Entorhinal grid coding as a functional link between tau accumulation and episodic memory in human aging: fMRI analysis

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This repository contains the MATLAB/SPM12 analysis scripts that implement the first-level fMRI model and region-of-interest representational similarity analyses used to quantify six-fold grid-cell-like representations and their temporal stability across runs. The repository includes: A first-level GLM in which translation events are grouped into six directional phase regressors. Each regressor combines movement directions separated by 60°, producing phase offsets of 0°, 10°, 20°, 30°, 40°, and 50°. An ROI-based representational similarity analysis that extracts voxelwise beta patterns for the six phase conditions from each run. Calculation of run-specific neural representational dissimilarity matrices (RDMs) using correlation distance Calculation of a cross-run neural RDM and its Spearman correlation with a theoretical six-fold model RDM (grid-cell-like signal magnitude) Assessment of temporal stability as the Spearman correlation between the unique off-diagonal elements of the run-1 and run-2 neural RDMs (temporal stability metric) A fully synthetic mock dataset generator for testing the complete analysis pipeline. The mock dataset follows the main acquisition and task structure, with a repetition time of 2.2 seconds, two runs of 246 volumes, six motion regressors per run, semicolon-delimited event files, and a synthetic ROI mask. The generated NIfTI images are small, non-anatomical artificial volumes containing simulated condition-dependent spatial patterns and noise. No participant-derived imaging data or identifiable information are included. The scripts are configured through a central configuration file and can be run as a complete example pipeline or as separate mock-data generation, first-level GLM, and RSA steps. Outputs include the estimated SPM model, beta images, subject- and ROI-level RSA results, temporal-stability estimates, saved RDM matrices, and editable and high-resolution RDM figures. Software requirements: MATLAB SPM12 MATLAB Statistics and Machine Learning Toolbox Researchers applying the scripts to other datasets should adapt the directory paths, file-naming conventions, acquisition parameters, and ROI definitions in the configuration file.

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