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Brain Research Protocols
Protocol
Regression analysis utilizing subjective evaluation of emotional
experience in PET studies on emotions
Sargo Aaltoa,b,*, Esa Walliusc,1, Petri Naatanend, Jaana Hiltunene, Liisa Metsahonkalab,f,
Hannu Sipilab, Hasse Karlssonb,g
aDepartment of Psychology, Abo Akademi University, FIN-20500 Turku, FinlandbTurku PET Centre, University of Turku, FIN-20520 Turku, Finland
cInstitute of Signal Processing, DMI, Tampere University of Technology, Tampere, FinlanddDepartment of Psychology, University of Helsinki, Finland
eSection of Clinical Neurosciences, Finnish Institute of Occupational Health, Helsinki, FinlandfDepartment of Child Neurology, Helsinki University Hospital, Helsinki, Finland
gDepartment of Psychiatry, University of Helsinki, 00014 Helsinki, Finland
Accepted 27 June 2005
Available online 29 August 2005
Abstract
A methodological study on subject-specific regression analysis (SSRA) exploring the correlation between the neural response and the
subjective evaluation of emotional experience in eleven healthy females is presented. The target emotions, i.e., amusement and sadness, were
induced using validated film clips, regional cerebral blood flow (rCBF) was measured using positron emission tomography (PET), and the
subjective intensity of the emotional experience during the PET scanning was measured using a category ratio (CR-10) scale. Reliability
analysis of the rating data indicated that the subjects rated the intensity of their emotional experience fairly consistently on the CR-10 scale
(Cronbach alphas 0.70–0.97). A two-phase random-effects analysis was performed to ensure the generalizability and inter-study
comparability of the SSRA results. Random-effects SSRAs using Statistical non-Parametric Mapping 99 (SnPM99) showed that rCBF
correlated with the self-rated intensity of the emotional experience mainly in the brain regions that were identified in the random-effects
subtraction analyses using the same imaging data. Our results give preliminary evidence of a linear association between the neural responses
related to amusement and sadness and the self-evaluated intensity of the emotional experience in several regions involved in the emotional
response. SSRA utilizing subjective evaluation of emotional experience turned out a feasible and promising method of analysis. It allows
versatile exploration of the neurobiology of emotions and the neural correlates of actual and individual emotional experience. Thus, SSRA
might be able to catch the idiosyncratic aspects of the emotional response better than traditional subtraction analysis.
D 2005 Elsevier B.V. All rights reserved.
Theme: Neural basis of behavior
Topic: Motivation and emotion
Keywords: Subject specific regression analysis; Regression analysis; Correlation analysis; Subjective rating; Emotion; Sadness; Amusement; Statistical
parametric mapping; Positron emission tomography
1385-299X/$ - see front matter D 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.brainresprot.2005.06.003
* Corresponding author. Department of Psychology, Abo Akademi
University, 20500 Turku, Finland. Fax: +358 2 231 8191.
E-mail address: sargo.aalto@utu.fi (S. Aalto).1 Has contributed more than is typical for 2nd writer.
1. Type of research
The statistical analyses of imaging studies on emotions
have almost exclusively utilized the subtraction method (see
[30]), where two conditions, typically ‘‘activation’’ and
reference (‘‘neutral’’) states, are subtracted from each other,
which is a bipolar and fairly simplistic approach. This
15 (2005) 142 – 154
S. Aalto et al. / Brain Research Protocols 15 (2005) 142–154 143
approach is based on external validation of emotion
induction and presumes reliable coupling between the
stimulus and the response, i.e., that almost similar emotional
states can be induced in the study sample as in the validation
sample. However, even in controlled laboratory conditions,
emotional experience varies considerably between subjects
(see, e.g., [43]). Therefore, in parallel with the ‘‘traditional’’
subtraction approach, there is a need for methods of analysis
that could capture more flexibly the inherent individuality of
emotional responses.
The theoretical works by Friston et al. [10–12] have
served as a basis for subject-specific regression analysis
(SSRA) in a voxel-based statistical analysis of imaging data.
With this method, it is possible to examine the association
between regional cerebral blood flow (rCBF) reflecting
neuronal activity and external variables, e.g. subjective
ratings. SSRA with subjective self-evaluation of the emo-
tional experience has several potential advantages compared
to subtraction analysis in studies on emotion. Firstly, the
method does not need a reference (neutral) condition or a
validation procedure of dichotomous emotion induction,
which are required for the subtraction method. Secondly,
this approach allows individual responses to the material
used for emotion induction because it is based on individual
ratings of emotional experience, and the statistical model is
fitted individually for each subject. Thirdly, by using a
versatile rating questionnaire, it is possible to explore the
different emotions or aspects of emotions induced by the
stimuli using the same (multiscan) imaging data. Thus,
SSRA might be able to catch more fully the idiosyncratic
aspects of the emotional response, as it allows and draws on
the natural variation of emotional experience. On the other
hand, despite the individualistic approach and the subject-
specific statistical model, the results of a multi-subject study
can be generalized at the population level, using random-
effects analysis (RFX). Despite its possible methodological
benefits, SSRA utilizing self-evaluation of subjective emo-
tional experience has been utilized only in a few recent
fMRI studies [31,32,46]. However, to the best of our
knowledge, it has been neither applied nor methodologically
evaluated in positron emission tomography (PET) studies on
emotions.
