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376 15 Imaging Blood Circulation Using Nuclear Magnetic Resonance
Stimulation setup
Projector
Screen
Mirror
Keyboard
PC
Head con
Head con
Magnet
Magnet
Figure 15.10 Schematic drawing of an fMRI experiment.
15.4.2.2 fMRI Analysis
Data acquired from an fMRI session generally takes the form of a time series
of three-dimensional matrices. Within these matrices, each value – known as a
‘‘voxel’’ – corresponds to the BOLD signal acquired from a small cuboid region
of space at a point in time. The number of data points resulting from a single
participant’s session can therefore be in the order of millions and requires a lengthy
and computationally intensive analysis.
A typical analysis includes preprocessing steps aimed at transforming data into
a standard format and detecting and removing imaging artifacts. This includes
a slice-timing correction (data are acquired sequentially in slices, so the top slice
will be recorded earlier than the bottom slice) and a realignment of scans within
participants, as they move slightly during the experiment. As brain morphology
differs considerably between people, the brain of any participant needs to be
aligned to what is considered a ‘‘standard’’ brain template. (What constitutes a
good template is an issue of contention, but most researchers agree that it should
be derived by averaging the neural anatomy of many people.)
At the next stage of the analysis, regressors are generated describing the time
course of the experiment, which are then compared to the time course of the
BOLD signal from all voxels. The regressors are then combined to create a general
linear model (GLM) with conditions relating to experimental manipulation and
regressors of no interest (e.g., movement parameters). The GLM allows an estimate
at each voxel of the relationship between each regressor and the BOLD signal. This
results in differences of strength of relationship between conditions for each voxel
of the brain. To obtain statistically confirmed statements about the location of
activity, the parameter estimates at each voxel are then subject to statistical tests.
The results of fMRI studies are frequently displayed as heat maps, superimposed
on an image of brain anatomy (Figure 15.11). The colors of the heat map correspond
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15.4 Blood Oxygenation Level-Dependent (BOLD) Contrast 377
x =−16 y =−8
8.0
6.0
t(7)
p < 0.0005
z =−17
Figure 15.11 Activation within the amygdala during the
reading of palatable food descriptions. The color bar indi-
cates each voxel’s result of the statistical test, in this case a
t-value. (From Piech et al. [74], their Fig. 1).
to t-values of each displayed voxel (t-values are statistical values indicating how
much the activity in a voxel is related to one experimental condition). Since
separate statistical tests are conducted for each voxel, the result must be corrected
for multiple comparisons.
To further characterize the activity of a region thought to be related to the
experimental manipulation, the raw BOLD signal in that ROI can be extracted at
specific time points. Figure 15.12 shows such an analysis. In that example, the
authors extracted signal from an area associated with reward while participants
were tasting wine. The separate plots correspond to conditions in which the same
wine is believed by the participant to be more or less expensive [66]. This study
suggests that the price of a product can influence associated BOLD activations
in regions thought to mediate the experience of reward as well as the reported
pleasure from consumption.
In the context of imaging analyses, parts of the brain can also become ROIs in
different ways. The selection can be based on functional considerations as in the
−0.5
−5
$10 wine $90 wine
Degustation onset
time (s)
x =−2 y = 48 z =−20
−2 −1
4 7 10 13 16 19
−0.4
−0.3
−0.2
−0.1
0
0.1
0.2
0.3
0.4
0.5
% signal change in mOFC
Figure 15.12 Development of activity dur-
ing the tasting of wine. In the two condi-
tions (blue and green plots in (a)), partici-
pants taste the same wine, believing that it
cost either $10 or $90. The activity signal
is extracted from an area of the medial pre-
frontal cortex (b), which is associated with
the experience of rewards. (From Plassmann
et al., [66], their Fig. 2D and E).
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378 15 Imaging Blood Circulation Using Nuclear Magnetic Resonance
wine tasting study [66] or on anatomical considerations. Studies may have a priori
hypotheses about certain regions or structures because of prior research. This can
have implications for the statistical thresholding, and acquisition of images. For
example, imaging smaller structures can allow for higher spatial and temporal
resolutions.
15.4.2.3 Limitations to Subtraction Paradigm
Using the subtraction paradigm as a basis of fMRI experimental design and
analysis has been a very useful approach, as it led to numerous insights in cognitive
neuroscience. However, the approach unquestionably has its limitations. On the
one hand, it relies on an additive model of cognitive processing, which may
not always apply. On the other hand, designing experiments with one condition
containing exactly one process more than another can be very difficult, and
increasingly so, the more complex the processes of interest get. This is not as
much a feature of fMRI research as an assumption typically made in cognitive
neuroscience. Experiments in perception are thus more suited for the subtraction
paradigm than the study of social interaction phenomena. To overcome some
of those limitations, a number of alternative approaches were developed. Those
include matching the BOLD signal to activation patterns, rather than activation
peaks, and combining data from fMRI experiments with other measures of neural
activity.
15.4.2.4 Alternative Experimental Designs
15.4.2.4.1 Multivoxel Pattern Analysis
Using the subtraction paradigm to analyze fMRI data focuses on activation ‘‘peaks.’’
