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| Home > In process > Software: Temporal instability weakens entorhinal grid-like coding in older adults, v1 |
| Home > In process > Software: Temporal instability weakens entorhinal grid-like coding in older adults, v1 |
| Software | DZNE-2026-01084 |
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2026
Zenodo
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Please use a persistent id in citations: doi:10.5281/zenodo.23106522
Abstract: This repository provides a compact MATLAB/SPM12 implementation of representational-similarity analyses for navigation fMRI data. It includes a fully reproducible mock-data workflow that generates synthetic two-run BOLD data, event files, motion regressors, and example pmEC, retrosplenial cortex, and presubiculum ROIs. No human participant data are included.The code implements two separate first-level models. The hexadirectional model groups movement headings into six 60°-periodic conditions (0°, 10°, 20°, 30°, 40°, and 50°), pooling headings separated by 60°. Direction-specific multivoxel beta patterns are used to construct run-specific and cross-run neural representational dissimilarity matrices (RDMs; correlation distance, 1 − r). Cross-run neural RDMs are compared with a six-fold model RDM, (1-\cos(6\Delta\theta)), using Spearman correlation to estimate grid-cell-like representational strength. The same workflow also quantifies three complementary properties of the six-fold signal: matching-bin temporal orientation stability, temporal representational-geometry stability, and across-voxel beta-pattern variability. An off-diagonal RSA estimate excludes matching-bin cells from the cross-run RDM.A separate 12-regressor model preserves full 360° heading space, using 30° centres with ±10° circular windows. It estimates the cross-run reproducibility of absolute-orientation multivoxel patterns in retrosplenial cortex and presubiculum.The repository saves CSV result tables, RDM matrices, and quality-control figures in PNG, PDF, and editable MATLAB formats. run_mock_pipeline.m executes the complete synthetic-data demonstration; analysis_config.m contains all paths and analysis settings for adaptation to empirical datasets.
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