Closing the Loop in Neuromodulation (2012)

There is increasing interest in the ability of oscillations in local field potentials (LFPs) to serve as biomarkers in different central nervous system (CNS) diseases. Although synchronized oscillatory activity is required for the normal organization of neural networks, abnormal synchrony in various frequency bands may reflect disease severity. Parkinson's disease and dystonia involve characteristic alterations in the beta frequency band in the basal ganglia-thalamocortical network,1 and epilepsy involves the occurrence of pathological high frequency oscillations in areas of seizure onset.2 Measuring oscillatory activity during deep brain stimulation (DBS) in these disease states could potentially be used to measure the effects of therapy and to monitor disease progression. Concurrent sensing and stimulation would best maximize usable neuronal data, minimize time delays for closed-loop actuation and measure effects of stimulation. Stanslaski et al (Design and Validation of a Fully Implantable, Chronic, Closed-Loop Neuromodulation Device With Concurrent Sensing and Stimulation. IEEE Trans Neural Syst Rehabil Eng. 2012 Jan 23. [Epub]) have described the first fully implantable, chronic, closed-loop neuromodulation system having this dual ability, and the authors validated an embedded algorithm that actuates neural modulation in response to seizure detection in an ovine model. The authors leveraged an existing DBS device to enable sensing and algorithmic capabilities while maintaining full functionality for delivery. Three new components were developed: an integrated circuit for detecting LFPs that permits both time amplification and spectral decomposition of signals measured from implanted leads, a three-axis accelerometer for detecting motion-derived biomarkers, and a module to classify signals in real time and record them for later download. As the authors point out, a major challenge in designing effective feedback loops is that because the ideal stimulation patterns are largely unknown, it is desirable to obtain a true neural signal by eliminating the effect of stimulation from the sensed signal. Electrode configuration, front-end filtering, pre-amplifier design, processor design, and stimulation parameters were modified in this device in order to overcome stimulation interference during sensing, in combination with algorithmic mitigation of residual stimulator interference. The research system was validated in a sheep implanted with one lead in the anterior nucleus of the thalamus, one lead in the ipsilateral hippocampus, and with the prototypic chronic device implanted in a subscapular pocket. The coincident response to stimulation in the hippocampus and thalamus was characterized by sensing during a sequence of no stimulation, and then gradually increasing amplitudes to 3.5V in either target with frequency fixed at 120 Hz and pulse width set to 90 μs. Half of the stimulations above 2.5V in the hippocampus produced seizure-like after-discharges. Although obscured in the time domain, the beginning of seizure-like activity during stimulation was observed using frequency domain analysis. Detection of this seizure-like activity was used to train a linear discriminant classifier, which was kept constant for 5 weeks after being uploaded to the device without any appreciable loss of seizure detection performance. The linear discriminant was then coupled to the stimulation control of the device to change stimulation in real time based on the measured state of the neural network. The device was successfully programmed to turn off stimulation when the linear discriminant triggered detection of seizure induction. This experiment modeled a closed-loop hippocampal stimulator for the treatment of epilepsy, in which the system addresses the risk of stimulation by monitoring for induced seizures and taking action. Concurrent sensing and neural stimulation is necessary to investigate neural circuit dynamics that may only be present during stimulation, including effects on electrophysiological biomarkers of disease states. Although particularly important in DBS for movement disorders, in which stimulation is always on, concurrent sensing and stimulation would enable accurate seizure count and minimize temporal delay to responsive neurostimulation in closed-loop systems for epilepsy, and may improve the performance of brain-machine interface technologies by enabling sensory feedback directly to neural structures. This work by Stanslaski et al is a key innovation that will aid the general application of closed-loop neuromodulatory systems to the treatment of multiple brain diseases.