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High-probability grants
According to our matching algorithm, Lila Davachi is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2007 — 2020 |
Davachi, Lila |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Medial Temporal Lobe Contributions to Episodic Memory
DESCRIPTION (provided by applicant): Despite the widespread appreciation that the medial temporal lobe (MTL) is necessary for episodic associative memory formation and retrieval, there is a fundamental gap in understanding the post-encoding processes by which memories are consolidate, or stabilized. This gap in knowledge is a critical problem because a host of psychiatric and neurologic disorders stem from a primary dysfunction of the MTL and how it contributes to associative memory. The long-term goal is to understand the mechanisms that support memory consolidation and what consequences these changes have on the integration of our new memories with past experience. The objective of the current proposal is to test a model of how post-encoding reactivation within MTL substructures known to be involved in encoding different aspects of an experience relate to the consolidation of those experiences. The central aim of the project is to establish reactivation as a mechanism for human episodic memory consolidation and to reveal distinct patterns of reactivation related to distinct kinds of memories. The rationale for the proposed research is that a better understanding of how the memories become stabilized over time will lead to a strong theoretical framework within which strategies for the understanding of mental disease disrupting memory will develop. The objective will be to identify, modulate and look for long-term consequences of reactivation which will be accomplished by pursuing three specific aims: 1) identify post-encoding patterns of reactivation that characterize recent prior experiences and relate to later associative memory for memories of different content; 2) modulate post-encoding reactivation by linking reactivation with the amount of prior learning and hippocampal activity; and 3) linking post-encoding reactivation with longer-term changes in the memory representations. Strong preliminary data demonstrate the feasibility of project aims in the applicant's hands. Under aim 1, evidence for reactivation of specific encoding experiences has been identified within the human hippocampus and evidence for distinct MTL interactions following encoding tasks presenting different memoranda. Under aim 2, preliminary data provide evidence that the magnitude of hippocampal activation during encoding correlates with post-encoding hippocampal-cortical interactions. Under aim 3, preliminary data identify expected patterns of change in the network representation of associative memories during reactivation that relate to behavioral measures of associative memory strength thus providing a much needed link between memory consolidation changes in the brain and strengthening of memories behaviorally. The approach is innovative and significant because we know very little about how interactions between MTL regions contribute to memory consolidation; it is highly programmatic because it is directly-motivated from our prior work on the role of MTL subregions to memory encoding and uses novel approaches to studying consolidation by looking for patterns of reactivation during post-encoding rest.
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1 |
2016 — 2019 |
Gureckis, Todd [⬀] Davachi, Lila |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Ncs-Fo: Using Computational Cognitive Neuroscience to Predict and Optimize Memory
The last decade has seen an explosion of research concerning the neural processes underlying memory formation and learning. As the basic research in this field becomes more mature, exciting possibilities for application of this knowledge have begun to emerge. This proposal aims to capitalize on these findings by developing assistive learning technologies that may revolutionize the way we teach and train people. Researchers at New York University will develop automated "adaptive teaching" technologies that guide learners through material in an individualized way. The goal is to increase retention or mastery of materials by tailoring instruction to individual learners. The novel contribution of this project is to combine insights from cognitive neuroscience and machine learning in the design and operation of such technologies. Successful development of this synergistic research would be a transformative application of neuroscience to our daily lives and may lead to new commercial technologies. The research will also provide a post-doctoral training opportunity for the next generation of scientists working at the intersection of neuroscience and computer science. The award is from the Integrated Strategies for Understanding Neural and Cognitive Systems program, with funding from the EHR Core Research (ECR) program, which supports fundamental research that advances the research literature on STEM learning, and from the Behavioral and Cognitive Sciences division in the SBE directorate.
The specific scientific goal is to explore novel applications of neuroscience methods (particularly fMRI) to improve how people learn. Neuroscience research has identified robust neural correlates of successful memory formation (e.g., activity in brain areas such as the medial temporal lobe and the hippocampus). The goal of this project is to use these variables to help predict the information needs of learners in an adaptive way. The project design involves scanning an individual's brain during the learning phase of a task. The research team then will identify which materials would benefit from additional study by combining computational models of the time course of learning and forgetting with theories mapping neural activation to successful memory formation. A computer algorithm then selects new materials for learners to re-study in a subsequent session. The goal is to show that generating a training sequence from a computer-based "neurofeedback" algorithm can enhance long-term memory retention more than when learners choose for themselves which items to re-study. This is a high-risk but potentially large reward project that merges basic science findings from neuroscience and cognitive science in ways that may transform the way we educate people.
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1 |
2018 — 2021 |
Davachi, Lila Goff, Donald C. [⬀] |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Hippocampal Memory Circuits in Delusions @ New York University School of Medicine
SUMMARY Hippocampal memory circuits are strongly implicated in the formation and persistence of delusions. In particular, the dentate gyrus (DG) and the CA1 subfield are vulnerable to early developmental stressors that are risk factors for schizophrenia; postmortem evidence points to deficits in inhibitory input and in neurogenesis. We hypothesize that delusions result from aberrant associative memory formation due to impaired functioning of DG/CA3 subfields and persist due to a failure to update false beliefs with new episodic information due to reduced mnemonic prediction error signaling in CA1 and reduced post-encoding consolidation of newly formed memories. Because antipsychotics target hippocampal memory circuits, it is important to study these circuits in unmedicated subjects. We propose to apply three task-based fMRI paradigms to examine early mnemonic associative processing, prediction error, and plasticity of circuits associated with encoding and retrieval in three experiments, each including 50 first episode nonaffective psychosis (FEP) subjects and 50 healthy matched controls. The medication-naïve FEP subjects will be re- studied after 8 weeks of antipsychotic treatment to examine the relationship between medication effects on delusional severity and on hippocampal memory circuits. Imaging will also be repeated after 8 weeks in healthy controls to assess learning effects. The first paradigm, a behavioral pattern separation task, has not previously been studied in medication-naïve first episode psychosis. The paradigms for assessment of CA1 activation during prediction error and of plasticity of connectivity between hippocampal subfields, the ventral tegmental area dopamine neurons and cortical regions were recently developed and validated by Dr. Davachi's team in healthy subjects; we have demonstrated feasibility of these paradigms in schizophrenia subjects. The proposed project will advance our understanding of circuits involved in delusions and their pharmacologic response, will provide validated imaging biomarkers for clinical studies and will identify new targets for treatment development.
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1 |