Closed loop deep brain stimulation: an evolving technology

Abstract

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.

Probing the sub-thalamic nucleus: development of bio-markers from very Local Field Potentials

“…constant adaptation to deal with habituation and tolerance to the parameter settings [43], minimisation of side effects with lower amplitude current stimulation, maximisation of battery life (and subsequent delay in operations to replace the battery), reducing the burden on the health care system by reducing required neurology consultation visits and introducing treatment strategies which work for people who have thus far been refractory to open loop DBS.…”

“…In classical control engineering we would refer to this as identifying the state estimator of the system [44]. These physiological characterisations of the patient are referred to as biomarkers [5], [43], [45]. There is a very large class of biomarkers which could be employed to 'measure' the state of the patient for a CLDBS algorithm and they can be broadly classified as internal or external biomarkers [43].…”

“…These physiological characterisations of the patient are referred to as biomarkers [5], [43], [45]. There is a very large class of biomarkers which could be employed to 'measure' the state of the patient for a CLDBS algorithm and they can be broadly classified as internal or external biomarkers [43]. The internal biomarkers are based on direct measurements of the neuro-dynamics and candidates include the LFP, individual spiking patterns, electroencephalograms, magnetoencephalography, near infrared spectroscopy and functional magnetic resonance imaging [43] and even chemical analysis of neurotransmitter metabolites [46].…”

“…There is a very large class of biomarkers which could be employed to 'measure' the state of the patient for a CLDBS algorithm and they can be broadly classified as internal or external biomarkers [43]. The internal biomarkers are based on direct measurements of the neuro-dynamics and candidates include the LFP, individual spiking patterns, electroencephalograms, magnetoencephalography, near infrared spectroscopy and functional magnetic resonance imaging [43] and even chemical analysis of neurotransmitter metabolites [46]. The external biomarkers are based on measurements of the clinical sequelae Parkinson's Disease such as accelerometers [47] or even video based monitoring systems [48] [43] which observe the motor pathology.…”

“…For a thorough review of candidate biomarkers for closed loop DBS see [43], [45]. In effect, all the candidate biomarkers have advantages and disadvantages.…”