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

项目概述：本提案的目标是提高我们准确推断neu性质的能力， 从更粗粒度的信息获得的非侵入性成像在人类的ral-level响应。 为了实现这一目标，该项目将利用功能选择性皮层反应。例如，许多新- 视觉皮层中的神经元表现出调谐功能，例如其中对于一个人来说放电率最大的响应曲线。 线的方向，并且对于与该方向逐渐不太相似的方向，福尔斯下降。有前途的新 分析功能性磁共振成像（fMRI）数据的方法显示， 血氧水平依赖（BOLD）信号。因为这些体素级调谐函数（VTF）是 表面上类似于电生理学观察到的神经调谐功能（NTFs），这是诱人的， 将VTF解释为反映了对它们有贡献的基础NTF的特征。但这 解释是不合理的，因为有多个替代帐户，通过这些帐户，NTF的变化 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- 将VTF ping回NTF。也就是说，对于功能磁共振成像提供神经水平机制的见解，逆概率- 将体素水平的fMRI信号映射回神经水平的反应是必须解决的问题。拟议工作 将通过考虑一组可能引起神经元水平变化的合理变化来解决这个逆问题。 观察体素水平fMRI反应的变化，并确定神经水平变化的模型最 可能使用模型恢复或分层贝叶斯估计，然后进行模型选择。 该目标将通过三个具体目标来实现：（1）确定允许我们区分的条件 神经水平调制的替代模型之间的简单调制方向选择性VTF （刺激对比）;（2）确定神经水平的机制调制的方向选择性 由知觉或认知状态的其他操纵引起的VTF;（3）确定神经水平的机制。 Nisms潜在的调制的两个进一步类的VTF。实现这三个目标的方法包括：i）收集 最佳的fMRI数据，ii）将神经水平调制的替代模型应用于fMRI数据，以解释 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来自电生理学的"地面实况"进行比较。亲- 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， 功能磁共振成像观察到的特征选择性体素反应调制的基础。此外，它将- 将研究细粒度神经机制的动物研究数据与 非侵入性成像在人类中，一系列的感知和认知现象。

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.