SPM12 STARTERS’ GUIDE
(c) Erno Hermans
General Information
SPM12 (Statistical Parametric Mapping) was developed at the Functional Imaging
Laboratory at University College London, by a team led by Karl Friston. It is a free
and open-source package that runs within the Matlab environment.
This beginner’s guide was first written for SPM99, for students in the MSc program
“Neuroscience and Cognition” at Utrecht University. The second edition was updated for SPM2, the third for SPM5, the fourth edition for SPM8, and the fifth for
SPM12 (with help from Bas Neggers and Mitzy Kennis of Utrecht University / UMC).
This guide is by no means comprehensive as only the most important functions of
SPM12 are described that are necessary to complete the course. Although things
have been simplified to a certain extent, this guide requires some background
knowledge in order to properly understand spatial pre-processing, and especially
statistics.
Among the most important topics that have been
left undescribed in this guide are more advanced
or newer features such as DARTEL, Bayesian
inference, dynamic causal modeling, and M/EEG
functions. The best starting point for more information is the SPM website at www.fil.ion.ucl.
ac.uk/spm.
File Type and File Selection
The NIfTI-1 format
SPM12 uses a standard 3D (or 4D in some cases) image format called NIfTI-1.1.
Each NIfTI format image consists of one file with an extension “.nii”.
The “.nii” file contains:
- A bitmap containing all the data in the image (i.e., grey values of each voxel)
- A transformation matrix placing the bitmap in a 3D coordinate system (a so-called
affine transformation: rotation, translation, zoom, and/or shear).
A time series of, e.g., 300 scans thus consists of 300 .nii files. When you manipulate scans using SPM, mostly the only thing that is changed in the NIfTI file is the
matrix containing the affine transformation. It is important to understand that the
actual data, the bitmap, is not changed until one chooses to “reslice” in whatever
stage of data processing. When an image is resliced (after realignment, or when
“written normalized”), the affine transformation matrix in the NIfTI file is reset, a new
bitmap is calculated, and a letter is prepended to the old filename (e.g., OLDFILE.
nii > rOLDFILE.nii) so that the old file is never overwritten.
Note: SPM12 can also use header (“.hdr”) and and image (“.img”) files like previous
(before SPM5) versions. When made in SPM12, these are also NIfTI compliant and
contain essentially the same information as “.nii” images. These files are therefore
different from the old img/hdr images that were used in previous SPM versions.
Analyze 7.5 format images made in SPM2 or older may not load properly in
SPM12, so do not switch SPM versions within projects.
See http://nifti.nimh.nih.gov/ for more information on NIfTI-1.1.
SPM12 starters guide - Erno Hermans - 1
Browsing and selecting files:
All SPM12 functions use the same file
browse dialog box shown here. In the left
panel, you can select a directory to go to.
The right panel will show the contents of a
directory. Clicking once is enough to
switch to a different directory. As is common in Unix-like environments, “..” stands
for “one level up”. Scans can be selected
by clicking them one by one, which will
make their names move to the box at the
bottom. Instead of clicking them all manually, you can right-click in the right panel
and subsequently click “select all”. Often, however, you will not want to select every
file, but only a large subset of files within a directory. In order to do this, you can
specify a filter (next to the button “Filt”). This filter works different from most common file filters. Here are the most important uses:
- “.*” : List all contents of a directory (but restricted to the type of image that your
SPM function requires).
- “funx” : list all files that contain “funx” in the filename.
- “^funx” : list all files with names that start with “funx”.
After selecting images, you can click the “Ed” button to edit the list of selected files.
Right-clicking in this window will take you back.
You can also click the question mark button for a more thorough explanation of the
file browse dialog.
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SPM12 main window
Spatial Pre-processing functions
Page 9
Statistical functions
Page 21
Visualization
Page 4
SPM12 starters guide - Erno Hermans - 3
Visualization
The visualization tools Display and Check Reg are used to:
- Look at scans
- Manually manipulate scans to set starting points for spatial
preprocessing
- Check all spatial pre-processing steps.
Display
The display function allows you to view and manipulate a single image.
Use the dialog to browse to your files and
select one (see page 2 for explanation).
When you select a “.nii” file it will appear at
the bottom of the dialog box. Clicking this
file again will deselect it.
You don’t have to double click to enter
directories. Clicking “..” takes you one
directory level up.
Click “Done” to continue.
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SPM12 starters guide - Erno Hermans - 5
The most important buttons in the display window are:
Crosshair position: the position of the blue line in the scans in either mm or voxels.
Right, forward and up: Move the scan in three directions. These directions only
make sense when your scan is in the same orientation as MNI standard space.
Pitch, roll, yaw: rotate the scan around the X, Y, or Z axis respectively.
Reorient images: Click here to save changes to the scan you are manipulating and
to apply the same changes to other scans. NOTE: here you have to select the
image you are working on again in order to save changes (see below).
On the right you can see some useful information about your scan: voxel size, origin, etc.
Except for just viewing images, the display function is used to manually match your
images before starting automatic spatial preprocessing. The reason to do this is that
if your scans do not approximately match, the algorithms that are used to make
exact matches can get stuck in a local minimum, resulting in scans that do not
match at all. It is advisable to put all your scans approximately into standard MNI
space and orientation before doing anything else, even when you are not going to
normalize them. Some scanners or conversion tools may already take care of this,
but it is wise to check anyway. In order to do this, use display to view a canonical
T1 image (e.g. located in the “canonical” subdirectory of the SPM12 directory, called
“single_subj_T1.nii”) and set the “mm” field under the “Crosshair position” to “0 0 0”
to move the crosshair to the origin of the coordinate system. Note the orientation of
the scan and its origin (the anterior commissure). Then display your own scans, set
the crosshair to “0 0 0” again, and use the right, forward, up, pitch, roll, and yaw
settings to manipulate your scan until you are satisfied with the result (note: rotations are given in radials; 180 degrees is 3.1416 radials). Do not forget to leave the
crosshair at mm 0,0,0 to be able to see where your origin is.
When you made adjustments to your scan, you
can apply these changes to any scan you like.
Any scan that is to be reoriented has to be
entered here, including the one you worked on.
You can add files by clicking on them. Lists of
files can be selected by specifying a filter (e.g.,
“^funx”) to show only the files you want, and
subsequently adding all of them by right-clicking in the box containing the names (“select
all”). Lists of selected files can also be edited
by clicking “Ed”.
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Check Reg
The Check Registration function can be used to check if two or more scans are
matched onto each other. This check should be performed after each step of spatial
pre-processing.
Select all the files you want to compare
before pressing “done”. (see page 2 for
explanation)
Click in the scans at different positions to see if all locations in the
scans match.
Here you see an MNI space
canonical (upper left), an individual
T1 anatomical image (upper right),
and an example functional image
(bottom left).
Note that the individual T1 and
functional scans are in the same
orientation as the MNI space
canonical. However, the origins
(where blue lines cross) do not
match. When at least the orientations match, SPM will mostly be
able to align the images using
automated functions (see next
part), but be sure to check.
