This clinical trial focuses on testing the efficacy of different digital interventions to promote re-engagement in cancer-related long-term follow-up care for adolescent and young adult (AYA) survivors of childhood cancer.
Humans have a remarkable ability to flexibly interact with the environment. A compelling demonstration of this cognitive flexibility is human's ability to respond correctly to novel contextual situations on the first attempt, without prior rehearsal. The investigators refer to this ability as 'ad hoc self-programming': 'ad hoc' because these new behavioral repertoires are cobbled together on the fly, based on immediate demand, and then discarded when no longer necessary; 'self-programming' because the brain has to configure itself appropriately based on task demands and some combination of prior experience and/or instruction. The overall goal of our research effort is to understand the neurophysiological and computational basis for ad hoc self-programmed behavior. The previous U01 project (NS 108923) focused on how these programs of action are initially created. The results thus far have revealed tantalizing notions of how the brain represents these programs and navigates through the programs. In this proposal, therefore, the investigators focus on the question of how these mental programs are executed. Based on the preliminary findings and critical conceptual work, the investigators propose that the medial temporal lobe (MTL) and ventral prefrontal cortex (vPFC) creates representations of the critical elements of these mental programs, including concepts such as 'rules' and 'locations', to allow for effective navigation through the algorithm. These data suggest the existence of an 'algorithmic state space' represented in medial temporal and prefrontal regions. This proposal aims to understand the neurophysiological underpinnings of this algorithmic state space in humans. By studying humans, the investigators will profit from our species' powerful capacity for generalization to understand how such state spaces are constructed. The investigators therefore leverage the unique opportunities available in human neuroscience research to record from single cells and population-level signals, as well as to use intracranial stimulation for causal testing, to address this challenging problem. In Aim 1 the investigators study the basic representations of algorithmic state space using a novel behavioral task that requires the immediate formation of unique plans of action. Aim 2 directly compares representations of algorithmic state space to that of physical space by juxtaposing balanced versions of spatial and algorithmic tasks in a virtual reality (VR) environment. Finally, in Aim 3, the investigators test hypotheses regarding interactions between vPFC and MTL using intracranial stimulation.
Mapping Algorithmic State Space in the Human Brain
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
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Sponsor: Baylor College of Medicine
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.