That means that the approach is sensitive for finding areas that are widely activated
by a certain cognitive process. At the same time, it may overlook areas that
are activated by the same process in a specific spatial pattern. The multivoxel
pattern analysis (MVPA) approach accounts for this possibility and analyzes
spatial ‘‘shapes’’ of activity, rather than overall means, thus improving the spatial
resolution of fMRI. The approach has been successfully used to address a range of
questions, including those regarding visual processing and attention [67–69]. It is
also probably the closest that neuroscience has come to mind-reading. The authors
of one such study were able to conclude from the fMRI data (with accuracy slightly
greater than chance), which of three object categories a participant was thinking
about [70].
15.4.2.5 Analysis Based on Mathematical Models of Behavior
The subtraction paradigm points to an area that is more active in one condition than
in another, but it does not necessarily tell us what process that difference in activity
reflects, that is, what the area is busy doing while activated. Model-based fMRI is an
analysis method, whichapplies the predictionsof computational modelsof behavior
to the BOLD signal. Computational models attempt to account for behaviors by
hypothesizing mathematical operations that operate on environmental inputs and
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15.4 Blood Oxygenation Level-Dependent (BOLD) Contrast 379
mental representations to give rise to behaviors. One then correlates activation
levels with the values such a function would produce in a given trial and a given
task. This approach improves the validity of conclusions based on fMRI data, as
it allows finely distributed parametric predictions of fMRI signal and of behavior
associated with a neural process [71].
15.4.2.6 Combination with EEG and TMS
In general, some limitations of fMRI can be overcome by combining fMRI with
other neuroscientific methods. Electroencephalography (EEG) signal, which can
be acquired simultaneously with the BOLD signal in an MRI scanner, displays
excellent time resolution, while sacrificing spatial precision. This makes it a good
candidate to compliment fMRI, which is precise spatially but has poor temporal
resolution [72]. Similarly, the correlative nature of fMRI can be overcome by
combining it with transcranial magnetic stimulation (TMS). In a typical fMRI
study, the experimenter aims to manipulate some cognitive process and observes
brain activity that goes along, or correlates, with that process. On the other hand,
the relationship is not necessarily causal. One cannot be sure if the observed brain
activity is essential to the cognitive process of interest or simply coincides with
it. TMS uses the rapid application of a magnetic field to temporarily disrupt the
functioning of small areas of the cortex. If used to disrupt an area previously
identified with fMRI, it can demonstrate whether the function in question is
compromised, providing evidence for a causal relationship between the neural
activity and the cognitive process [73].
15.4.2.7 Recent Applications
The advancement and improved accessibility of fMRI technology, as well as
user-friendly analysis tools, have contributed to an increased impact of the tech-
nique. In the last decade, the questions investigated with fMRI have increasingly
pertained to areas outside classic cognitive neuroscience. Those areas include affec-
tive science, tackling questions about neural underpinnings of emotion, motivation,
and their regulation [74–76] (Figure 15.11). Within social science, fMRI is used
to investigate otherwise methodologically challenging questions such as prejudice
[77]. Social interaction can be investigated with two or more participants being
scanned simultaneously, something referred to as hyperscanning [78]. Furthermore,
questions that economists have struggled to answer with their conventional tools
can be aided by brain imaging [79, 81]. fMRI has also been used to investigate
issues pertaining to law, justice, and politics [82–84] (Figure 15.13).
In psychiatry and clinical science, fMRI has been widely used to investigate
the neural basis of behavioral deficits and abnormalities in patients. For a review,
see Linden and Fallgatter [80]. Some advances have also been made toward
clinical use of fMRI. DeCharms and colleagues [85] showed patients with chronic
pain their real-time brain activity related to pain during a scanning session and
enabled them to significantly reduce pain levels through self-control. (In analogy to
biofeedback, this techniquehas been termedneurofeedback.) Technical development
also allowed the creation of MRI scanners with particularly strong magnetic fields
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380 15 Imaging Blood Circulation Using Nuclear Magnetic Resonance
(t)
3.5
q(FDR) < 0.05
R
y = 37
8
Figure 15.13 Activity in the right dor-
solateral prefrontal cortex, correspond-
ing to participants assessing respon-
sibility for a crime. (From Buckholtz
et al. 2008, their Figure 2A).
(e.g., 7 T, compared to more standard 3 T), improving their resolution and signal
levels. Those high-field scanners for humans are, for example, able to show
the precise location of single-digit activations in the somatosensory cortex [86]
(Figure 15.14).
X=44
X=45
X=46
D4 - D2
q(FDR) < 0.001
−8.00
4.58
−4.58
8.00
t(402)
p < 0.000006
10 mm10 mm10 mm
3b
CS
A
1
Figure 15.14 This high-resolution image shows the separa-
tion of activity related to digit 2 (blue and green) and digit 4
(orange and yellow) sensation in area 3b of the somatosen-
sory cortex. (From Stringer et al. [86], their Fig. 2A).
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References 381
15.4.2.8 Limitations
Brain imaging, and especially the use of fMRI, has made an important contribution
to our understanding of brain function. Nevertheless, its usefulness and promise
are restricted by a number of limitations. Some of them are linked to technological
progress and will be overcome, while others are inherent to the technique. At the
center of the limitations is the BOLD signal, which reflects blood oxygenation and
is thus an indirect, and rather slow, index of brain activity. BOLD is also a relative,
rather than an absolute measure, so comparisons must always be made within one
experiment, rather than between experimental sessions or studies. Finally, brain
imaging studies provide information on neural correlates of cognitive processing
rather than their causes. Regarding more practical issues, MRI scanners are bulky
and costly to purchase and maintain. Given that they rely on a electrical magnet
that needs to create an incredibly strong field, this is likely to remain so in the
future.
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