Using fMRI to Measure the Neural-level Signals Underlying Population-level Responses

项目摘要：这项建议的目标是提高我们准确推断神经网络特性的能力。 从人类非侵入性成像获得的更粗粒度的信息中获得的视神经水平的反应。 为了实现这一目标，该项目将利用功能选择性皮质反应。例如，许多新大学- 视觉皮层中的神经元表现出调节功能，例如其中一个人的放电率最大的反应轮廓 一条线的方向，并随着与该方向逐渐不太相似的方向而下降。前景看好的新产品 分析功能磁共振成像(FMRI)数据的方法揭示了类似的特征调整 血氧水平依赖(BOLD)信号。因为这些体素级调谐函数(VTF) 表面上类似于用电生理学观察到的神经调谐功能(NTF)，它诱人地 将VTF解释为反映了构成它们的底层NTF的特征。不过，这个 解释是不合理的，因为非关税壁垒的变化有多种不同的解释。 可以在VTF中产生给定的变化。为了区分这些账户，我们需要一种地图的方法- 将VTFS ping回NTFS。也就是说，对于功能磁共振成像提供对神经水平机制的洞察，相反的问题是- 必须解决将体素水平的fMRI信号映射回神经水平的LEM问题。拟议中的工作 将通过考虑一组看似合理的神经水平变化来解决这个逆问题，这些变化可能会导致 观察体素水平功能磁共振反应的变化，并确定神经水平变化最大的模型 可能使用模型恢复或分层贝叶斯估计，然后进行模型选择。 这一目标将通过三个具体目标来实现：(1)确定使我们能够区分 用于定向选择性VTF简单调制的神经级调制的不同模式之间的比较 (刺激对比)；(2)确定定向选择性调制的神经机制 由知觉或认知状态的其他操作引起的VTFS；以及(3)识别神经水平的机制-- NISM是另外两类VTF调制的基础。实现这三个目标的方法包括：i)收集 最佳fMRI数据，ii)将替代的神经水平调制模型应用于fMRI数据以说明 体素级调制，iii)基于模型恢复或分层贝叶斯执行模型选择 估计，iv)将模型选择的结果与电生理学的“地面事实”进行比较。支持- 因此，该项目将开发一种实验和模型选择程序，以揭示神经水平的机制-- 在功能磁共振成像观察到的特征选择性体素反应中的调制背后的NISM。此外，它还将使- 研究细粒度神经机制的动物研究数据与来自 对一系列知觉和认知现象进行非侵入性成像。

Project Summary: The goal of this proposal is to advance our ability to accurately infer the properties of neu- ral-level responses from the more coarse-grained information obtained with non-invasive imaging in humans. To achieve this goal, the project will capitalize on feature-selective cortical responses. For example, many neu- rons in visual cortex exhibit a tuning function such as a response profile in which firing rate is greatest for one orientation of a line, and falls off for orientations progressively less similar to that orientation. Promising new methods for analyzing functional Magnetic Resonance Imaging (fMRI) data reveal analogous feature-tuning in the blood oxygenation level-dependent (BOLD) signal. Because these voxel-level tuning functions (VTFs) are superficially analogous to the neural tuning functions (NTFs) observed with electrophysiology, it is tempting to interpret VTFs as mirroring the characteristics of the underlying NTFs that contribute to them. However, this interpretation is not justified because there are multiple alternative accounts by which changes in the NTFs could produce a given change in the VTF. To distinguish between these accounts, we need a means of map- ping VTFs back to NTFs. That is, for fMRI to provide insights into neural-level mechanisms, the inverse prob- lem of mapping voxel-level fMRI signals back to neural-level responses must be solved. The proposed work will tackle this inverse problem by considering a plausible set of neural-level changes that may give rise to an observed change in voxel-level fMRI responses, and determining which model of neural-level change is most likely using either model recovery or hierarchical Bayesian estimation, followed by model selection. The goal will be accomplished via three specific aims: (1) Determine the conditions that allow us to distinguish between alternative models of neural-level modulation for a simple modulation of orientation-selective VTFs (stimulus contrast); (2) Identify the neural-level mechanisms underlying modulations of orientation-selective VTFs induced by other manipulations of perceptual or cognitive state; and (3) Identify the neural-level mecha- nisms underlying modulation of two further classes of VTF. The approach for all three aims entails: i) collecting optimal fMRI data, ii) applying alternative models of neural-level modulation to the fMRI data to account for voxel-level modulations, iii) performing model selection based upon model recovery or hierarchical Bayesian estimation, iv) comparing the outcome of model selection with ‘ground truth’ from electrophysiology. The pro- ject will thereby develop an experimental and model selection procedure for revealing the neural-level mecha- nisms that underlie modulations in feature-selective voxel responses observed with fMRI. Moreover, it will ena- ble the comparison of data from animal studies investigating fine-grained neural mechanisms with data from non-invasive imaging in humans, for a range of perceptual and cognitive phenomena.