to relieve stimulation-induced dysarthria while maintaining therapeutic tremor suppression ().
published the first case series (n = 4) of successful ILS use to treat dystonia in patients who had not previously responded to conventional high-frequency pallidal DBS. Zhang et al. (2018) later published a case report with similar findings, supporting the need for further exploration of ILS use in dystonia patients not responding to HFS DBS. Finally, Shu et al. (2018) described a case of pallidal DBS for Meige syndrome where ILS settings greatly improved the patient’s symptoms compared to HFS DBS.
Burst cycling
Unlike traditional continuous HFS DBS (; ), a temporal pattern of “burst cycling” stimulation ON and OFF has also gained traction (Figure 3). This pattern has been tested in a variety of neurologic disorders with varying results. For PD-associated tremor, there is some evidence that cycling stimulation (10s/1s or 30s/5s ON/OFF) in the STN or thalamus may help prevent tremor rebound and tolerance to DBS (). A later study found that the implementation of a cycling stimulation pattern improved FOG in one PD patient who had developed tolerance to DBS (). On the other hand, Wong et al. (2021) did not find significant differences between cycling and conventional HFS in the treatment of FOG in patients with PD. Montgomery et al. found that continuous HFS of the STN provided greater symptom relief than burst cycling, showing a linear relationship between cycling interval and motor performance, with increasing efficacy of cycling from 0.1 to 0.5 s, both of which were inferior to continuous HFS. However, their results were likely underpowered and did not provide definitive conclusion (). While stimulating the thalamus in the treatment of postural tremor, similarly found that cycling parameters more closely matching continuous HFS produced the greatest reduction in tremor power.
In dystonia, burst cycling has shown some therapeutic effects while increasing battery life and reducing the frequency of battery replacements (Tai et al., 2011). Loeffler et al. employed a unique cycling stimulation paradigm in an attempt to treat tremor in a patient with FGF-14 associated spinocerebellar ataxia. Their cycling paradigm used HFS (180 Hz) during the day and switched to LFS at night (30 Hz). This group found that alternating high and low frequency in the daytime and nighttime, respectively, led to a significantly better tremor response than with stimulation OFF at night (). Burst cycling has also been used to treat non-motor conditions, the most common being various forms of drug-related epilepsy (DRE). Velasco et al. (2007) applied a burst cycling paradigm consisting of 1 min ON and 4 min OFF in the centromedian nucleus of the thalamus to effectively treat generalized tonic-clonic seizures and atypical absence seizures in Lennox-Gastaut syndrome. A double-blind, randomized control trial (ESTEL trial) found cycling 145 Hz for 1 min ON and 5 min OFF significantly reduced seizures in Lennox-Gastaut syndrome compared to no stimulation (). The stimulation parameters selected for this trial were in large part influenced by a prior double-blind, randomized control trial (SANTE trial) that found a significant reduction in seizure frequency in patients with refractory epilepsy using cycling stimulation of the thalamus (). These findings have been corroborated by other groups, such as , who showed a 60% seizure reduction 24 months after surgery with a 145 Hz 1 min ON, 5 min OFF cycling paradigm. A similar cycling paradigm (1 min ON, 4 min OFF) was used with success to stimulate the subiculum in six patients with mesial temporal lobe epilepsy associated with hippocampal sclerosis (Vázquez-Barrón et al., 2021). This is in line with conclusion of another study with mesial temporal lobe epilepsy patients where amygdalohippocampal cycling stimulation (1 min ON, 3 min OFF) resulted in seizure reductions at 2 and 18 months (). When directly comparing anterior nucleus of thalamus cycling stimulation to conventional stimulation in DRE patients, observed no significant difference in seizure frequency; however, they found an increase in restlessness with increased cycling frequency (shorter duration OFF period). Finally, cycling stimulation has been shown to be useful to avoid after-discharges and resultant seizures when using anterior cingulate DBS to treat chronic pain ().
Coordinated reset
Synchronous neuronal firing is thought to underlie critical behavioral processes such as memory formation. In fact, recent studies suggest that greater neuronal synchrony between mesial temporal lobe structures correlates with improved memory performance (). However, hypersynchronous activity in neural circuits may also underlie pathological brain states, such as PD, epilepsy, and tinnitus (). For example, synchronization among populations of neurons in the thalamus and basal ganglia in PD and ET is associated with characteristic pathological movements that are typically seen in these movement disorders. More importantly, studies exploring pharmacological interventions with dopaminergic drugs (e.g., Levodopa) and/or surgical interventions with DBS have recently shown a direct correlation between reduced synchronized oscillatory activity in the β-band (8–35 Hz) and improved motor performance, suggesting the potentially critical role that synchronized neuronal activity may play in the pathogenesis of movement disorders such as PD ().
CR-DBS is an alternative stimulation protocol that delivers intermittent high-frequency bursts to disrupt hypersynchronous neuronal firing (Figure 3). Typical CR-DBS parameters consist of high-frequency trains with an intra-burst frequency of 130 Hz (current: 2.0–4.0 mA), cycle repetition rate of 3–20 Hz, and pulse width ranging from 60 to 120 μs. Each pulse train typically consists of three to five pulses and the total duration of a pulse train ranges from 23 to 38 ms (). This pattern of stimulation can be applied to electrode contacts in either a serial (non-shuffled) or shuffled (random order stimulation of electrode contacts) fashion (Wang et al., 2022). Developed in 2003, the rationale of CR-DBS is to desynchronize neuronal populations in the basal ganglia-motor cortical circuit to achieve therapeutic benefits in movement disorders such as PD (Tass, 2003). In a proof-of-concept study by , six PD patients underwent bilateral implantation of quadripolar DBS electrodes in the STN. The patients were evaluated before and after once-daily 2-h CR-DBS treatments for a total of 3 days. Their CR-DBS stimulation protocol (detailed above) was specifically delivered to the three distal contacts of the electrode. They demonstrated that after 3 days of CR-DBS treatment, there was a significant reduction in β-band activity, with a mean reduction in β-power of 42% (p = 0.03), and an associated significant improvement in motor function [mean Unified Parkinson’s Disease Rating Scale (UPDRS) score reduction of 58%, p = 0.03] (). It is important to note that this study was limited by a lack of comparison to traditional HFS DBS, however, it is the first to show the therapeutic efficacy of CR-DBS in managing PD motor symptoms.
Temporally optimized stimulation
Traditional HFS DBS parameters are selected empirically based on individual patient testing and programmer experience. Newer alternative patterns have focused on using biomarkers or computational models to predict the “best settings” for a stimulation paradigm. Temporally optimized stimulation is a non-standardized computationally optimized pattern of DBS. In theory, a temporally optimized pattern of stimulation would significantly reduce energy consumption and frequency of implantable pulse generator (IPG) replacement, improving overall clinical outcomes.
In 2013, tested a wide variety of temporally irregular DBS patterns in PD patients (n = 10) while assessing motor outcomes via a finger-tapping task in the operating room. This group found that stimulation pattern, and not rate, more significantly impacted DBS efficacy. Interestingly, they employed several patterns that relieved motor symptoms more effectively than temporally regular HFS, including “absence,” “presence,” “unipeak,” and “uniform,” with varying pulse entropy and frequencies (see for detailed review).
