Name: Programación de la configuración del detector de dispositivos para la neuroestimulación de circuito cerrado
Uploaded: 2023-07-07T12:40:00
Description: 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

Estimulación cerebral profunda para el tratamiento de la depresión mayor

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.

Autor destacado: Beneficio terapéutico de la estimulación cerebral profunda de circuito cerrado en el tratamiento de la depresión 

Neuroestimulación de circuito cerrado para el tratamiento personalizado del trastorno depresivo mayor basado en biomarcadores

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

Programación de la configuración del detector de dispositivos para la neuroestimulación de circuito cerrado

Comience iniciando sesión en el sistema de gestión de datos del paciente, utilizando el programador. A continuación, seleccione la detección correcta de pacientes, programación y cambios. En función del biomarcador personalizado seleccionado, seleccione el canal correcto para detectar la actividad.Para la detección de actividad, seleccione Actividad rítmica, que connota un detector de paso de banda. Haga clic en Más controles en Configuración avanzada. Seleccione la frecuencia mínima y máxima deseada.Asegúrese de activar el paso de banda y desactivar la longitud de línea en Configuración de área. Además, establezca la lógica de inversión en No invertida. Programe el detector ajustando la amplitud mínima al 0,8% y la duración mínima a 0,64 segundos.Después de seleccionar todas las configuraciones del detector, haga clic en el botón Listo para cerrar todas las ventanas de programación. Coloque la varilla sobre el neuroestimulador implantable del paciente y seleccione Revisar todos los cambios en el RNS del programa. El descubrimiento de los síntomas mediante biomarcadores se realizó en función del poder neuroespectral entre los estados de depresión baja y depresión alta.Se encontró que la mejor opción para un biomarcador de frecuencia específica en este ejemplo es de 13 a 30 hercios de OFC, de tres a cuatro.

Research

JoVE Journal

Neuroscience

Programming Device Detector Settings for Closed-Loop Neurostimulation

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

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major ...