The aim of the study is methodological evaluation of
SSRA utilizing subjective evaluation of emotional experi-
ence in imaging studies on emotion. We present a PET study
utilizing SSRA and subjective evaluation of emotional
experience. Two target emotions, amusement and sadness,
were induced with validated film stimuli. The rCBF values
were measured using PET, and the subjective intensity of
the emotional experience was rated on a category ratio scale
[5] after each PET scan. The correlation between rCBF and
the subjective rating data was analyzed using subject-
specific regression analysis with RFX to achieve results
generalizable at the population level. For comparison,
subject-specific subtraction analysis with RFX was per-
formed as well. In the present study, we utilize the PET data
with eleven subjects used in our earlier study presenting the
results of fixed-effects subtraction analysis [2]. The present
study is mainly methodological, and the discussion focuses
on the usability, benefits, and restrictions of SSRA in studies
on emotions.
2. Time required
Training of subjects to use the CR-10 scale: 30 min per
subject.
Manipulation check: 60 min.
Evaluation of rating data: 60 min.
Magnetic resonance imaging of the brain: 35 min per
subject.
PET data acquisition: 150 min per subject.
PET data preprocessing: 10 min per subject.
Voxel-based statistical analyses: 120 min.
Total time required to run the protocol with 11 subjects:
approximately 45 h.
3. Materials
3.1. Subjects
Eleven right-handed healthy female volunteers (mean
age 33.4; range 18 to 44 years) participated in the study
approved by the Ethics Committee of Turku University/
Turku University Central Hospital. The volunteers gave
written informed consent. (For a detailed description of the
study sample, see [2]).
3.2. Stimulus material
The stimulus material consisted of 12 film clips, each
lasting for an average of 2:30 min, with four films in each
category, to induce amusement, sadness and a ‘‘neutral’’
state. Four of the emotional film clips (When Harry met
Sally (amusement), two clips from Kramer vs. Kramer
(sadness), and The Champ (sadness)) were chosen from
previously validated film sets [16,33]. All the additional
films were validated for the purposes of the present study
with a sample of 51 university students. From this set, three
episodes of the film Bean-The Ultimate Disaster Movie
(amusement) and one episode from Kramer vs. Kramer
(sadness) were chosen. The four neutral films were
professional video material containing views of social
scenes, talking people, and scenes of nature with movement
and sound. The neutral films with minimal emotional
content were chosen in such a way that the basic sensory
properties (sound, movement, color) were similar to those of
the emotional films. To eliminate potential order effects, the
sequence of films was pseudo-random, but similar for all
subjects. The present set of films has been previously used
by Aalto et al. [2].
S. Aalto et al. / Brain Research Protocols 15 (2005) 142–154144
4. Detailed procedure
4.1. Measurement of the intensity of emotional experience
After each PET scan, the subjects rated their subjective
emotional experience during the film using a questionnaire,
which covered the thematic (appraisal), action readiness,
and feeling aspects of sadness, amusement, and a few
covariate emotions (interest, joy, disgust, fear, anger). The
items for amusement were: ‘‘Humorous or funny things
happened’’ (core-relational theme), ‘‘I wanted to smile or
laugh’’ (action readiness), and ‘‘I felt amused’’ (feeling).
The items for sadness were: ‘‘Something important was
lost’’ (core-relational theme), ‘‘I wanted to comfort’’
(action readiness), and ‘‘I felt sad’’ (feeling). The intensity
ratings of the feeling items of sadness and amusement
were used in this study. The intensities of the experienced
emotions were measured with a verbally anchored 12-step
(0, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) category ratio (CR-10)
scale [5], which allows linear quantification of perceptual
intensities [7,39]. To obtain maximal accuracy and
reliability of the intensity ratings, the subjects were trained
and tested with a special blackness test to use the CR-10
scale [6]. The subjects’ experiential responses to linearly
increasing blackness were within the 95% confidence
interval compared to the normal values [6]. The subjects
were instructed to rate their average emotional experience
during the film. Ratings based on one’s subjective
experience instead of normative expectations were encour-
aged. The questions were projected on the film screen
after each PET scan and the subjects answered them
verbally.
4.2. Manipulation check
(1) To check the effectiveness of the films in inducing the
target emotions of the present study, two repeated-measures
analyses of variance (rmANOVA) were conducted on the
amusement and sadness ratings separately across the twelve
films. Simple contrasts with two parameters were used. The
first parameter compared the ratings of amusement or
sadness between the corresponding target emotion films
and neutral films. The second parameter compared the
amusement or sadness ratings between the two target
emotion films. The ratings of both target emotions were
significantly the highest in the corresponding target emotion
films in comparison with the neutral or the other target film,
the lowest F(1, 10) = 57.2, P < 0.001.
(2) To ensure that amusement and sadness were the
most conspicuous components of the induced emotional
states, paired t tests were conducted to compare the mean
ratings of the target emotions to all the other covariate
emotions. Among all the feeling ratings, the sadness ratings
of the sadness films and the amusement ratings of the
amusement films reached significantly the highest level, the
lowest T = 3.4, P < 0.01. Statistical analyses concerning
the rating data were computed with SPSS for Windows,
Release 11.0.1.