Later, in a proof-of-concept study, Brocker et al. used model-based computational evolution to develop a temporally optimized stimulation paradigm (). They coupled a model of the basal ganglia with a genetic algorithm (GA) thought to operate similarly to evolution, with “natural selection” occurring to optimize stimulation. Three patterns were tested: temporally regular 185 Hz HFS, temporally regular 45 Hz LFS, and the optimized, GA pattern of stimulation, with an average frequency of 45 Hz. In a finger-tapping task in bradykinesia-dominant PD patients (n = 4), there was no significant difference in the rate and regularity of finger tapping between the HFS and GA groups, though both improved these parameters compared to baseline. Similarly, in the tremor-dominant PD subjects (n = 4), they found no significant difference in the reduction of tremor between the HFS and GA groups, though both significantly decreased tremor compared to baseline. Thus, despite significant study limitations, GA was tentatively deemed equivalent to HFS in terms of efficacy, but superior to HFS in terms of energy efficiency and battery life preservation ().
Recently, in a prospective, randomized, cross-over, multi-center feasibility study (n = 26), Okun et al. tested both versions of temporally optimized stimulation (TOPS) from the two aforementioned Brocker et al. studies [TOPS1 () and TOPS2 ()] (). They defined TOPS as pulse trains with a repeating sequence of non-regular and non-random intervals between pulses. TOPS2 used a long burst sequence (inter-burst interval ∼50 ms) followed by a short burst sequence (inter-burst interval ∼5 ms), with an average frequency of ∼158 Hz [see “absence” in ]. Like Brocker et al., this group found that TOPS reduced motor symptoms as effectively as conventional HFS DBS (). As this was a safety and feasibility study, the results were not powered to provide statistically significant conclusion on efficacy. Nonetheless, the promising findings of potential non-inferiority of TOPS vs. HFS, with the added knowledge that TOPS is more energy efficient, should lead to well-powered randomized, controlled clinical trials comparing TOPS against HFS.
Closed-loop and adaptive stimulation
Current DBS systems use open-loop stimulation in which stimulation is always ON. Closed-loop adaptive neuromodulation relies on a control signal to initiate changes in stimulation parameters in real-time. Biomarker-controlled DBS may be useful, as symptoms fluctuate throughout the day in many neurologic and neuropsychiatric disorders. In PD, tremors occur at rest and rigidity occurs with the onset of movement (Reich and Savitt, 2019), whereas in ET, tremor occurs with movement (Shanker, 2019). In neuropsychiatric conditions such as treatment-resistant OCD, symptoms fluctuate throughout the day, with obsessive fixation alternating with ritualistic compulsive behaviors, all of which vary widely from patient to patient (). In treatment-resistant depression (TRD), symptoms may be more pronounced in times of stress and minimally present at rest, though this disease is also characterized by highly heterogeneous symptoms (). In Tourette syndrome (TS), tics emerge frequently when the patient is under high stress, but manifest in different areas of the body, with variable duration and intensity (). Thus, neuromodulation strategies for these diseases might be more effective, more efficient, or better tolerated with on-demand stimulation paradigms. The goal of adaptive DBS (aDBS) is to treat each patient’s constellation of symptoms in an individualized manner, to reduce stimulation-induced side effects (including during sleep), and to prolong battery life through more efficient energy consumption.
The majority of studies in aDBS focus on PD (, ,; Rosa et al., 2015, 2017; ; , , ; Tinkhauser et al., 2017; , ; Swann et al., 2018; Velisar et al., 2019; ; ; ; ; Sasaki et al., 2021; Sarikhani et al., 2022). Several groups suggest that STN aDBS is equivalent or non-inferior to open-loop or constant DBS (cDBS) in reducing the UPDRS score in PD (; , ; Rosa et al., 2017; ; Swann et al., 2018; Velisar et al., 2019; Sasaki et al., 2021; Sarikhani et al., 2022). Some studies found greater score reductions with aDBS compared to cDBS (, ; Rosa et al., 2015; ). A potentially important limitation of these studies is that they were conducted with externalized leads and experimental pulse generators in a controlled research environment in the immediate post-lead-implantation period. Further studies are needed to demonstrate the generalizability of these findings in long-term studies and non-clinical environments.
Adaptive DBS is also of interest in neurocognitive aspects of PD. investigated a binary (ON-OFF) aDBS paradigm in eight PD patients with bilateral STN leads. They developed a threshold-based algorithm using beta oscillations (13–30 Hz) that switched ON and OFF automatically with a ramp up/down time of 250 ms (). They administered a speech intelligibility test (SIT) at baseline, during cDBS, and during aDBS (15-min duration) to measure speech-related side effects and improvement. They found that aDBS was associated with improved SIT scores compared to both baseline and cDBS (baseline SIT 67.9%; aDBS 70.4%; cDBS 60.5%; p = 0.02).
In ET, several studies report outcomes of unilateral VIM or zona incerta aDBS combined with recordings from subdural strip electrodes over primary motor cortex (,; ; ; , ; ). Two studies showed greater tremor suppression with aDBS (; ) and one study showed aDBS to be equivalent or non-inferior to cDBS (). On the other hand, demonstrated decreased tremor control with aDBS compared to cDBS.
Dystonia presents both opportunities and challenges for aDBS applications. found that short-term GPi aDBS (n = 7) did not lead to acute changes in low frequency oscillations (4–12 Hz) or to any significant clinical changes. However, dystonia typically has a delayed response to cDBS such that aDBS effects could be difficult to extrapolate in the context of short-term stimulation. DBS programming in dystonia patients is often more complex than for PD or ET, such that robust, effective closed loop stimulation strategies could play a useful role in these patients.
In neuropsychiatric disease, aDBS has been described in TRD (Scangos et al., 2021) and TS (). In TRD, Scangos et al. (2021) demonstrated safety and feasibility of a fully integrated aDBS system in a single patient. They found that bilateral amygdala gamma power is correlated to elevated symptom severity with high reproducibility. Stimulating at the VC/VS, they demonstrated improved depressive symptoms correlating to reduced amygdala gamma power in two of five stimulation trials. compared aDBS to cDBS in four TS patients with bilateral centromedian-parafascicular complex thalamic leads. Using a subdural strip overlying M1 and thalamic leads, they found increased low-frequency thalamic power (3–10 Hz) at the onset of involuntary tics that was not present during voluntary movements. Though there was no statistically significant difference between the two, both cDBS and aDBS significantly reduced symptoms compared to DBS OFF, suggesting possible non-inferiority of aDBS versus cDBS in TS. As in all indications, seeking a suitable control signal presents challenges, but recent studies show that aDBS for OCD may be on the horizon (, ). For example, recently found that delta-band (0–4 Hz) power showed a strong negative correlation with symptom severity in five patients with OCD implanted with sensing-capable IPGs and bilateral ventral capsule/ventral striatum (VC/VS) electrodes. Though this remains to be tested with aDBS, this finding may represent a suitable biomarker. While aDBS is of increasing interest, these studies are all limited by small sample sizes and require more robust investigation.