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MRIcron
MRIcron is another free program. It is not part of SPM and does not run within
Matlab like SPM. MRIcron is oriented towards visualization of statistical results and
is more flexible for this purpose than SPM. The two can be used alongside. For
download and manual, see http://www.cabiatl.com/mricro/mricron/index.html (note:
do not use the older “MRIcro” in combination with SPM12 because it does not handle the NIFTI file format properly). This is an example of an individual T1 in mricron:
To visualize your group-statistics results using MRIcron, load an anatomical background image from the “open templates” submenu in the “file” drop-down menu.
Next, pick “add” from the “overlay” menu to load your statistical results (e.g., files
called “spmT_000n.img”).
The resulting image will look strange until you set the threshold properly. You can
do this by altering the values in the middle two boxes (here set to 3.68 and 13).
This means that all statistical (T) values above 3.68 will be plotted onto the image,
and that value of 8 or higher will yield the brightest color. Thus, in MRIcron you
have to set threshold levels to the appropriate values manually (e.g., choose the
ones that SPM has determined for you).
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Spatial pre-processing functions
The goals of spatial pre-processing are:
1. To match all scans of an individual subject.
2. To match scans of all subjects into standard space.
The most important tools are: realign (and unwarp), slice timing
correction, coregister, normalize, and smooth. These are
described below.
Realign
Realign is the most basic function to match images. It uses a rigid body transformation to manipulate the scans. This means that it allows only translations (moving the
image in X, Y, or Z direction) and rotations (over the X, Y, and Z axis). By trial and
error it tries to find the manipulation that minimizes the difference between two
scans. The cost function that is minimized is the sum of squared differences
between the two scans. As a consequence, it can only be used within modalities,
i.e., on scans that have been acquired with the same pulse sequence. It is mostly
used to correct for motion of the subject during the functional scans (hence the
name realign). Realignment results in changes to the (affine) transformation that is
incorporated into your “.nii” files. You can also “reslice” these images into new files
containing altered (interpolated) bitmaps.
Translations
Rotations
In the SPM12 main window, under Realign, use the
drop down menu to select:
Estimate: determine parameters for rigid body
transformation and incorporate these changes into
the “.nii” file.
Reslice: create new bitmap image files (an r will be
added at the beginning of the filename).
Estimate and reslice: do both in one go.
You are now taken to SPM12’s batch manager.
Reslicing after every transformation is not always
necessary and can reduce quality. However, reslicing is necessary before starting
statistical analysis.
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Suppose you chose estimate and reslice. You
can now see that a job called “Realign:
Estimate & Reslice” is shown in the module list
in the left panel. Note that you can add more
modules to create a batch using the menu bar
(mainly under "SPM").
One thing needs to be specified, namely, the
images that need to be realigned. Note that initially, “<-X” is shown to the right of the item
“Data”. This means that fields within this item
still need to be completed. This is not necessary for the other options, which contain settings that are taken from the defaults, but may
be altered here. For instance, you can change
the letter that is prepended to your filenames
after reslicing under "filename prefix".
When you double-click “Data”, you get the
option to add sessions to your “Data” item.
This can be used when multiple time-series of
a single subject were scanned. Subsequently,
you can specify “.nii” files for each session by
double clicking “Select Files”. You then get to
the file browser dialog box that you should now
be familiar with (or see page 2). Browse to
your files, and use the filter or the mouse to
select the right images; right-click to select all.
You can edit lists of entered files using “Ed”.
Unwarp
EPI-based functional pulse sequences may exhibit strong spatial
distortions around air-filled cavities in the head which are caused
by inhomogeneities in the magnetic field. Unwarp can be used 1)
to correct the resulting static deformations based on a B0 field
map and 2) correct changes in these deformations that occur
because of movement. Such deformations can be thought of as
comparable to moving up and down in front of a funny mirror.
Thus, not only the position, but also the shape of the volume changes as a function
of time. When these distortions are present, realignment using rigid body transformations as described above is insufficient to remove motion artifacts. When you
choose unwarping, these deformations are accounted for by (un)warping the images so that they match onto each other again. Note, however, that you should only
use unwarping when there is reason to believe that your scans are warped in the
first place. The default option in SPM12 is to unwarp only with respect to pitch and
roll movements, which correspond to nodding yes and no, respectively. These are
the most common types of movement inside a scanner.
SPM12 starters guide - Erno Hermans - 10
Slice Timing
Most functional pulse sequences (like 2D EPI, as opposed to three dimensional
pulse sequences such as 3D EPI or PRESTO) do not acquire every slice in a volume at the same moment. Because the time it takes to scan a volume is typically in
the order of seconds, substantial timing differences can occur as a function of
acquisition order of the slices within a scan. In fMRI models where timing is an
important factor (i.e., in event related designs), this timing difference should therefore be taken into account. SPM solves this problem by allowing the opportunity to
correct (i.e., equalize) the timing of your functional series.
In the batch editor window you now
get the options for “Slice Timing”.
The first field that needs to be completed is the “Data” field. This works
exactly the same as specifying filenames for realignment. You can also
use multiple sessions here.
The second field can be completed by
first clicking “Number of Slices” and
then “Edit value”. Here you must enter
the number of EPI slices you
acquired.
The third field will ask you for the (volume) TR, i.e., the acquisition duration of each of your functional scans. Enter this
value in seconds.
In the next field, TA, you have to enter the time between acquisition of the first slice
and the last slice. As stated, this is usually given by:
TR - (TR / NumberofSlices)
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Here you are asked to enter the Slice Order of you scans. You will have to make
absolutely sure what this order was by checking the settings during scanning. Two
types are common:
Ascending or descending: Slices were
acquired one by one from top to bottom or the other way around. In the
slice order field, enter:
[1:30]: for 30 slices, bottom to top.
[30:-1:1]: for 30 slices, top to bottom.
(note: these are Matlab expressions)
Interleaved: In interleaved EPI scans,
first all even-numbered slices are
acquired, after which odd-numbered
slices are done (or reversed).
These orders can be entered as:
[2:2:30,1:2:29]: ascending, even first
[29:-2:1,30:-2:2]: descending, odd first.
You can check these Matlab expressions by typing them in the Matlab command
window first. Alternatively, you can enter any order manually: [1,3,5,7,2,4,6,8]:
ascending, odd first.
Finally, enter the reference slice. This is the slice that the others are corrected to.
Most people will opt for the slice that was acquired halfway the scan. This way you
minimize the timing corrections that are made to your data.
Optionally, you can alter the prefix that is prepended to your file names after slice
timing correction by altering the Filename Prefix".
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Coregister
The Coregister function is used to match scans of different modalities. For instance,
when these two scans (T1 and T2 weighted) need to be matched:
-
=
As this example shows, minimization of the sum of squared differences will not
work because the difference is very high when the scans match. Therefore, a different cost function is used, called Mutual Information:
1
2
This is the mutual information 2D-histogram of the above two scans when perfectly
matched. For each gray level of the first scan, represented as the X-axis, the distribution of gray levels of the other scan (in the voxels that have this grey level) is
plotted vertically. Two identical images would thus result in a diagonal line from bottom left to upper right. In this case, you can see there is partly a positive relation
between the two scans (line one above) in the darker gray levels. This is because
both images have a black background, gray skin and black skull. There is also,
however, a negative relation between the grey levels in cerebrospinal fluid of the
two images (black in the left scan, white in the right scan, see line two above).
