Early warning signals and triggers of MIGRAINE attacks measured @HOME
Our project will bring together TU Delft, LUMC and TMSi in a newly established public private partnership to result in the first methods and insights to enable us to predict migraine attacks by integrating information from our E-Headache diary and home-based recordings of brain activity.
Migraine is a brain disorder characterized by unpredictable attacks with enormous personal and social impact affecting 1 in 7 of our population. Patient’s main wish is to gain better control over their attacks with insight on personalized triggers, such as sleep deprivation, stress, menstruation, and certain foods and warning signals when their brain is most susceptible to an attack. This is not yet possible because we lack information on: (I) how (personal) triggers cause attacks; and (II) reliable early signs (‘markers’) of upcoming attacks.
Specific personal combinations of triggers together with internal changes in the brain’s properties play a pivotal role that can be determined using an integrated approach. Our approach will combine an innovative E-Headache diary to collect detailed information on provoking factors and migraine features combined with monitoring migraine susceptibility by recording brain activity (EEG) at home. We are the first to record in the home environment where patients are exposed to their routine daily combination of triggers. This ensures that we obtain individualized information about migraine attacks as they ‘happen in the real world’, which has never been done.
Ultimately our project will result in a unique data set recorded at the home of the migraine patient. The data enables us to conclude on specific migraine triggers, feasibility of obtaining reliable markers of changes in brain activity related to an upcoming migraine attack and present a first iteration of a personalized prediction model for migraine attacks. This will give migraine patients the opportunity to take steps on days their brain is most susceptible for upcoming attacks.
