Serra E. Favila - US grants

Project Summary Memory allows us to use previous experience to guide current behavior. Computational models of episodic memory propose that recalling an event from the past involves reinstating patterns of cortical activity that were evoked when the event occurred. While there is considerable evidence for reinstatement within early sensory areas, recent evidence suggests that reinstatement also occurs in higher-level regions, such as lateral parietal cortex, that are strongly predictive of memory success. However, the significance of reinstatement in lateral parietal cortex and its relation to reinstatement in sensory areas is not well understood. Understanding how areas like lateral parietal cortex represent, transform, and prioritize information from sensory areas will be critical to building a full model of how distributed brain networks support healthy and dysfunctional memory. Addressing these gaps in understanding will require a multidisciplinary approach that relies on innovative fMRI analyses, computational modeling, and integrating evidence from neuroimaging and electrophysiology. My dissertation work so far has used fMRI approaches to test the hypothesis that lateral parietal cortex represents sensory content during memory retrieval. This work has produced several significant results in favor of this hypothesis and has involved the development of new fMRI methods. In the proposed research and training program, I will use computational models of visual encoding to discriminate between competing hypotheses of how topographic maps in parietal cortex encode spatial information during memory retrieval. This experience will provide training in visual neuroscience and computational modeling, allowing me to develop expertise with diverse approaches to studying episodic memory. In the proposed postdoctoral training phase, I will gain theoretical and methodological training in electrophysiological measurements collected from humans. The combination of fMRI and electrophysiology measurements will allow me to test novel hypotheses about how memory signals propagate through time across distinct brain regions, and to generate findings that unify research performed in human and non-human animal systems. This research program has significant potential to generate novel understanding of the cortical mechanisms that support memory retrieval, to bridge findings across measurements techniques, and to inform the treatment of aging- and disease-related memory disorders. I am confident that the training provided by this research program will allow me to develop the diverse theoretical and methodological expertise needed to conduct independent research on a core set of questions in systems and cognitive neuroscience.

Project Summary Memory allows us to use previous experience to guide current behavior. Computational models of episodic memory propose that recalling an event from the past involves reinstating patterns of cortical activity that were evoked when the event occurred. While there is considerable evidence for reinstatement within early sensory areas, recent evidence suggests that reinstatement also occurs in higher-level regions, such as lateral parietal cortex, that are strongly predictive of memory success. However, the significance of reinstatement in lateral parietal cortex and its relation to reinstatement in sensory areas is not well understood. Understanding how areas like lateral parietal cortex represent, transform, and prioritize information from sensory areas will be critical to building a full model of how distributed brain networks support healthy and dysfunctional memory. Addressing these gaps in understanding will require a multidisciplinary approach that relies on innovative fMRI analyses, computational modeling, and integrating evidence from neuroimaging and electrophysiology. My dissertation work so far has used fMRI approaches to test the hypothesis that lateral parietal cortex represents sensory content during memory retrieval. This work has produced several significant results in favor of this hypothesis and has involved the development of new fMRI methods. In the proposed research and training program, I will use computational models of visual encoding to discriminate between competing hypotheses of how topographic maps in parietal cortex encode spatial information during memory retrieval. This experience will provide training in visual neuroscience and computational modeling, allowing me to develop expertise with diverse approaches to studying episodic memory. In the proposed postdoctoral training phase, I will gain theoretical and methodological training in electrophysiological measurements collected from humans. The combination of fMRI and electrophysiology measurements will allow me to test novel hypotheses about how memory signals propagate through time across distinct brain regions, and to generate findings that unify research performed in human and non-human animal systems. This research program has significant potential to generate novel understanding of the cortical mechanisms that support memory retrieval, to bridge findings across measurements techniques, and to inform the treatment of aging- and disease-related memory disorders. I am confident that the training provided by this research program will allow me to develop the diverse theoretical and methodological expertise needed to conduct independent research on a core set of questions in systems and cognitive neuroscience.