Addictive disorders are a severe health concern, as they can be very complex and often result in severe consequences for the individual. Conventional therapies have moderate success and the probability of relapse after treatment remains high. The team want to develop a system that simultaneously listens to brain signals and when necessary, e.g. when substance craving occurs, can use small electrical impulses to help patients overcome the urge to consume the substance, something known as a closed-loop system. Electrodes would be used to detect biomarkers associated with these addictive behaviours and the system would deploy stimulation via the same electrodes.
This interdisciplinary effort is needed, as engineers are able to solve certain aspects of the problem, but clinicians apply the technology in disease.
Professor Ivan Minev
ACSE Academic
Dr Arvaneh’s expertise lies in analysing the electrical impulses generated by the brain and explores methods in which information can be extracted from these brain signals. Professor Minev looks at the development of hardware for obtaining these signals and in the review discusses some current trends in their design. Their research is complementary and highlights their collaboration with Technische Universität Dresden, who have developed a pre-clinical rodent model where biomarkers of addiction, e.g. how does the brain signal change in addicted patients, can be studied.
Through this partnership they have been able to discuss the necessary steps for the design of the entire system - from the hardware to signal analysis to validating the system.
Brain stimulation is already used in clinics around the world, for example to treat the symptoms of Parkinson's disease. In current practice brain stimulation is on constantly, however no feedback from the brain is used. If recording the brain signals is combined to fine tune the stimulation, the treatment can be more precise and opened up to treat other diseases.
A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation (technology that acts directly upon nerves) adapted to the individual patient. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms.
This work in collaboration with colleagues at the Techinsche Universität Dresden has recently been published in the journal Bioelectronic Medicine. Read the full journal article.