Discussion
As our familiarity with continuous high-frequency DBS, the gold-standard stimulation paradigm in the treatment of both neurologic and neuropsychiatric disorders, grows, clinicians and scientists increasingly recognize the need for programming optimization and improved efficacy, which has led to the development of numerous alternative patterns of stimulation. In this review, we provide a snapshot of current human evidence supporting these patterns–including TBS, paired pulse, VFS, ILS, burst cycling, CR-DBS, TOPS, and aDBS–as possible alternatives to HFS DBS. With small case series in most cases, the existing literature is not sufficient to reach definitive conclusion regarding non-inferiority/superiority of an alternative pattern versus HFS DBS. However, based on our findings, in addition to these being safe and technically feasible, they generally present two specific advantages over HFS DBS: (1) optimization leads to a more energy-efficient delivery of stimulation, prolonging battery life and reducing the frequency of costly IPG replacements; (2) non-continuous paradigms have the potential to simultaneously deliver therapeutic levels of stimulation, treating both motor and non-motor symptoms of various neurologic and neuropsychiatric diseases, while mitigating stimulation-induced side effects. By the temporal nature of their designs (i.e., non-continuous), many of the alternative patterns of stimulation discussed here would draw less power from an IPG than continuous HFS DBS when delivering stimulation. In TBS, specifically iTBS, stimulation trains or bursts are followed by pauses (DBS OFF periods) (). Similar to iTBS, VFS presumably would draw less electrical energy than HFS alone because of the alternating periods of LFS and HFS in a given cycle. However, though theoretically iTBS and VFS would consume less battery life than cTBS or HFS DBS alone, direct evidence of this is limited. Continuous TBS appears to improve motor PD symptoms, although the therapeutic threshold is higher with low intraburst frequencies (∼50 Hz vs. 100 Hz) (). The impact of burst cycling on battery longevity has been directly investigated, and one of the main motivations for using burst cycling stimulation over conventional continuous HFS has been to conserve device battery and decrease the frequency of IPG replacements (; Wong et al., 2021). In the case of TOPS and aDBS battery life is conserved in a more indirect approach, using computational modeling or biomarker-driven auto-regulating systems, respectively, which ultimately leads to more efficient energy consumption. Studies of ILS have shown conflicting results, with some indicating that ILS may lead to higher energy consumption and decreased battery life (; ), and others showing evidence that certain ILS settings may conserve energy relative to continuous HFS DBS (). In contrast to the primary motivation of increasing battery longevity, several studies focused on the potential mitigation of stimulation-induced side effects provided by these non-traditional paradigms, specifically VFS, ILS, burst cycling, CR-DBS, and aDBS. Many VFS studies showed improvement in gait dysfunction, FOG, and stimulation-induced speech disorders (e.g., dysarthria, hypophonia) with the use of VFS. Additionally, ILS studies observed improvements in rigidity, bradykinesia, tremor, and gait (e.g., FOG) symptoms as well as mitigation of unwanted side effects such as dysarthria, dyskinesias, diplopia, and paresthesias.
Finally, we found that some alternative patterns of stimulation may mimic a more physiologic neuronal firing pattern, which may be an explanation for why some alternative stimulation patterns seem to better address cognitive or memory-related symptoms of movement disorders and other neuropsychiatric disorders. For example, there is evidence that dorsolateral prefrontal cortex TBS can modulate networks involved in mood and cognitive function (e.g., memory). The ability to increase theta-power on-demand may provide a way to modulate specific neuronal populations, since it is believed that higher power theta oscillations can entrain high-frequency activity more efficiently (). Indeed, theta-gamma coupling (and theta band activity entraining single neuron firing) has functional relevance for cognition in humans across multiple domains including memory and decision making.
We acknowledge that our broad review of alternative patterns of stimulation has some methodological limitations. The most important limitation of this study is that, though we hope to review all alternative patterns of stimulation with published human data in a comprehensive manner, we are unable to account for those patterns for which we did not search. Additionally, there are likely some alternative patterns which may not have a standard naming convention and thus are difficult to review. For example, conducted a study using square biphasic pulses and other irregular pulse patterns in PD (n = 8) and ET (n = 3) patients. Briefly, their findings showed that certain non-conventional patterns of stimulation may extend battery life and minimize stimulation-associated side effects. The goal of their randomized, blinded pilot study was to provide a framework for rigorously testing non-HFS patterns of stimulation; however, they specify that further testing is needed to assess efficacy of alternative patterns of stimulation and compare them to conventional HFS ().
Conclusion
Alternative patterns of stimulation such as theta burst, paired pulse, variable frequency, interleaving, burst cycling, coordinated reset, temporally optimized stimulation, and adaptive DBS are promising novel patterns of stimulation that may provide improved efficacy for both motor and non-motor symptoms of neurologic and neuropsychiatric disorders. In doing so, these patterns may also extend battery life and lead to fewer replacements, ultimately improving quality of life for these patients. However, current evidence is limited and warrants rigorous trials before implementing in the clinical domain.
Statements
Author contributions
RN, AM, AK, UB, VD, HW, and JB: conceptualization, writing—original draft preparation, and writing—review and editing. VD, HW, and JB: supervision. All authors have read and agreed to the published version of the manuscript.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1
AdamchicI.HauptmannC.BarnikolU. B.PawelczykN.PopovychO.BarnikolT. T.et al (2014). Coordinated reset neuromodulation for Parkinson’s disease: proof-of-concept study.Mov. Disord.291679–1684. 10.1002/mds.25923
2
AgnesiF.JohnsonM. D.VitekJ. L. (2013). Deep brain stimulation: how does it work?Handb. Clin. Neurol.11639–54. 10.1016/B978-0-444-53497-2.00004-8
3
AkbarU.RaikeR. S.HackN.HessC. W.SkinnerJ.Martinez-RamirezD.et al (2016). Randomized, blinded pilot testing of nonconventional stimulation patterns and shapes in Parkinson’s Disease and Essential Tremor: Evidence for Further Evaluating Narrow and Biphasic Pulses.Neuromodulation J. Int. Neuromodulation Soc.19343–356. 10.1111/ner.1397
4
AndersonT. R.HuB.IremongerK.KissZ. H. T. (2006). Selective attenuation of afferent synaptic transmission as a mechanism of thalamic deep brain stimulation-induced tremor arrest.J. Neurosci.26841–850. 10.1523/JNEUROSCI.3523-05.2006
5
AquinoC. C.DuffleyG.HedgesD. M.VorwerkJ.HouseP. A.FerrazH. B.et al (2019). Interleaved deep brain stimulation for dyskinesia management in Parkinson’s disease.Mov. Disord.341722–1727. 10.1002/mds.27839
6
ArlottiM.ColomboM.BonfantiA.MandatT.LanotteM. M.PirolaE.et al (2021). A new implantable closed-loop clinical neural interface: First application in Parkinson’s Disease.Front. Neurosci.15:763235. 10.3389/fnins.2021.763235
7
ArlottiM.MarcegliaS.FoffaniG.VolkmannJ.LozanoA. M.MoroE.et al (2018). Eight-hours adaptive deep brain stimulation in patients with Parkinson disease.Neurology90e971–e976. 10.1212/WNL.0000000000005121
8
AwadM. Z.IrwinZ. T.VadenR. J.GuthrieB. L.WalkerH. C. (2020). Short latency cortical evoked potentials elicited by subthalamic nucleus deep brain stimulation: Commentary and results from paired pulse studies.Clin. Neurophysiol.131465–467. 10.1016/j.clinph.2019.11.015
9
AwadM. Z.VadenR. J.IrwinZ. T.GonzalezC. L.BlackS.NakhmaniA.et al (2021). Subcortical short-term plasticity elicited by deep brain stimulation.Ann. Clin. Transl. Neurol.81010–1023. 10.1002/acn3.51275
10
BakerK. B.MontgomeryE. B.RezaiA. R.BurgessR.LüdersH. O. (2002). Subthalamic nucleus deep brain stimulus evoked potentials: physiological and therapeutic implications.Mov. Disord.17969–983. 10.1002/mds.10206
11
BarbeM. T.DembekT. A.BeckerJ.RaethjenJ.HartingerM.MeisterI. G.et al (2014). Individualized current-shaping reduces DBS-induced dysarthria in patients with essential tremor.Neurology82614–619. 10.1212/WNL.0000000000000127
12
BaumannC. R.ImbachL. L.Baumann-VogelH.UhlM.SarntheinJ.SürücüO. (2012). Interleaving deep brain stimulation for a patient with both Parkinson’s disease and essential tremor.Mov. Disord.271700–1701. 10.1002/mds.25221
13
BenabidA. L.ChabardesS.MitrofanisJ.PollakP. (2009). Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson’s disease.Lancet Neurol.867–81. 10.1016/S1474-4422(08)70291-6
14
BenazzouzA.HallettM. (2000). Mechanism of action of deep brain stimulation.Neurology55S13–S16.
