Learn more: https://www.brainlab.com/fibertracking-software 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.

1. 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.
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.
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.
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2. 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 preprocessing 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.
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 multiparametric Gauss model (Figure 1) and a Gamma
model (Figure 2).
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
REFERENCES
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James L. Leach & Scott K. Holland: Functional
MRI in children: clinical and research applications,
[1]
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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