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

• Published in: Human brain mapping Vol. 31; no. 3; pp. 391 - 397
• Main Author: Rodriguez, Paul F.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.03.2010; Wiley-Liss
• Subjects: Adult; Biological and medical sciences; Brain - physiology; cognitive map; Electrodiagnosis. Electric activity recording; Environment; Female; Goals; hippocampus; Hippocampus - physiology; Humans; Investigative techniques, diagnostic techniques (general aspects); Least-Squares Analysis; Magnetic Resonance Imaging - methods; Male; Medical sciences; multivoxel pattern analysis; Nervous system; Parietal Lobe - physiology; place coding; prefrontal cortex; Prefrontal Cortex - physiology; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Signal Processing, Computer-Assisted; Space Perception - physiology; Spatial Behavior - physiology; User-Computer Interface; Young Adult; Human; Cartography; Nervous system diseases; Navigation; Radiodiagnosis; Central nervous system; Prefrontal cortex; Nuclear magnetic resonance imaging; Encephalon; place coding; Coding; multivoxel pattern analysis; Pattern analysis; Hippocampus; cognitive map
• ISSN: 1065-9471; 1097-0193; 1097-0193
• 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. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.