4.3. Evaluation of rating data
The inter-subject consistency of the ratings, as measured
with Cronbach alphas of the amusement and sadness ratings
for the target films, varied from high (a = 0.84, amusement
ratings) to very high (a = 0.97, sadness ratings). The
corresponding alphas for both the target and the neutral
films (with very low emotional intensity) varied from 0.70
(amusement, excluding one neutral film of inconsistent
amusement ratings) to 0.79 (sadness). Thus, considering the
inter-personal context, the subjects rated their emotional
experience in a considerably stable manner throughout the
experiment, which supports the reliability of the ratings.
Considering the intra-personal context, the scatter plots
of the CR-10 ratings of the intensity of the emotional
experience show that the variety in the intra-subject ratings
was large and the profiles differed considerably between
subjects (Fig. 1, left panels). The films seem to provide the
necessary intra-subject variation for subject-specific regres-
sion estimates, although some individuals rated the films in
a more dichotomous manner. The scatter plots of the CR-10
ratings (Fig. 1, left panels) also show that some of the films
did not have the expected effect on some individuals (see,
e.g., subject 5, who experienced one of the neutral films as
highly amusing).
4.4. PET data acquisition
The PET data covering the whole brain were gathered in
the 3 D mode using a whole-body PET scanner (GE
Advance, Milwaukee, WI, USA), which provided 35
cantomeatal slices of 4.25 mm thickness and with 2.34 �2.34 mm in-plane voxel size. The performance characteristic
tests made on this scanner ensure that the transaxial spatial
resolution is 6 mm in the radial and 5 mm in the tangential
direction, and the axial resolution is 6.5 mm [24]. In each of
the 12 scans, the film was initiated approximately 20 s
before the injection of an intravenous 300 MBq [15O]-water
bolus (10 ml) into the left antecubital vein. The data were
integrated into a single 90-s frame. (For the details of PET
scanning, see [2]).
4.5. Preprocessing of imaging data
The preprocessing and statistical analyses were per-
formed using the Statistical Parametric Mapping [10]
software version 99 (SPM99), Statistical non-Parametric
Mapping 99 (SnPM99; [27]) and Matlab 5.3 for Windows
(Math Works, Natick, MA, USA). Realignment and spatial
normalization were made using the standard procedure of
SPM99 (see [2]) and, finally, the images were smoothed
with an isotropic Gaussian filter of 14 mm FWHM (full
width at half maximum).
Fig. 1. Scatter plots (left panels) and mean scores (right panels) of subjective intensity ratings of amusement (upper panels) and sadness (lower panels) based on
a category ratio scale (CR-10). In the scatter plots of subjective intensity ratings, the eleven subjects are shown on the horizontal axis and the intensity ratings of
the four emotion-inducing and the four neutral films on the vertical axis. The observations of emotion-inducing films are depicted as circles and the
observations of the neutral films as quadrangles. In the mean scores of the subjective intensity ratings, the CR-10 ratings of the target emotions during the
corresponding target emotion films are denoted by S and the ratings during the neutral films by N. The error bars represent standard error of mean (SEM).
S. Aalto et al. / Brain Research Protocols 15 (2005) 142–154 145
4.6. Voxel-based statistical analyses
Statistical testing was done using two types of procedure:
an exploratory analysis covered the entire brain without any
a priori hypothesis about the location of effects, while a
hypothesis-driven (confirmatory) VOI-based (volume of
interest) analysis enabled higher sensitivity.
4.6.1. Exploratory regression analysis
At the first phase of RFX analysis, SSRAs testing the
correlation between rCBF and the subjective ratings (CR-
10) utilized eight scans per subject, out of which four scans
corresponded to the four films inducing specific emotions
(amusement or sadness) and the other four to the ‘‘neutral’’
films. Separate SSRAs for both emotions were conducted
with a model including subject-by-covariate interaction.
Subject-specific AnCova global normalization was used,
and the mean voxel values were subject-specifically scaled
to 50 ml/dl per minute. The model had 33 parameters,
leaving 55 df for the estimation. The parameters in SSRAs
consisted of three parts: a subject part, a block part, and a
nuisance part. The subject part consisted of a block of
subjective ratings for each subject, yielding 11 parameters.
The block part contained 11 parameters from the number of
subjects, and the nuisance part included 11 subject-specific
parameters for global normalization of CBF.
After the SSRA model specification, subject-specific
contrasts were made and written to contrast images, which
served as the first, within-subject phase of RFX. As the
second, between-subjects phase of RFX, the null hypotheses
of no significant positive or negative correlation effects in
the contrast images were statistically tested with one-tailed
one-sample t tests using SnPM99. A corrected P value of
0.05 at a cluster level was applied, which had been obtained
by a corrected height threshold P value of 0.05 at the voxel
level combined with a critical suprathreshold size of 16
voxels. SnPM’s cluster level inference differs relevantly
from that of SPM. In SnPM, such results are presented that
(i) are significant at the cluster level or (ii) exceed the
critical threshold of the voxel-level inference. In the
analysis, variance smoothing of 12 mm and 2048 permu-
tations were used. Variance smoothing pools variance
estimates over neighboring voxels, giving additional
degrees of freedom and decreasing noise. It also makes
the statistic image, now called pseudo-t image, no longer t-
distributed. With more variance smoothing, the pseudo-t
thresholds for the analysis become lower, increasing the
sensitivity (statistical power) of the analysis. The corre-
sponding pseudo-t height thresholds were 4.55 in amuse-
ment and 4.29 in sadness.
