This OSF page contains data and code accompanying the paper: **Frontal language areas do not emerge in the absence of temporal language areas: A case study of an individual born without a left temporal lobe** by Greta Tuckute, Alexander Paunov, Hope Kean, Hannah Small, Zachary Mineroff, Idan Blank, Evelina Fedorenko.
The preprint is available on *bioRxiv* (https://www.biorxiv.org/content/10.1101/2021.05.28.446230v2).
The page is structured as follows:
- **Demographics**: Contains two files, one for each control group (CG) *CG1_demographics.csv* and *CG2_demographics.csv*. Each contains subject IDs, gender, and age at scan. Additionally, for CG2 there is information about subjects' second language of native-like fluency.
- **Images**: Contains three anatomical images for EG used in Fig. 2A of the paper.
- **Neural_Data**: Contains three files in the main folder: 1) *data_blockwise.csv* with effect size BOLD responses per block of each fMRI run and per condition for each subject (across both control groups and both EG sessions) for language and multiple demand (MD) regions, 2) *data_averaged_all_blocks_and_runs.csv* with effect size responses averaged across blocks and runs for each subject (across both control groups and both EG sessions) for language and multiple demand (MD) regions, and 3) *data_to_plot.csv* contains the data that is plotted in Fig. 2 of the paper (block-wise effect size responses for EG and effect sizes averaged across blocks and runs for the control group subjects).
Additionally, the folder **Language_parcels** contains the anatomical language 'parcels' that were used to constrain the voxel responses based on the functional language localizer (read more under https://evlab.mit.edu/funcloc/ and Fedorenko et al., 2010 JNeurophys).
- **Scripts**: Contains two folders:
- **Statistics**: Contains three scripts: 1) *Outlier_Detection.R* for visualizing and detecting outliers based on block-wise fMRI data, 2) *LME_statistics.R* for running linear mixed effects (LME) models and hypothesis tests, and 3) *Crawford_statistics.R* for running the Bayesian Crawford statistics.
- **Visualization**: contains the script *main_figure.py* for plotting mean effect sizes and individual data points for each condition of interest across both control groups and both EG sessions (Fig. 2 in the paper).
