## Study and Citation
Scholz, R., Villringer, A., & Martins, M. D. J. D. (2022). **Distinct hippocampal and cortical contributions in the representation of hierarchies.** [bioRxiv](https://www.biorxiv.org/content/10.1101/2022.06.15.496057v1
).
This repository: [https://osf.io/56nxh/](https://osf.io/56nxh/)
Preregistration: [https://osf.io/4bq2n](https://osf.io/4bq2n), DOI: 10.17605/OSF.IO/4BQ2N
## Whole-Brain MRI Analysis
Folder: `analysis/spm_whole_brain`
### Standard Analysis
This is based on the raw subject specific mri-data, that can not be provided.
| Num | File | Stage Name |
|:------|:-------------|:------------|
| 0 | spm12_preprocessing.m | Preprocessing of all MRI data |
| 1 | spm12_firstlv_standard.m | Common first level analysis |
| 2 | spm12_group_standard.m | Script to analyze group contrasts (here: Task x Pos, but can also be adapted to Task x Experience |
### Grid Analysis
Takes preprocessed data as input
| Num | File | Stage Name |
|:------|:-------------|:------------|
| G1A | spm12_firstlv_grid_gridcat.m | GridCat first level script adapted |
| G1B | spm12_firstlv_grid_custom.m | Custom First level grid analysis |
| G2 | spm12_group_grid.m | Group Analysis |
## Results (whole brain group contrasts)
Folder: group_contrasts
* **Task x Pos SPM Contrasts** - `gflexall5c_txp_new28.tar.gz`
* **Task x Exp SPM Contrasts** - `gflexall5c_txELb_28.tar.gz`
### Task x Pos SPM Contrasts
File archive: `gflexall5c_txp_new28.tar.gz`
| Numbering | Contrast| Comment |
|:------|:-------------|:------------|
| con_0001 | ALL2-ALL1 | |
| con_0002 | H2-H1 | |
| con_0003 | I2-I1 | |
| con_0004 | Hier-Iter | Figure 2A (red) & Table S1, Effect of Task |
| con_0005 | Hier1-Iter1 | |
| con_0006 | Hier2-Iter2 | |
| con_0007 | TxP IA 1: (H2-H1)-(I2-I1) | Figure 2A (green) & Table 1, 1st Interaction of Task x Position |
| con_0008 | ALL1-ALL2 | |
| con_0009 | H1-H2 | |
| con_0010 | I1-I2 | |
| con_0011 | Iter-Hier | |
| con_0012 | Iter1-Hier1 | |
| con_0013 | Iter2-Hier2 | |
| con_0014 | TxP IA 2: (I2-I1)-(H2-H1) ~ (H1-H2)-(I1-I2) | Figure 2A (blue) & Table S1, 2nd Interaction of Task x Position |
### Task x Experience SPM Contrasts
File archive: `gflexall5c_txELb_28.tar.gz`
| Numbering | Contrast| Comment |
|:------:|:-------------|:------------:|
| con_0001 | H-I | |
| con_0002 | LH-EH | |
| con_0003 | LI-EI | |
| con_0004 | LH-LI | |
| con_0005 | EH-EI | |
| con_0006 | TxEL 1 (HL-HE-[IL-IE]) | Figure 3A (red) & Table S2 |
| con_0007 | I-H | |
| con_0008 | EH-LH | |
| con_0009 | EI-LI | |
| con_0010 | LI-LH | |
| con_0011 | EI-EH | |
| con_0012 | TxEL 2 (IL-IE-[HL-HE]) | |
## ROI-based Analysis
Folder: `analy/roi_based`
* activations were extracted using [REX](https://www.nitrc.org/projects/rex/) for each ROI (using the respective masks) and then concatenated into a single CSV-file for each contrast
* we used Jamovi to analyze this data (making use of its filtering function); for each `*.csv` file there is a corresponding `*.omv` file in the folder
* the plots were created using python/matplotlib/seaborn; this is summarized as jupyter notebook: `create_plots.ipynb`
| Contrast | CSV-File | Comment |
|:------|:------|:-------------|
| Task x Pos | ROIs_txp31_betas2.csv| Figure 2B & C |
| Task x Pos | HIP_gradient_txp31_bilateral.csv | Figure 2D |
| Task x Exp | ROIs_txELb31_betas3_corrMapd.csv | Figure 3B&C |
## Behavioral Analysis
Behavioral data (.csv) and the corresponding jamovi file can be found under:
Folder: `analy/behavioral`
## Experimental paradigm
The nessesary scripts for executing the experimental procedure (as completed by participants in the scanner) can be found under:
Folder: `paradigm/HCSMTL`
The experiment is coded entirely in python and makes use of the psychopy toolbox.
