Temporal Gradient Figures

This is the code accompanying the manuscript Microstructural underpinnings and macroscale functional implications of temporal lobe connectivity gradients. It will allow you to reproduce the main figures of the manuscript. The development version of the BrainSpace toolbox is required for running the code herein.

Usage

First, make sure to add this toolbox and the BrainSpace development version to your MATLAB path:

System Message: ERROR/3 (<string>, line 15)

Error in "code-block" directive: maximum 1 argument(s) allowed, 3 supplied.

.. code-block:: matlab
    addpath(gepnath('/path/to/micaopen/temporal_gradients'))
    addpath(genpath('/path/to/brainspace/matlab'))

Next, you can download the required data by running temporal_gradients.download_data. The plots for, for example figure 1, can be produced by running temporal_gradients.master_figure1. The resulting figures will be stored in the directory micaopen/temporal_gradients/+temporal_gradients/figures/figure1.

Note that the sizes of some graphics objects in the figures depend on screen resolution (and others don't), so generated figures may look slightly different from those in the manuscript.

Data

The function temporal_gradients.download_data will automatically download a data file called figure_data.mat and store it inside micaopen/temporal_gradients/+temporal_gradients/data. The contents of figure_data.mat are as follows:

bigbrain: the MPC gradients

c69_20k: structure containing conte69 surfaces downsampled to 20000 vertices, indices for the downsampling, and a midline mask.

connectivity_distance: connectivity distance of the temporal lobe

connectivity_vector_3829: connectivity of the left temporal pole (vertex 3829)

degree_centrality: degree centrality of the temporal lobe

evo_data: functional homology index and cortical expansion of the temporal lobe.

gm_hcp_discovery: GradientMaps object of the discovery dataset.

gm_hcp_replication: GradientMaps object of the replication dataset.

gm_mics: GradientMaps object of the MICS dataset.

HAR_expression: Human accelerated gene expression map for the left hemispheric Schaefer200 regions.

include: vertices included in the yeo network analysis (overlap of vertices not in the midline and those included in the yeo networks).

kfold_fc_r: subjectwise correlation between empirical and predicted functional connectivity in the discovery dataset.

microstructural_features: curvature, cortical thickness, and t1w/t2w intensities

mics_fc_r: subjectwise correlation between empirical and predicted functional connectivity in the MICS dataset.

r: vertexwise correlation between predicted and empirical data in the discovery dataset

r_ho: vertexwise correlation between predicted and empirical data in the replication and MICS datasets

repl_fc_r subjetwise correlation between empirical and predicted functional connectivity in the replication dataset.

sc_mask: log-transformed (for visualization only!) structural connectivity of the discovery dataset.

schaefer_200_tl: Schaefer 200 regions in the left temporal lobe.

sjh: parcellation used for the MPC gradient

surf_lh: left hemispheric surface

surf_rh: right hemispheric surface

temporalLobe_msk: temporal lobe mask

yeo: Yeo networks

yeo_tl: Yeo networks in the temporal lobe.

yeo_predicted: Predicted yeo networks in the discovery dataset of one of the folds.

License

The data and code herein are published under a BSD-3 license. Please note that this license only applies to the temporal_gradients sub-directory of micaopen and overrides the license in the root directory.

If you use any of the code or data in this package, then please consider citing Vos de Wael et al., 2020, bioRxiv.

File	Mode	Size
+temporal_gradients
export_fig
.gitignore	-rw-r--r--	18 bytes
LICENSE	-rw-r--r--	1.5 KB
README.rst	-rw-r--r--	4.0 KB
figure.png	-rw-r--r--	4.4 MB

https://github.com/MICA-MNI/micaopen/

README.rst