Leeds Daniel D, Tarr Michael J
Fordham University, Computer and Information Science Department, Bronx, New York, USA; Carnegie Mellon University, Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
Carnegie Mellon University, Psychology Department, Pittsburgh, PA, USA; Carnegie Mellon University, Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
Neuroimage. 2016 Jun;133:529-548. doi: 10.1016/j.neuroimage.2016.02.071. Epub 2016 Mar 11.
The properties utilized by visual object perception in the mid- and high-level ventral visual pathway are poorly understood. To better establish and explore possible models of these properties, we adopt a data-driven approach in which we repeatedly interrogate neural units using functional Magnetic Resonance Imaging (fMRI) to establish each unit's image selectivity. This approach to imaging necessitates a search through a broad space of stimulus properties using a limited number of samples. To more quickly identify the complex visual features underlying human cortical object perception, we implemented a new functional magnetic resonance imaging protocol in which visual stimuli are selected in real-time based on BOLD responses to recently shown images. Two variations of this protocol were developed, one relying on natural object stimuli and a second based on synthetic object stimuli, both embedded in feature spaces based on the complex visual properties of the objects. During fMRI scanning, we continuously controlled stimulus selection in the context of a real-time search through these image spaces in order to maximize neural responses across pre-determined 1cm(3) rain regions. Elsewhere we have reported the patterns of cortical selectivity revealed by this approach (Leeds et al., 2014). In contrast, here our objective is to present more detailed methods and explore the technical and biological factors influencing the behavior of our real-time stimulus search. We observe that: 1) Searches converged more reliably when exploring a more precisely parameterized space of synthetic objects; 2) real-time estimation of cortical responses to stimuli is reasonably consistent; 3) search behavior was acceptably robust to delays in stimulus displays and subject motion effects. Overall, our results indicate that real-time fMRI methods may provide a valuable platform for continuing study of localized neural selectivity, both for visual object representation and beyond.
中高级腹侧视觉通路中视觉对象感知所利用的属性目前尚不清楚。为了更好地建立和探索这些属性的可能模型，我们采用了一种数据驱动的方法，即使用功能磁共振成像（fMRI）反复询问神经单元，以确定每个单元的图像选择性。这种成像方法需要使用有限数量的样本在广泛的刺激属性空间中进行搜索。为了更快地识别人类皮层对象感知背后的复杂视觉特征，我们实施了一种新的功能磁共振成像协议，其中视觉刺激基于对最近显示图像的血氧水平依赖（BOLD）反应进行实时选择。开发了该协议的两种变体，一种依赖于自然对象刺激，另一种基于合成对象刺激，两者都基于对象的复杂视觉属性嵌入特征空间。在fMRI扫描期间，我们在通过这些图像空间进行实时搜索的背景下持续控制刺激选择，以最大化跨预先确定的1立方厘米感兴趣区域的神经反应。我们在其他地方报告了这种方法所揭示的皮层选择性模式（利兹等人，2014年）。相比之下，这里我们的目标是介绍更详细的方法，并探索影响我们实时刺激搜索行为的技术和生物学因素。我们观察到：1）在探索参数化更精确的合成对象空间时，搜索收敛更可靠；2）对刺激的皮层反应的实时估计相当一致；3）搜索行为对刺激显示延迟和受试者运动效应具有可接受的鲁棒性。总体而言，我们的结果表明，实时fMRI方法可能为持续研究局部神经选择性提供一个有价值的平台，无论是对于视觉对象表征还是其他方面。
