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

Example usage (link to sample data):

$ miracl stats ace \
     -c ./ctrl/ \
     -e ./treated/ \
     -sao ./output_dir \
     -n 1000 \
     -a ./atlas/ \
     -r 25 \
     -sfwhm 3 \
     -start 0.05 \
     -step 5 \
     -h 2 \
     -e 0.5

Flag	Parameter	Type	Description	Default
-c, --control	CONTROL_BASE_DIR	str	path to base control directory	None (required)
-e, --experiment	EXPERIMENT_BASE_DIR	str	path to base experiment directory	None (required)
-sao, --sa_output_folder	SA_OUTPUT_FOLDER	str	path to output directory	None (required)
-n, --num_perm	NUM_PERM	int	number of permutations	100
-a, --atlas_dir	ATLAS_DIR	str	path to atlas directory	miracl_home
-r, --img_resolution	IMG_RESOLUTION	int	image resolution (atlas resolution 10 or 25 um)	25
-sfwhm, --smoothing_fwhm	SMOOTHING_FWHM	int	fwhm of Gaussian kernel in pixel	3
-start, --tfce_start	TFCE_START	float	tfce threshold start	0.01
-step, --tfce_step	TFCE_STEP	float	tfce threshold step	10
-h, --tfce_h	TFCE_H	float	tfce H power	2
-e, --tfce_e	TFCE_E	float	tfce E power	0.5
-c, --cpu_load	CPU_LOAD	float	percent of cpus used for parallelization	0.9
-p, ---step_down_p	STEP_DOWN_P	float	step down p value	0.3
-m, --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.