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Deep brain stimulation (DBS) represents one of the major clinical breakthroughs in the age of translational neuroscience. In 1987, Benabid and colleagues demonstrated that high-frequency stimulation can mimic the effects of ablative neurosurgery in Parkinson's disease (PD), while offering two key advantages to previous procedures: adjustability and reversibility. Deep brain stimulation is now an established therapeutic approach that robustly alleviates symptoms in patients with movement disorders, such as Parkinson's disease, essential tremor, and dystonia, who present with inadequate or adverse responses to medication. Currently, stimulation electrodes are implanted in specific target regions of the basal ganglia–thalamic circuit and stimulation pulses are delivered chronically. To achieve optimal therapeutic effect, stimulation frequency, amplitude, and pulse width must be adjusted on a patient-specific basis by a movement disorders specialist. The finding that pathological neural activity can be sampled directly from the target region using the DBS electrode has inspired a novel DBS paradigm: closed-loop adaptive DBS (aDBS). The goal of this strategy is to identify pathological and physiologically normal patterns of neuronal activity that can be used to adapt stimulation parameters to the concurrent therapeutic demand. This review will give detailed insight into potential biomarkers and discuss next-generation strategies, implementing advances in artificial intelligence, to further elevate the therapeutic potential of DBS by capitalizing on its modifiable nature. Development of intelligent aDBS, with an ability to deliver highly personalized treatment regimens and to create symptom-specific therapeutic strategies in real-time, could allow for significant further improvements in the quality of life for movement disorders patients with DBS that ultimately could outperform traditional drug treatment.

Closed-loop strategies for deep brain stimulation (DBS) are paving the way for improving the efficacy of existing neuromodulation therapies across neurological disorders. Unlike continuous DBS, closed-loop DBS approaches (cl-DBS) optimize the delivery of stimulation in the temporal domain. However, clinical and neurophysiological manifestations exhibit highly diverse temporal properties and evolve over multiple time-constants. Moreover, throughout the day, patients are engaged in different activities such as walking, talking, or sleeping that may require specific therapeutic adjustments. This broad range of temporal properties, along with inter-dependencies affecting parallel manifestations, need to be integrated in the development of therapies to achieve a sustained, optimized control of multiple symptoms over time. This requires an extended view on future cl-DBS design. Here we propose a conceptual framework to guide the development of multi-objective therapies embedding parallel control loops. Its modular organization allows to optimize the personalization of cl-DBS therapies to heterogeneous patient profiles. We provide an overview of clinical states and symptoms, as well as putative electrophysiological biomarkers that may be integrated within this structure. This integrative framework may guide future developments and become an integral part of next-generation precision medicine instruments.

In Parkinson’s disease (PD), subthalamic nucleus (STN) beta burst activity is pathologically elevated. These bursts are reduced by dopamine and deep brain stimulation (DBS). Therefore, these bursts have been tested as a trigger for closed-loop DBS. To provide better targeted parameters for closed-loop stimulation, we investigate the spatial distribution of beta bursts within the STN and if they are specific to a beta sub-band. Local field potentials (LFP) were acquired in the STN of 27 PD patients while resting. Based on the orientation of segmented DBS electrodes, the LFPs were classified as anterior, postero-medial, and postero-lateral. Each recording lasted 30 min with (ON) and without (OFF) dopamine. Bursts were detected in three frequency bands: ±3 Hz around the individual beta peak frequency, low beta band (lBB), and high beta band (hBB). Medication reduced the duration and the number of bursts per minute but not the amplitude of the beta bursts. The burst amplitude was spatially modulated, while the burst duration and rate were frequency dependent. Furthermore, the hBB burst duration was positively correlated with the akinetic-rigid UPDRS III subscore. Overall, these findings on differential dopaminergic modulation of beta burst parameters suggest that hBB burst duration is a promising target for closed-loop stimulation and that burst parameters could guide DBS programming.

