# Experiment 1 (StudyForrest) Contains scripts for connectopic and retinotopic analyses of the StudyForrest dataset in Experiment 1. 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. > Note that we use our own Freesurfer reconstructions of the subject anatomies, > rather than the ones provided with the StudyForrest repository (although they > should be similar). This is because we were unable to get the Freesurfer > surfaces in the StudyForrest repository to download successfully. > The movie-watching and retinotopy timeseries were pre-processed in FEAT. The > resulting *filtered_func_data* timeseries serve as the inputs to both the > connectopy and retinotopy analysis pipelines. * `unpack_data.sh` - Copies/symlinks data downloaded from the StudyForrest repository into a nicer directory structure. This sets up the basic structure of subject directories that further scripts rely on. * `freesurfer_bbr.sh` - Runs boundary based registration between functional volumes and Freesurfer surfaces. * `fsl_bbr+fnirt.sh` - Runs volume registrations, first as boundary based registrations between each subject's functional and anatomical volumes, then as non-linear registrations between the anatomical volumes and MNI space. * `xfm_aseg2vol.sh` - Transforms Freesurfer *aseg* atlas to volume spaces for both the MNI brain and individual subjects. This is used to derive white-matter and ventricular masks to be used for normalising the functional timeseries (see *normalise_timeseries.py*), as well as a sub-cortical gray matter mask to be used for masking the sub-cortical volume data (see *xfm_normalised_timeseries2MNI_subcortex.sh*). * `normalise_timeseries.py` - Normalises the pre-processed functional timeseries (the *filtered_func_data* files produced by FEAT) by first converting to percent signal change, and then regressing out the mean white-matter and ventricular timeseries and motion parameters. * `xfm_normalised_timeseries2surf.sh` - Transforms normalised timeseries onto _fsaverage6_ cortical surface. * `xfm_normalised_timeseries2MNI_subcortex.sh` - Transforms normalised timeseries to MNI volume and then masks by subcortical gray matter. ## retinotopy Scripts are used for performing travelling wave analyses of retinotopy data, and allow defining V1 labels. * `calc_phase_maps.sh` - Uses a combination of AFNI and Freesurfer to perform travelling wave analysis on cortical surface. Also depends on *fix_gifti_intent.py* and *fix_phaseshifts.py*. * `display_in_freeview.sh` - Display phase maps in freeview for manual drawing of V1 ROIs. These ROIs were saved out as Freesurfer label files. * `restrict_maps_to_labels.py` - Once V1 labels have been defined, this script restricts the phase maps to to those labels. The restricted maps serve as one of the inputs to the connectopic mapping pipeline. * `fix_gifti_intent.py`, `fix_phaseshifts.py` - Utility scripts used by the *calc_phase_maps.sh* script. ## connectopy Scripts for calculating connectivity fingerprints and performing connectopic mapping. * `utils/manifold.py` - Module contains assorted functions used for manifold learning. * `utils/surface.py` - Module contains assorted functions for loading and saving surface data. * `calc_connectivity_fingerprints.py` - Calculates the functional connectivity fingerprints within V1 by correlating the normalised timeseries. Provides options for measuring connectivity between V1 and all other cortical and subcortical structures, just cortical structures, just subcortical structures, cortical and subcortical structures 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. Runs a repeated-measures ANOVA with factors for the map-type (eccentricity, polar angle) and graph type. 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 paired-samples t-tests to compare correlations between map types (eccentricity, polar angle). 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 repeated-measures ANOVA with factors for the map and algorithm type. * `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). Performs a repeated-measures ANOVA with factors for the algorithm type, map type, and connectivity source. * `cross_corr_embedding.py` - Calculates correlations between connectopic maps produced by each connectopic mapping algorithm. * `pyUtils.py`, `R_utils.R` - Utility scripts, contain functions used by other scripts.