Even after realignment there is considerable variance in fMRI time series that covary with, and is most probably caused by, subject movements. It is also the case that this variance is typically large compared to experimentally induced variance. Anyone interested can include the estimated movement parameters as covariates in the design matrix, and take a look at an F-contrast encompassing those columns. It can be quite dramatic. The result is loss of sensitivity, and if movements are correlated to tas specificity. I.e. we may mistake movement induced variance for true activations. Because the movement induced variance is often very large compared to "true" activations false positives may ensue even from a relatively modest correlation between task and movement.
The problem is well known, and several solutions have been suggested. A quite pragmatic (and conservative) solution is to include the estimated movement parameters (and possibly squared) as covariates in the design matrix. Since we typically have loads of degrees of freedom in fMRI we can usually afford this. The problem occurs when movements are correlated with the task, since the strategy above will discard "good" and "bad" variance alike.
The "covariate" strategy described above was predicated on a model where variance was assumed to be caused by "spin history" effects, but will work pretty much equally good/bad regardless of what the true underlying cause is.
Others have assumed that the residual variance is caused mainly by errors caused by the interpolation kernel in the resampling step of the realignment. One has tried to solve this through higher order resampling (huge Sinc kernels, or k-space resampling).
The "Unwarp" toolbox is based on a different hypothesis regarding the residual variance. EPI images are not particularly faithful reproductions of the object, and in particular there are severe geometric distortions in regions where there is an air-tissue interface (e.g. orbitofronal cortex and the anterior medial temporal lobes). In these areas in particular the observed image is a severely warped version of reality, much like a funny mirror at a fair ground. When one moves in front of such a mirror ones image will distort in different ways and ones head may change from very elongated to seriously flattened. If we were to take digital snapshots of the reflection at these different positions it is rather obvious that realignment will not suffice to bring them into a common space.
The situation is similar with EPI images, and an image collected for a given subject position will not be identical to that collected at another. Hence, even after a "successful" realignment there will be residual variance caused by the object having different shape at different time points. We call this effect susceptibility-by-movement interaction. "Unwarp" is predicated on the assumption that the susceptibility-by-movement interaction is responsible for a sizeable part of residual movem nt related variance.
Assume that we know how the deformations change when the subject changes position (i.e. we know the derivatives of the deformations with respect to subject position). That means that for a given time series and a given set of subject movements we should be able to predict the "shape changes" in the object and the ensuing variance in the time series. It also means that, in principle, we should be able to formulate the inverse problem, i.e. given the observed variance (after realignment) and known (esti ated) movements we should be able to estimate how deformations change with subject movement.
We have made an attempt at formulating such an inverse model, and at solving for the "derivative fields". A deformation field can be thought of as little vectors at each position in space showing how that particular location has been deflected. A "derivative field" is then the rate of change of those vectors with respect to subject movement. Given these "derivative fields" we should be able to remove the variance caused by the susceptibility-by-movement interaction. Since the underlying model is so re tricted we would also expect experimentally induced variance to be preserved. Our experiments have also shown this to be true. Indeed one particular experiment even indicated that in some cases the method will reintroduce experimental variance that had been obliterated by movement related variance.
In theory it should be possible to estimate also the "static" deformation field, yielding an unwarped (to some true geometry) version of the time series. In practice that doesn't really seem to work, hence the method deals only with residual movement related variance induced by the susceptibility-by-movement interaction.
This means that the time-series will be undistorted to some "average distortion" state rather than to the true geometry. If one wants additionally to address the issue of anatomical fidelity one should combine Unwarp with a measured field-map. A field-map in the format that Unwarp expects can be created using the FieldMap toolbox.
The description above can be thought of in terms of a Taylor expansion of the field as a function of subject movement. Unwarp alone will estimate the first (and optionally second, see below) order terms of this expansion. It cannot estimate the zeroth order term (the distortions common to all scans in the time series) since that doesn't introduce (almost) any variance in the time series. The measured fieldmap takes the role of the zeroth order term. Refer to the FieldMap toolbox and the documents FieldMap.man and FieldMap_principles.man for a description of how to obtain fieldmaps in the format expected by Unwarp.
