Predicting Posttraumatic Epilepsy Using Transparent, Flexible Multielectrode Arrays and Simultaneous Glutamate Imaging
Abstract
Traumatic brain injuries (TBI) are the leading cause of death and disabilities in children and the aged. Post-traumatic epilepsy (PTE) often occurs following TBI, which significantly impairs rehabilitation and impacts patient quality of life. There are limited therapeutic options for TBI, none of which have proven to be efficacious in improving neurological outcomes and preventing PTE across diverse groups of TBI patients. One of the biggest challenges in treating these conditions is that there is currently no way to tell which TBI patients will go on to develop PTE. If it was possible to identify the TBI patients who are at particular risk of developing PTE, then targeted interventions could be utilized to minimize the development of epilepsy. Developing quantifiable assays with predictive value of PTE (biomarkers) would allow identification of at-risk patients and appropriate early intervention. This has been a long-standing goal of the field that has yet to be realized. Currently, there are no predictive biomarkers for PTE. Blood-based assays, imaging-based markers, and electroencephalographic (EEG) signatures all have potential to serve as biomarkers of PTE. Our previous research has identified cellular, molecular, and network-level changes that occur after TBI and may contribute to the development of PTE. These include increases in electrical activity, enhanced excitatory signaling through glutamate, and compromised inhibition in the cortex. Based on these studies, we propose to monitor electrical activity and glutamate imaging in vivo following TBI to identify candidate biomarkers of PTE. We will utilize a multifaceted approach to measure electrical and glutamatergic activity in the brain following TBI. Electrical activity will be monitored by implanting high density multi-electrode arrays onto the cortical surface following controlled cortical impact (CCI, an established mouse model of TBI). These electrodes can be stably implanted into the brain and can monitor activity from 256 sites simultaneously. We will also perform in vivo meso-scale (large field of view) glutamate imaging following CCI. Mice will be genetically engineered to express a fluorescent glutamate sensor protein throughout the brain. Imaging glutamate allows us to visualize excitatory neurotransmission in real time, and our meso-scale approach allows us to see the entire cortical surface. Importantly, the multi-electrode arrays we will use have been engineered to be transparent, allowing them to be used simultaneously with in vivo imaging modalities. Glutamate imaging and multi-electrode array recording will be performed simultaneously for 3 months post-TBI. We will then go on to monitor which animals develop epilepsy after CCI using 24/7 chonic video/EEG monitoring. Using this rich data set, we will identify signals in our MEA and glutamate imaging data, which may serve as predictive, activity-based biomarkers of PTE. This is an especially exciting and promising approach because it applies technologies that have never been attempted in an in vivo model of TBI and it allows collection of two unique types of data simultaneously. Longitudinal studies with our new experimental paradigm will allow us to examine how biomarkers develop over time between TBI and PTE onset. Potential MEA-based biomarkers include theta rhythm degradation, enhanced high frequency oscillations, elevated inter-ictal spiking, and abnormal slow oscillations. Candidate glutamate-imaging biomarkers include increased spontaneous glutamate activity, altered resting glutamate levels, and changes in synchronized glutamate activity across the brain. These proposed biomarkers are in line with activity-based biomarkers recently identified in other forms of epilepsy, including increased glutamate levels in human hippocampal epileptic foci and decreased EEG activity in an animal model of epilepsy. These proposed approaches are extremely innovative, have never be
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 29, 2018
- Source ID
- W81XWH1810699
Entities
People
- Chris G Dulla
Organizations
- Tufts University School of Medicine
- United States Army