4.6.2. Exploratory subtraction analysis
The comparative method, subject-specific subtraction
analysis, was applied separately for both emotions using a
condition-by-subject interaction model. Otherwise, the
model settings in the first phase of RFX were the same
S. Aalto et al. / Brain Research Protocols 15 (2005) 142–154146
as in SSRA. The model specifications resulted in a design
with 44 parameters leaving 55 df for the estimation. In
subtraction analyses, the subject part of the parameters
comprised a block of specific emotion and neutral scans
for each subject, yielding 22 parameters. The block part
(11 parameters) and the nuisance part (11 parameters)
were the same as in the SSRAs. The second phase of
RFX testing the null hypotheses of no significant
activation and deactivation effects was conducted as
described earlier in the context of SSRA. The resulting
pseudo-t height thresholds were 4.92 in amusement and
4.87 in sadness.
4.6.3. VOI-based analysis
The hypothesis-driven VOI analysis performed with
SPM was directed onto the amygdala, medial frontal
cortex (MFC), and subcallocal cingulate (SCC), which are
frequently shown to be involved in sadness, amusement,
or visually induced emotional state (for meta-analysis, see
[30]). The VOIs were drawn with the Imadeus software
(version 1.5, Forima Inc., Turku, Finland) on a ‘‘template’’
image averaged from spatially normalized T1-weighted
magnetic resonance images. The circular VOI with a 10
mm radius was placed bilaterally onto the four consec-
utive slices in the amygdala. The VOI for SCC was
placed onto the eight consecutive planes inferior to the
anterior genu of corpus callosum, corresponding mainly to
the undermost part of the Brodmann areas 32. The VOI
for MFC was placed onto the nine consecutive planes
including the medial aspects of the Brodmann areas 9 and
10, extending from the anterior boundary of the anterior
cingulate to the frontopolar tip. The search volume
covering bilaterally the amygdala, MFC, and SCC was
3300 voxels, which equals 26.4 cm3. Fig. 2 shows the
location and shape of the VOIs. The binary VOI image
was multiplied by the contrast images to obtain images
including only a region corresponding to VOI. A
corrected P value of 0.05 was used in the regression
and subtraction VOI analyses.
Fig. 2. Visualization of anatomical localization and
4.6.4. Localization and calculation of correlation
coefficients
For localization, the coordinates of the local maxima of
the clusters resulting from statistical analysis were converted
into Talairach coordinates [41] using the mni2tal conversion
software by Brett (http://www.mrc-cbu.cam.ac.uk/Imaging/
mnispace.html) and localized using the Talairach Deamon
software [20]. The correlation coefficient for the peak voxel
of each cluster was computed with Matlab after adjustment
for the effects of interest contrast.
5. Results
Fig. 3 presents the results of random-effects regression
and subtraction analyses for amusement (amusement vs.
neutral) and sadness (sadness vs. neutral) with three
orthogonal projections of a glass brain. The corresponding
anatomic regions are given in Table 1 for amusement and in
Table 2 for sadness.
In amusement, the most extensive and intensive (highest
pseudo-t statistics) cluster with a positive correlation
occurred in the right occipitotemporal area (BA19,
BA22, BA39; Talairach coordinates of the peak voxel
[44, �76, �6]) (Fig. 3, upper left panel). Clusters showing
positive correlations were also seen in the occipitotemporal
(BA18; [�36, �82, �4] and BA22; [59, �33, 7]) and
fusiform (BA37; [�42, �51, �13]) areas and in the
superior temporal gyrus on the left (BA38; [�26, 24,
�33]), in the right middle temporal lobe (BA21; [53, 10,
�29]), and in the cerebellum on the left [�10, �71, �15].
Clusters with negative correlations were seen in right
medial frontal gyrus (BA10; [8, 38, �9]) and in the AC
bilaterally (BA24), in the right inferior frontal gyrus
(BA46; [48, 39, 13], BA47; [14, 19, �18]), in the left
superior and middle frontal gyri (BA8; [�22, 24, 45],
BA10; [�22, 52, �3]; BA11, BA46; [�42, 28, 17]), in the
right supraorbital area (BA11; [4, 41, �31]), bilaterally in
the inferior and middle temporal lobes (BA20; right [65,
shapes of VOIs onto a MRI template image.
