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iPlan® BOLD MRI MAPPING Clinical White Paper

OVERVIEW With iPlan BOLD MRI Mapping, anatomical images are enhanced with functional maps showing areas of the brain that are responsible for important motoric or cognitive functions. iPlan BOLD MRI Mapping can be easily combined with iPlan FiberTracking to provide a powerful and comprehensive functional package with information about vital functional areas and significant white matter structures. References [1-2] describe application of the software for patient treatment.

INTRODUCTION The basis of BOLD MRI Mapping is the BOLD (“Blood oxygen level dependent”) effect, which is caused by the patient performing different motoric or cognitive tasks in the MR Scanner during a functional experiment. Thereby, induced activation leads to complex local changes in the relative blood oxygenation and changes in the local cerebral blood flow. As the MR signal of blood varies slightly depending on the level of oxygenation, the BOLD effect can then be visualized using appropriate MR scanning sequences. In order to better distinguish the variations in the brain activity

(BOLD signal changes), the scanning procedure requires a high number of scan repetitions. With iPlan BOLD MRI Mapping the correlation between expected and measured brain response to the stimulation is calculated and displayed as activation overlays onto the corresponding anatomical images.

Figure 1: Correlation

between an activated

voxel's time series (white)

and the Gauss-modelled

hemodynamic response

(pink). The background

shows the underlying

boxcar-model.

Figure 2: Correlation

between an activated

voxel's time series (white)

and the Gamma-modelled

hemodynamic response

(yellow). The background

shows the underlying

boxcar-model.

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TECHNICAL DESCRIPTION IMPORT BOLD MRI DATA Prior to the analysis, BOLD MRI DICOM data has to be imported and sorted according to the time course of the functional experiment. This can be done with the Load & Import task in iPlan. Currently Philips, GE and Siemens scanners are supported. During data import, it is possible to apply three different pre-processing steps: smoothing, slice time correction or motion correction. The smoothing option uses a two-dimensional 3x3 Gaussian kernel. To reduce signal artefacts caused by slightly different slice acquistion times, Slice Time Correction can be used. Apart from standard ascending / descending acquisition order, the slices are also often acquired in an interleaved order (H >> F or F >> H) to avoid "cross talk" effects between adjacent slices. Motion Correction is based on a rigid Mutual Information algorithm (see reference [3] for more technical details). Assuming that the structures in the image sets behave like a rigid body, six transformation parameters (three degrees of freedom (x, y, z) for translation and rotation, resp.) have to be determined in order to realign the image sets successfully. For each voxel in the reference series (1st image set), the position of the corresponding voxel in the 2nd image set is calculated. A similarity measure is then computed from the sequence of all obtained voxel pairs. To control the Motion Correction results and the data quality, the transformation parameters are visualized both in the import step and in the BOLD MRI Mapping task. ANALYSIS OF THE BOLD DICOM DATA For the BOLD MRI analysis the expected reaction of the brain to the applied experimental stimulation must be modelled. This model, also known as the Design Matrix, is then compared to the measured time series. The underlying approach is commonly known as Statistical Parametric Mapping (SPM), which is based on the usage of a General Linear Model (regression analysis). The goal of the general linear model is to explain the variation of the measured time series in terms of a linear combination of explanatory variables and an error term. The explanatory variables are also known as predictors, which predict the course of the hemodynamic response of the brain to stimulation in every voxel. This can be expressed as Y=Xβ+ε

Y is a time series of any length at a given location in the brain, which can be approximated with a linear combination of predictor time series in the Design Matrix X. X contains all effects that may have an influence on the required signal. ε is the residual error. The parameter β can then be estimated by using a ‘least squares’ approach to find the best fit. From the results of this analysis, a student t-statistic is created independently for each voxel and then displayed as a statistical map. To model the expected hemodynamic response in terms of the Design Matrix X, the user has to specify the experimental paradigm with a boxcar function. The software then does an iterative optimization to obtain a more realistic representation for the hemodynamic response in the brain, which is used to calculate the actual statistical map. To initally better approximate the hemodynamic response of the brain, it is possible to convolute the simple boxcar-model with a so-called hemodynamic response function. Currently two functions can be applied: a multi-parametric Gauss model (Figure 1) and a Gamma model (Figure 2). REFERENCES [1] James L. Leach & Scott K. Holland: Functional MRI in children: clinical and research applications, Pediatr.Radiol., Vol. 40: 31–49, 2010.

[2] Thomas Gasser,T Oliver Ganslandt, Erol Sandalcioglu, Dietmar Stolke, Rudolf Fahlbusch, Christopher Nimsky: Intraoperative functional MRI: Implementation and preliminary experience, NeuroImage, Vol.26: 685– 693, 2005

[3] R.S.J. Frackowiak, K.J. Friston, C. Frith, R. Dolan, K.J. Friston, C.J. Price, S. Zeki, J. Ashburner, W.D. Penny, editors, Human Brain Function, Academic Press, 2nd edition, 2003.

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