# Experiment 2 (HCP) Contains scripts for connectopic and retinotopic analyses of the HCP dataset in Experiment 2. We outline the contents of the repository below, in the (approximate) order they need to be run. > Many of the scripts make reference to a `basedir` variable - this should be > set to the top-level directory containing all of the subject directories. ## preproc Scripts used for setting up directory structure, and running initial pre-processing stages. * `unpack_data.sh` - Copies/symlinks data downloaded from the HCP into a nicer directory structure. This sets up the basic structure of subject directories that further scripts rely on. * `anat2freesurfer.sh` - Converts HCP surface files from GIFTI to Freesurfer format for improved compatibility with Freesurfer. * `curv2freesurfer.py` - Converts HCP curvature files from CIFTI to Freesurfer format for improved compatibility with Freesurfer. * `preproc_data.py` - Applies additional pre-processing to functional data. Specifically, converts timeseries to percent signal change, and applies spatial smoothing (surface and volume based). ## retinotopy Scripts are used for performing pRF analyses of retinotopy data and defining V1 labels. * `extract_occipital_label.sh` - Extracts occipital lobe label from Freesurfer atlas and transforms to HCP surface. This is used to provide an approximate label that can be used to restrict pRF fits to the occipital lobe to save on computation time. * `fit_pRF.m` - Performs pRF fits. Adapted from code provided by Benson et al. (2018), Journal of Vision; https://osf.io/bw9ec/ . Code has additional external dependencies which we do not redistribute here - see docstring in script for details on how to obtain these. * `convert_pRF_to_freesurfer.sh` - Converts outputs from *fit_pRF.m* script from CIFTI to MGZ format for compatibility with Freesurfer. * `display_in_freeview.sh` - Display phase maps in freeview for manual drawing of V1 ROIs. These ROIs were saved out as Freesurfer label files. * `convert_labels_to_cifti.sh` - Once V1 labels have been defined, this script converts them from Freesurfer back to CIFTI format. * `restrict_maps_to_V1.py` - Once V1 labels have been defined, this script restricts the pRF eccentricity and polar maps to those labels. Saves in CIFTI, MGZ, and numpy formats. Mostly useful for providing retinotopic inputs to connectopic mapping scripts. ## connectopy Scripts for calculating connectivity fingerprints and performing connectopic mapping. * `calc_connectivity_fingerprints.py` - Calculates the functional connectivity fingerprints within V1 by correlating the pre-processed timeseries. Provides options for measuring connectivity between V1 and all other cortical and subcortical grayordinates, just cortical grayordinates, just grayordinates structures, cortical and subcortical grayordinates substituted with noise, or purely within V1. * Various scripts perform connectopic mapping, each offering different algorithms: * `do_diffusion_map.py` - Diffusion maps * `do_isomap.py` - Isomap * `do_kernel_pca.py` - Kernal PCA, using polynomial and RBF kernels * `do_linear_embedding.py` - PCA and ICA * `do_LLE.py` - (M)LLE and LTSA * `do_spectral_embedding.py` - Spectral Embedding * `do_tSNE.py` - t-SNE ## stats Runs statistical analyses on the prediction accuracies (absolute correlations between retinotopic and connectopic maps). * First, various scripts are used to extract the prediction accuracies (correlations) from each connectopic mapping algorithm and compile the values over subjects. The outputs are used as inputs to further statistical analyses. * `extract_corrs-spectral_embedding.py` - Extracts correlations from spectral embedding algorithm. Dedicated script is needed to handle the different graph types (weighted/unweighted $\times$ full/radius/kNN). * `extract_corrs-kernel_pca.py` - Extracts correlations from kernel PCA algorithm. Dedicated script is needed to handle the different kernel types (polynomial and RBF). * `extract_corrs-other_embeddings.py` - Extracts correlations for all other connectopic mapping technqiues (PCA, ICA, MLLE, LTSA, Isomap, diffusion maps, and t-SNE). * `analyse_corrs-spectral_embedding.R` - Performs analysis of correlations within spectral embedding algorithm. Dedicated script is needed to handle the different graph types. Runs a three-way repeated-measures ANOVA with factors for the map type (eccentricity, polar angle), graph type, and task (rest, movie-watching). Also performs one-sample t-tests to test correlations in each case against zero. * `analyse_corrs-other_embeddings.R` - Performs analysis of correlations for all other algorithms (PCA, ICA, kernel PCA (polynomial and RBF), LLE, LTSA, Isomap, diffusion maps, and t-SNE). Runs a two-way repeated-measures ANOVA with factors for the map type (eccentricity, polar angle) and task (rest, movie-watching). Also performs one-sample t-tests to test correlations in each case against zero. * `compare_corrs_between_embeddings.R` - Compares correlations across connectopic mapping algorithms via a three-way repeated-measures ANOVA with factors for the map type (eccentricity, polar angle), algorithm type, and task (rest, movie-watching). * `compare_corrs_between_sources.R` - For spectral embedding, Isomap, and diffusion map algorithms, compares correlations for connectopic maps based on different connectivity sources (cortex+subcortex, cortex-only, subcortex-only, within-V1, and cortex+subcortex substituted with noise). Runs a four-way repeated-measures ANOVA with factors for the algorithm type, map type (eccentricity, polar angle), connectivity source, and task (rest, movie-watching). * `cross_corr_embedding.py` - Calculates correlations between connectopic maps produced by each connectopic mapping algorithm. ## python_utils Assorted utility functions and classes for use by other python scripts * `cifti.py` - Tools for handling data from CIFTI files. * `manifold.py` - Contains assorted functions and classes used for manifold learning. * `stats.py` - Tools used by statistics Python scripts. ## R_utils Contains `R_utils.R`, containing tools used by statistics R scripts.