The treatment of psychiatric diseases with Deep Brain Stimulation (DBS) is becoming more of a reality as studies proliferate the indications and targets for therapies. Opinions on the initial failures of DBS trials for some psychiatric diseases point to a certain lack of finesse in using an Open Loop DBS (OLDBS) system in these dynamic, cyclical pathologies. OLDBS delivers monomorphic input into dysfunctional brain circuits with modulation of that input via human interface at discrete time points with no interim modulation or adaptation to the changing circuit dynamics. Closed Loop DBS (CLDBS) promises dynamic, intrinsic circuit modulation based on individual physiologic biomarkers of dysfunction. Discussed here are several psychiatric diseases which may be amenable to CLDBS paradigms as the neurophysiologic dysfunction is stochastic and not static. Post-Traumatic Stress Disorder (PTSD) has several peripheral and central physiologic and neurologic changes preceding stereotyped hyper-activation behavioral responses. Biomarkers for CLDBS potentially include skin conductance changes indicating changes in the sympathetic nervous system, changes in serum and central neurotransmitter concentrations, and limbic circuit activation. Chemical dependency and addiction have been demonstrated to be improved with both ablation and DBS of the Nucleus Accumbens and as a serendipitous side effect of movement disorder treatment. Potential peripheral biomarkers are similar to those proposed for PTSD with possible use of environmental and geolocation based cues, peripheral signs of physiologic arousal, and individual changes in central circuit patterns. Non-substance addiction disorders have also been serendipitously treated in patients with OLDBS for movement disorders. As more is learned about these behavioral addictions, DBS targets and effectors will be identified. Finally, discussed is the use of facial recognition software to modulate activation of inappropriate responses for psychiatric diseases in which misinterpretation of social cues feature prominently. These include Autism Spectrum Disorder, PTSD, and Schizophrenia-all of which have a common feature of dysfunctional interpretation of facial affective clues. Technological advances and improvements in circuit-based, individual-specific, real-time adaptable modulation, forecast functional neurosurgery treatments for heretofore treatment-resistant behavioral diseases.

Deep brain stimulation (DBS), including optical stimulation and electrical stimulation, has been demonstrated considerable value in exploring pathological brain activity and developing treatments for neural disorders. Advances in DBS microsystems based on implantable microelectrode array (MEA) probes have opened up new opportunities for closed-loop DBS (CL-DBS) in situ. This technology can be used to detect damaged brain circuits and test the therapeutic potential for modulating the output of these circuits in a variety of diseases simultaneously. Despite the success and rapid utilization of MEA probe-based CL-DBS microsystems, key challenges, including excessive wired communication, need to be urgently resolved. In this review, we considered recent advances in MEA probe-based wireless CL-DBS microsystems and outlined the major issues and promising prospects in this field. This technology has the potential to offer novel therapeutic options for psychiatric disorders in the future.

Neural decoding is a field involving the use of signal processing and machine learning methods to decode brain activity for various applications including assistive technology for people living with paralysis and diagnosing brain-related diseases such as Parkinson’s, Alzheimer’s, schizophrenia and obsessive compulsive disorder. The use of neural decoding, however, could be extended to applications in bioelectronic medicine, a field focused on the treatment and diagnosis of organ and other diseases through neurostimulation and neurosensing in the central and peripheral nervous system. Specifically, there is increasing evidence that neurostimulation can upregulate or downregulate the immune system, and, just as the nervous system innervates our organs and helps regulate their function, it also can modulate the immune system response and even affect acute inflammation response. Previous research in bioelectronic medicine has been focused primarily on open-loop neurostimulation without sensing methods or algorithms to automatically control the spatial and temporal characteristics of the stimulation. Research involving neural decoding methods has been conducted for other neurostimulation applications including the treatment of epilepsy and Parkinson’s disease. Introducing sensing and neural decoding methods into the bioelectronic medicine field could improve the diagnosis and treatment of a wide variety of diseases by closing the loop, which would allow automatic and adaptive neurostimulation that could increase its overall efficacy. There will be important related research questions and challenges to address as we attempt to place a control system on top of an already existing control system—a vast, complex and dynamic one—the human nervous system.

