FSL

# Featquery - FEAT Results Interrogation

## Introduction

Featquery is a tool which allows you to interrogate FEAT results within a mask or at a given co-ordinate. The mask/co-ordinate can be in standard-space, highres-space or lowres-space. Within this mask (or at the given co-ordinate), Featquery calculates a number of image and timeseries statistics. For example, you might define a standard-space mask for the motor cortex, and Featquery will tell you the mean (and peak) % signal change associated with your modelled experimental paradigm within that area.

## Choosing FEAT directories to interrogate

First you must select a previously-created FEAT output directory whose results you wish to investigate. You can run multiple queries by changing the Number of FEAT directories from the default of 1. The results of running Featquery will be saved in a directory (by default named "featquery") inside each FEAT directory that you select here. If you have already run Featquery on a FEAT directory, a "+" will be appended to the Featquery output name, e.g. "featquery+".

## Choosing stats images to investigate

Once you have selected a FEAT directory, a list of stats images inside that FEAT directory will appear. Click the buttons next to those that you are interested in. For each of these in turn, Featquery will calculate various quantities within the selected mask, for example, the number of non-zero voxels within the mask, the mean and max values of the stats image within the mask, and the co-ordinates of the max image value within the mask.

## Setting up a mask or voxel co-ordinates

You must now choose a Mask image. This would normally be a binary image in standard-space, highres-space or lowres-space, with a region-of-interest (ROI), for example, the visual cortex, created by any method (e.g. hand-drawn or activation from a multi-subject higher-level FEAT analysis). Featquery will automatically detect which space this mask is in (standard-space, highres-space or lowres-space) and will transform it into the native lowres space of example_func; of course this can only work if FEAT registration was setup and carried out.

If you want a different mask for each selected FEAT directory, specify a mask name as a relative filename (i.e., without a "/"). This mask will then be looked for in each FEAT directory.

Alternatively, you can specify a single co-ordinate (in voxels or mm) at which to extract values from the chosen stats images. This still requires a "mask" image to be chosen, as the co-ordinate is specified in the space of the mask. Thus if you want the co-ordinates to be in lowres space, just select "mask" or "example_func" from the FEAT directory. If you want to specify standard-space co-ordinates (e.g. MNI152 space) then choose "reg/standard". If you choose to specify the co-ordinate in mm (instead of voxels) and the mask image is one with a co-ordinate origin set (e.g. reg/standard which by default is in MNNI152 space, with the origin at the anterior commissure) then the co-ordinates that you enter will be relative to this origin, not the corner of the volume.

Finally, you can also automatically generate a mask from one of the various standard-space atlases provided with FSL. To do this, select an atlas from the menu, and then select a structure by pressing the Select label button.

Featquery saves the mask, once transformed into the native space of the selected FEAT directory, inside the Featquery output directory, called "mask" (even if this only contains a single non-zero voxel, in the case of selecting a co-ordinate).

## Further options

If you select an atlas, extra information will be added into the Featquery output. For each stats image, the position of the maximum voxel (within the input mask) will be used to query the selected atlas, and each structure which overlaps this voxel will be reported (for the probabilistic atlases, the % probability for each overlapping structure will be given).

If you select Convert PE/COPE values to % (VARCOPE to %^2), any PE or COPE parameter estimate or contrast values will be converted to percentage signal change values before reporting. This is achieved by scaling the PE or COPE values by (100*) the peak-peak height of the regressor (or effective regressor in the case of COPEs) and then by dividing by mean_func (the mean over time of filtered_func_data). If you are running Featquery on a higher-level FEAT directory, mean_func is the mean of all the lower-level mean_func images, so the % change values are reported correctly. For VARCOPE variance images, the equivalent transformation is also made, this time in units of % squared (i.e., take the square root to compare with the size of the COPE % change).

If you turn on Do not binarise mask (allow weighting) then if your mask is non-binary, its non-zero values will weight all Featquery output values rather than treating the mask as binary. In this case the mask will be scaled such that its mean (over non-zero voxels) is 1.

If you have selected a mask image in standard or highres space, this will get transformed into lowres space as described above. This involves interpolation; at the edges of the mask there will be a continuous range of values from 1 down to 0. In order to get back to a binary mask, this must be thresholded at some value - the default is 0.5. However, if you want the mask to be slightly more or less inclusive than that default, you can Change post-interpolation thresholding of mask - for example, by reducing the value to 0.3, the final lowres mask will be slightly larger.

If, as well as masking the stats images that you selected above, you want to only consider voxels from the images that are above above a given threshold, then turn on Threshold stats images as well as masking and select a value.

By default the output from Featquery is saved in a directory called "featquery", created inside each selected original FEAT directory (or is called "featquery+", etc., if you have already run Featquery before). At Featquery output directory name you can change this default name to anything else in order to more easily distinguish different runs of Featquery.

## Go

When you press Go, Featquery produces a web page report with all requested stats, as well as a raw text version of the main report table. Above the main table are links to "Mean time series (masked/weighted)" (this is a raw text file containing the mean time series, within the mask, of the 4D data file filtered_func_data) and "Masked time series plots" (this is a full graphical time series report, similar to those shown in the FEAT report, but with everything masked by the Featquery mask/co-ordinate, including peri-stimulus plots).

## Featquery output

The first column in the main table shows the "stats image", i.e. lists the different FEAT stats images that you asked Featquery to report on; each of these is also a link to a raw text file giving the data timeseries plot at the position of the maximum image value within the mask. The next column tells you the number of non-zero voxels within the mask.

The next group of columns give various statistics derived from each image's values within the Featquery mask. Most of these are self-explanatory (min, mean, median, max); the 10% and 90% columns show the image values at 10% and 90% of the way through the ordered list of values (i.e., at 10%/90% of the cumulative distribution function) - so these could be considered a "robust range" of the data values, ignoring the tails/outliers of the distribution.

The final group of columns gives the co-ordinates of the maximum image voxel within the Featquery mask, both in voxels in native space and in mm in standard space (assuming that FEAT registration was run), as well as overlapping atlas strucures, if requested.

## Scripting the running of Featquery and the extraction of values generated by Featquery

When you press Go, if you started the Featquery GUI from a command-line terminal then you will see the command-line call to the featquery script that is made. You can easily adapt this to script Featquery, if you want to make lots of calls to featquery automatically. Type "featquery" to get the full command-line usage.

It is easy to extract data from the raw text output, using unix commands like grep and awk. For example, to extract the "# featdir" value (column 1) and the median (column 7) of the "cope2" image, you might use:

    cat ~/grot.feat/featquery/report.txt | grep stats/cope2 | awk '{print $1 " " $7}'

Copyright © 2002-2006, University of Oxford. Written by S. Smith.