Advances in closed-loop deep brain stimulation devices

Abstract

Background: Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner.

Methods: This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research.

Results: Although we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state.

Conclusions: The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized.

Keywords: Biomarker; Closed-loop control; Deep rain simulation; Sensor; Signal conditioning; Stimulator.

Fig. 1

Overview of open-loop DBS (a) versus closed-loop DBS (b). In open-loop DBS, a neurologist manually adjusts the stimulation parameters every 3–12 months after DBS implantation. On the other hand, in closed-loop DBS, programming of the stimulation parameters is performed automatically based on the measured biomarker. c Demonstration of two different brain states and the action of open-loop and closed-loop DBS. When the brain enters a specific state, it remains in that state for a short or long time. Closed-loop DBS gets deactivated when the brain enters the normal state. Open-loop DBS continues the stimulation regardless of the brain state

Fig. 2

a A schematic representing different brain layers and measurable electrophysiological signals. Recording from higher depths results in potentials with higher strength and quality. The higher the distance of electrode from the potential source means a larger impedance. Therefore, proportional to the distance, the potentials are attenuated and high-frequency components are rejected due to the low-pass filtering behavior of the brain layers [159, 160]. In addition, recording from an electrode with smaller contact area enables measuring potentials from fewer neurons [161]. b Amplitude vs frequency characteristics of the human brain potentials of interest. c The spatial resolution of electrophysiological signals. d Three-shell head model. Different layers of the head, particularly the skull with a large resistivity, induce a distorting effect on the potentials that reach the scalp surface

Fig. 3

The process of real-time closed-loop DBS programming. The recording unit records the biomarker signal via an inserted electrode inside (I) or outside (II) of the brain based on the biomarker type. After signal conditioning (amplification and filtration), the biomarker signal is digitized and then sent to the controller unit. Then, through a computational model (A), the biomarker signal is evaluated from different aspects (e.g. amplitude, frequency, and pulse-width, etc.) to define the response signal, which is then employed to predict optimized stimulation parameters. The bottom model (B) has been adopted from [105] and then modified. It represents the structure of controller where X (t), Y (t), and Z (t) are the input vector, neural circuit states, and biomarker response, respectively. The mapping functions from input to the neural state and from neural state to the biomarker response are demonstrated by f (X,t) and g (X,Y,t), respectively. The k (Z,t) is the controller that evaluates the biomarker response and updates stimulation parameters. The estimated parameters are adjusted in the stimulation unit to create the stimulation pulses for applying to the stimulation electrodes. In this real-time process, a very short time-window of the recorded signal is used for prediction of the stimulation parameters. The time-window of biomarker signal is pushed forward continuously and simultaneous computations are done to predict and update the next stimulation-window

Fig. 4

Categorization of different stimulation patterns. For additional details regarding the pros and cons of each waveform refer to [89, 162]

Fig. 5

Closed-loop DBS research challenges. These challenges are classifiable in three major parts including monitoring issues (blue part), stimulation challenges (yellow part), and design expectation concerns (red part)