Visual Neuroscience Group, School of Psychology, University of Nottingham, Nottingham, UK.
Neuroimage. 2012 Nov 15;63(3):1623-32. doi: 10.1016/j.neuroimage.2012.07.066. Epub 2012 Aug 17.
Previous studies have demonstrated that the perceived direction of motion of a visual stimulus can be decoded from the pattern of functional magnetic resonance imaging (fMRI) responses in occipital cortex using multivariate analysis methods (Kamitani and Tong, 2006). One possible mechanism for this is a difference in the sampling of direction selective cortical columns between voxels, implying that information at a level smaller than the voxel size might be accessible with fMRI. Alternatively, multivariate analysis methods might be driven by the organization of neurons into clusters or even orderly maps at a much larger scale. To assess the possible sources of the direction selectivity observed in fMRI data, we tested how classification accuracy varied across different visual areas and subsets of voxels for classification of motion-direction. To enable high spatial resolution functional MRI measurements (1.5mm isotropic voxels), data were collected at 7T. To test whether information about the direction of motion is represented at the scale of retinotopic maps, we looked at classification performance after combining data across different voxels within visual areas (V1-3 and MT+/V5) before training the multivariate classifier. A recent study has shown that orientation biases in V1 are both necessary and sufficient to explain classification of stimulus orientation (Freeman et al., 2011). Here, we combined voxels with similar visual field preference as determined in separate retinotopy measurements and observed that classification accuracy was preserved when averaging in this 'retinotopically restricted' way, compared to random averaging of voxels. This insensitivity to averaging of voxels (with similar visual angle preference) across substantial distances in cortical space suggests that there are large-scale biases at the level of retinotopic maps underlying our ability to classify direction of motion.
先前的研究表明,通过使用多元分析方法(Kamitani 和 Tong,2006),可以从枕叶皮层的功能磁共振成像(fMRI)响应模式中解码出视觉刺激的感知运动方向。一种可能的机制是,在体素之间,方向选择皮层柱的采样存在差异,这意味着,使用 fMRI 可能可以获得小于体素大小的信息。或者,多元分析方法可能受到神经元在更大尺度上聚类甚至有序图谱的组织的驱动。为了评估 fMRI 数据中观察到的方向选择性的可能来源,我们测试了分类准确性如何在不同的视觉区域和运动方向分类的体素子集之间变化。为了实现高空间分辨率功能磁共振成像测量(1.5mm 各向同性体素),在 7T 下收集数据。为了测试运动方向的信息是否在视网膜映射的尺度上表示,我们在训练多元分类器之前,在视觉区域(V1-3 和 MT+/V5)内跨不同体素组合数据,观察分类性能。最近的一项研究表明,V1 中的朝向偏好在解释刺激朝向的分类时既必要又充分(Freeman 等人,2011)。在这里,我们结合了在单独的视网膜测量中确定具有相似视野偏好的体素,并观察到,与随机平均体素相比,以这种“视网膜限制”的方式平均时,分类准确性得以保留。这种对跨越皮质空间中大量距离的体素(具有相似视角偏好)的平均不敏感,表明在我们能够分类运动方向的视网膜映射底层存在大尺度偏差。