Neural decoding of goal locations in spatial navigation in humans with fMRI

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.