Functionality

The core functionality of this toolbox is to compute the following features:

Variable	Description
subject	Subject ID
wm_snr_orig	Signal-to-noise ratio for white matter in orig.mgz
gm_snr_orig	Signal-to-noise ratio for gray matter in orig.mgz
wm_snr_norm	Signal-to-noise ratio for white matter in norm.mgz
gm_snr_norm	Signal-to-noise ratio for gray matter in norm.mgz
cc_size	Relative size of the corpus callosum
lh_holes	Number of holes in the left hemisphere
rh_holes	Number of holes in the right hemisphere
lh_defects	Number of defects in the left hemisphere
rh_defects	Number of defects in the right hemisphere
topo_lh	Topological fixing time for the left hemisphere
topo_rh	Topological fixing time for the right hemisphere
con_lh_snr	WM/GM contrast signal-to-noise ratio in the left hemisphere
con_rh_snr	WM/GM contrast signal-to-noise ratio in the right hemisphere
rot_tal_x	Rotation component of the Talairach transform around the x axis
rot_tal_y	Rotation component of the Talairach transform around the y axis
rot_tal_z	Rotation component of the Talairach transform around the z axis

The program will use an existing output directory (or try to create it) and write a CSV table into that location. The CSV table will contain the above metrics plus a subject identifier.

The program can also be run on images that were processed with FastSurfer (v1.1 or later) instead of FreeSurfer. In that case, simply add a --fastsurfer switch to your shell command. Note that FastSurfer’s full processing stream must have been run, including surface reconstruction (i.e. brain segmentation alone is not sufficient).

In addition to the core functionality of the toolbox, there are several optional modules that can be run according to need:

screenshots module

This module allows for the automated generation of cross-sections of the brain that are overlaid with the anatomical segmentations (asegs) and the white and pial surfaces. These images will be saved to the ‘screenshots’ subdirectory that will be created within the output directory. These images can be used for quickly glimpsing through the processing results. Note that no display manager is required for this module, i.e. it can be run on a remote server, for example.

surfaces module

This module allows for the automated generation of surface renderings of the left and right pial and inflated surfaces, overlaid with the aparc annotation. These images will be saved to the ‘surfaces’ subdirectory that will be created within the output directory. These images can be used for quickly glimpsing through the processing results. Note that no display manager is required for this module, i.e. it can be run on a remote server, for example.

skullstrip module

This module allows for the automated generation cross-sections of the brain that are overlaid with the colored and semi-transparent brainmask. This allows checking the quality of the skullstripping in FreeSurfer. The resulting images will be saved to the ‘skullstrip’ subdirectory that will be created within the output directory.

fornix module

This is a module to assess potential issues with the segmentation of the corpus callosum, which may incorrectly include parts of the fornix. To assess segmentation quality, a screenshot of the contours of the corpus callosum segmentation overlaid on the norm.mgz will be saved as ‘cc.png’ for each subject within the ‘fornix’ subdirectory of the output directory.

modules for the amygdala, hippocampus, and hypothalamus

These modules evaluate potential missegmentations of the amygdala, hippocampus, and hypothalamus. To assess segmentation quality, screenshots will be created. These modules require prior processing of the MR images with FreeSurfer’s dedicated toolboxes for the segmentation of the amygdala and hippocampus, and the hypothalamus, respectively.

shape module

The shape module will run a shapeDNA / brainprint analysis to compute distances of shape descriptors between lateralized brain structures. This can be used to identify discrepancies and irregularities between pairs of corresponding structures. The results will be included in the main csv table, and the output directory will also contain a ‘brainprint’ subdirectory.

outlier module

This is a module to detect extreme values among the subcortical (‘aseg’) segmentations as well as the cortical parcellations. If present, hypothalamic and hippocampal subsegmentations will also be included.

The outlier detection is based on comparisons with the distributions of the sample as well as normative values taken from the literature (see References).

For comparisons with the sample distributions, extreme values are defined in two ways: nonparametrically, i.e. values that are 1.5 times the interquartile range below or above the 25th or 75th percentile of the sample, respectively, and parametrically, i.e. values that are more than 2 standard deviations above or below the sample mean. Note that a minimum of 10 supplied subjects is required for running these analyses, otherwise NaNs will be returned.

For comparisons with the normative values, lower and upper bounds are computed from the 95% prediction intervals of the regression models given in Potvin et al., 2016, and values exceeding these bounds will be flagged. As an alternative, users may specify their own normative values by using the ‘–outlier-table’ argument. This requires a custom csv table with headers label, upper, and lower, where label indicates a column of anatomical names. It can be a subset and the order is arbitrary, but naming must exactly match the nomenclature of the ‘aseg.stats’ and/or ‘[lr]h.aparc.stats’ file. If cortical parcellations are included in the outlier table for a comparison with aparc.stats values, the labels must have a ‘lh.’ or ‘rh.’ prefix. upper and lower are user-specified upper and lower bounds.

The main csv table will be appended with the following summary variables, and more detailed output about will be saved as csv tables in the ‘outliers’ subdirectory of the main output directory:

Variable	Description
n_outliers_sample_nonpar	Number of structures that are 1.5 times the IQR above/below the 75th/25th percentile
n_outliers_sample_param	Number of structures that are 2 SD above/below the mean
n_outliers_norms	Number of structures exceeding the upper and lower bounds of the normative values