If we think of the field as a function of subject movement it should in principle be a function of six variables since rigid body movement has six degrees of freedom. However, the physics of the problem tells us that the field should not depend on translations nor on rotation in a plane perpendicular to the magnetic flux. Hence it should in principle be sufficient to model the field as a function of out-of-plane rotations (i.e. pitch and roll). One can object to this in terms of the effects of shimming (object no longer immersed in a homogenous field) that introduces a dependence on all movement parameters. In addition SPM/Unwarp cannot really tell if the transversal slices it is being passed are really perpendicular to the flux or not. In practice it turns out thought that it is never (at least we haven't seen any case) necessarry to include more than Pitch and Roll. This is probably because the individual movement parameters are typically highly correlated anyway, which in turn is probably because most heads that we scan are attached to a neck around which rotations occurr.
On the subject of Taylor expansion we should mention that there is the option to use a second-order expansion (through the defaults) interface. This implies estimating also the rate-of-change w.r.t. to some movement parameter of the rate-of-change of the field w.r.t. some movement parameter (colloquially known as a second derivative). It can be quite intresting to watch (and it is amazing that it is possible) but rarely helpful/necessarry.
In addition to inducing residual (after realignment) movement-related variance, movement-by-susceptibility distortion changes may bias the estimation of the movement. For each iteration Unwarp gets a better idea of the true shape of each scan, and can potentially get a better estimate of movement. In Unwarp there is an option (default) to re-estimate (do a new realign) the movements between each iteration of estimating the fields. Our testing indicates that this is a good idea.
In the defaults there is also an option to include Jacobian intensity modulation when estimating the fields. "Jacobian intensity modulation" refers to the dilution/concentration of intensity that ensue as a consequence of the distortions. Think of a semi-transparent coloured rubber sheet that you hold against a white background. If you stretch a part of the sheet (induce distortions) you will see the colour fading in that particular area (you can also think of future appearance of a tattoo obtained when young and slim). In theory it is a brilliant idea to include also these effects when estimating the field (see e.g. Andersson et al, NeuroImage 20:870-888). In practice for this specific problem it is NOT a good idea.
It should be noted that this is a method intended to correct data afflicted by a particular problem. If there is little movement in your data to begin with this method will do you no good. If on the other hand there is appreciable movement in your data (>1mm or >1deg) it will remove some of that unwanted variance. If, in addition, movements are task related it will do so without removing all your "true" activations.
The method attempts to minimise total (across the image volume) variance in the data set. It should be realised that while (for small movements) a rather limited portion of the total variance is removed, the susceptibility-by-movement interaction effects are quite localised to "problem" areas. Hence, for a subset of voxels in e.g. frontal-medial and orbitofronal cortices and parts of the temporal lobes the reduction can be quite dramatic (>90%).
It should also be noted that there are several reasons for residual movement related variance. Notably:
Unwarp will deal only with the first of these, which means that even after that correction the other components will remain. It is difficult to say which of these effetcs dominate. It will depend on ones scanner, and even on the specific data set.
Friston KJ, Williams SR, Howard R, Frackowiak RSJ and Turner R (1995) Movement-related effect in fMRI time-series. Magn Reson Med 35:346-355
Jezzard P and Balaban RS (1995) Correction for geometric distortions in echoplanar images from B0 field variations. Magn Reson Med 34:65-73
Wu DH, Lewin JS and Duerk JL (1997) Inadequacy of motion correction algorithms in functional MRI: Role of susceptibility-induced artefacts. J Magn Reson Imag 7:365-370
The software was developed in part while JLRA was supported by STINT (Stiftelsen for internationalisering av högre utbildning och forskning.)
This software has been tested by the authors on data collected on two different make MR scanners. Undoubtedly there are numerous bugs that will surface when more people start using it. We are grateful for bug reports, and other input, and the more we receive the faster bugs will be weeded out.
Unwarp Toolbox for SPM5 can be downloaded with the latest version of SPM5
Since Unwarp is a part of main SPM2 this distribution will (eventually, when bugs are fewer) be part of the general SPM updates. Meanwhile we recommend the following method for installing the update.