Analytic Tools for Post-traumatic Epileptogenesis Biomarker Search in Multimodal Dataset of an Animal Model and Human Patients

Abstract

Epilepsy is among the most common serious disabling disorders of the brain, and the global burden of epilepsy exerts a tremendous cost to society. Most people with epilepsy have acquired forms of the disorder, and the development of antiepileptogenic interventions could potentially prevent or cure epilepsy in many of them. However, the discovery of potential antiepileptogenic treatments and clinical validation would require a means to identify populations of patients at very high risk for epilepsy after a potential epileptogenic insult, to know when to treat and to document prevention or cure. A fundamental challenge in discovering biomarkers of epileptogenesis is that this process is likely multifactorial and crosses multiple modalities. Investigators must have access to a large number of high quality, well-curated data points and study subjects for biomarker signals to be detectable above the noise inherent in complex phenomena, such as epileptogenesis, traumatic brain injury (TBI), and conditions of data collection. Additionally, data generating and collecting sites are spread worldwide among different laboratories, clinical sites, heterogeneous data types, formats, and across multi-center preclinical trials. Before the data can even be analyzed, these data must be standardized. The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is a multi-center project with the overarching goal that epileptogenesis after TBI can be prevented with specific treatments. The identification of relevant biomarkers and performance of rigorous preclinical trials will permit the future design and performance of economically feasible full-scale clinical trials of antiepileptogenic therapies. We have been analyzing human data collected from UCLA and rat data collected from the University of Eastern Finland, both centers collecting data for EpiBioS4Rx, to identify biomarkers of epileptogenesis. Big data techniques and rigorous analysis are brought to longitudinal data collected from humans and an animal model of TBI, epilepsy, and their interaction. The prolonged continuous data streams of intracranial, cortical surface, and scalp EEG from humans and an animal model of epilepsy span months. By applying our innovative mathematical tools via supervised and unsupervised learning methods, we are able to subject a robust dataset to recently pioneered data analysis tools and visualize multivariable interactions with novel graphical methods.

Body mass index, time of day and genetics affect perivascular spaces in the white matter

“…In the past two decades, improvements in imaging and post-processing techniques as well as the more widespread use of ultra-high field MRI systems supported significantly enhanced evaluation of PVS, and increasing attention has been dedicated to PVS, their pathophysiological variations, and their potential role as a diagnostic biomarker. [6][7][8][9] In fact, to date increased PVS visibility on human MRI studies has been found associated with aging 10 and a number of pathologic conditions, such as neuropsychiatric and sleep disorders, [11][12][13][14][15] multiple sclerosis, 16,17 mild traumatic brain injury, 18,19 Parkinson's disease, 20 post-traumatic epilepsy, 19 myotonic dystrophy, 21 systemic lupus erythematosus, 22 cerebral small vessel disease, [23][24][25][26][27] and cerebral amyloid-b pathologies, including Alzheimer's disease and cerebral amyloid angiopathy. [28][29][30][31] These findings suggest that a higher number of MRI-visible PVS might be an indicator of impaired brain health, although not specific for any single disease.…”

Imaging biomarkers of posttraumatic epileptogenesis

“…[23][24][25][26] Imaging biomarkers are appealing because they are based on noninvasive procedures that are routinely performed as part of a patient's workup protocol and are capable of detecting patterns across the whole brain that may indicate or precede epileptogenesis. [23][24][25][26] Imaging biomarkers are appealing because they are based on noninvasive procedures that are routinely performed as part of a patient's workup protocol and are capable of detecting patterns across the whole brain that may indicate or precede epileptogenesis.…”

“…Advancements in neuroimaging have greatly advanced PTE research by allowing researchers to identify global and subtle structural and functional alterations that follow injury and coincide with subsequent epilepsy diagnosis. [23][24][25][26] Imaging biomarkers are appealing because they are based on noninvasive procedures that are routinely performed as part of a patient's workup protocol and are capable of detecting patterns across the whole brain that may indicate or precede epileptogenesis. Brain imaging is especially important in TBI, where injuries are spatially heterogenous, 9 involve both cortical and subcortical structures, 9 and vary between patients with similar clinical severity of injury.…”

“…94,95 Multifiber modeling can also resolve regions with crossing fibers, allowing for more nuanced analysis of structural plasticity. 24 Additional MRI techniques have shown promise for lesion characterization and outcome prediction and should be applied to investigations of epileptogenesis. These methods include susceptibility-weighted imaging and derived quantitative susceptibility mapping that have been utilized to characterize diffuse axonal injury and identify chronic hemosiderosis better than existing sequences 96 ; magnetic transfer ratio imaging, which has been shown to decrease in association with axonal injury 97 ; and chemical exchange saturation transfer echoplanar imaging, which was recently shown to successfully identify tissue at risk for chronic injury due to cerebral acidosis in a clinical setting.…”

Neuroanatomic markers of post-traumatic epilepsy based on magnetic resonance imaging and machine learning

“…This heterogeneity needs to be accounted for in statistical analyses due to its potentially confounding effect. The prediction of post-traumatic seizure onset and frequency based on neurological and radiological examinations has been only moderately successful and more research is needed to understand the relationship between TBI and PTE [46,45,17,14,24]. Thus, the identification of imaging biomarkers can help in developing better PTE prediction strategies.…”