Abstract Deep brain stimulation (DBS) is a powerful tool for the treatment of circuitopathy-related neurological and psychiatric diseases and disorders such as Parkinson's disease and obsessive-compulsive disorder, as well as a critical research tool for perturbing neural circuits and exploring neuroprostheses. Electrically mediated DBS, however, is limited by the spread of stimulus currents into tissue unrelated to disease course and treatment, potentially causing undesirable patient side effects. In this work, we utilize infrared neural stimulation (INS), an optical neuromodulation technique that uses near to midinfrared light to drive graded excitatory and inhibitory responses in nerves and neurons, to facilitate an optical and spatially constrained DBS paradigm. INS has been shown to provide spatially constrained responses in cortical neurons and, unlike other optical techniques, does not require genetic modification of the neural target. We show that INS produces graded, biophysically relevant single-unit responses with robust information transfer in rat thalamocortical circuits. Importantly, we show that cortical spread of activation from thalamic INS produces more spatially constrained response profiles than conventional electrical stimulation. Owing to observed spatial precision of INS, we used deep reinforcement learning (RL) for closed-loop control of thalamocortical circuits, creating real-time representations of stimulus-response dynamics while driving cortical neurons to precise firing patterns. Our data suggest that INS can serve as a targeted and dynamic stimulation paradigm for both open and closed-loop DBS.

Deep brain stimulation is an effective and safe medical treatment for a variety of neurological and psychiatric disorders including Parkinson’s disease, essential tremor, dystonia, and treatment resistant obsessive compulsive disorder. A closed loop deep brain stimulation (CLDBS) system automatically adjusts stimulation parameters by the brain response in real time. The CLDBS continues to evolve due to the advancement in the brain stimulation technologies. This paper provides a study on the existing systems developed for CLDBS. It highlights the issues associated with CLDBS systems including feedback signal recording and processing, stimulation parameters setting, control algorithm, wireless telemetry, size, and power consumption. The benefits and limitations of the existing CLDBS systems are also presented. Whilst robust clinical proof of the benefits of the technology remains to be achieved, it has the potential to offer several advantages over open loop DBS. The CLDBS can improve efficiency and efficacy of therapy, eliminate lengthy start-up period for programming and adjustment, provide a personalized treatment, and make parameters setting automatic and adaptive.

The clinical application of DBS has become manifold and there has been a tremendous growth in DBS technology in the last few decades making it safer and user friendly. The earlier concept of its delayed application in motor fluctuations of Parkinson's disease has been replaced by Class-I evidence of EARLY-STIM trial in 2013, leading to its FDA approval to be used in early-stage despite criticism. Various studies have provided evidence of beneficial effects of bilateral STN-DBS on both motor as well as nonmotor symptoms and different new targets such as the pedunculopontine nucleus, posterior subthalamic area or caudal zona incerta, centromedian-parafascicular complex, and substantia nigra pars reticulata have now become a new area of interest in addition to the subthalamic nucleus and globus pallidus internus for the alleviation of both motor and nonmotor symptoms of Parkinson's disease. New data has confirmed that the DBS is clinically as effective and safe in elderly patients as it is in younger ones. Technological advances like current steering, directional leads, and closed-loop DBS are directed towards reducing the stimulation-induced adverse effects and preservation of the battery life for a longer period. Results of the long-term efficacy of DBS on Parkinson's disease are now available. These have shown that as the motor benefit continues, the clinical progression of Parkinson's disease also continues. We plan to discuss all these in this paper.

or intelligent neuromodulation allows adjustable, personalized neuromodulation which usually incorporates the recording of a biomarker, followed by implementation of an algorithm which decides the timing ( ) and strength ( ) of stimulation. Closed-loop neuromodulation has been shown to have greater benefits compared to neuromodulation, particularly for therapeutic applications such as pharmacoresistant epilepsy, movement disorders and potentially for psychological disorders such as depression or drug addiction. However, an important aspect of the technique is selection of an appropriate, preferably neural biomarker. Neurochemical sensing can provide high resolution biomarker monitoring for various neurological disorders as well as offer deeper insight into neurological mechanisms. The chemicals of interest being measured, could be ions such as potassium (K ), sodium (Na ), calcium (Ca ), chloride (Cl ), hydrogen (H ) or neurotransmitters such as dopamine, serotonin and glutamate. This review focusses on the different building blocks necessary for a neuromodulation system including biomarkers, sensors and data processing algorithms. Furthermore, it also highlights the merits and drawbacks of using this biomarker modality.