Simply said, the final degree of fit of two images is determined by the “sharpness”
of the lines in the 2D histogram. This sharpness is roughly the measure that SPM
maximizes when coregistering.
SPM12 starters guide - Erno Hermans - 13
Just like realign, coregister allows only rigid body transformations (translations and
rotations) by default.
Translations
Rotations
In the main window drop down
menu under coregistration, select:
Estimate: To determine rigid body
transformation parameters, and
incorporate them into the “.nii” file
of the scan without actually changing the bitmap.
Reslice: To apply this transformation to a scan and create new bitmap image files.
Estimate & Reslice: both at once
For estimate, you will be asked for:
- a reference image: The scan to which another scan should be matched.
- a source image: The scan that should be manipulated onto the reference scan.
- (optionally) other images: these will undergo the same manipulation as the
source image.
Note: the other images should thus already be registered (or realigned) with the
source image.
SPM12 starters guide - Erno Hermans - 14
SPM will also allow you to
change the options taken from
default for both estimation and
reslicing. For most cases the
defaults settings will be fine.
In case you choose to reslice
during this step, you may consider changing the
“Interpolation” field to, e.g., 4th
degree B-Spline, which is slower but better than trilinear interpolation. This is especially recommended when reslicing was
not performed after realingment.
Normalize
The Normalize function is used to manipulate scans into standard stereotactic (MNI)
space. MNI space as used in SPM12 is defined by a template that was created by
nonlinear registration of 152 T1-weighted images.
The (new) default in SPM12 is to use a procedure called “unified segmentation” for
spatial normalization. This procedure combines three steps into one model: segmentation, bias correction, and spatial normalization. Segmentation (see also the
description of the Segment function below) refers to the separation of different tissue classes within an image, such as grey matter, white matter, and CSF, in anatomical scans. Bias correction is procedure for removing smoothly varying intensity
differences across images (e.g., a darker area in the middle of the brain). Spatial
normalization is achieved by generating “deformation fields”. This is an example of
the deformation fields for spatial normalization of a T1-weighted scan:
T1-weighted scan
X deformation field Y deformation field Z deformation field
Deformation fields are images that quantify the amount of displacement for each
location in 3D space. For example, in the X deformation field above, a bright color
indicates that the location needs to be shifted to the right, while a dark color indicates a shift to the left. Thus, a dark to bright gradient across the plane indicated in
red quantifies an enlargement (zoom) across the left-right direction in the coronal
plane. Once these deformation fields have been generated, they can be applied to
other images of different modalities (e.g., the functional scans), as long as these
have previously been coregistered with the anatomical image of that subject.
SPM12 starters guide - Erno Hermans - 15
In the main window, use the drop
down menu under normalise to
select:
Estimate: To calculate the deformation fields for spatial normalization and save them for later
use.
Write: Similar to “reslice”, apply
deformation fields that have
been determined previously to
specific scans, and make new
bitmaps. This creates new files
with “w” (or other optional prefix)
before the original filename.
Mostly, both are done at once
using Estimate and Write.
Suppose you choose
Estimate&Write. The following
fields need to be completed:
First go to data, add a subject,
then click “Image to align” and
specify the file that is to be used
for calculating the deformation
fields for matching onto MNI
space. These can subsequently
be applied to other images.
Usually, the highest quality
image is used for this.
If you wish, you can use a
“Source weighting image” in
order to mask lesions in damaged brains.
Next, you need to specify which images should be resliced after applying the transformation ("Images to write"). This will create new bitmap images with a “w” (or
optional other prefix selected in the writing options) before the original filename.
Next, take a look at the “estimation options”. The default settings usually give optimal results. However, problems in normalization may arise, for instance, in case of
strong smooth variation of signal intensity across an image ("bias"). In such case,
one may try to decrease the bias regularisation setting.
SPM12 starters guide - Erno Hermans - 16
Before starting normalization,
you may consider changing
some of the default "writing"
options. One important setting is
the “bounding box”. This setting
defines the portion of MNI space
that is to be incorporated in your
new files. The default setting
contains the entire brain, so if
you did not scan the entire brain
you may choose to use a smaller
bounding box (by looking at an
MNI space template).
Bounding boxes are specified with two rows and three columns of numbers. The
two rows define the starts and ends, respectively, and columns stand for dimensions in X (left-right), Y (posterior-anterior), and Z (bottom to top) directions.
Next, you may alter the voxel size of your new scans. By default, SPM will reslice
your new normalized images at a voxel size of 2*2*2 mm. In some circumstances, it
is advisable to change this setting to your original voxel size or the closest round
number. Smaller voxels will substantially increase the size of your dataset and the
time required for statistical calculations, but may be beneficial for smoothness estimations that SPM uses for multiple comparisons corrections.
Also, make sure your voxels fit into the bounding box. For instance, 4*4*4 voxels fit
into a -80 to 80,-112 to 76, -68 to 88] bounding box, because all these boundaries
can be devided by four.
Finally, consider changing the interpolation method that is used when reslicing the
images, that is, the method that is used when calculating the voxel values of your
new MNI space images. The default option is 4th degree B-spline interpolation.
When computational time is critical, the faster trilinear interpolation could be sufficient, in particular when reslicing with 4th degree B-splines is already used after
realignment.
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Smooth
The Smooth function is used as a final step in spatial pre-processing to blur the
functional images. The reason to do this is to correct for slight remaining functional/
anatomical differences between subjects. The trade-off, however, is that you lose
resolution by smoothing. Thus, the amount of smoothing that you should use is
determined partly by the question you want answered. When you are interested in
very small structures, for instance, you should not use much smoothing.
Smoothing is achieved by averaging every voxel with a weighted sum of its neighbors, with the weighting defined by a Gaussian kernel:
The size of the Gaussian is given by its Full Width at Half Maximum (FWHM). The
larger the FWHM, the more smoothing you get. As a rule of thumb, most fMRI
researchers use a Gaussian with a FWHM that is twice (a single dimension of) the
voxel size.
First, click “images to smooth“ and
“select files” to choose the files you
want to be smoothed.
In the field below, the FWHM can be
entered. It is set to 8 mm in all directions by default.
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Segment
Segment can be used to separate tissue classes such as grey matter, white matter,
CSF, in anatomical scans. Segmenting an anatomical scan thus results in a set of
different images, which can be used, e.g., for varying purposes, such as volumetry
(voxel based morphometry). Segmentation is also used for normalization (see
above). Segmentation is achieved by using tissue probability maps, which quantify
the probability of the presence of a certain tissue type for each voxel. These look as
follows:
Grey matter White matter
CSF
Skull/bones
Soft tissue
Air
To perform segmentation, first choose
your “Data”: normally, this should be an
anatomical (T1-weighted) image. Below,
options can be set to produce different
types of output images.
