Author: Alejandro Ojeda

Publisher:

Published: 2019

Total Pages: 160

ISBN-13:

Despite several decades of research, most mental health treatments are based on pharmacological manipulations that globally affect the nervous system. Such treatments often lead to undesired side effects and short term symptomatic relief. The difficulty of diagnosing and treating mental health illnesses stems from the overwhelming complexity of the brain and is exacerbated by the fact that our ability to probe, simultaneously, the activity of dynamic and distributed brain networks is limited. In this dissertation, I propose an alternative way to tackle the mental health problem by using high-resolution imaging-based brain-computer interface (BCI) neurotechnology. I focus on new neuroimaging technology that allows us to monitor the electrical activity of cortical networks at low-cost and high spatiotemporal resolution using noninvasive electroencephalographic (EEG) measurements. This technology will serve as the "neural decoder" component of yet to come imaging-based closed-loop systems that can effectively restore impaired cognition. The decoder allows a BCIs to dynamically probe specific cognitive abilities of the subject in search for signatures of circuit dysfunctions. Then, various types of feedback can be designed to induce the engagement of neural populations that can compensate for the detected aberrant neuronal activity. In this dissertation, first, I develop the mathematical framework to efficiently map scalp EEG responses back into the cortical space, and by doing so, I show that the biological mechanisms responsible for the neurocognitive processes of interest are easy to study. Of theoretical and practical relevance, I demonstrate that this framework successfully unifies three of the most common problems in EEG analysis: data cleaning, source separation, and imaging. Then, I develop the algorithmic and software machinery necessary to implement high-resolution imaging-based BCIs. Finally, I analyze data from healthy adults performing a self-paced unconstrained schoolwork-like computerized task and show that within the proposed framework, I can identify brain network correlates of attention switches at a millisecond time scale. Since attention-related dysfunctions are linked to several psychiatric disorders, these results represent a step forward towards developing BCI interventions to treat several mental health illnesses.