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Abstract

Frontoparietal brain networks are integral to high-level cognition, influencing diverse cognitive processes such as working memory, reasoning and cognitive control. Yet, despite extensive research efforts, important insights into the mechanisms governing frontoparietal network function remain elusive. In this talk, I will trace the trajectory of my research journey, spanning past, present, and future research efforts aimed at deepening our understanding of frontoparietal brain network mechanisms in human high-level cognition.
Drawing from my doctoral studies, I will introduce Neuroadaptive Bayesian Optimization - a novel brain-computer interface combining real-time fMRI and a branch of machine learning called active sampling. This closed-loop framework provides a powerful strategy to efficiently explore many more experimental conditions than is currently possible with standard neuroimaging methodology. I will showcase its applications, from elucidating the unique functional role of frontoparietal networks in healthy individuals to mapping cognitive dysfunction in aphasic stroke patients and personalizing non-invasive brain stimulation parameters.
Additionally, I will share insights from ongoing research investigating frontoparietal network function layer-specifically using fMRI at ultrahigh field strengths. Specifically, I will present three studies where we use layer-fMRI (GE-BOLD and VASO) to probe the laminar circuitry of the prefrontal cortex during working memory.