Title: Mapping Brain Tissue Microstructure in Epilepsy with Multi-modal MRI

Abstract: Developing non-invasive methods to reliably map features of brain tissue microstructure is valuable for early diagnosis and the investigation of brain mechanisms in both health and disease. In this regard, Magnetic Resonance Imaging (MRI) provides a unique ability to sensitize the signal to the presence of microstructural components within each voxel.

My doctoral work consists of three studies. First, we showed how diffusion-sensitized MRI can be used to discern microstructural abnormalities in the hippocampal subfields indicative of mesial temporal sclerosis (MTS). Our results suggest that diffusion metric analysis at the subfield level, especially in dentate gyrus, can be beneficial for clinical confirmation of MTS.

Second, we leveraged magnetization transfer and diffusion MRI for estimation of g-ratios across major white matter tracts in a mouse model of generalized epilepsy with progressive absence seizures. Our findings revealed widespread myelin structural changes that are specific to the absence seizure network and demonstrated the potential utility and importance of MRI-based g-ratio estimation to non-invasively detect myelin plasticity.

In the third study, we showed how deep learning can improve microstructural feature extraction from MRI data. We presented a novel approach that combines synthetic MRI generation and transfer learning to allow a reliable inference of brain tissue microstructure with a minimal amount of paired MRI-histology data. Our approach showed decreased error compared to biophysical model fitting and increased prediction capabilities that are consistent with electron microscopy validation and previous biological studies.

Please contact Madelyn Bernstein for the Zoom link