Table 1
Anatomical brain regions showing statistically significant effects in random-effects regression and subtraction analyses in amusement
Amusement
Random effects regression analysis Random effects subtraction analysis
Positive correlation of rCBF and amusement ratings Amusing > Neutral film blocks
Brain region Talairach
coordinates
of the peak
voxel
Local
maxima
(BA)
Cluster
size
pseudo-t
value
R Brain region Talairach
coordinates
of the peak
voxel
Local
maxima
(BA)
Cluster
size
pseudo-t
value
Right occipitotemporal
area
[44, �76, �6] BA 19,
22, 39
3501 9.23 0.65 Right occipitotemporal
area and gyrus
fusiformis
[48, �74, �3] BA19,
BA22
BA36
4193 12.90
Right middle temporal
gyrus
[53, 10, �29] BA 21 118 5.37 0.67 Right middle temporal
gyrus
[53, 6, �27] BA21 109 5.60
[65, �12, �6] BA 21 27 4.92 0.61
Left occipitotemporal
area
[�36, �82, �4] BA 18 1022 7.77 0.63 Left occipitotemporal
area and gyrus
fusiformis
[�38, �80, 1] BA19,
BA22
BA37
3398 11.53
[�59, �33, 7] BA 22 464 6.01 0.54
Left fusiform gyrus [�42, �51, �13] BA 37 277 6.59 0.70
Left superior temporal
gyrus
[�26, 24, �33] BA 38 34 6.45 0.54 Left superior temporal
gyrus
[�46, 16, �31] BA38 76 5.44
Left cerebellum [�10, �71, �15] 331 5.92 0.54 Left cerebellum [�12, �69, �12] 269 6.82
Negative correlation of rCBF and amusement Neutral > amusing film blocks
Right medial frontal
gyrus
[8, 38, �9] BA10 825 6.40 0.56 Right medial frontal
gyrus, left medial and
middle frontal gyrus
and anterior cingulate
[�24, 40, �17] BA11 3111 7.83
Anterior cingulate BA24 BA32
Right orbital gyrus [4, 41, �31] BA11 22 5.26 0.54
Left superior and
middle frontal gyrus
[�22, 52, �3] BA10,
BA11
417 5.59 0.53
Left middle frontal
gyrus
[�42, 28, 17] BA46 125 5.29 0.64
Right inferior frontal
gyrus
[48, 39, 13] BA46 236 5.87 0.65 Right inferior
frontal gyrus
[48, 37, 11] BA46 379 7.81
Right inferior frontal
gyrus
[14, 19, �18] BA47 30 4.90 0.53
Right middle frontal
gyrus
[32, 20, 47] BA8 27 5.16
Left superior frontal
gyrus
[�22, 24, 45] BA8 77 5.45 0.63 Left middle frontal
gyrus
[�22, 22, 45] BA8 80 5.64
Right inferior parietal
lobule
[57, �39, 44] BA40 138 5.46 0.58 Right inferior parietal
lobule
[57, �37, 44] BA40 208 6.35
Right inferior
temporal gyrus
[65, �24, �19] BA20 108 5.20 0.56 Right inferior temporal
gyrus
[67, �30, �17] BA20 494 7.25
Left inferior parietal
lobule
[38, �64, 42] BA39 23 4.81 0.60 Left inferior parietal
lobule
[�36, �64, 42] BA39 400 7.02
Left middle temporal
gyrus
[�61, �45, �11] BA20 35 4.85 0.61 Posterior cingulate and
paracentral lobule
bilaterally
[�4, �28, 33] BA31
BA5,
BA6
295 6.04
Brain regions showing statistically significant effects in random-effects regression (left tables) and subtraction analyses (right tables) in amusement. The upper
left table represents the areas showing a positive correlation, the lower left table contains negative correlations, while the upper right table shows activations
and the lower right table deactivations, respectively. A corrected P value of 0.05 at the cluster level (corrected height threshold P value of 0.05; critical
suprathreshold size of 16 voxels) was considered statistically significant in SnPM analyses. The results are localized according to the coordinates of the peak
voxel of the cluster and its local maxima. Anatomical brain regions, Brodmann areas (BA, for all local maxima of the cluster), cluster sizes in 2 � 2 � 2 mm
voxels, pseudo-t statistic values, and for regression analyses, correlation coefficients (denoted by R) for the peak voxels of the clusters are given.
S. Aalto et al. / Brain Research Protocols 15 (2005) 142–154 147
�24, �19], left [�61, �45, �11]), and bilaterally in the
parietal lobules (right BA40; [57, �39, 44]; left BA39;
[38, �64, 42]).
