ACE Cluster Only

Apply cluster-wise analysis including a cluster-wise TFCE test and correlation analysis on voxelized and warped segmentation maps.

Main Inputs

Control and Treated directories, containing voxelized and warped segmentation maps for each group.

CLI

To get more information about the workflow and its required arguments use the following command on the CLI:

$ miracl stats ace -h

Main outputs

./
|-- clust_final/
   |-- f_obs.nii.gz
   |-- p_values.nii.gz
   |-- pvalue_heatmap_mean_plot.tiff
|-- corr_final/

• clust_final: Contains the cluster-wise TFCE permutation statistics at the atlas space (f_obs.nii.gz), the p-value image of the F-statistics (p_values.nii.gz), and the p-value heatmap projected onto the Allen atlas space (pvalue_heatmap_mean_plot.tiff). All p-values are expressed as -log10(p-value).

• corr_final: Contains the correlation analysis output including correlation maps and p_value maps.

Example usage (link to sample data):

Note

You must download the sample data before running the below command. To do so, run:

$ docker exec -it <CONTAINER_NAME> bash
$ cd <WHERE YOU WANT TO DOWNLOAD DATA>
$ download_sample_data

This will open an interface where you can select which data you want to download. For this tutorial, you will need to download option 2.

$ miracl stats ace \
     --control ./ctrl/ \
     --treated ./treated/ \
     --sa_output_folder ./output_dir \
     --sctp_num_perm 1000 \
     --rwc_voxel_size 25 \
     --sctp_smoothing_fwhm 3 \
     --sctp_tfce_start 0.05 \
     --sctp_tfce_step 5 \
     --sctp_tfce_h 2 \
     --sctp_tfce_e 0.5

Flag	Parameter	Type	Description	Default
-c, --control	CONTROL_BASE_DIR	str	path to base control directory	None (required)
-t, --treated	TREATED_BASE_DIR	str	path to base treated directory	None (required)
-sao, --sa_output_folder	SA_OUTPUT_FOLDER	str	path to output directory	None (required)
-ua, --u_atlas_dir	ATLAS_DIR	str	path to atlas directory	os.environ.get(ARA_ENV, None)
-sctpn, --sctp_num_perm	NUM_PERM	int	number of permutations	100
-rwcv, --rwc_voxel_size	IMG_RESOLUTION	int	image resolution (atlas resolution 10 or 25 um)	25
-sctpfwhm, --sctp_smoothing_fwhm	SMOOTHING_FWHM	int	fwhm of Gaussian kernel in pixel	3
-sctpstart, --sctp_tfce_start	TFCE_START	float	tfce threshold start	0.01
-sctpstep, --sctp_tfce_step	TFCE_STEP	float	tfce threshold step	10
-sctph, --sctp_tfce_h	TFCE_H	float	tfce H power	2
-sctpe, --sctp_tfce_e	TFCE_E	float	tfce E power	0.5
-sctpc, --sctp_cpu_load	CPU_LOAD	float	percent of cpus used for parallelization	0.9
-sctpsp, ---sctp_step_down_p	STEP_DOWN_P	float	step down p value	0.3
-sctpm, --sctp_mask_thr	MASK_THR	int	percentile to be used for binarizing difference of the mean	95

Jupyter notebook

An accompanying Jupyter notebook for this tutorial can be found here.