Abstract
Human functional magnetic resonance imaging (fMRI) studies examining the putative firing of grid cells (i.e., the grid code) suggest that this cellular mechanism supports not only spatial navigation, but also more abstract cognitive processes. Despite increased interest in this research, there remain relatively few human grid code studies, perhaps due to the complex analysis methods, which are not included in standard fMRI analysis packages. To overcome this, we have developed the Matlab-based open-source Grid Code Analysis Toolbox (GridCAT), which performs all analyses, from the estimation and fitting of the grid code in the general linear model (GLM), to the generation of grid code metrics and plots. The GridCAT, therefore, opens up this cutting-edge research area by allowing users to analyze data by means of a simple and user-friendly graphical user interface (GUI). Researchers confident with programming can edit the open-source code and use example scripts accompanying the GridCAT to implement their own analysis pipelines. Here, we review the current literature in the field of fMRI grid code research with particular focus on the different analysis options that have been implemented, which we describe in detail. Key features of the GridCAT are demonstrated via analysis of an example dataset, which is also provided online together with a detailed manual, so that users can replicate the results presented here, and explore the GridCAT’s functionality. By making the GridCAT available to the wider neuroscience community, we believe that it will prove invaluable in elucidating the role of grid codes in higher-order cognitive processes.
Highlights
Identifying the neural mechanisms supporting spatial navigation remains a key goal for neuroscience
Consistent with the analysis strategy of Doeller et al (2010), in GLM1 we found that the orientations of grid codes in voxels of both right and left entorhinal cortex showed significant non-uniformity, or clustering
Rayleigh’s test for non-uniformity of circular data can be carried out, in order to test whether the orientations of the grid code in voxels within an region of interest (ROI) show greater clustering than would be expected by chance
Summary
Identifying the neural mechanisms supporting spatial navigation remains a key goal for neuroscience. Significant progress has been made with the discovery of the grid cell in the rat medial entorhinal cortex, a neuron exhibiting firing properties that could provide a spatial metric underlying navigational functions such as path integration (Hafting et al, 2005). Given that each cell’s grid is spatially offset (to varying degrees) relative to a neighboring one, it has been hypothesized that these cells may provide the neural mechanism for complex spatial navigation abilities such as path integration (Hafting et al, 2005)
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