Leveraging substructural brain mechanics to understand brain pathophysiology and develop intervention systems

Abstract

Neurodegenerative diseases and traumatic brain injuries (TBIs) represent significant public health challenges globally, particularly in the United States, where they are projected to impact 88 million individuals by 2050. These conditions, often linked to sports that involve repetitive head impacts, can lead to conditions like chronic traumatic encephalopathy (CTE) and are associated with considerable disability and mortality. Despite decades of research, there remains a critical need for advancements in detection, diagnosis, prevention, and treatment strategies.This thesis explores the missing mechanical factors that influence neurodegenerative diseases, focusing on how mechanical forces and brain substructure dynamics can be utilized to enhance understanding and management of these diseases. Specifically, it investigates the biomechanics of brain injuries from the perspective of substructural mechanics and the effects of morphological changes within the brain, providing a comprehensive view of how such dynamics relate to disease processes. The research begins with an evaluation of the biomechanical performance of current sports helmets, particularly bicycle helmets that incorporate various impact mitigation strategies. Findings indicate that helmets equipped with rotation damping systems and other novel technologies outperform traditional helmets in managing kinematics at low impact velocities and angular momentum. This uncovers the necessity for re-considering helmet designs to better prevent cycling-related TBIs, advocating for more inclusive impact testing that considers a range of motion and impact scenarios.\\ Further, the study utilizes dynamic amplified MRI (aMRI) to assess \textit{in vivo} displacement of brain substructures in patients with Normal Pressure Hydrocephalus (NPH), examining how these movements are altered by surgical interventions such as shunt placement. The research demonstrates that aMRI can effectively indicate shunt functionality, offering a non-invasive method to monitor and evaluate surgical outcomes. Additionally, leveraging data from the DIAGNOSE CTE project, the thesis applies machine learning algorithms to predict neurocognitive and neuropsychiatric outcomes based on blood and cerebrospinal fluid (CSF) biomarkers, alongside magnetic resonance imaging (MRI) morphometrics. This innovative approach highlights the potential of integrating computational methods with clinical data to better understand and manage TBIs and neurodegenerative diseases.\\ Overall, this thesis investigates a multidisciplinary approach that integrates mechanical assessments, advanced imaging techniques, and machine learning to provide new insights into the prevention, diagnosis, and management of neurodegenerative diseases and TBIs. The findings contribute to the broader field of neurology by suggesting that mechanical factors are crucial for understanding the complex dynamics of brain diseases and by offering promising new directions for enhancing patient care and treatment strategies.