15
BentleyJ. N.IrwinZ. T.BlackS. D.RoachM. L.VadenR. J.GonzalezC. L.et al (2020). Subcortical Intermittent Theta-Burst Stimulation (iTBS) Increases Theta-Power in Dorsolateral Prefrontal Cortex (DLPFC).Front. Neurosci.14:41. 10.3389/fnins.2020.00041
16
BirdnoM. J.CooperS. E.RezaiA. R.GrillW. M. (2007). Pulse-to-pulse changes in the frequency of deep brain stimulation affect tremor and modeled neuronal activity.J. Neurophysiol.981675–1684. 10.1152/jn.00547.2007
17
BirdnoM. J.KuncelA. M.DorvalA. D.TurnerD. A.GrillW. M. (2008). Tremor varies as a function of the temporal regularity of deep brain stimulation.Neuroreport19599–602. 10.1097/WNR.0b013e3282f9e45e
18
BoongirdA.BoongirdA.KhongkhatithumC.ThampratankulL.VisudtibhanA. (2016). Deep Brain Stimulation of Anterior Thalamic Nuclei for Intractable Epilepsy in Thailand: Case Report.J. Med. Assoc. Thail. Chotmaihet Thangphaet99S126–S129.
19
BrockerD. T.SwanB. D.SoR. Q.TurnerD. A.GrossR. E.GrillW. M. (2017). Optimized temporal pattern of brain stimulation designed by computational evolution.Sci. Transl. Med.9:3532. 10.1126/scitranslmed.aah3532
20
BrockerD. T.SwanB. D.TurnerD. A.GrossR. E.TatterS. B.KoopM. M.et al (2013). Improved efficacy of temporally non-regular deep brain stimulation in Parkinson’s disease.Exp. Neurol.23960–67. 10.1016/j.expneurol.2012.09.008
21
Bueno-JuniorL. S.LeiteJ. P. (2018). Input convergence, synaptic plasticity and functional coupling across hippocampal-prefrontal-thalamic circuits.Front. Neural Circuits12:40. 10.3389/fncir.2018.00040
22
CagleJ. N.OkunM. S.CerneraS.EisingerR. S.OpriE.BowersD.et al (2022). Embedded human closed-loop deep brain stimulation for tourette syndrome: A nonrandomized controlled trial.JAMA Neurol.791064–1068. 10.1001/jamaneurol.2022.2741
23
CagnanH.PedrosaD.LittleS.PogosyanA.CheeranB.AzizT.et al (2017). Stimulating at the right time: phase-specific deep brain stimulation.Brain J. Neurol.140132–145. 10.1093/brain/aww286
24
CampbellB. A.Favi BoccaL.Escobar SanabriaD.AlmeidaJ.RammoR.NagelS. J.et al (2022). The impact of pulse timing on cortical and subthalamic nucleus deep brain stimulation evoked potentials.Front. Hum. Neurosci.16:1009223. 10.3389/fnhum.2022.1009223
25
Castaño-CandamilS.FerlegerB. I.HaddockA.CooperS. S.HerronJ.KoA.et al (2020). A Pilot Study on Data-Driven Adaptive Deep Brain Stimulation in Chronically Implanted Essential Tremor Patients.Front. Hum. Neurosci.14:541625. 10.3389/fnhum.2020.541625
26
CerneraS.OkunM. S.GunduzA. (2019). A review of cognitive outcomes across movement disorder patients undergoing deep brain stimulation.Front. Neurol.10:419. 10.3389/fneur.2019.00419
27
ChangB.MeiJ.XiongC.ChenP.JiangM.NiuC. (2021). Bilateral globus pallidus interna combined with subthalamic nucleus variable frequency deep brain stimulation in the treatment of young-onset Parkinson’s Disease with refractory dyskinesia: A case report.Front. Neurosci.15:782046. 10.3389/fnins.2021.782046
28
ChenK.-H.TangA. M.GilbertZ. D.Martin Del Campo-VeraR.SebastianR.GogiaA. S.et al (2022). Theta low-gamma phase amplitude coupling in the human orbitofrontal cortex increases during a conflict-processing task.J. Neural Eng.19:ac4f9b. 10.1088/1741-2552/ac4f9b
29
ChungJ. W.BurciuR. G.OforiE.CoombesS. A.ChristouE. A.OkunM. S.et al (2018). Beta-band oscillations in the supplementary motor cortex are modulated by levodopa and associated with functional activity in the basal ganglia.Neuroimage Clin.19559–571. 10.1016/j.nicl.2018.05.021
30
ConwayZ. J.SilburnP. A.PereraT.O’MaleyK.ColeM. H. (2021). Low-frequency STN-DBS provides acute gait improvements in Parkinson’s disease: a double-blinded randomised cross-over feasibility trial.J. Neuroengineering Rehabil.18:125. 10.1186/s12984-021-00921-4
31
DalicL. J.WarrenA. E. L.BullussK. J.ThevathasanW.RotenA.ChurilovL.et al (2022). DBS of Thalamic Centromedian Nucleus for Lennox–Gastaut Syndrome (ESTEL Trial).Ann. Neurol.91253–267. 10.1002/ana.26280
32
DayalV.RajabianA.JahanshahiM.Aviles-OlmosI.CowieD.PetersA.et al (2021). Pedunculopontine nucleus deep brain stimulation for Parkinsonian disorders: A Case Series.Stereotact. Funct. Neurosurg.99287–294. 10.1159/000511978
33
DeuschlG.Schade-BrittingerC.KrackP.VolkmannJ.SchäferH.BötzelK.et al (2006). A randomized trial of deep-brain stimulation for Parkinson’s Disease.N. Engl. J. Med.355896–908. 10.1056/NEJMoa060281
34
DinkelbachL.MuellerJ.PoeweW.DelazerM.ElbenS.WoltersA.et al (2015). Cognitive outcome of pallidal deep brain stimulation for primary cervical dystonia: One year follow up results of a prospective multicenter trial.Parkinsonism Relat. Disord.21976–980. 10.1016/j.parkreldis.2015.06.002
35
EbertM.HauptmannC.TassP. A. (2014). Coordinated reset stimulation in a large-scale model of the STN-GPE circuit.Front. Comput. Neurosci.8:154. 10.3389/FNCOM.2014.00154/BIBTEX
36
EnatsuR.KitagawaM.MorishitaT.SasagawaA.KuribaraT.HiranoT.et al (2020). Effect of cycling thalamosubthalamic stimulation on tremor habituation and rebound in Parkinson Disease.World Neurosurg.14464–67. 10.1016/j.wneu.2020.08.141
37
FerlegerB. I.HoustonB.ThompsonM. C.CooperS. S.SonnetK. S.KoA. L.et al (2020). Fully implanted adaptive deep brain stimulation in freely moving essential tremor patients.J. Neural Eng.17:56026. 10.1088/1741-2552/abb416
38
FilatovaE. V.ShadrinaM. I.SlominskyP. A. (2021). Major depression: One brain, one disease, one set of intertwined processes.Cells10:1283. 10.3390/cells10061283
39
FisherR.SalanovaV.WittT.WorthR.HenryT.GrossR.et al (2010). Electrical stimulation of the anterior nucleus of thalamus for treatment of refractory epilepsy.Epilepsia51899–908. 10.1111/j.1528-1167.2010.02536.x
40
FrançaC.BarbosaE. R.IglesioR.TeixeiraM. J.CuryR. G. (2019). Interleaving Stimulation in Parkinson Disease: Interesting to Whom?World Neurosurg.130e786–e793. 10.1016/j.wneu.2019.06.223
