Brain MRI to pre-symptomatically predict seizure onset for Sturge-Weber Syndrome - Abstract Sturge-Weber syndrome (SWS) is a rare neurological disease, and its biggest concern is neurocognitive impairments by school age (6-10 years). To improve neurocognitive outcomes, Kenndy Krieger Institute (Dr. Comi, co-PI of this R21) and Boston Children’s Hospital (Dr. Pinto, co-PI of this R21) have been the leading or key sites in various clinical trials, to test new treatment (NCT02332655 (2014-19); NCT0304980 (2017-19); NCT04447846 (2019-21)), or to develop neuroimaging biomarkers that can select at-risk patients (NCT01345305 (2010-2012); NCT01425944 (2010-2020); NCT04717427 (2021-2024)). However, all these trials focus on the post-symptomatic phase – after seizure symptoms have occurred. Our recent evidence suggested that pre-symptomatic treatment – treating patients before seizure symptoms occur, ideally before 2 years of age – may delay or avoid seizure symptoms. This is important, because those without seizure symptoms by 2 years of age (10-25% of SWS patients) often enjoy good neurocognitive outcomes by school age. Motivated by this, multidisciplinary experts gathered and reached a consensus in 2018, 2019, and 2021, calling for immediate investigations of pre-symptomatic treatments. In a timely response to this call, KKI and BCH, the two largest national centers that treat SWS pre-symptomatically, are planning for a trial to comprehensively evaluate the effect of anti-epilepsy drugs Levetiracetam, in two treatment arms (Levetiracetam with versus without low-dose aspirin) for pre-symptomatic treatment. The bottleneck issue for this planned trial, though, is the lack of a biomarker to accurately and pre-symptomatically identify SWS patients who are at risk to develop seizure symptoms by 2 years of age. Those at-risk patients should be ideal candidates to be included in our planned trial. This R21 aims to address this bottleneck biomarker problem. We plan to retrospectively build the largest multi-site presymptomatic database from clinical data in KKI and BCH (Aim 1). We will thoroughly evaluate two clinical and brain MRI biomarkers to identify at-risk patients pre-symptomatically (Aim 2). The central hypothesis is that sophisticated features that artificial intelligence (AI) algorithms extract from clinical and brain MRI could serve as a biomarker to pre-symptomatically identify SWS patients at risk of developing seizure symptoms by 2 years of age. This is the first AI-powered, large-dataset-driven rigorous study for presymptomatic clinical and MRI biomarkers for this rare disease. Such a biomarker will be immediately used in our planned clinical trials to evaluate Levetiracetam with or without low-dose aspirin for pre-symptomatic treatment.