The results of subtraction analyses (Fig. 3, upper right
panels) differed slightly from those of regression analyses
in amusement. A small cluster in the right middle temporal
Table 2
Anatomical brain regions showing statistically significant effects in random-effects regression and subtraction analyses in sadness
Sadness
Random effects regression analysis Random effects subtraction analysis
Positive correlation of rCBF and sadness rating Sad > Neutral film blocks
Brain region Talairach
coordinates
of the peak
voxel
Local
maxima
(BA)
Cluster
size
pseudo-t
value
R Brain area Talairach
coordinate
of the peak
voxel
Local
maxima
(BA)
Cluster
size
pseudo-t
value
Right middle occipital
gyrus
[44, �76, �10] BA 18 214 4.81 0.70 Right occipital
area and cerebellum
[44, �80, �9] BA18 1992 8.84
Right fusiform gyrus [40, �51, �14] BA 36, 37 62 4.60 0.58
Right superior temporal
gyrus
[34, 26, �33] BA 38 30 4.96 0.60 Right fusiform
gyrus
[44, �46, �18] BA37 55 5.43
Right cerebellum [32, �81, �25] 41 4.72 0.53 Right middle
temporal gyrus
[65, �14, �3] BA21 715 8.72
[55, 7, �21] BA21 380 6.68
[46, 0, �42] BA38 65 5.70
Left inferior occipital
gyrus
[�38, �82, �4] BA 19 387 5.40 0.69 Left superior and
middle temporal
area
[�61, �16, 1] BA22,
BA38
3664 11.24
Left temporal lobe [�61, �16, 1] BA 22 824 6.99 0.73 Left inferior
occipital gyrus
[�40, �82, �6] BA18 841 8.74
[�26, 24, �33] BA 21, 38 465 6.71 0.57
[�40, �8, �42] BA 20 20 4.59 0.55
Left precentral gyrus [�44, �10, 43] BA 4 114 5.14 0.61 Left precentral gyrus [�48, �8, 41] BA4 92 5.55
Left superior frontal
gyrus
[�6, 54, 29] BA9 59 5.68
Right superior frontal
gyrus
[6, 9, 57] BA6 18 5.15
Negative correlation of rCBF and sadness rating Neutral > sad film blocks
Right middle frontal
gyrus
[26, 36, �17] BA11 432 7.61 0.90 Right inferior frontal
gyrus
[26, 34, �18] BA11 316 7.34
Right inferior frontal
gyrus
[40, 45, 0] BA46 189 4.88 0.88 Right inferior frontal
gyrus
[48, 39, 7] BA46 225 5.98
Right inferior parietal
lobule
[53, �35, 44] BA40 336 5.49 0.91 Right inferior
parietal lobule
[61, �37, 42] BA40 325 6.99
Right inferior temporal
gyrus
[65, �32, �20] BA20 816 8.70 0.81 Right inferior and
middle temporal
gyrus
[63, �30, �22] BA20 797 8.93
Left superior frontal
gyrus
[�26, 59, 6] BA10 78 5.00 0.87 Left middle frontal
gyrus
[�24, 38, �19] BA11 171 6.89
Left inferior frontal
gyrus
BA11 50 4.95 [�30, 59, 6] BA10 144 6.27
Left middle frontal
gyrus
[�26, 38, �19] BA11 50 4.95 0.88
Left middle and inferior
temporal gyrus
[�61, �45, �13] BA20 266 5.89 0.83 Left middle and
inferior temporal
gyrus
[�61, �45, �13] BA20 348 7.27
Left inferior parietal
lobule
[�40, �60, 45] BA40 128 4.69 0.89 Left parietal lobule [�48, �50, 39] BA40,
BA7
527 6.22
Right middle frontal
gyrus
[47, 27, 32] BA9 18 4.60 0.90 Anterior cingulate [�2, �28, 33] BA23 382 6.24
Left inferior frontal
gyrus
[�44, 37, 6] BA46 138 5.64
Right precuneus [18, �68, 44] BA7 112 5.80
Brain regions showing statistically significant effects in random-effects regression (left tables) and subtraction analyses (right tables) in sadness. The upper left
table depicts the areas showing a positive correlation, the lower left table includes negative correlations, and the upper right table shows activations and lower
right table deactivations, respectively. A corrected P value of 0.05 at the cluster level (corrected height threshold P value of 0.05; critical suprathreshold size of
16 voxels) was considered statistically significant in SnPM analyses. The results are localized according to the coordinates of the peak voxel of the cluster and
its local maxima. Anatomical brain regions, Brodmann areas (BA, for all local maxima of the cluster), cluster sizes in 2 � 2 � 2 mm voxels, pseudo-t statistic
values, and for regression analyses, correlation coefficients (denoted by R) for the peak voxels of the clusters are given.
S. Aalto et al. / Brain Research Protocols 15 (2005) 142–154148
Fig. 3. SnPM results of random-effects regression analyses (left panels) and random-effects subtraction analyses (right panels) of amusement (upper two rows)
and sadness (lower two rows) projected on a glass brain. The upper row in both emotions depicts positive correlations (left panel) and activation (right panel)
and the lower row negative correlations (left panel) and deactivation (right panel), respectively. The statistically significant effects of using a corrected P value
of 0.05 at the cluster level are shown (corrected height threshold P value of 0.05; critical suprathreshold size of 16 voxels). The images are presented in
accordance with the neurological convention (Right is right).
S. Aalto et al. / Brain Research Protocols 15 (2005) 142–154 149
gyrus (BA21; [65, �12, �6]) showed only a positive
correlation and no activation (amusement > neutral) in the
subtraction analysis. One cluster in the left middle
temporal gyrus (BA20; [�61, �45, �11]) showed a
negative correlation in the regression analysis, but did
not show deactivation (amusement < neutral) in the
subtraction analysis. The posterior cingulate gyrus
(BA31; [�4, �28, 33]) and the paracentral areas (BA 5,
BA6) showed only deactivation and no negative correla-
tion. The left frontal (BA 11; [�24, 40, �17]) and right
temporal areas (BA 20; [67, �30, �17]) with deactivation
were more extensive than the corresponding areas with
negative correlations.
In sadness, the cluster with the most extensive and
intensive positive correlation was situated in the left
temporal lobe (BA22; [�61, �16, 1]) (Fig. 3, lower left
panel). Several other clusters showing positive correla-
tions were also situated in the left occipitotemporal area
(BA21; [�26, 24, �33], BA38, BA20; [�40, �8, �42]),
BA19; [�38, �82, �4]). Positive correlations were
further seen in clusters in the superior temporal gyrus
(BA38; [34, 26, �33]), the middle occipital gyrus (BA18;
[44, �76, �10]), and the fusiform gyrus (BA36; [40,
�51, �14], BA37) on the right. Clusters showing positive
correlations were also found in the precentral gyrus (BA4;
[�44, �10, 43]) on the left and in the cerebellum [32,
�81, �25] on the right. In sadness, clusters with negative
correlations were found bilaterally in the inferior and
middle temporal gyri (BA20; right [65, �32, �20], left
[�61, �45, �13]), in the right inferior (BA46; [40, 45,
0]) and middle frontal gyri (BA9; [47, 27, 32], BA11;
[�26, 38, �19]), in the left superior and middle frontal
gyri (BA10; [�26, 59, 6], BA11; [�26, 38, �19]), and
bilaterally in the parietal lobe (BA40; right [53 ,�35, 44],
left [�40, �60, 45]).