41
GilronR.LittleS.PerroneR.WiltR.de HemptinneC.YaroshinskyM. S.et al (2021). Long-term wireless streaming of neural recordings for circuit discovery and adaptive stimulation in individuals with Parkinson’s disease.Nat. Biotechnol.391078–1085. 10.1038/s41587-021-00897-5
42
GoftariM.KimJ.JohnsonE.PatriatR.PalnitkarT.HarelN.et al (2020). Pallidothalamic tract activation predicts suppression of stimulation-induced dyskinesias in a case study of Parkinson’s disease.Brain Stimulat.131821–1823. 10.1016/j.brs.2020.09.022
43
GoodmanW. K.GriceD. E.LapidusK. A. B.CoffeyB. J. (2014). Obsessive-compulsive disorder.Psychiatr. Clin. North Am.37257–267. 10.1016/j.psc.2014.06.004
44
GrillW. M.SnyderA. N.MiocinovicS. (2004). Deep brain stimulation creates an informational lesion of the stimulated nucleus.Neuroreport151137–1140. 10.1097/00001756-200405190-00011
45
GroverT.GeorgievD.KalliolaR.MahlknechtP.ZachariaA.CandelarioJ.et al (2019). Effect of low versus high frequency subthalamic deep brain stimulation on speech intelligibility and verbal fluency in Parkinson’s Disease: A double-blind study.J. Park. Dis.9141–151. 10.3233/JPD-181368
46
HammondC.AmmariR.BioulacB.GarciaL. (2008). Latest view on the mechanism of action of deep brain stimulation.Mov. Disord.232111–2121. 10.1002/mds.22120
47
HashimotoT.ElderC. M.OkunM. S.PatrickS. K.VitekJ. L. (2003). Stimulation of the subthalamic nucleus changes the firing pattern of pallidal neurons.J. Neurosci.231916–1923. 10.1523/JNEUROSCI.23-05-01916.2003
48
HeS.BaigF.MostofiA.PogosyanA.DebarrosJ.GreenA. L.et al (2021). Closed-Loop deep brain stimulation for essential tremor based on thalamic local field potentials.Mov. Disord.36863–873. 10.1002/mds.28513
49
HeS.DebarrosJ.KhawaldehS.PogosyanA.MostofiA.BaigF.et al (2020). Closed-loop DBS triggered by real-time movement and tremor decoding based on thalamic LFPs for essential tremor.Annu. Int. Conf. IEEE Eng. Med. Biol. Soc.20203602–3605. 10.1109/EMBC44109.2020.9175433
50
HerronJ. A.ThompsonM. C.BrownT.ChizeckH. J.OjemannJ. G.KoA. L. (2017a). Chronic electrocorticography for sensing movement intention and closed-loop deep brain stimulation with wearable sensors in an essential tremor patient.J. Neurosurg.127580–587. 10.3171/2016.8.JNS16536
51
HerronJ. A.ThompsonM. C.BrownT.ChizeckH. J.OjemannJ. G.KoA. L. (2017b). Cortical brain-computer interface for closed-loop deep brain stimulation.IEEE Trans. Neural Syst. Rehabil. Eng.252180–2187. 10.1109/TNSRE.2017.2705661
52
HornM. A.GulbertiA.GülkeE.BuhmannC.GerloffC.MollC. K. E.et al (2020). A New Stimulation Mode for Deep Brain Stimulation in Parkinson’s Disease: Theta Burst Stimulation.Mov. Disord.351471–1475. 10.1002/mds.28083
53
HuangY.CheeranB.GreenA. L.DenisonT. J.AzizT. Z. (2019). Applying a sensing-enabled system for ensuring safe anterior cingulate deep brain stimulation for pain.Brain Sci.9:150. 10.3390/brainsci9070150
54
HuangY.-Z.EdwardsM. J.RounisE.BhatiaK. P.RothwellJ. C. (2005). Theta burst stimulation of the human motor cortex.Neuron45201–206. 10.1016/j.neuron.2004.12.033
55
JankovicJ.KurlanR. (2011). Tourette syndrome: evolving concepts.Mov. Disord.261149–1156. 10.1002/mds.23618
56
JensenA. L.DurandD. M. (2009). High frequency stimulation can block axonal conduction.Exp. Neurol.22057–70. 10.1016/j.expneurol.2009.07.023
57
JiaF.GuoY.WanS.ChenH.HaoH.ZhangJ.et al (2015). Variable frequency stimulation of subthalamic nucleus for freezing of gait in Parkinson’s disease.Parkinsonism Relat. Disord.211471–1472. 10.1016/j.parkreldis.2015.10.002
58
JiaF.GuoY.WanS.LiL. (2017). Severe subthalamic stimulation-induced dysarthria alleviated by a novel paradigm: A case report.J. Neurol. Sci.372270–271. 10.1016/j.jns.2016.11.048
59
JiaF.Wagle ShuklaA.HuW.AlmeidaL.HolandaV.ZhangJ.et al (2018). Deep Brain Stimulation at Variable Frequency to Improve Motor Outcomes in Parkinson’s Disease.Mov. Disord. Clin. Pract.5538–541. 10.1002/mdc3.12658
60
JiaF.ZhangJ.WangH.LiangZ.LiuW.WangX.et al (2019). Variable- versus constant-frequency deep-brain stimulation in patients with advanced Parkinson’s disease: study protocol for a randomized controlled trial.Trials20:749. 10.1186/s13063-019-3884-4
61
JohnsonV.WiltR.GilronR.AnsoJ.PerroneR.BeudelM.et al (2021). Embedded adaptive deep brain stimulation for cervical dystonia controlled by motor cortex theta oscillations.Exp. Neurol.345:113825. 10.1016/j.expneurol.2021.113825
62
JonesK. T.JohnsonE. L.BerryhillM. E. (2020). Frontoparietal theta-gamma interactions track working memory enhancement with training and tDCS.NeuroImage211:116615. 10.1016/j.neuroimage.2020.116615
63
JutrasM. J.BuffaloE. A. (2010). Synchronous neural activity and memory formation.Curr. Opin. Neurobiol.20150–155. 10.1016/j.conb.2010.02.006
64
KahanaM. J.SekulerR.CaplanJ. B.KirschenM.MadsenJ. R. (1999). Human theta oscillations exhibit task dependence during virtual maze navigation.Nature399781–784. 10.1038/21645
65
KarlJ. A.OuyangB.GoetzS.MetmanL. V. (2020). A Novel DBS Paradigm for Axial Features in Parkinson’s Disease: A Randomized Crossover Study.Mov. Disord.351369–1378. 10.1002/mds.28048
66
KarlJ. A.OuyangB.Verhagen MetmanL. (2019). A Novel Dual-Frequency Deep Brain Stimulation Paradigm for Parkinson’s Disease.Neurol. Ther.8483–489. 10.1007/s40120-019-0140-5
67
KaufmannE.BötzelK.VollmarC.MehrkensJ.-H.NoachtarS. (2020). What have we learned from 8 years of deep brain stimulation of the anterior thalamic nucleus? Experiences and insights of a single center.J. Neurosurg.[Epub ahead of print]. 10.3171/2020.6.JNS20695
68
KernD. S.PicilloM.ThompsonJ. A.SammartinoF.di BiaseL.MunhozR. P.et al (2018). Interleaving Stimulation in Parkinson’s Disease, Tremor, and Dystonia.Stereotact. Funct. Neurosurg.96379–391. 10.1159/000494983
69