Under "Warping & MRF" and "deformation fields", you can choose to generate
deformation fields for spatial normalization, and also deformation fields for
reverse normalization.
Below is an example of segmented grey
and white matter images of a single individual. These images are in native
space (i.e., in alignment with the original
T1 image of that individual).
Grey matter
White matter
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Statistical Analysis Functions
The statistical analysis functions are used to:
- Create a model of the expected BOLD signal during your
experiment using multiple regressors.
- Estimate and test the fit of this model independently in every
voxel in the brain, by specifying contrasts.
During design specification, SPM12 will allow you to set numerous options to specify your design, so make sure you have all information available. If you need information stored on your computer (e.g. onsets of stimuli), you may need to use
Matlab commands to load and / or manipulate your data. Make sure to do all this
after starting up SPM, because SPM empties the workspace at startup. Before
actually running your model specification / estimation, be sure to save everything
you have entered into a batch file. This will allow you to return to your model specification without starting from scratch when something went wrong.
The first thing you’ll need is to assign the onsets of your trials to variables for each
condition. When these are very few, such as in slow block designs, you may enter
them manually in the graphical interface of SPM12. Otherwise, make sure SPM is
running, and switch to the Matlab command window (which can be “docked” in the
main Matlab window, or have its own window). Here you can enter commands in
the Matlab programming language (which is not unlike Unix/Linux). Here are a few
important commands:
pwd
ls
cd
load trialdata
onsets=load(‘onsets.txt’)
whos
[type variable name]
[pressing arrow up]
some_variable(4,2)
some_variable(:,1)
:
:
:
:
:
Print Working Directory
List (contents of a directory)
Change Directory
Loads a Matlab data file called “trialdata.mat”
Loads an ascii file called ”onsets.txt” into a variable
called “onsets”. Works fine for single column files with
numbers.
: Shows all variables presently in
the workspace
: Shows the contents of a variable
: Copy previous command
: Shows the content of the cell at
row 4, column 2 of a variable
called “some_variable”.
: Shows contents of all rows of
column 1 of this variable
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Specify 1st-level
“Specify 1st level” (or fMRI model specification) is used for model specification at
the single subject time-series level. In the steps below, the most important options
you can set are described.
Directory: Specify the directory in which
your design specification and all related
files will be stored. It is very useful to
create a new directory for this purpose.
This way you can keep your data and
analyses separated.
Timing parameters:
The units for design indicate if you specify your design (i.e., the onsets of your
events/blocks) in either seconds or
scans. On either scale, the (start of the)
first scan will be timepoint zero (not 1!).
When choosing scans, the (start of the)
second scan will be timepoint 1, etc.
The interscan interval refers to the period between the start of subsequent
scans. Normally, this should be the (volume) TR.
If your timing is not critical, as in slower block designs, you may leave the microtime
resolution and microtime onset settings at default and skip the following.
When SPM creates a model of your expected signal, it will need to know when you
acquired your data exactly. Often, this will time point will correspond to the middle of
your interscan interval. This can be because you have adjusted the timing yourself
(using slice timing correction) to correspond to the slice that was acquired halfway
the interscan interval. Whichever you chose there, you’ll need to tell SPM.
Now you’ll be asked for the microtime resolution, or number of time bins and the
microtime onset, or the sampled bin. The number of bins can be set to correspond
with the number of EPI slices you acquired. The “microtime onset” then is the one
that the timing was adjusted to. So, if you have a 30 slice EPI dataset that has been
acquired in, e.g., interleaved mode (slice order 1,3,5,...etc, 2,4,6, ...etc), that has
been slice timing corrected to the slice halfway your interscan interval (that is, slice
#2, counting bottom-up), you have to enter 30 for microtime resolution, and 15 for
“microtime onset”. You can also leave the microtime resolution at default and estimate the right value for the onset (note that exact timing is not very crucial because
of the inherently low temporal resolution of the BOLD signal).
SPM12 starters guide - Erno Hermans - 21
Data & Design: Here, you should first click “New
Subject/Session” until you have the number of
sessions in your design. A session usually is a
repetition of an experiment within a subject. For
example, in drug studies, the drug and placebo
conditions, even though scanned on separate
days, can be entered as two sessions. An example of a three sessions design would be an
experiment that consisted of three runs, separated by two short rest periods. Make sure to never
enter scans that were acquired in separate runs
as a single session!
Within each session, you first have to specify
the scans that belong to this session using the
regular file select dialog. For “conditions”, you
need to click “New Condition” once for each
condition you want to have in your model. In the
most basic experiment possible, an on-off stimulation paradigm, the answer would be 1 (not 2),
because only one regressor is necessary to
model the difference between two conditions.
For a design with two conditions plus resting
blocks, you would need two regressors, etc.
For each “Condition”, you will need to set a
number of parameters. First, enter a name for
your condition. For “onsets”, enter the vector of
onsets for this condition. You can do two things
here:
1. Enter a comma separated vector manually,
e.g., [10,60,100,130,160]. Make sure you enter
these onsets in terms of scans if you selected
this option before.
2. Enter a name of a variable in Matlab workspace that contains a vector of numbers representing the onsets of this condition.
For durations, enter the duration(s) the events/
blocks in this condition. In block designs, this
will be the length of your blocks. For event related designs, the duration of all events can be set
to zero. If all events/blocks have the same duration, you can enter a single value. Otherwise,
enter a vector with the same length as the vector describing the onsets.
Time and Parametric modulations are explained
shortly on the next page.
SPM12 starters guide - Erno Hermans - 22
Time / parametric modulations are used to relate the size of a response in a certain
condition throughout the experiment to a third variable. This third variable can be,
e.g., be a linear downward trend (e.g., when responses are expected to habituate this is an example of a time modulation), or reaction time measurements (when,
e.g., a shorter reaction time is expected to accompany a larger BOLD response this is an example of a parametric modulation).
When you choose a time or parametric modulation for a certain condition, SPM12
will add one or more regressors to your model. Consider this example of a time
modulation:
The upper figure shows a typical block design. In the middle figure, its time modulation regressor is depicted. When you sum these two regressors, you get the lower
figure. As you can see, this models a habituating response. The more this response
habituates, the larger the contribution of the middle regressor will be. This is reflected in high parameter estimates for this regressor when you fit the model. You can
choose “time modulation” and subsequently “1st order” for the example here. In
more advanced designs, you may wish to use higher order polynomials as well. If
you want to use, e.g., reaction time as a modulator, you will need to choose “parametric modulations”, add a new “Parameter”, and enter a name and a vector of
numbers representing the reaction time per trial of that condition. Again, you may
assume a linear (i.e., first order polynomial) or higher order polynomial relation
between reaction time and response size.
SPM12 starters guide - Erno Hermans - 23
Multiple Conditions: Instead of using the conditions option described in the previous section,
you can refer to a file that contains all this information. Using this option requires some Matlab
experience and is beyond this guide. Read the
instructions at the bottom of the windows for
more information.
