Localized fMRI of heterogeneous neural responses

描述（由申请人提供）：功能磁共振成像（fMRI）是不可否认的神经成像方法，已经成为神经科学和心理学研究人员的主要工具，他们希望获得与人类行为相关的大脑生理变化的局部测量。功能性磁共振成像测量的高价值是基于这样一个事实，即它们一次又一次地显示出与局部神经群体反应的线性相关。然而，最近文献中的一些文章表明，fMRI反应与测量或假设的神经活动之间存在不匹配。这些不匹配似乎仅限于实验中，只有一小部分神经群受到刺激；当局部神经兴奋和抑制之间的平衡倾向于抑制时，它们似乎也最有可能发生。这些关于功能磁共振成像反应的报告未能与神经反应相关联，这充其量是令人困惑的，对于那些想从功能磁共振成像数据中得出神经群体活动定量结论的科学家来说，这可能会带来麻烦。我们提出的第一个系列实验将描述采样分辨率对fMRI对小刺激反应的可解释性的影响。侧翼负反应不仅会混淆fMRI对小块神经活动反应的准确解释，因为边界区域与总神经反应相比较大，而且依赖于大小的内在抑制塑造了神经反应，但在血流动力学反应中却有未知的表现。第一个系列实验的结果将是一个计算模型，用于描述(1)孤立的神经活动斑块边缘的神经-血流动力学耦合，以及(2)抑制性神经活动对fMRI反应的贡献。我们的第二个系列实验将描述神经网络在兴奋和抑制之间的不同平衡所引起的fMRI反应。所有局部神经编码都包含输入和输出之间的平衡；局部计算使用激励和抑制的平衡来塑造输入和定义输出尖峰率。在这一系列的实验中，我们将探讨我们最近研究的意义，该研究表明单个图像斑块的局部fMRI不能简单地从对这些刺激反应最好的神经元的反应中预测。利用一个计算模型，演示如何使用整个局部神经群反应来预测fMRI反应，第二系列实验将寻求识别当异质神经反应掩盖了神经元亚群编码的关键信息时存在的标志性血流动力学反应特征。总之，这些实验将提高我们使用高分辨率功能磁共振成像来表征模式神经活动的能力，提高功能磁共振成像在神经外科计划和癫痫发作位点检测等临床应用中的实用性。

DESCRIPTION (provided by applicant): Functional magnetic resonance imaging (fMRI) is undeniably the neuroimaging methodology that has become the workhorse for neuroscience and psychology researchers who want access to localized measurements of physiological changes in the brain that correlate with human behavior. The high value of fMRI measurements is based on the fact that they have been shown, time and again, to exhibit a linear correlation with the local neural population response. There are, however, a recent smattering of articles in the literature indicating a mismatch between the fMRI response and the measured or presumed neural activity. These mismatches appear limited to experiments in which only a small neural population is stimulated; they also seem most likely to occur when the balance between local neural excitation and inhibition is tipped in favor of inhibition. These reports of fMRI responses that fail to correlate with neural responses are puzzling at best, and potentially troublesome for scientists who want to draw quantitative conclusions about neural population activity from fMRI data. Our first series of proposed experiments will characterize the effects of sampling resolution on the interpretability of the fMRI response to small stimuli. Not only do flanking negative responses confound accurate interpretation of the fMRI response to small patches of neural activity, because the boundary regions are large compared to the total neural response, but size-dependent intrinsic inhibition shapes the neural response yet has an unknown representation in the hemodynamic response. The result of the first series of experiments will be a computational model characterizing (1) neuro-hemodynamic coupling at the edges of isolated patches of neural activity, and (2) the contribution of inhibitory neural activity to the fMRI response. Our second series of experiments will characterize fMRI response evoked by neural networks with different balances between excitation and inhibition. All local neural codes contain a balance between input and output; local computations use a balance of excitation and inhibition to shape the input and define output spiking rates. In this series of experiments, we will investigate the implications of our recent study showing that localized fMRI of individual image patches cannot be predicted simply from the responses of the neurons that respond best to those stimuli. Working with a computational model that demonstrates how the entire local neural population response can be used to predict fMRI responses, this second series of experiments will seek to identify signature hemodynamic response characteristics that are present when heterogeneous neural responses mask key information encoded in a sub-population of neurons. Together, these experiments will improve our ability to use high-resolution fMRI to characterize patterned neural activity, improving the utility of fMRI for clinicl applications such as neurosurgical planning and seizure locus detection. PUBLIC HEALTH RELEVANCE: Scientists who study the brain need high-resolution imaging tools in order to understand how different patterns of neural activity correlate with different aspects of behavior; functional magnetic resonance imaging (fMRI) is one of the tools that can provide the highest imaging resolution. However, we still need to answer some fundamental questions about the relationship between the fMRI signal and the underlying neural activity in the brain. The work funded by this grant will develop more accurate models for linking neural activity patterns to fMRI responses when (1) the neural response occupies only a small portion of cortex, and (2) sub- populations of neurons right next to each other have different responses. This ability to detect activity or dysregulation of activity in a subpopulation of neurons is key fr high-resolution localization of neural function, for neurosurgical planning or seizure locus detection, as well as for quantifying biomarkers of diseases such as schizophrenia, which differentially affects inhibitory neurons in visual cortex.