KimK.SchedlbauerA.RolloM.KarunakaranS.EkstromA. D.TandonN. (2018). Network-based brain stimulation selectively impairs spatial retrieval.Brain Stimulat.11213–221. 10.1016/j.brs.2017.09.016
70
KovácsN.JanszkyJ.NagyF.BalásI. (2012). Changing to interleaving stimulation might improve dystonia in cases not responding to pallidal stimulation.Mov. Disord.27163–165. 10.1002/mds.23962
71
KuncelA. M.BirdnoM. J.SwanB. D.GrillW. M. (2012). Tremor reduction and modeled neural activity during cycling thalamic deep brain stimulation.Clin. Neurophysiol.1231044–1052. 10.1016/j.clinph.2011.07.052
72
LeeD. J.DrummondN. M.SahaU.De VlooP.DallapiazzaR. F.GramerR.et al (2021). Acute low frequency dorsal subthalamic nucleus stimulation improves verbal fluency in Parkinson’s disease.Brain Stimulat.14754–760. 10.1016/j.brs.2021.04.016
73
LittleS.BeudelM.ZrinzoL.FoltynieT.LimousinP.HarizM.et al (2016a). Bilateral adaptive deep brain stimulation is effective in Parkinson’s disease.J. Neurol. Neurosurg. Psychiatry87717–721. 10.1136/jnnp-2015-310972
74
LittleS.TripolitiE.BeudelM.PogosyanA.CagnanH.HerzD.et al (2016b). Adaptive deep brain stimulation for Parkinson’s disease demonstrates reduced speech side effects compared to conventional stimulation in the acute setting.J. Neurol. Neurosurg. Psychiatry871388–1389. 10.1136/jnnp-2016-313518
75
LittleS.PogosyanA.NealS.ZavalaB.ZrinzoL.HarizM.et al (2013). Adaptive deep brain stimulation in advanced Parkinson disease.Ann. Neurol.74449–457. 10.1002/ana.23951
76
LoefflerM. A.SynofzikM.CebiI.KlockeP.HormoziM.GasserT.et al (2022). Case Report: Deep brain stimulation improves tremor in FGF-14 associated spinocerebellar ataxia.Front. Neurol.13:1048530. 10.3389/fneur.2022.1048530
77
LouieK. H.LuC.AbdallahT.GuziorJ. C.TwedellE.NetoffT. I.et al (2021). Gait phase triggered deep brain stimulation in Parkinson’s disease.Brain Stimulat.14420–422. 10.1016/j.brs.2021.02.009
78
LowetE.KondaboluK.ZhouS.MountR. A.WangY.RavasioC. R.et al (2022). Deep brain stimulation creates informational lesion through membrane depolarization in mouse hippocampus.Nat. Commun.13:7709. 10.1038/s41467-022-35314-1
79
LozanoA. M.LipsmanN.BergmanH.BrownP.ChabardesS.ChangJ. W.et al (2019). Deep brain stimulation: current challenges and future directions.Nat. Rev. Neurol.15148–160. 10.1038/s41582-018-0128-2
80
MalekmohammadiM.HerronJ.VelisarA.BlumenfeldZ.TragerM. H.ChizeckH. J.et al (2016). Kinematic adaptive deep brain stimulation for resting tremor in Parkinson’s Disease.Mov. Disord.31426–428. 10.1002/mds.26482
81
McIntyreC. C.SavastaM.Kerkerian-Le GoffL.VitekJ. L. (2004). Uncovering the mechanism(s) of action of deep brain stimulation: Activation, inhibition, or both.Clin. Neurophysiol.1151239–1248. 10.1016/j.clinph.2003.12.024
82
MillerJ. P.SweetJ. A.BaileyC. M.MunyonC. N.LudersH. O.FastenauP. S. (2015). Visual-spatial memory may be enhanced with theta burst deep brain stimulation of the fornix: a preliminary investigation with four cases.Brain J. Neurol.1381833–1842. 10.1093/brain/awv095
83
MinB.GuomingL.JianZ. (2013). Treatment of mesial temporal lobe epilepsy with amygdalohippocampal stimulation: A case series and review of the literature.Exp. Ther. Med.51264–1268. 10.3892/etm.2013.968
84
MiocinovicS.KhemaniP.WhiddonR.ZeilmanP.Martinez-RamirezD.OkunM. S.et al (2014). Outcomes, management, and potential mechanisms of interleaving deep brain stimulation settings.Parkinsonism Relat. Disord.201434–1437. 10.1016/j.parkreldis.2014.10.011
85
MolinaR.HassC. J.CerneraS.SowalskyK.SchmittA. C.RoperJ. A.et al (2021). Closed-Loop deep brain stimulation to treat medication-refractory freezing of gait in Parkinson’s Disease.Front. Hum. Neurosci.15:633655. 10.3389/fnhum.2021.633655
86
MontgomeryE. B. (2005). Effect of subthalamic nucleus stimulation patterns on motor performance in Parkinson’s disease.Parkinsonism Relat. Disord.11167–171. 10.1016/j.parkreldis.2004.12.002
87
NakajimaA.ShimoY.FuseA.TokugawaJ.HishiiM.IwamuroH.et al (2021). Case Report: Chronic adaptive deep brain stimulation personalizing therapy based on Parkinsonian State.Front. Hum. Neurosci.15:702961. 10.3389/fnhum.2021.702961
88
OkunM. S.FernandezH. H.WuS. S.Kirsch-DarrowL.BowersD.BovaF.et al (2009). Cognition and Mood in Parkinson Disease in STN versus GPi DBS: The COMPARE Trial.Ann. Neurol.65586–595. 10.1002/ana.21596
89
OkunM. S.HickeyP. T.MachadoA. G.KuncelA. M.GrillW. M. (2022). Temporally optimized patterned stimulation (TOPS®) as a therapy to personalize deep brain stimulation treatment of Parkinson’s disease.Front. Hum. Neurosci.16:929509. 10.3389/fnhum.2022.929509
90
OpriE.CerneraS.MolinaR.EisingerR. S.CagleJ. N.AlmeidaL.et al (2020). Chronic embedded cortico-thalamic closed-loop deep brain stimulation for the treatment of essential tremor.Sci. Transl. Med.12:eaay7680. 10.1126/scitranslmed.aay7680
91
OstremJ. L.RacineC. A.GlassG. A.GraceJ. K.VolzM. M.HeathS. L.et al (2011). Subthalamic nucleus deep brain stimulation in primary cervical dystonia.Neurology76870–878. 10.1212/WNL.0b013e31820f2e4f
92
OzturkM.ViswanathanA.ShethS. A.InceN. F. (2021). Electroceutically induced subthalamic high-frequency oscillations and evoked compound activity may explain the mechanism of therapeutic stimulation in Parkinson’s disease.Commun. Biol.4:393. 10.1038/s42003-021-01915-7
93
PaekS. B.KnightE. J.ChangS.-Y.LujanJ. L.JangD. P.BennetK. E.et al (2013). Dopamine measurement during prolonged deep brain stimulation: a proof-of-principle study of paired pulse voltammetry.Biomed. Eng. Lett.322–31. 10.1007/s13534-013-0086-y
94
ParsonsT. D.RogersS. A.BraatenA. J.WoodsS. P.TrösterA. I. (2006). Cognitive sequelae of subthalamic nucleus deep brain stimulation in Parkinson’s disease: a meta-analysis.Lancet Neurol.5578–588. 10.1016/S1474-4422(06)70475-6
95
PedrosaD. J.AuthM.EggersC.TimmermannL. (2013). Effects of low-frequency thalamic deep brain stimulation in essential tremor patients.Exp. Neurol.248205–212. 10.1016/j.expneurol.2013.06.009
