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 predetermined 1 cm3 brain regions. Elsewhere we have reported the patterns of cortical selectivity revealed by this approach (). 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 are 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.
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机译：视觉对象感知在中，高级腹侧视觉通路中利用的性质了解甚少。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，在该方法中，我们使用功能性磁共振成像（fMRI）反复询问神经单元以建立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阔的刺激特性空间中进行搜索。为了更快速地识别人类皮层对象感知的复杂视觉特征，我们实施了一种新的功能磁共振成像协议，其中基于对最近显示的图像的BOLD响应实时选择视觉刺激。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，两者均基于对象的复杂视觉特性嵌入特征空间中。在fMRI扫描期间，我们在这些图像空间的实时搜索范围内连续控制刺激的选择，以最大化预定1 cm 3 大脑区域的神经反应。在其他地方，我们已经报道了这种方法揭示的皮质选择性模式（）。相反，这里我们的目标是提出更详细的方法，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）在探索更精确参数化的合成对象空间时，搜索收敛更可靠； 2）皮层对刺激反应的实时估计是合理一致的； 3）对于刺激显示和主体运动效果的延迟，搜索行为具有可接受的鲁棒性。总的来说，我们的结果表明，实时功能磁共振成像方法可能为继续研究局部视觉选择性以及其他方面的局部神经选择性提供有价值的平台。
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