Project 461490
Multimodal prediction of seizure recurrence after unprovoked first seizure to guide clinical decision-making: A multi-centre study of cognition, mood and brain connectivity as predictors
Multimodal prediction of seizure recurrence after unprovoked first seizure to guide clinical decision-making: A multi-centre study of cognition, mood and brain connectivity as predictors
Project Information
| Study Type: | Unclear |
| Research Theme: | Clinical |
Institution & Funding
| Principal Investigator(s): | Winston, Gavin P; Omisade, Antonina |
| Co-Investigator(s): | Gallivan, Jason P; Ikeda, Kristin M; Schmidt, Matthias H |
| Institution: | Queen's University (Kingston, Ontario) |
| CIHR Institute: | Neurosciences, Mental Health and Addiction |
| Program: | |
| Peer Review Committee: | Behavioural Sciences - B-2 |
| Competition Year: | 2022 |
| Term: | 5 yrs 0 mth |
Abstract Summary
One in 10 people have a seizure during their life. Usually no cause is identified. Seizures without an identified cause are called unprovoked first seizure (UFS). Most of these people do not have further seizures. Those that do are diagnosed with epilepsy, a neurological condition with recurrent seizures. After UFS, several things can increase the risk of further seizures. These include an identifiable cause such as head injury, abnormal brain scan findings or the seizure occurring during sleep. Most people with UFS do not have any of these, so it is difficult to know who will have more seizures. Being able to predict the risk of more seizures as soon as possible would help doctors decide whether to suggest treatment after UFS. Giving treatment to people with a high chance of more seizures can reduce that risk. Equally, doctors can avoid giving unnecessary medications to people with a low chance of more seizures. Studies show that seizures are associated with changes in brain structure and function that are difficult to detect with standard assessments but can be detected with advanced techniques. Changes in connections between brain regions are also linked to subtle problems in thinking and mood. We will examine brain connections using detailed brain scans, thinking, and mood in 200 people with UFS. Using this information, we will develop an accurate method for calculating the risk of further seizures. This study will be carried out by two epilepsy centers with clinics dedicated to seeing people with UFS. It will be the first study to combine many advanced techniques to estimate the chance of more seizures. We hope that a reliable method for predicting further seizures after UFS will enable better treatment of people in this common clinical situation.
No special research characteristics identified
This project does not include any of the advanced research characteristics tracked in our database.