96
PedrosaD. J.AuthM.PaulsK. A. M.RungeM.MaaroufM.FinkG. R.et al (2014). Verbal fluency in essential tremor patients: the effects of deep brain stimulation.Brain Stimulat.7359–364. 10.1016/j.brs.2014.02.012
97
Perestelo-PérezL.Rivero-SantanaA.Pérez-RamosJ.Serrano-PérezP.PanettaJ.HilarionP. (2014). Deep brain stimulation in Parkinson’s disease: meta-analysis of randomized controlled trials.J. Neurol.2612051–2060. 10.1007/s00415-014-7254-6
98
PetrucciM. N.NeuvilleR. S.AfzalM. F.VelisarA.AnidiC. M.AndersonR. W.et al (2020). Neural closed-loop deep brain stimulation for freezing of gait.Brain Stimulat.131320–1322. 10.1016/j.brs.2020.06.018
99
Piña-FuentesD.BeudelM.LittleS.BrownP.OterdoomD. L. M.van DijkJ. M. C. (2019). Adaptive deep brain stimulation as advanced Parkinson’s disease treatment (ADAPT study): protocol for a pseudo-randomised clinical study.BMJ Open9:e029652. 10.1136/bmjopen-2019-029652
100
Piña-FuentesD.BeudelM.Van ZijlJ. C.Van EgmondM. E.OterdoomD. L. M.Van DijkJ. M. C.et al (2020a). Low-frequency oscillation suppression in dystonia: Implications for adaptive deep brain stimulation.Parkinsonism Relat. Disord.79105–109. 10.1016/j.parkreldis.2020.08.030
101
Piña-FuentesD.van DijkJ. M. C.van ZijlJ. C.MoesH. R.van LaarT.OterdoomD. L. M.et al (2020b). Acute effects of adaptive Deep Brain Stimulation in Parkinson’s disease.Brain Stimulat.131507–1516. 10.1016/j.brs.2020.07.016
102
Piña-FuentesD.LittleS.OterdoomM.NealS.PogosyanA.TijssenM. A. J.et al (2017). Adaptive DBS in a Parkinson’s patient with chronically implanted DBS: A proof of principle.Mov. Disord.321253–1254. 10.1002/mds.26959
103
ProvenzaN. R.MattesonE. R.AllawalaA. B.Barrios-AndersonA.ShethS. A.ViswanathanA.et al (2019). The Case for Adaptive Neuromodulation to Treat Severe Intractable Mental Disorders.Front. Neurosci.13:152. 10.3389/fnins.2019.00152
104
ProvenzaN. R.ShethS. A.Dastin-van RijnE. M.MathuraR. K.DingY.VogtG. S.et al (2021). Long-term ecological assessment of intracranial electrophysiology synchronized to behavioral markers in obsessive-compulsive disorder.Nat. Med.272154–2164. 10.1038/s41591-021-01550-z
105
Ramirez-ZamoraA.KahnM.CampbellJ.DeLaCruzP.PilitsisJ. G. (2015). Interleaved programming of subthalamic deep brain stimulation to avoid adverse effects and preserve motor benefit in Parkinson’s disease.J. Neurol.262578–584. 10.1007/s00415-014-7605-3
106
RazmkonA.AbdollahifardS.TaherifardE.RoshanshadA.ShahrivarK. (2022). Effect of deep brain stimulation on freezing of gait in patients with Parkinson’s disease: a systematic review.Br. J. Neurosurg.[Epub ahead of print]. 10.1080/02688697.2022.2077308
107
ReichS. G.SavittJ. M. (2019). Parkinson’s Disease.Med. Clin. North Am.103337–350. 10.1016/j.mcna.2018.10.014
108
RosaM.ArlottiM.ArdolinoG.CogiamanianF.MarcegliaS.Di FonzoA.et al (2015). Adaptive deep brain stimulation in a freely moving Parkinsonian patient.Mov. Disord.301003–1005. 10.1002/mds.26241
109
RosaM.ArlottiM.MarcegliaS.CogiamanianF.ArdolinoG.FonzoA. D.et al (2017). Adaptive deep brain stimulation controls levodopa-induced side effects in Parkinsonian patients.Mov. Disord.32628–629. 10.1002/mds.26953
110
RosenbaumR.ZimnikA.ZhengF.TurnerR. S.AlzheimerC.DoironB.et al (2014). Axonal and synaptic failure suppress the transfer of firing rate oscillations, synchrony and information during high frequency deep brain stimulation.Neurobiol. Dis.6286–99. 10.1016/j.nbd.2013.09.006
111
Sáenz-FarretM.LohA.BoutetA.GermannJ.EliasG. J. B.KaliaS. K.et al (2021). Theta burst deep brain stimulation in movement disorders.Mov. Disord. Clin. Pract.8282–285. 10.1002/mdc3.13130
112
SarikhaniP.FerlegerB.MitchellK.OstremJ.HerronJ.MahmoudiB.et al (2022). Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson’s disease and essential tremor.J. Neural Eng.19ac86a2. 10.1088/1741-2552/ac86a2
113
SasakiF.OyamaG.SekimotoS.NuermaimaitiM.IwamuroH.ShimoY.et al (2021). Closed-loop programming using external responses for deep brain stimulation in Parkinson’s disease.Parkinsonism Relat. Disord.8447–51. 10.1016/j.parkreldis.2021.01.023
114
ScangosK. W.KhambhatiA. N.DalyP. M.MakhoulG. S.SugrueL. P.ZamanianH.et al (2021). Closed-loop neuromodulation in an individual with treatment-resistant depression.Nat. Med.271696–1700. 10.1038/s41591-021-01480-w
115
SchlenstedtC.ShalashA.MuthuramanM.FalkD.WittK.DeuschlG. (2017). Effect of high-frequency subthalamic neurostimulation on gait and freezing of gait in Parkinson’s disease: a systematic review and meta-analysis.Eur. J. Neurol.2418–26. 10.1111/ene.13167
116
ShankerV. (2019). Essential tremor: diagnosis and management.BMJ366:14485. 10.1136/bmj.l4485
117
ShuW.LiY.LiJ.ZhangY. (2018). Interleaving programming in pallidal deep brain stimulation improves outcomes in a patient with Meige syndrome.Br. J. Neurosurg.32661–662. 10.1080/02688697.2018.1504883
118
SolomonE. A.SperlingM. R.SharanA. D.WandaP. A.LevyD. F.LyalenkoA.et al (2021). Theta-burst stimulation entrains frequency-specific oscillatory responses.Brain Stimulat.141271–1284. 10.1016/j.brs.2021.08.014
119
SuD.ChenH.HuW.LiuY.WangZ.WangX.et al (2018). Frequency-dependent effects of subthalamic deep brain stimulation on motor symptoms in Parkinson’s disease: a meta-analysis of controlled trials.Sci. Rep.8:14456. 10.1038/s41598-018-32161-3
120
SwannN. C.de HemptinneC.ThompsonM. C.MiocinovicS.MillerA. M.GilronR.et al (2018). Adaptive deep brain stimulation for Parkinson’s disease using motor cortex sensing.J. Neural Eng.15:46006. 10.1088/1741-2552/aabc9b
121
TaiC.-H.WuR.-M.LiuH.-M.TsaiC.-W.TsengS.-H. (2011). Meige syndrome relieved by bilateral pallidal stimulation with cycling mode: case report.Neurosurgery69E1333–E1337. 10.1227/NEU.0b013e31822a9ad2
122