In sadness, too, there were some differences between the
results of subtraction (Fig. 3, lower right panels) and the
regression analyses. The activated (sadness > neutral)
clusters in the cerebellum and in the right temporal lobe
(BA18; [44, �80, �9], BA37; [44, �46, �18]) were larger
than the clusters with positive correlations in the corre-
sponding brain areas. A small cluster in the superior frontal
gyrus (BA 9; [�6, 54, 29]) on the left and another in the
superior frontal gyrus (BA 6; [6, 9, 57]) on the right also
showed only activation and no positive correlation. A small
cluster in the right middle frontal gyrus (BA9; [47, 27, 32])
showed only a negative correlation and no deactivation
(sadness < neutral). Clusters in the left inferior frontal gyrus
(BA46; [�44, 37, 6]), in the anterior cingulate gyrus (BA
23; [�2, �28, 33]), and in the right precuneus (BA7; [18,
�68, 44]) showed only deactivation and no negative
correlation.
S. Aalto et al. / Brain Research Protocols 15 (2005) 142–154150
VOI-based analysis directed onto the amygdala, the
medial frontal cortex (MFC), and the subcallocal cingulate
(SCC) did not yield correlation or activation in either of the
emotions.
6. Discussion
Imaging studies of the neurobiological basis of emotions
have so far almost exclusively applied the subtraction method
(see [30] for a review), which reveals only dichotomous
activation or deactivation and does not allow a more versatile
evaluation of the neurobiology of emotional experience.
Subject-specific regression analysis (SSRA) utilizing sub-
jective evaluation of emotional experience might open up
new possibilities in studies of emotions. Although analysis of
covariance has been utilized earlier in PET studies on
emotions [23], SSRA utilizing self-ratings has not been
previously applied to PET studies exploring the correlation
between rCBF and subjective emotional experience. In the
present study, we utilized SSRA to explore the association
between rCBF and post-film subjective evaluation of the
intensity of emotional experience. The generalizability of the
results and inter-study comparability were ensured using
random-effects analysis (RFX). For comparison, subject-
specific subtraction analysis with RFX was also performed.
We found that the areas revealed by regression analyses
were mainly the same as those found using subtraction
analyses (see Fig. 3). In general, the comparison of the
results of SSRAs to the results of the previous studies on
emotions is not straightforward, as the earlier studies have
not utilized a subject-specific statistical model to explore the
correlation between self-evaluation of emotional experience
and rCBF. Furthermore, as far as we know, the previous PET
studies on emotions utilizing subtraction analysis have not
utilized RFX which limits the generalizability and compar-
isons between studies. Despite the considerable methodo-
logical differences between the present and previous studies,
the overlapping brain areas seen in both regression and
subtraction analyses have been detected in several earlier
studies on emotions using film stimuli. Activation of the
occipital cortex is a common finding when using visual
stimuli for emotions [30]. Activation in the temporal [22,28]
and occipital areas bilaterally [28,44] has been seen during
amusement in studies using film stimuli. Deactivation in the
left frontal and right parietal areas and in the anterior and
middle cingulate has also been reported previously to be
associated with film-induced amusement/happiness [28]. As
regards sadness, even other studies using film induction have
revealed increased activation in the cerebellum and bilat-
erally in the temporal area [4,22]. As the common and
distinct neural substrates of amusement and sadness were
examined and discussed in our earlier study [2], their
discussion is not included in the present study.
In some areas, however, the findings of regression and
subtraction analyses differed. The most prominent exclusive
findings of subtraction analyses were the deactivation of the
posterior cingulate without a negative correlation in amuse-
ment and the inactivation of the ventral part of AC (BA23)
bilaterally without a negative correlation in sadness.
Although activation of the AC would better agree with its
suggested role in emotional awareness [21], deactivation of
the AC related to emotions has also been reported [26,28].
In general, the lack of correlation might indicate a nonlinear
neural response or non-additive interaction [11]. On the
other hand, there were also regions that were only found in
the regression analyses. In amusement, a cluster in the right
middle temporal gyrus showed only a positive correlation
and no activation, and one cluster in the left middle temporal
gyrus showed a negative correlation but no deactivation. In
sadness, a cluster in the right middle frontal gyrus showed
only a negative correlation and no deactivation. As far as we
know, precisely, these areas have no previously identified
function in the neurobiology of emotion. It is not easy to
give a convincing interpretation of the deactivations or the
corresponding negative correlations found in the frontal
lobe. They might relate to functional interaction between
cognition and emotion (e.g., [15]). However, at least a
partial explanation might be that distinct emotional stimuli
probably provoke less cognition in comparison to clearly
non-emotional stimuli with otherwise equal content (for
further discussion, see [2]).