Regressors and Multiple regressors: Here you can add more regressors to your
model. Mostly, these will be variables that model additional variance due to causes
such as movement of the subject in the scanner. Alternatively, in connectivity analyses, regressors can be added that contain a representative time course of a certain
region.
To add a single regressor, click “regressors” and choose “new regressor”. Then
enter a name and (variable containing) a vector of numbers with the length as your
number of scans. In case you want to enter the parameters of realignment (i.e., the
movement of the subject in the scanner) into your model, it is easier to use the
“multiple regressors” option. During realignment, a file was created in the directory
of the first image that was entered into realignment. This file is named after this first
image, but starts with “rp_” and ends with “.txt”. The file contains six columns.
These represent position (not movement) of the subject in terms of X, Y, and Z displacement and X, Y, and Z axis rotation with respect to the first scan entered. If this
file has the same number of rows (scans) as the number of functional scans in this
session, you can enter the entire file right away into SPM by choosing “multiple
regressors” and pointing at this file.
If not, for instance because you have used realignment to align another scan such
as a reference scan with your functional scans, you will need to manipulate the file
in the Matlab command window. To do this, load it into Matlab workspace (e.g., type
“ rp=load(‘rp_[scan name].txt’) ”), and select the columns you need one by one
(e.g., rp_column1 = rp(1:488,1) for scan 1 through 488, column 1). Then use the
“regressor” option six times to enter these variable names (in this example, “rp_column1”).
It is generally recommended to include the realignment parameters in your model.
However, the use of realignment parameters as additional regressors is not unquestioned. One should bear in mind that not all movement-related artefacts are properly
modeled using these six parameters.
The regressors option can also be used for a simple seed region-based functional
connectivity analysis. To enter extracted data stored in a VOI file (see last paragraph of this guide), load the VOI file into matlab workspace and enter "Y" as a
value under "regressor".
SPM12 starters guide - Erno Hermans - 24
High-pass filter: Next, enter the high-pass filter
that you want to use (in order to remove low
frequencies from your signal). Filtering in SPM
works by adding a discrete cosine set to your
model. Although this set of regressors is hidden, it is fitted onto your signal together with
the rest of the model (which is orthogonalized
with respect to your filter).
This cosine set consists of any number of
cosine functions, starting with a half cosine, a
full cosine, 1.5 cosines, etc., and ending at (or
before) the cut-off frequency that you entered.
The figure to the left shows five such cosine
waves. The cut-off period in this case is calculated as: session duration in secs / 2.5 periods.
Note that, for instance, the left column is capable of filtering linear drifts out of your signal.
A lower cut-off period will yield more regressors
(each one will cost you one degree of freedom
in your statistics). Although the cost of adding
more regressors does not weigh up to the benefits of better noise filtering, you should be
careful not to include cosines at a frequency in
the range of your task. If so, task related activation can be filtered out, resulting in
decreased power. SPM12 has a default cut-off
of 128 secs. Although this may be fine for most
designs, it is worthwhile to consider lowering
the cut-off in fast event-related designs.
(More advanced users are advised to calculate
correlations between filters and task regressors
manually)
Factorial design: Using this option, you can
assign the “conditions” specified earlier to cells
in a multifactorial design. For instance, suppose
your design consists of two factors: “presentation time” (three levels, 1, 5, or 10 sec) and
“familiarity” (two levels: familiar picture versus
novel picture). This means that you need to
have entered (3*2=) 6 conditions in the conditions option above. If you have entered them as
“familiar 1sec”, “novel 1sec”, “familiar 5sec”,
“novel 5sec”, “familiar 10sec”, and “familiar 10sec”, enter “presentation time” as the
first level and familiarity as the second factor here.
Note that the factorial design option will only add contrasts testing for main and
interaction effects. You can also specify these manually in the contrast manager
(see below).
SPM12 starters guide - Erno Hermans - 25
Basis functions: As you will probably know,
neuronal activity does not translate directly to
the Blood Oxygen Level Dependent (BOLD)
signal that you measure in fMRI. Therefore, you
need a model of how the BOLD response
relates to neuronal activity. By far the most
widely used is the hemodynamic response
function (HRF). Thus, most people will choose
the upper option in the upper right panel,
namely “Canonical HRF”. The other options are
beyond the scope of this guide.
Example block model with three blocks (X axes in seconds)
Boxcar function
Convolved with HRF
When you choose one of these, all blocks or events that you have specified will be
“convolved” with the “canonical” (i.e., standardly assumed) HRF. Above you can see
an example of a block design. The upper graph shows the expected neuronal activity (on during a certain condition, off in between). Below you can see the expected
hemodynamic changes as a result of this neuronal activity. During statistical analysis, SPM will determine for every voxel in the brain to what extent it exhibits this
pattern.
It is important to understand that in fMRI (in the context of SPM analyses), the difference between event related designs and block designs is only a matter of the
width of the input function. You can easily see this by looking at the example of an
event related design on the next page. Here, neural activity is modeled as a socalled delta function which assumes a short burst of activity at the time of each
event (upper graph). Convolution with the HRF results in a model of the BOLD
response triggered by this burst (second graph).
SPM12 starters guide - Erno Hermans - 26
Example Event-related model with three events (X axes given in seconds)
Delta function
Convolved with HRF
Time Derivative
Dispersion Derivative
Of course, using convolution with the HRF makes certain assumptions about the
shape of the BOLD response. In order to introduce some freedom in shape of
responses that you can model, other regressors can be added that can alter the
shape of the HRF. To do this, click “canonical HRF”, “Model derivatives”, and
“Specify menu item”. The time derivative is used to allow subtle shifts in time (see
third graph). Mathematically, this function is the partial derivative with respect to
time of the convolved delta function.
Instead of one regressor, your signal is now modeled by a linear combination of two regressors:
b1*X+b2*Y, where X is the convolved HRF, Y is
the time derivative and b1 and b2 are the betas
(parameter estimates) that give the best fit onto
your data. On the left you can see what happens
when b2 increases: this results in a shift back in
time of your model (red dots).
You can also choose to add another regressor: the
dispersion derivative (see fourth graph above).
This is the partial derivative with respect to the duration of the pulse you have convolved with the HRF. In practice, this regressor can be used to allow alterations of
the width of a response, as you can see below: when its beta decreases, the HRF
will get wider.
Note that adding more regressors to a model
comes at a (small) cost: each regressor will
decrease the degrees of freedom in your statistical
tests with one. In block designs, timing errors in the
onsets of the blocks, or the width of the blocks,
become such a minor factor that it does not weigh
up to the cost of losing degrees of freedom.
Therefore, time and dispersion derivatives are not
useful for block designs.
SPM12 starters guide - Erno Hermans - 27
Model Interactions (Volterra): This can be used
to model interactions among trials or conditions, e.g. because a BOLD response to a stimulus of a certain condition is expected to be
larger when preceded by a trial of a certain
other condition. This option is rarely used and
will not be described in detail here.
Global Normalisation: Choose “scale” if you
want to scale each voxel value of each scan to
the global (overall) mean of that scan (thus,
equalize the global mean of all scans in your
time series).
Caution should be taken when conditions you
want to compare are expected to show large
differences across the whole brain, as in that
case, task related activation may be scaled out.
