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Abstract To report the results of ‘responsive’ deep brain stimulation (DBS) for Tourette syndrome (TS) in a National Institutes of Health funded experimental cohort. The use of ‘brain derived physiology’ as a method to trigger DBS devices to deliver trains of electrical stimulation is a proposed approach to address the paroxysmal motor and vocal tic symptoms which appear as part of TS. Ten subjects underwent bilateral staged DBS surgery and each was implanted with bilateral centromedian thalamic (CM) region DBS leads and bilateral M1 region cortical strips. A series of identical experiments and data collections were conducted on three groups of consecutively recruited subjects. Group 1 (n = 2) underwent acute responsive DBS using deep and superficial leads. Group 2 (n = 4) underwent chronic responsive DBS using deep and superficial leads. Group 3 (n = 4) underwent responsive DBS using only the deep leads. The primary outcome measure for each of the 8 subjects with chronic responsive DBS was calculated as the pre-operative baseline Yale Global Tic Severity Scale (YGTSS) motor subscore compared to the 6 month embedded responsive DBS setting. A responder for the study was defined as any subject manifesting a ≥ 30 points improvement on the YGTSS motor subscale. The videotaped Modified Rush Tic Rating Scale (MRVTRS) was a secondary outcome. Outcomes were collected at 6 months across three different device states: no stimulation, conventional open-loop stimulation, and embedded responsive stimulation. The experience programming each of the groups and the methods applied for programming were captured. There were 10 medication refractory TS subjects enrolled in the study (5 male and 5 female) and 4/8 (50%) in the chronic responsive eligible cohort met the primary outcome manifesting a reduction of the YGTSS motor scale of ≥ 30% when on responsive DBS settings. Proof of concept for the use of responsive stimulation was observed in all three groups (acute responsive, cortically triggered and deep DBS leads only). The responsive approach was safe and well tolerated. TS power spectral changes associated with tics occurred consistently in the low frequency 2–10 Hz delta-theta-low alpha oscillation range. The study highlighted the variety of programming strategies which were employed to achieve responsive DBS and those used to overcome stimulation induced artifacts. Proof of concept was also established for a single DBS lead triggering bi-hemispheric delivery of therapeutic stimulation. Responsive DBS was applied to treat TS related motor and vocal tics through the application of three different experimental paradigms. The approach was safe and effective in a subset of individuals. The use of different devices in this study was not aimed at making between device comparisons, but rather, the study was adapted to the current state of the art in technology. Overall, four of the chronic responsive eligible subjects met the primary outcome variable for clinical effectiveness. Cortical physiology was used to trigger responsive DBS when therapy was limited by stimulation induced artifacts.

Deep brain stimulation (DBS) has advanced treatment options for a variety of neurologic and neuropsychiatric conditions. As the technology for DBS continues to progress, treatment efficacy will continue to improve and disease indications will expand. Hardware advances such as longer-lasting batteries will reduce the frequency of battery replacement and segmented leads will facilitate improvements in the effectiveness of stimulation and have the potential to minimize stimulation side effects. Targeting advances such as specialized imaging sequences and “connectomics” will facilitate improved accuracy for lead positioning and trajectory planning. Software advances such as closed-loop stimulation and remote programming will enable DBS to be a more personalized and accessible technology. The future of DBS continues to be promising and holds the potential to further improve quality of life. In this review we will address the past, present and future of DBS.

Deep brain stimulation (DBS) is a plausible therapy for various neuropsychiatric disorders, though continuous tonic stimulation without regard to underlying physiology (open-loop) has had variable success. Recently available DBS devices can sense neural signals which, in turn, can be used to control stimulation in a closed-loop mode. Closed-loop DBS strategies may mitigate many drawbacks of open-loop stimulation and provide more personalized therapy. These devices contain many adjustable parameters that control how the closed-loop system operates, which need to be optimized using a combination of empirically and clinically informed decision making. We offer a practical guide for the implementation of a closed-loop DBS system, using examples from patients with chronic pain. Focusing on two research devices from Medtronic, the Activa PC+S and Summit RC+S, we provide pragmatic details on implementing closed- loop programming from a clinician’s perspective. Specifically, by combining our understanding of chronic pain with data-driven heuristics, we describe how to tune key parameters to handle feature selection, state thresholding, and stimulation artifacts. Finally, we discuss logistical and practical considerations that clinicians must be aware of when programming closed-loop devices.

Cognitive fatigue and cognitive fatigability are distinct constructs. Cognitive fatigue reflects perception of cognitive fatigue outside of the context of activity level and duration and can be reliably assessed via established instruments such as the Fatigue Severity Scale (FSS) and the Modified Fatigue Impact Scale (MFIS). In contrast, cognitive fatigability reflects change in fatigue levels quantified within the context of the level and duration of cognitive activity, and currently there are no reliable measures of cognitive fatigability. A recently published scale, the Pittsburgh Fatigability Scale (PFS), attempts to remedy this problem with a focus on the aged population. While the physical fatigability subscore of PFS has been validated using physical activity derived measures, the mental fatigability subscore of PFS remains to be tested against equivalent measures derived from cognitive activities. To this end, we recruited 35 older, healthy adult participants (mean age 73.77 ± 5.9) to complete the PFS as well as a prolonged continuous performance of a Stroop task (>2 h). Task-based assessments included time-on-task changes in self-reported fatigue scores (every 20 min), reaction time, and pupil diameter. Defining subjective fatigability, behavioral fatigability, and physiologic/autonomic fatigability to be the slope of change over time-on-task in the above three assessed variables, we found that the PFS mental subscore was not correlated with any of the three task-based fatigability measures. Instead, the PFS mental subscore was correlated with trait level fatigue measures FSS (ρ = 0.63, p < 0.001), and MFIS cognitive subsection (ρ = 0.36, p = 0.03). This finding suggested that the PFS mental fatigability subscore may not be an adequate measure of how fatigued one becomes after a given amount of mental work. Further development efforts are needed to create a self-report scale that reliably captures cognitive fatigability in older adults.