Journal: Frontiers in Neuroinformatics

Article Title: The GridCAT: A Toolbox for Automated Analysis of Human Grid Cell Codes in fMRI

doi: 10.3389/fninf.2017.00047

Figure Lengend Snippet: The graphical user interface (GUI) of the Grid Code Analysis Toolbox (GridCAT). A grid code analysis can be carried out via the GUI by specifying data, parameters and settings, depending on the individual experimental design and research question. The GUI offers all analysis options of the grid code analysis pipeline as well as a set of additional tools to generate grid code metrics, visualize results and export the resulting data. A detailed explanation of all GUI options and how to use all functions of the GridCAT via the GUI, is provided in the GridCAT user manual that is distributed along with the open-source code. Please note that the visual appearance of the GUI might differ between operating systems and versions of Matlab.

Article Snippet: Given the increasing interest in the role of grid cells in human cognition, and the absence of standard analysis tools to examine grid codes in fMRI, we present here the Matlab-based Grid Code Analysis Toolbox (GridCAT), which generates grid code metrics from functional neuroimaging data.

Techniques:

Journal: Frontiers in Neuroinformatics

Article Title: The GridCAT: A Toolbox for Automated Analysis of Human Grid Cell Codes in fMRI

doi: 10.3389/fninf.2017.00047

Figure Lengend Snippet: Grid code analysis pipeline. All that is required to perform a grid code analysis are functional brain images (which, depending on the user’s wishes, may have undergone standard fMRI data preprocessing such as smoothing etc.) together with a file detailing events of interest during the fMRI time course and their corresponding timing information. The GridCAT partitions these data into an estimation dataset and a test dataset, offering multiple options as to how to split the data depending on the experimental design and the user’s needs. Using the estimation dataset, the GridCAT then estimates voxel-wise grid orientations of the grid code in a first general linear model (GLM1). As a result, voxel-wise grid orientations are stored and can be plotted using the GridCAT’s specific plotting options to visualize grid code stability both within and between voxels, or can be exported in several formats for further analysis such as group level analyses, statistical testing, or multivariate analysis methods. Moreover, within any region of interest (ROI), the GridCAT can calculate an ROI-specific mean grid orientation, providing that the mask image (e.g., anatomically or functionally defined) and functional data are registered to one another. Finally, in a second GLM (GLM2) the GridCAT allows events in the test dataset to be modeled with respect to their alignment with the ROI-specific mean grid orientation, in order to quantify the grid code response magnitude individually for all brain voxels or averaged over voxels within an ROI. All results and grid code metrics can be exported for further use with statistical and neuroimaging analysis tools of the researcher’s choice.

Article Snippet: Given the increasing interest in the role of grid cells in human cognition, and the absence of standard analysis tools to examine grid codes in fMRI, we present here the Matlab-based Grid Code Analysis Toolbox (GridCAT), which generates grid code metrics from functional neuroimaging data.

Techniques: Functional Assay

Journal: Frontiers in Neuroinformatics

Article Title: The GridCAT: A Toolbox for Automated Analysis of Human Grid Cell Codes in fMRI

doi: 10.3389/fninf.2017.00047

Figure Lengend Snippet: GridCAT polar histogram plots showing coherence of the grid orientation between voxels in right and left entorhinal cortex ROIs. The length of each bar indicates the number of voxels that share a similar grid orientation, and the blue numbers indicate the number of voxels represented by each ring of the polar plot. The GridCAT also allows the user to calculate and visualize the mean grid orientation (red arrow) for each plot (which is used in GLM2 to model grid events with respect to their deviation from the mean grid orientation). Depending on the user’s choice of model, the mean grid orientation can be calculated separately for run 1 (left column) and run 2 (middle column), or alternatively, the mean grid orientation over multiple runs (right column) can be calculated by averaging the parameter estimates. Furthermore, users can choose to carry out Rayleigh’s test for non-uniformity of circular data. Rayleigh’s test indicated that voxels in both the right (top row) and left (bottom row) entorhinal cortex showed significant clustering (i.e., coherence) in their orientations (all p < 0.00001).

Article Snippet: Given the increasing interest in the role of grid cells in human cognition, and the absence of standard analysis tools to examine grid codes in fMRI, we present here the Matlab-based Grid Code Analysis Toolbox (GridCAT), which generates grid code metrics from functional neuroimaging data.

Techniques:

Journal: Frontiers in Neuroinformatics

Article Title: The GridCAT: A Toolbox for Automated Analysis of Human Grid Cell Codes in fMRI

doi: 10.3389/fninf.2017.00047

Figure Lengend Snippet: GridCAT polar plots showing coherence of the grid orientation within voxels, across runs 1 and 2, in right and left entorhinal cortex ROIs. In both the right and left entorhinal cortex, the majority of voxels maintained the same grid orientation (±15°) across the two runs; the proportion of voxels maintaining the same orientation across runs is calculated automatically for the user. The two black rings in each plot represent the two different runs (inner ring: run 1, outer ring: run 2), and the orientation of the grid code for each voxel is indicated with a circular marker; a line connects the orientations of each voxel. Green solid lines indicate voxels with stable orientations, whereas red dotted lines indicate voxels with unstable orientations. The GridCAT allows the user also to customize the plots, including the color schemes, line styles, as well as adapting the threshold for classifying a voxel as stable.

Article Snippet: Given the increasing interest in the role of grid cells in human cognition, and the absence of standard analysis tools to examine grid codes in fMRI, we present here the Matlab-based Grid Code Analysis Toolbox (GridCAT), which generates grid code metrics from functional neuroimaging data.

Techniques: Marker