Global normalization may also cause artifacts
related to motion or other effects that cause
large scale signal changes. Most researchers
therefore agree not to use global normalisation.
Explicit mask: This option allows you to specify
a mask that is used for your analyses, which
means that the analyses can be restricted to
certain part of the brain. Example uses are:
1) An image describing where in the brain grey
matter voxels are located. Such an image can
be created using “segmentation” (see above).
2) Restricting your analyses to only the area
you are interested in, e.g., the frontal lobe. This
requires that you have an image that describes
the location of this area (which is not standard
in SPM). Statistics outside of this area will not
be calculated. Alternatively, you can use region
of interest analyses described further below.
Most people will opt to calculate all statistics
across the brain, and thus not to mask at this
stage.
SPM12 starters guide - Erno Hermans - 28
Serial correlations: In the BOLD signal that is
measured using fMRI, the measured signal is
mostly slower than the sample rate (scans/sec).
As a consequence, your data is “oversampled”,
or in other words, your subsequent datapoints
are not fully independent observations.
To make this problem more understandable,
consider this example: When you want to calculate the correlation between age and income,
and you “measure” only two people, it will be
impossible to find a statistically significant relation. You might, however, if you measure the
same people 10 times, acting as if they were
separate persons. Doing so, you wrongfully
overestimate the number of independent observations in your test.
Because regular statistics assume that your observations are independent (uncorrelated serially), a correction needs to be applied for fMRI time-series. SPM12 uses
the AR(1) (autoregression) model to achieve this and lower your resulting T value if
such correlations exist. Using this correction is necessary when you want to report
statistics from a regular single subject time-series analysis. It will, however, not
affect your parameter estimates (betas), and therefore, is not relevant for group
studies where normally (contrasts of) betas are tested across a group.
You are now ready specifying your first level model. Make sure to save all your settings into a batch file. This will allow you to re-load it and make changes without
starting from scratch again. After running the model, you will get a file called “SPM.
mat” in the directory you specified for the analysis
(see above). You will also see a screen like the
one to the left here. What you see in the middle
is the so-called design matrix. In this case, the
design is very simple and consists of one session
and one condition (stimulation vs. rest).
Columns of a design matrix represent regressors,
and rows represent scans. Values are represented by grey levels, the brighter the grey level, the
higher the value.
Here, the first regressor depicts a block design
with rest periods in between. The six regressors
next to it are the realignment parameters of that
subject. By clicking your mouse in the design
matrix you can view its values. Below it, you’ll
find a list of the choices you made during model
specification.
SPM12 starters guide - Erno Hermans - 29
Specify 2nd-level
“Specify 2nd-level” is used exclusively for group analyses. So skip this paragraph if
you are currently working on the single subject (first) level and continue to the next.
In many ways, second level analyses are less complicated than first-level analyses.
Before starting a 2nd level design specification, make sure you fulfill all of the following requirements:
1) You have preprocessed your data in such a way that all your images are now in
the same space (usually MNI space). Thus, all your subjects need to have been
normalized.
2) You have successfully specified and estimated a first-level model, and have
specified and calculated contrasts of interest for each subject. For instance, in a
design with two conditions, e.g., left versus right, you could calculate the left versus
right contrast at the first level. This would result in contrast images that contain one
value for each voxel, namely the percentage signal difference between those conditions. Now if every subject has a positive value here, you can claim that there is an
effect in this voxel across the group. As you probably know, testing if a group of
subjects score consistently different from zero can be done using a simple one
sample t-test. So the 2nd level analysis in this example would consist of a one sample t-test for every voxel in the brain. This is the most simple 2nd level statistic, but
you’ll learn that many questions can be answered using this simple 2nd level design
by calculating the proper contrasts at the first level. More complicated designs,
involving paired samples t-tests, two (independent) samples t-tests, and full factorial
ANOVAs can also be used in SPM, and follow essentially the same logic.
Below factorial design specification you
can set a number of options
Design: Here, you can enter the type of
statistical test that you want to perform
across subjects. The following tests are
available:
1) One sample t-test. This will test the
null-hypothesis of a population mean of
zero. Simply enter the contrast images
of all subjects (as “Scans”).
2) Two sample t-test. This will test the
null hypothesis of a zero difference
between the means of two separate
groups. Because usually separate
groups are involved, you may assume
independence. However, assuming
equal variance may not be warranted
when testing, e.g., patients versus control subjects.
SPM12 starters guide - Erno Hermans - 30
3) Paired t-test. This will test the null hypothesis of a zero difference between pairs
of scans, usually repetitions of a certain measurement within a subject. A paired
t-test is mathematically identical to a one-sample t-test over differences between
pairs.
4) Multiple regression. This option will allow you to test the null hypothesis of zero
correlation between your series of scans and one or more covariates (e.g., age,
questionnaire scores, etc).
5) Full factorial. This option allows you to specify a full factorial analysis of variance
with any combination of within (repeated measures) and between group factors.
First click “new factor” for each factor you want to add. Now you need to enter a
name and a number of levels. Subsequently, choose “yes” for “independence” if
your factor is a between-group factor. For within group factors like repetitions of a
certain measurement, pick “no”. For variance, you can leave the default at
“unequal” if you’re not sure you can assume that all your cells have equal variance.
Note that a full factorial model with only one two-level independent factor is actually
identical to a 2-sample t-test.
6) Flexible factorial. This option allows you to specify a factorial model in a more
flexible way, namely, with the option of not including the full set of main effects and
interactions. A detailed description of this is beyond the scope of this guide.
Covariates: Here you can enter any covariates that may be of interest or may
explain additional variance. For instance, when testing an effect across a single
group, you may want to control for factors such as age, personality characteristics,
etc., or explicitly test for effects of these.
Masking, Global calculation, and Global normalisation: Masking out voxels outside
of the brain is usually not done at this stage and will not be described here. The
other three options are used for PET.
Directory: In this field you have to enter a directory where the files will be stored
that belong to your analysis. It is advisable to separate your datafiles from your
analyses.
SPM12 starters guide - Erno Hermans - 31
Review
The review function is used to review the specifications of a design. It allows you to
view all parameters necessary to check the efficiency / accuracy of your design.
Review can be used for both first and second level models. Below, only first level
review will be shown, but options are similar for second level.
After selecting “review”, choose the SPM.
mat file that was created during design
specification in the previous step.
After clicking “design” in the window to the
left, you have three options:
Design matrix: This will show the design
matrix again, exactly as depicted on the
previous page.
Design orthogonality: When you choose
this option, you will see your design matrix
again, but now with a triangular figure
below it. This figure depicts the orthogonality of your design: the degree to which your
regressors correlate with one another
(regressors are orthogonal when they are
not correlated). Higher correlations are
depicted by darker grey-levels.
Orthogonality is an important issue,
because when two regressors are highly
correlated, they will explain almost the
same variance in your signal, which
decreases your power and causes instability in the model fit.
Mostly, however, this issue is dealt with
while constructing a design, that is, prior to
scanning. Once scanned, there is not
much you can do about it anymore. So the
message is: specify your design in SPM
already before running your first subject.
