Researchers from the Centre for Biomedical Technology (CTB) at Universidad Politécnica de Madrid (UPM) have participated in a joint research with Universidad Carlos III de Madrid and University of Reading (United Kingdom) that have revealed possible subtypes of Parkinson's disease patients. By applying this finding, researchers have used artificial intelligence techniques for tremor detection in patients of the discovered groups. The goal is to achieve a demand driver deep brain stimulation that intelligently alleviates the symptoms of the disease. All this would enhance the life quality of patients.
Parkinson's disease is a degenerative disorder of the central nervous system whose main symptomatology includes temblor, rigidity and bradykinesia. The motor function is a balance which is carefully regulated by a set of neurotransmitters in the circuits of the basal ganglia. When a neurotransmitter is not correctly released, the information among cores is inefficient and results in diseases of the motor system. This is the case of Parkinson's disease, mainly caused by the death of dopamine-secreting neurons. The research work of the group of Cognitive and Computational Neuroscience of CTB-UPM is focused on the study of the most known symptoms of Parkinson's disease, the tremor.
There are diverse types of tremors in Parkinson's disease depending on the circumstances in which it appears. The resting tremor (RT) is the most common symptom. RT is a rhythmic movement that appears when the patient is at rest and usually disappears when the patient starts a movement. This symptom hinders patients from performing daily life tasks.
Diverse studies suggest that Parkinson's disease is actually a denomination that groups together different subtypes of this disease, since not all patients have the same symptomatology or respond in the same way to the treatment.
The treatment of Parkinson's disease is a complex task today. The first choice is oral medication that consists on levodopa supply (main precursor of dopamine). Its usage started in the mid 60s and it is still the most commonly Parkinson's disease used treatment. Levodopa therapy alleviates the main symptomatology of Parkinson's disease, but it does not adapt properly to the disease. After a few years using this treatment, about 5, patients start to experience motor fluctuations.
This effect is known as the ON-OFF phenomenon, alternate periods in which drugs work (ON) with others that not (OFF). Besides, OFF periods increase as this treatment is used for longer. Therefore, oral medication is increasingly inappropriate.
Due to these complications when using oral medication, some patients need surgery to be treated with driven deep brain stimulation. This therapy consists of the implantation of electrodes that apply an electrical stimulation just on the affected brain structures, which is usually the subthalamic nucleus in Parkinson's disease. This stimulation breaks away from abnormal activity of the neurons, which tend to an excessive synchronization and imposes a suitable performance.
Although neurostimulators are known as 'brain pacemakers' they do not work in the same way. Pacemakers are able to detect atypical episodes of heart signals and adjust the stimulation according to the patients' needs at every moment, while the neurostimulators, once are implanted, carry out a continuously stimulation. This means that the device battery has to be replaced every 3-4 years and the patients need to be operated again.
The main contribution of this research was to find results that, with a high degree of accuracy, give evidence of two subtypes of patients, or more specifically temblor of type 1, according to the Consensus Statement of the Movement Disorder Society on Tremor.
These results can lead to the development of diverse treatments for the different types of patients involved.
Based on these findings, the study proposes a real-time tremor detection system based on artificial neural networks. Researchers suggest a tool than can learn different characteristics of the brain signal when the patient suffers a tremor. Through training this device, this would be able to take a decision when the patient is suffering a tremor. The idea is, when a demand driven stimulation system is 100% reliable, the mentioned system will be embedded in the neurostimulator.
A demand driven stimulation device would be capable to detect when the patient is trembling, and only then, provide him with stimulation. All this has a double benefit: firstly, brain structures will be able to work properly when the patients do not show symptoms instead of being stimulated at all times. Secondly, battery would be more efficient extending its useful life, and consequently, extending the time before going under surgery to replace it.
This way, we can provide implantable medical devices with certain intelligence. Therefore, those devices could learn the pathology of the patient who wear the device and to have the capacity to take decisions in order to enhance the treatment and daily lives of patients.
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The above story is based on materials provided by Universidad Politécnica de Madrid. Note: Materials may be edited for content and length.
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