programming device detector settings for closed-loop neurostimulation

Deep Brain Stimulation for Treatment of Major Depression

Major depressive disorder (MDD) is a neuropsychiatric disease characterized by network-level aberrant activity and connectivity1. The disease manifests a variety of symptoms that vary across individuals, fluctuate over time, and may stem from different neural circuits2,3. Approximately 30% of individuals with MDD are refractory to standard-of-care treatments4, highlighting a need for new approaches.
Deep brain stimulation (DBS) is a form of neuromodulation in which electrical current is delivered to targeted areas of the brain with the goal of modulating the activity. DBS for the treatment of MDD has been very successful in some applications5,6, but has also failed to replicate in larger studies7,8. All of the cited studies employed open-loop stimulation9, in which the delivery of putative therapeutic stimulation was continuous with fixed parameters. In contrast, closed-loop stimulation delivers stimulation based on a programmed biomarker or neural activity pattern associated with the symptom state10. There are two main implementations of closed-loop stimulation: responsive stimulation and adaptive stimulation11. Responsive stimulation delivers bursts of stimulation with constant parameters (e.g., frequency, amplitude, pulse width) when the programmed criteria are met. With adaptive stimulation, stimulation parameters dynamically change as a function of the measured biomarker, according to the algorithm, which may have multiple fix points or automated continuous adjustment. Stimulation can be continuous or intermittent with adaptive stimulation. Adaptive stimulation has shown superior efficacy to open-loop stimulation in controlling symptoms of Parkinson&#39;s disease12. Responsive neurostimulation for epilepsy13 is Food and Drug Administration (FDA)-approved, while early investigations of responsive stimulation for MDD14 and adaptive stimulation for Tourette syndrome15 and essential tremor16 also show therapeutic benefit.
To implement closed-loop stimulation, a physiological signal must be selected and tracked to inform when stimulation should be delivered. This feedback is the key difference between open-loop and closed-loop stimulation and is realized by selecting a biomarker. This protocol provides a procedure for determining a personalized biomarker according to the constellation of symptoms experienced by a given individual. Future meta-analyses across patients will reveal if there are common biomarkers across individuals or if the heterogeneous presentation of MDD symptoms and the underlying circuitry necessitates a personalized approach17,18. Using DBS devices capable of both sensing neural activity and delivering electrical stimulation allows for both the discovery of this biomarker and the subsequent implementation of closed-loop neuromodulation. This approach presupposes a close temporal relationship between neural activity and specific symptom states and may not be applicable for all indications or symptoms.
While indications such as Parkinson&#39;s disease and essential tremor have symptoms that can be measured using peripheral sensors (e.g., tremor, rigidity), symptoms of MDD are typically reported by the patient or assessed by a clinician using standardized questions and observation. In the context of amassing sufficient data to calculate a personalized biomarker, clinician assessments are not practical, and thus patient reports of symptoms through rating scales are used. Such scales include visual analog scales of depression (VAS-D), anxiety (VAS-A), and energy (VAS-E)19, and the six-question form of the Hamilton Depression Rating Scale (HAMD-6)20. Concurrent recordings of neural activity and completion of these self-report symptom ratings provide a paired dataset that can be used to look at relationships between spectral features of the neural signal related to or predictive of high-symptom states.
Computational approaches, such as state-space modeling, can be used to uncover relationships between symptom states and neural features. Graph theoretic methods are attractive for characterizing a state-space21 because they enable the discovery of states over different timescales by explicitly modeling the temporal proximity between measurements22. A symptom state-space model identifies periods of time in which there is a common phenotype of the patient&#39;s symptoms and may pinpoint symptom sub-states in which the ratings on specific dimensions of the patient&#39;s depression differ based on the environment or context. A closed-loop approach relies on the detection of symptom states based on underlying brain activity. Machine learning classification is a final step that helps identify a combination of statistical features derived from brain activity signals that best distinguishes two or more symptom states14. This two-stage approach explains the variability in a patient&#39;s symptoms over time and links systematic patterns of symptom variation to brain activity.
The present protocol utilizes the NeuroPace Responsive Neurostimulation System (RNS)13,23. Procedures to determine the optimal stimulation site(s) and parameters are outside the scope of this protocol. However, the stimulation capabilities of a given device are important to consider when designing closed-loop neurostimulation. For the device used in this protocol, stimulation is current-controlled and delivered between the anode(s) and cathode(s). One or more electrode contacts or the Can (implantable neurostimulator [INS]) can be selected as the anode(s) or cathode(s). Stimulation frequency (1-333.3 Hz), amplitude (0-12 mA), pulse width (40-1000 &#181;s per phase), and duration (10-5000 ms, per stim) are all pre-programmed. The prior parameters can be set independently for up to five stimulation therapies; these therapies are delivered sequentially if the detection criteria continue to be met. It is not possible to deliver multiple stimulation waveforms simultaneously (e.g., one cannot deliver two different frequencies of stimulation concurrently). The stimulation waveform is a symmetric biphasic rectangular wave and cannot be changed.

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment 

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

We are assessing the therapeutic benefit of closed-loop personalized deep brain stimulation for the treatment of major depressive disorder. This protocol outlines how to identify a personalized neural biomarker that is correlated with symptom severity, and then program an implanted neurostimulator to administer stimulation using that biomarker. Prior applications of deep brain stimulation for depression used continuous stimulation, which was always on, regardless of symptom status.This protocol describes using closed-loop stimulation in which we only deliver stimulation during high symptom states. Continued work to identify biomarkers for control of closed-loop stimulation will facilitate an approach where we can target the unique time courses of different symptoms.

Watch this Scientific Journal Video about Programming Device Detector Settings for Closed-Loop Neurostimulation at JoVE.com

Programming Device Detector Settings for Closed-Loop Neurostimulation

Begin by logging onto the patient Data Management System, using the programmer. Next, select the correct Patient, Programming, and Change Detection. Based on the selected personalized biomarker, select the correct channel for detecting the activity.For activity detection, select Rhythmic Activity, which connotes a band pass detector. Click More Controls in Advanced Settings. Select the desired minimum and maximum frequency.Ensure to turn on the band pass and turn off the line length in Area Settings. Additionally, set the inversion logic to Not Inverted. Program the detector by setting the minimum amplitude to 0.8%and the minimum duration to 0.64 seconds.After selecting all the detector settings, click the Done button to close all programming windows. Place the wand over the patient's implantable neurostimulator, and select Review All Changes in Program RNS. The biomarker discovery of symptoms was performed based on the neurospectral power between the low depression and the high depression states.It was found that the best choice for a frequency-specific biomarker in this example is 13 to 30 hertz of OFC, three to four.

programming-device-detector-settings-for-closed-loop-neurostimulation

Research

JoVE Journal

Neuroscience

79 Views.  University of California, San Francisco. Begin by logging onto the patient Data Management System, using the programmer. Next, select the correct Patient, Programming, and Change Detection. Based on the selected personalized biomarker, select the correct channel for detecting the activity.For activity detection, select Rhythmic Activity, which connotes a band pass detector. Click More Controls in Advanced Settings. Select the desired minimum and maximum frequency.Ensure to turn on the band pass and turn off the line le...