SPM12 starters guide - Erno Hermans - 32
Explore: Using explore, you can take a closer
look at individual regressors. In the graphics window you’ll see three graphs that characterize a
single condition in your design.
Upper left is the hemodynamic response model
over the total duration of the currently selected
session. Next to it, you will find the frequency
spectrum of this model. Note that the grey area
in the frequency spectrum indicate the frequencies that are being filtered out by the high-pass
filter.
Below is a depiction of the basis set (here, the
hemodynamic response function) you have chosen. Here, a six-second block is convolved with
the canonical HRF to result in this model for the
response to each block.
Estimate
Once you have specified your model and
reviewed the design, you can continue to
actually fit the entire design onto the data.
This may take some time, depending on
the size of your dataset and model.
Second-level models usually take less time
to estimate.
In the window to the left, enter the SPM.
mat file again (it is in the directory that you
specified above). For method, we assume
here that you choose “Classical”. The other
options, using Bayesian statistics, are
beyond the scope of this guide.
SPM12 starters guide - Erno Hermans - 33
Results
Now you are ready to start looking at results.
Press “results” and load your SPM.mat file
again. This will bring you into the contrast
manager, which looks like this:
Here, by selecting ‘define new contrast’, you can specify which comparisons, or
contrasts, you want to test. The index listed before the {T} corresponds to the index
of the filename in your workdirectory your results will be written to (e.g.,
spmT_0001.img for contrast #1).
Remember that, when a model is fitted, each regressor (in each voxel of the brain),
gets a beta, a parameter estimate that determines the amount of variance explained
by that regressor. By defining contrasts, you simply make combinations of these
betas. In the example above, where on/off periods of a certain condition are modeled using a single regressor, a simple [1] contrast will suffice to test whether the
beta attached to the first regressor differs from zero. In cases where you model
responses to two different conditions (vs. rest), modeled by two different regressors,
you can use a [1,-1] contrast to compare the two conditions. This will subtract the
second beta from the first before testing against zero. Another example would be to
look at the sum of these two betas (represented by a [1,1] contrast). In that case,
you will test in each voxel in the brain if it is activated during the two conditions of
the task combined.
After defining T-contrasts, the statistical test SPM will perform is a T-test. To simplify
things a little bit, this last step of the creation of a T-map is that SPM calculates T
values for the whole brain by assessing the ratio of explained variance (the contrasted betas) and unexplained variance (the amount of variance that could not be
fitted into your model).
SPM12 starters guide - Erno Hermans - 34
Now consider an event-related design with 2 canonical hrf functions and their temporal derivatives. The proper test for this situation is not a [1,1,-1,-1] contrast,
because in such a T-contrast, the betas of the HRF and temporal derivative are
summed. Thus, when you have a high positive beta for the HRF and an equally
negative beta for the temporal derivative (i.e., a shift forward in time), the sum of
these two can be zero, which means that you will get a T value of zero. Therefore,
you need a test that can assess whether any of two betas, regardless of directions,
explains a significant amount of the variance. Such a test is provided by the
F-contrasts. Using these contrasts, you simultaneously specify a number of
T-contrasts (which are entered in different rows). The test subsequently assesses if
this contrast as a whole explains a significant amount of variance.
To give an example: if your design contains two
event-related conditions, and both have a temporal
derivative, the proper F contrast to compare these
two conditions is (like in the figure):
[1,0,-1,0]
[0,1,0,-1]
The first row of this contrast compares the canonical
hrf’s, and the second the temporal derivatives. The
resulting F value represents the combination of
these two.
After you are finished specifying contrasts, you can
proceed to have SPM calculate the T or F-maps, by
selecting one and pressing OK and ‘done’.
Now you can select if you want to mask your
contrast, which means that you can use another
contrast to restrict the areas in which you perform your test to those that are significant in
another contrast. If you choose to do so, you will
be asked for a contrast and an uncorrected p
level threshold for your mask (see below for
thresholding). Finally it will ask if you want to perform your new test only inside (inclusive) or outside (exclusive) of this masking area.
In the remainder it is assumed that you do not
use masking.
SPM12 starters guide - Erno Hermans - 35
The next step is to determine the threshold you are
going to use. This question is related to the socalled problem of multiple comparisons. Normally,
in statistical testing, an alpha level of .05 is used,
which means that you regard a result as significant
when there is a chance of less then 5 percent that
your effect occurs merely by chance. In fMRI however, you often perform 20.000 tests all at once.
Using the alpha of .05, this means that an expected 20000*.05=1000 voxels will show a "significant"
result. The solution to this problem is to lower your
alpha. There are, however, different approaches as
to what a proper correction is. The simplest solution is to divide your alpha by your number of tests, a procedure referred to as a
Bonferroni correction. In SPM, you can use this correction by answering "none"
here. Subsequently, you will be asked for a p threshold. As mentioned above, you
can calculate your threshold by dividing .05 by the number of voxels you are testing. The Bonferroni correction, however, is an overly conservative correction (i.e.,
biased towards misses instead of false positives). The reason for this is that
Bonferroni assumes that all your tests are independent, while in reality they are not.
This means that while you may be performing 20.000 T-tests, you do not actually
have 20.000 independent possibilities to find an effect, because your images are
spatially correlated. In other words, you are often testing almost the same thing.
Therefore, SPM has implemented a different correction that is based on so-called
random field theory to estimate the true number of independent tests, and correct
the threshold accordingly (an exact explanation of random field theory is beyond
this guide). In order to use this correction, select 'FWE’ (family wise error) here, and
subsequently enter the corrected alpha (where you can enter the normal alpha of
.05, because it's now corrected). This alpha of .05 now means that you allow a five
percent chance to identify a single voxel across the brain as being activated while it
is actually not.
Another method to solve the problem of multiple comparisons is to simply restrict
the number of tests that you perform. If, for instance, you have a specific hypothesis
about a certain small brain area, it does not make sense to correct for the whole
brain (doing so would increase the risk of type II error - or false negatives). In these
cases, a more liberal uncorrected p threshold can be chosen here (e.g., p < .001,
uncorrected for multiple comparisons, depending on the size of the hypothesized
region). Subsequently, regional hypotheses can be tested from within the next window (see below). To do so, choose “none” in this window, and subsequently enter
the requested threshold.
SPM12 starters guide - Erno Hermans - 36
After entering either a corrected or uncorrected alpha or t threshold, you can enter
an extent threshold. This means that you can restrict your results to clusters of voxels of at least the specified size (in number of voxels). The default is option here is
zero, which allows single activated voxels to be detected. After this, you will finally
see your results, for instance:
Above, you see a so-called glass brain, arguably not the best way to visualize your
results, but after some practice you'll get used to it. The activation shown here is
located in the temporal lobe. Note that only activation that exceeds your threshold is
drawn. You can navigate the glass brain dragging and dropping the little red arrow
or right-clicking in it. In this way you can select a cluster or voxel you are interested
in. Next to the glass brain, your design matrix will appear, above which the chosen
contrast is depicted graphically (here simply [1,0,0,0,0,0,0,0]).
