John Medaglia - US grants

DESCRIPTION (provided by applicant): This application requests 2 years of funding to support John Medaglia's pre-doctoral training in functional neuroimaging data modeling and clinical neuroscience research. The proposed research will apply novel techniques to understand the role of the cerebellum as a latent support mechanism for working memory performance following moderate-to-severe traumatic brain injury (TBI) to better understand the processes underlying cognitive deficits and recovery. This project is distinct from traditional fMR research that attempts to isolate regional differences between individuals with TBI and matched healthy controls in that it affords explicit quantitative and qualitative examinations of how neura networks are affected by injuries. This proposal consists of 3 aims, each with an associated experimental approach. Specific Aim 1 is to examine the role of a traditionally understudied region, the cerebellum, in a distributed working memory (WM) system with a critical role in learned timing, pattern detection, associative learning, and speed of information processing. It is hypothesized that the cerebellum will be highly related to previously identified regions involved in WM (i.e., the dorsolateral prefrontal cortex, anterior cingulate cortex, and parietal cortex) during task performance and that the strengths of these relationships will predict performance, particularly those between the cerebellum and the prefrontal cortex. Specific Aim 2 is to test the hypothesis that the primary large-scale networks observed during WM tasks (i.e., involving the dorsolateral prefrontal cortex, anterior cingulate cortex, parietal cortex, and cerebellum) in controls will be disrupted in TBI and that disruption will predict behavioral performance. Importantly, this extends beyond Aim 1 by considering the joint functions of large networks as important to behavior as opposed to each part in isolation. It is hypothesized that controls will have more closely interrelated functional networks loosely constrained by anatomical connections, whereas individuals with TBI will have fractionated networks with specific disruptions in cerebellar and prefrontal functional connections that are predictive of cognitive dysfunction. Aim 3 will seek to corroborate functional findings in brain structural connectivity using diffusion tensor imaging. It is hypothesized that anatomical integrity will predict the degre of functional connectivity across the brain as well as specific functional relationships between the dorsolateral prefrontal cortex and parietal cortex, which have anatomical connections with the cerebellum. The results from this proposal will advance our understanding of the mechanisms of how the brain responds to injury as a neurocognitive system as opposed to previous findings that do not account for the complex relationships among regions in the brain during cognitive processing. This is a critical step toward future aggressive treatment of severe injury because it will aid our understanding of how disrupted activity in certain parts of the neurl system affects others, which may have critical implications for neurosurgery, medication, and cognitive rehabilitation. This proposal will also prepare the Applicant with advanced expertise in signal analysis, linear and nonlinear equation modeling, the utility of graph theory in understanding the brain, and structural connectivity techniques which will provide the basis for a productive independent research career.

? DESCRIPTION (provided by applicant): Executive functions, and in particular cognitive control functions, contribute to or are affected by numerous psychiatric and neurological conditions. Understanding how brain network dynamics support cognitive control function is crucial for clarifying the basis of resilience to injury and identifying opportunities for substantive advancements in intervention. While network science (e.g., graph theory) has led to enlightenment in the organization of the brain and basis of human cognition, elucidating translational implications requires an explicit focus. I propose to do so. I aim to apply recent innovations in dynamic network analysis (recent extensions of graph theory) and network control theory in neuroimaging data to examine the basis of cognitive control function in health and dysfunction in stroke. The program integrates approaches from cognitive neuroscience, network science, and control theory. The goal is to produce a theoretical advance in the use of noninvasive brain stimulation treatments for cognitive dysfunction. The specific aims for this project are to: 1) Quantify structural and dynamic brain network properties underlying cognitive control function in health and dysfunction following stroke 2) Use network control theory to intervene in brain networks that support cognitive control There are two main components of this project: (1) the analysis of network structure and function underlying adaptive cognitive control and (2) the use of network control theory applied to diffusion tractography data to (a) discriminate between network mechanisms of cognitive control and (b) facilitate cognitive control recovery in individuals that have suffered from stroke. This would provide a substantial advance in our knowledge of how cognitive control processes exert their influences across brain networks. While some research has begun to emerge in this area, I propose to use state of the art techniques within dynamic network analysis in conjunction with well-validated behavioral measures. This will serve as an important benchmark for work outside of the current application. It will also begin to characterize reference states underlying adaptive task performance that will be used to guide later control theory-based approaches to brain stimulation. Here, network control theory will be used to target noninvasive brain stimulation on an individual basis. This could lead to a substantive advance in our understanding of the variance in responsiveness to noninvasive brain stimulation and lead to a control theory based framework for intervention in cognitive control dysfunction. More broadly, the outcome this work will provide a step toward true integration between network neuroscience and systems engineering-based translation in neurological and psychiatric populations. These fields are developing rapidly, but an explicit focus on cognition and integration with the physical sciences will be required to conceptualize potent opportunities for intervention. This project offers the first opportunity to establish this intersection and promote a new interdisciplinary conversation between the fields represented.

PROJECT ABSTRACT Electroencephalography (EEG) is an essential clinical diagnostic and research tool in neurology, neurorehabilitation, cognitive, and behavioral neuroscience. However, in more than 100 years of EEG research, the fundamental EEG technology has remained primitive and game-changing technological innovations have been few and far between. Most current EEG systems rely on gelled silver/silver- chloride or metal electrodes affixed on the scalp with conductive gels or pastes. These devices suffer from the large size of the electrodes, cost, risk of corrosion, preparation, and cleaning. In addition, gels and pastes are necessary to achieve adequate impedance and signal quality, but can be irritating to the skin and dry out over time. Dry (i.e., gel-free) EEG systems can bypass some of the issues of these wet EEG devices, but are still critically limited in terms of subject comfort and signal quality. Finally, MRI-compatible EEG systems for multimodal brain mapping are often highly specialized and expensive. Here, we propose to validate a fully novel, dry EEG system based on MXene materials. MXenes offer high biocompatibility, stability, conductivity, flexibility and low electrochemical impedance. In addition, they can be processed at a low cost, easily integrated into functional neural devices with a variety of geometries and shapes, record brain electrical activity with high fidelity without the need for gels or pastes, and interact weakly with magnetic fields. These properties make MXene ideal to serve as enabling material for the next-generation EEG technologies. In this proposal, we will build on promising pilot data to scale-up and optimize the fabrication and design of MXene EEG electrodes. Specifically, we will aim to outperform the electrodes used in our pilot studies while maintaining fast, cost-effective, and reliable fabrication. Then, we will validate the performance of the best performing MXene electrodes on well-established behavioral tasks associated with readily identifiable EEG spectral characteristics. Finally, we will examine the MRI compatibility of a customized multichannel MXene EEG system for simultaneous EEG/MRI mapping using quantitative and clinician ratings of signal quality, an essential step to propel its widespread adoption in brain research and clinical contexts. By completing this project, we expect to move the field forward by generating a novel dry EEG technology with superior resolution, signal fidelity, and usability compared to current tools. These advantages could pave the way for fundamental innovations in a number of domains including clinical neurology, rehabilitation, and cognitive neuroscience.