Deep brain stimulation has been used systematically for nearly two decades now in a variety of disorders. One of the more prominent uses is for alleviation of tremor in Parkinson’s disease. Although considerable progress has been made and knowledge about brain stimulation is still growing, closed loop systems like those seen in artificial heart pacemakers since the early 1990s are still non-existent. Current deep brain stimulators deliver a constant stimulation, even at moments where this might not be necessary, e.g. in the momentary absence of an actual tremor. Using a closed loop system to detect when stimulation is needed and deliver stimulation accordingly could have great benefits. According to a recent study in Clinical Neurophysiology by Hirschmann and colleagues, this could significantly reduces battery usage if stimulation is activated less then 94% of the time. This would in turn mean that patients will have to come back less often to get batteries replaced, which involves minor surgery. In this study, conducted in Nijmegen in the Netherlands and Düsseldorf in Germany, the researchers used Hidden Markov Models to analyse and quantify the signals measured through the deep brain stimulation electrodes in the subthalamic nucleus. By employing these statistical models that are fully data driven, they were able to accurately measure when tremors occurred and when patients were temporarily tremor free.
There is of course a lot more information needed than rest versus tremor to successfully develop a closed loop deep brain stimulator. Other symptoms such as akinesia, bradykinesia or rigidity would still need to be alleviated as well. To complicate matters further, this would all need to be analysed within milliseconds while patients are also using different kinds of medication. This all still leaves out voluntary movement as well. So although there is clearly still much that needs to be investigated, this appears to be a great step forwards in the field of deep brain stimulation.