TassP. A. (2003). A model of desynchronizing deep brain stimulation with a demand-controlled coordinated reset of neural subpopulations.Biol. Cybern.8981–88. 10.1007/s00422-003-0425-7
123
TinkhauserG.PogosyanA.LittleS.BeudelM.HerzD. M.TanH.et al (2017). The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson’s disease.Brain J. Neurol.1401053–1067. 10.1093/brain/awx010
124
TitizA. S.HillM. R. H.MankinE. A.AghajanM. Z.EliashivD.TchemodanovN.et al (2017). Theta-burst microstimulation in the human entorhinal area improves memory specificity.eLife6:e29515. 10.7554/eLife.29515
125
VallabhajosulaS.HaqI. U.HwynnN.OyamaG.OkunM.TillmanM. D.et al (2015). Low-frequency versus high-frequency subthalamic nucleus deep brain stimulation on postural control and gait in Parkinson’s disease: a quantitative study.Brain Stimulat.864–75. 10.1016/j.brs.2014.10.011
126
Vázquez-BarrónD.Cuéllar-HerreraM.VelascoF.VelascoA. L. (2021). Electrical Stimulation of Subiculum for the Treatment of Refractory Mesial Temporal Lobe Epilepsy with Hippocampal Sclerosis: A 2-Year Follow-Up Study.Stereotact. Funct. Neurosurg.9940–47. 10.1159/000510295
127
VelascoF.VelascoA. L.VelascoM.JiménezF.Carrillo-RuizJ. D.CastroG. (2007). Deep brain stimulation for treatment of the epilepsies: the centromedian thalamic target.Acta Neurochir. Suppl.97337–342. 10.1007/978-3-211-33081-4_38
128
VelisarA.Syrkin-NikolauJ.BlumenfeldZ.TragerM. H.AfzalM. F.PrabhakarV.et al (2019). Dual threshold neural closed loop deep brain stimulation in Parkinson disease patients.Brain Stimulat.12868–876. 10.1016/j.brs.2019.02.020
129
VivekanandaU.BushD.BisbyJ. A.BaxendaleS.RodionovR.DiehlB.et al (2021). Theta power and theta-gamma coupling support long-term spatial memory retrieval.Hippocampus31213–220. 10.1002/hipo.23284
130
WangJ.FergusS. P.JohnsonL. A.NebeckS. D.ZhangJ.KulkarniS.et al (2022). Shuffling Improves the Acute and Carryover Effect of Subthalamic Coordinated Reset Deep Brain Stimulation.Front. Neurol.13:716046. 10.3389/fneur.2022.716046
131
WilsonD.MoehlisJ. (2015). Clustered Desynchronization from High-Frequency Deep Brain Stimulation.PLoS Comput. Biol.11:e1004673. 10.1371/journal.pcbi.1004673
132
WojteckiL.VesperJ.SchnitzlerA. (2011). Interleaving programming of subthalamic deep brain stimulation to reduce side effects with good motor outcome in a patient with Parkinson’s disease.Parkinsonism Relat. Disord.17293–294. 10.1016/j.parkreldis.2010.12.005
133
WongJ. K.HuW.BarmoreR.LopesJ.MooreK.LegacyJ.et al (2021). Safety and Tolerability of Burst-Cycling Deep Brain Stimulation for Freezing of Gait in Parkinson’s Disease.Front. Hum. Neurosci.15:651168. 10.3389/fnhum.2021.651168
134
XieC.-L.ShaoB.ChenJ.ZhouY.LinS.-Y.WangW.-W. (2016). Effects of neurostimulation for advanced Parkinson’s disease patients on motor symptoms: A multiple-treatments meta-analysas of randomized controlled trials.Sci. Rep.6:25285. 10.1038/srep25285
135
XieT.BloomL.PadmanabanM.BertacchiB.KangW.MacCrackenE.et al (2018). Long-term effect of low frequency stimulation of STN on dysphagia, freezing of gait and other motor symptoms in PD.J. Neurol. Neurosurg. Psychiatry89989–994. 10.1136/jnnp-2018-318060
136
YamawakiN.MagillP. J.WoodhallG. L.HallS. D.StanfordI. M. (2012). Frequency selectivity and dopamine-dependence of plasticity at glutamatergic synapses in the subthalamic nucleus.Neuroscience2031–11. 10.1016/j.neuroscience.2011.12.027
137
YuH.TakahashiK.BloomL.QuaynorS. D.XieT. (2020). Effect of Deep Brain Stimulation on Swallowing Function: A Systematic Review.Front. Neurol.11:547. 10.3389/fneur.2020.00547
138
ZafarS. M.RajanR.KrishnanS.KesavapisharadyK.KishoreA. (2021). Interleaved Stimulation for Freezing of Gait in Advanced Parkinson’s Disease.Neurol. India69457–460. 10.4103/0028-3886.314570
139
ZavalaB.DameraS.DongJ. W.LunguC.BrownP.ZaghloulK. A. (2017). Human Subthalamic Nucleus Theta and Beta Oscillations Entrain Neuronal Firing During Sensorimotor Conflict.Cereb. Cortex N. Y. N.27496–508.
140
ZengK.DrummondN. M.GhahremaniA.SahaU.KaliaS. K.HodaieM.et al (2021). Fronto-subthalamic phase synchronization and cross-frequency coupling during conflict processing.Neuroimage238:118205.
141
ZhangC.PanY.ZhouH.XieQ.SunB.NiuC. M.et al (2019). Variable High-Frequency Deep Brain Stimulation of the Subthalamic Nucleus for Speech Disorders in Parkinson’s Disease: A Case Report.Front. Neurol.10:379.
142
ZhangS.WangY.LiP.WangW. (2018). Interleaving pallidal deep brain stimulation improves the degree of benefit obtained in a patient with dystonia.Neurol. India66S138–S141. 10.4103/0028-3886.226442
143
ZhangS.ZhouP.JiangS.WangW.LiP. (2016). Interleaving subthalamic nucleus deep brain stimulation to avoid side effects while achieving satisfactory motor benefits in Parkinson disease: A report of 12 cases.Medicine95:e5575. 10.1097/MD.0000000000005575
144
ZuckerR. S.RegehrW. G. (2002). Short-term synaptic plasticity.Annu. Rev. Physiol.64355–405. 10.1146/annurev.physiol.64.092501.114547
Summary
Keywords
theta-burst stimulation, deep brain stimulation, coordinated reset stimulation (CRS), paired pulses, closed-loop, interleaved stimulation, neuromodulation, cycling stimulation
Citation
Najera RA, Mahavadi AK, Khan AU, Boddeti U, Del Bene VA, Walker HC and Bentley JN (2023) Alternative patterns of deep brain stimulation in neurologic and neuropsychiatric disorders. Front. Neuroinform. 17:1156818. doi: 10.3389/fninf.2023.1156818
Received
01 February 2023
Accepted
06 June 2023
Published
21 June 2023
Volume
17 - 2023
Edited by
Hemant Bokil, Boston Scientific, United States
Reviewed by
J. Luis Lujan, Mayo Clinic, United States; Anders J. Asp, Mayo Clinic, United States
Updates
Copyright
© 2023 Najera, Mahavadi, Khan, Boddeti, Del Bene, Walker and Bentley.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: J. Nicole Bentley, nbentl@uab.edu
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