Department of Radiology, University of California at San Diego, La Jolla, 92037, USA.
Hum Brain Mapp. 2010 Mar;31(3):391-7. doi: 10.1002/hbm.20873.
We demonstrate that multivoxel pattern analysis can be used to decode place-related information in fMRI. Subjects performed a working memory version of the Morris water maze task in a virtual environment with a single wall cue. The voxel data that corresponds to when subjects were located at the goal was extracted for seven regions implicated in spatial navigation, and then used to train a pattern classifier based on partial least squares. Using a leave-one-out (LOO) test procedure, goal locations at E, W, N positions (relative to the cue as S) were predicted significantly better than a naïve classifier for voxels in medial prefrontal cortex, hippocampus, and inferior parietal cortex. Prediction with voxels from other regions involved in navigation was also better than a naïve classifier, which raises the possibility that goal-location information is widely disseminated among the navigation network. It turns out that predictive capability of all regions combined significantly decreases, relative to no change, only when voxel data from the hippocampus is left out. This implies that the hippocampus contains some unique information that identifies goal locations, whereas other regions contain information that also identifies goal locations but is more redundant. Classification of goal locations is an important step toward decoding a variety of place-related information in spatial navigation with fMRI.
我们证明多体素模式分析可用于解码 fMRI 中的与位置相关的信息。被试在具有单个壁提示的虚拟环境中执行工作记忆版的 Morris 水迷宫任务。从与被试位于目标位置相对应的体素数据中提取出七个与空间导航相关的区域，然后使用基于偏最小二乘法的模式分类器进行训练。使用留一法（LOO）测试程序，与 naïve 分类器相比，目标位于 E、W、N 位置（相对于提示为 S）的预测显著更好，这对于内侧前额叶皮层、海马体和下顶叶皮层的体素来说是如此。来自其他导航相关区域的体素的预测也优于 naïve 分类器，这表明目标位置信息在导航网络中广泛传播。事实证明，与没有变化相比，只有当排除海马体中的体素数据时，所有区域的组合预测能力才会显著降低。这意味着海马体包含一些可识别目标位置的独特信息，而其他区域包含的信息也可识别目标位置，但更冗余。目标位置的分类是用 fMRI 解码空间导航中各种与位置相关的信息的重要步骤。
