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Abstract This project will characterize the functions of the human pulvinar (PUL) and mediodorsal (MD) nuclei and their role in brain-wide networks promoting a variety of high-level cognitive functions and behaviors. We propose to perform whole-brain fMRI to test the Center hypothesis that PUL and MD play crucial roles in coordinating information within and across cortical networks. Working closely with P5 (Woodward) and Core C (Chen), Aim 1 will resolve the internal organization of PUL and MD in healthy individuals based on functional connectivity with cortical networks involved in high-level cognitive functions. We will compare network topologies to those found in schizophrenia (P5) to identify dysfunction of cortical and thalamic nodes and in their interconnectivity. In Aim 2, we will adapt the Center’s APU and HDM tasks to assess the PUL and MD’s roles as centralized hubs for integrating information across cognitive control networks. The use of common tasks with tree shrews and macaques (P1-3) will allow for bridging across animal models spanning scales of representation from neurons to circuits to networks. In Aim 3, we will use a variant of the HDM task to identify human-unique characteristics of thalamocortical cognitive control networks that support flexible rule switching and generalization. We will use computational cognitive modeling to bridge between neuroimaging and behavior. Resolving the network-level architecture of PUL-cortical and MD-cortical function during complex cognitive behaviors will provide empirical and computational insights into the principles guiding behaviorally-relevant thalamocortical function (Center Aim 1), which will serve as a basis for assessing dysfunction (Center Aim 2) and inform the development of a mechanistic model for human thalamic function (Center Aim 3).
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Developing artificial neural network tools for cognitive modeling
  • 批准号:
    10641215
  • 项目类别:
  • 资助金额:
    $22.85万
  • 财政年份:
    2023
  • 负责人:
    Anne G.E. Collins
  • 依托单位:
The neural computations supporting hierarchical reinforcement learning
  • 批准号:
    10359201
  • 项目类别:
  • 资助金额:
    $38.01万
  • 财政年份:
    2019
  • 负责人:
    Anne G.E. Collins
  • 依托单位:
The neural computations supporting hierarchical reinforcement learning
  • 批准号:
    10113371
  • 项目类别:
  • 资助金额:
    $38.06万
  • 财政年份:
    2019
  • 负责人:
    Anne G.E. Collins
  • 依托单位:
The neural computations supporting hierarchical reinforcement learning
  • 批准号:
    10576384
  • 项目类别:
  • 资助金额:
    $37.94万
  • 财政年份:
    2019
  • 负责人:
    Anne G.E. Collins
  • 依托单位:
海外基金