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Abstract
Multi-voxel pattern analysis (MVPA), or ‘decoding’, of fMRI activity has gained popularity in the neuroimaging community in recent years. MVPA differs from standard fMRI analyses by focusing on whether information relating to specific stimuli is encoded in patterns of activity across multiple voxels. If a stimulus can be predicted, or decoded, solely from the pattern of fMRI activity, it must mean there is information about that stimulus represented in the brain region where the pattern across voxels was identified. This ability to examine the representation of information relating to specific stimuli (e.g., memories) in particular brain areas makes MVPA an especially suitable method for investigating memory representations in brain structures such as the hippocampus. This approach could open up new opportunities to examine hippocampal representations in terms of their content, and how they might change over time, with aging, and pathology. Here we consider published MVPA studies that specifically focused on the hippocampus, and use them to illustrate the kinds of novel questions that can be addressed using MVPA. We then discuss some of the conceptual and methodological challenges that can arise when implementing MVPA in this context. Overall, we hope to highlight the potential utility of MVPA, when appropriately deployed, and provide some initial guidance to those considering MVPA as a means to investigate the hippocampus.
Highlights
It has been clear for many decades that the hippocampus is critical for memory
Multi-voxel pattern analysis (MVPA) analyses have revealed that it is possible to decode complex, realistic information such as allocentric spatial locations within a virtual environment (Hassabis et al, 2009; Rodriguez, 2011)
Using MVPA to probe the nature of hippocampal representations more closely, evidence emerged that the hippocampus maintains distinct representations of highly overlapping episodic-like memories, providing a link between theories of pattern separation and complex episodic memories (Chadwick et al, 2011)
Summary
It has been clear for many decades that the hippocampus is critical for memory. Lesions to this structure leave afflicted patients with dense anterograde amnesia and significant retrograde memory loss for their personal experiences (Scoville & Milner, 1957; Mayes, 1988; Spiers, Maguire, & Burgess, 2001; Mayes & Montaldi, 2001; Mayes & Roberts, 2001; Mayes, 2008; Winocur & Moscovitch, 2011). An alternative approach to fMRI analysis has emerged which exploits the intrinsically multivariate nature of fMRI data The motivation for this change stems from the belief that there may be information present in the distributed pattern of activation across voxels that is missed when looking at each voxel independently as in the mass-univariate method (Haynes & Rees, 2006; Norman, Polyn, Detre, & Haxby, 2006). A clear demonstration of the potential of MVPA was provided by Haxby et al (2001), who found that neural representations of object categories, such as places and faces, were more widely distributed and overlapping within the ventral temporal cortex than had been thought previously They examined specific regions where the individual voxels (using a mass-univariate approach) responded strongly to one category or another, and found that within these supposedly category-selective regions, there still existed considerable information in the distributed pattern of activation about the non-preferred category. Our goal is to promote greater application of MVPA to the study of the hippocampus, as we believe that appropriate deployment of this method has the potential to provide important new perspectives on the function of the human hippocampus
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