Also, you get a window such as the one here
on the left.
In the section p-values you can click whole
brain or current cluster to see statistical information on all clusters in the brain, or the chosen cluster, respectively.
SPM12 starters guide - Erno Hermans - 37
When you click "whole brain", you will see a table like this one:
The following information can be found here (starting with the bottom table):
Height threshold: The p-level that you have chosen as uncorrected alpha, or that
has been calculated by SPM as a corrected one. The T-threshold is the T value in a
T distribution with your number of degrees of freedom that corresponds to the given
p-value. Statistical images such as shown in the glass brain usually consist of T (or
F) values. You will therefore only see T values above your T threshold.
Extent threshold: The minimum number of adjacent supra-threshold voxels.
Degrees of freedom: Normally, the degrees of freedom in a general linear model
are calculated as the number of observations minus the number of regressors in the
model.
Smoothness FWHM: Only relevant if you have chosen corrected alphas. This is an
estimation of the smoothness of you statistical image, from which SPM calculates
the size of ResEls (resolution elements) and the true number of independent statistical tests.
Finally, you'll see the total volume, number of voxels and resels, and voxel size.
In the table above, a summary is shown of all statistics.
In fMRI research papers, mostly voxel-level statistics are reported. The values you
see here are p levels that are corrected (left) or uncorrected (right) for multiple comparisons. As explained above, the p value depends upon the T level and the number of degrees of freedom. The Z value shown here, however, is directly derived
from the p value. This value is often reported as it is easier (than T values) to interpret across studies with widely varying degrees of freedom.
SPM12 starters guide - Erno Hermans - 38
In many cases, a researcher will not perform a blind search across the entire brain
to identify areas that respond to a certain task, but will aim to test a specific hypothesis about the regional response in a certain a priori region of interest (ROI). In
those cases, it is appropriate to apply a small volume correction, i.e., a correction
that is restriced to the multiple tests performed within the ROI. Suppose, in the current example, we wished to test the hypothesis that the inferior frontal gyrus would
respond to the auditory stimulation paradigm. We now first need a definition of this
region of interest. A proper way to do this would be to create an anatomical mask
consisting of this region, which can, for instance, be made using an SPM add-on
toolbox called the WFU pickatlas. This would work as follows. After selecting "toolboxes" and "wfu pickatlas" from the SPM12 main window (if installed), double click
"TD labels" in the panel shown on the left
here. Now select the inferior frontal gyrus,
click "add" in the upper middle, and then
click "save mask" to save the ROI mask.
Now use "check reg" (see visualization,
above) to check whether your mask
matches your ROI on an MNI-space
image.
There are also other methods to define
ROIs. Some examples are masks based
on results of an independent statistical
contrast or ROIs defined as a spherical
search region around peak coordinates
previously reported in literature.
To view results within your ROI, select your contrast,
and use a liberal initial threshold (e.g., P < .001). In
the window show to the left, now select "small volume". You can then select "image" and continue to
select your ROI mask image, and click "done". The
table with statistics (bottom left) now contains only
voxels that are significant after correcting for your
search region (note that they need to reach your initial threshold as well). The peak voxel in IFG is significant when correcting for IFG, even though it did
not reach whole-brain correction.
Alternatively, you may use a sphere (or
box) to define your ROI. To do this, enter
your hypothesized coordinates into the X,
Y, and Z boxes at the bottom of the middle
left window. Then click "small volume",
select "sphere", and enter a radius for the
sphere to be used for small volume correction. The size of the sphere needs to chosen considering the size of the hypothesized region and the amount of smoothing
applied to the images. Again, any results
now shown in the table are corrected only
for the spherical ROI.
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The SPM results window also offers various
possibilities for data visualization and extraction. The two most important options will be
described here.
Data visualization: If you want to visualize
your results spatially, in order to facilitate
anatomical localization of activity, you can
click “overlays” and select, for instance, “slices”. You will then be asked to select an
image for rendering on, which may be any
anatomical image, provided that it is in the
same stereotactic space as your T-map.
When you have normalized your functional
images during spatial pre-processing, you
can project the activity onto any MNI space
image, yielding nice pictures such as the
ones below. You can also use MRIcron (see
above) for this type of visualization.
SPM12 can also visualize your data temporally, which means that you can plot the
changes in BOLD signal over time. For this, you can use single voxels or clusters of
voxels.
When you click “plot” in the visualization section, you can choose various options for visualization of your activity. Now, choosing “fitted
responses” (followed by the contrast of your
interest) will allow you to view the changes in
activation throughout the whole session.
Choose “adjusted” to view the fit of your entire
model, and plot it against “scan or time”.
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Ideally, the result will look somewhat like this. Note that the grey
line does not represent the actual
signal that was measured, but the
entire fitted model, including the
contribution of other regressors
such as high pass filters and
realignment parameters. Although
this may look like a realistic BOLD
signal, the reality of the BOLD signal is very noisy, as you can see
from the dotted line in the background.
The option shown here is more useful for block designs. For event related designs,
choose “event related responses” and “fitted response and PSTH” to view fitted
BOLD responses to your stimuli.
When you choose to use F-contrasts for your comparisons, note that F values have
no sign, i.e., they are positive regardless of the direction of your effect. Thus, when
you find a statistically significant effect in a certain voxel, it may be useful to view
the directions of the actual effects. To do
this, go to the voxel you are interested
in, a then press “plot” and select “contrast estimates and 90%CI” from the
drop-down menu. You are then asked
for a contrast. If you choose “effects of
interest” here, you’ll get to see the
parameter estimates for all regressors
separately. One the left is an example
with only two.
Data extraction: SPM12 also allows you to
extract data from an activated voxel or cluster
of voxels. This can be done by clicking the
"eigenvariate" button in the window shown on
the left. First, specify a name for the region to
extract data from. Then specify an adjustment
contrast. This allows you to remove task-related (or other) effects from the extracted time
series data. Finally, you can select how to
define the region: either using a sphere, a
box, a cluster, or a mask. Select cluster to
extract data from a cluster of significantly activated voxels.
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In the graphics window, you will
now see the extracted time
series from the selected cluster.
Note that this time series is not
simply based on the average
time series across all voxels in
the cluster. SPM calculates the
first eigenvariate of the these
time courses, i.e., the first principle component resulting from a
principle component analysis.
SPM will now save a .mat file
into your current directory which contains this eigenvariate in a variable called "Y".
This variable can, for instance, subsequently be used perform connectivity analyses
such as simple seed region functional connectvity analyses. To do such an analysis,
simply enter the time course in the Y variable as an additional regressor into a firstlevel statistical analysis.
Extracted data can also be used for more complicated (effective) connectivity analyses, for instance, analyses that assess interactions between a task and interregional connectivity. Such models are called "psycho-physiological interaction" models.
Similar procedures are also used for dynamic causal modelling. These models,
however, are beyond the scope of this guide.
The SPM results window offers various other possibilities which needed to be left
undescribed here. Hopefully, the topics that have been described in this guide will
get you started with doing your first analyses!
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