To enhance the sensitivity of the analysis, we conducted
VOI-based analyses focused onto the amygdala, the sub-
callosal cingulate (SCC), and the medial frontal cortex
(MFC), which did not reveal any findings related to sadness
or amusement. Although visual emotional stimuli generally
activate the amygdala [30], our results are in line with some
previous studies showing that film-induced emotional states
do not activate the amygdala [22,28]. In addition, our results
of a female sample are well in line with the findings reported
by Schneider et al. [37], who used ROI analysis to
demonstrate that females, unlike males, fail to show
amygdala activation or any association between amygdala
activity and mood change. VOI analysis did not reveal
activation or correlation in SCC, either, which is considered
to be involved in sadness [30]. However, even this agrees
with the previous studies showing that film-induced sadness
does not activate SCC [4,22,44]. Finally, VOI analysis did not
yield a positive correlation or activation in theMFC, although
MFC activation was seen in almost 40% of emotion studies
using visual induction [30]. However, some studies have also
shown deactivation of the MFC [14,26]. Although the lack of
findings in the amygdala, SCC, and MFC is not surprising in
view of the results of the above-mentioned eight studies, one
should also note that almost all earlier studies have been
performed without random-effects analysis, which weakens
the generalizability of their results. Apart from this methodo-
logical factor, the differences in the stimuli and the imaging
protocols might also partially explain why the recent meta-
analysis of emotion studies showed no specific brain area to
be activated consistently in the different studies [30].
S. Aalto et al. / Brain Research Protocols 15 (2005) 142–154 151
There were only a few regional differences between the
results of the regression and subtraction analyses, which
might be partially due to the properties of our data. Despite
the considerable variation in the subjective ratings (see Fig.
1, left panels), the intensities of the emotions were fairly
polarized at the group level due to the ‘‘neutral’’ films (see
Fig. 1, right panels), which increased the convergence of the
results. On the other hand, the very low intensity of
emotional involvement during the neutral films may have
decreased the accuracy of the ratings. This was demon-
strated by the lower reliabilities of the emotional ratings
over the neutral films, possibly influencing the results of
SSRA. There is also evidence to suggest that the activation
of the emotional system must reach a certain level to cause
congruence between the different response systems (e.g.,
facial expression and experience) [42,35], which might also
apply to the relationship between brain activity and self-
evaluation (ratings). Thus, emotional stimuli that induce a
response of at least moderate intensity and also cover the
entire range of intensity of a certain emotion would be most
suitable for performing SSRA (see [1]). However, SSRA
also seems feasible for the present fairly polarized data in
spite of the above-mentioned factors that detract from the
accuracy of ratings.
The rating of emotional experience may be considerably
inaccurate, especially when the object of a single rating is
the average intensity of the emotional experience over the
whole scanning session (at least 1 min in PET). An average
retrospective rating used in the present study does not reflect
the primary emotional experience in the best possible way
because there is no guarantee that the rated state remains
stable over this period. In addition, several factors, such as
the demand characteristics, stereotypes and temporal fea-
tures of the response, may hamper the validity of
retrospective subjective ratings and should be taken into
consideration when planning the experimental design (see,
e.g., [8] and [45]). However, the moderately high alphas of
the ratings through the films give some support for the
notion that there is true variation behind the retrospective
ratings of this study. The usability of subjective ratings
made after the scanning session was also demonstrated in a
recent fMRI study [31]. There is also empirical support for
the accuracy of retrospective rating of emotional state, as the
facial expressions induced by 1- to 2-min films and the
retrospective ratings made after a lag of at least 30 s
considerably overlap ([35], see also [36]). As fMRI studies
allow operation at short time intervals, they might offer
good opportunities to catch the immediate and momentary
aspects of emotional experience (see [31,32]).
All first-phase SPM analyses are fixed-effects analyses
that ignore the between-subject component of variance and
thus produce results that cannot be generalized beyond the
study sample [18]. Hence, when generalizable results and
reliable inter-study comparisons are wanted, RFX is the
only appropriate method of analysis. Due to the low degrees
of freedom, RFX was performed using SnPM with variance
smoothing, to increase the statistical power (sensitivity) of
the analysis. Although additional hypothesis-driven VOI
analysis enhanced the sensitivity of the analyses, the
relatively small study sample (11 subjects) may have
precluded us from detecting correlations in some regions
that might play a role in the neurobiology of emotional
experience. One should note that the fairly small sample size
also weakens the generalizability of the results. Finally,
although we have conducted mainly a comparison between
the statistical properties of the results, one should note that
statistical significance, per se, does not guarantee biological
relevance of the results.
SSRA utilizing subjective evaluation of emotional
experience has some methodological properties that open
up interesting possibilities for studies on emotion. It enables
emotions to be studied basically from an individual
subjective point of view, contrary to subtraction analysis,
which is based on validation and dichotomous selection of
the stimuli beforehand. Validation may not be appropriate
when stimuli are used in an actual imaging situation or for a
dissimilar group of study subjects, e.g., psychiatric patients.
SSRA is based on individual ratings made in the actual
experimental conditions and acknowledges that subjects can
experience a given stimulus in considerably different ways,
which enhances its psychological validity. Another advant-
age of regression analysis is that no particular reference
condition of the kind needed in subtraction analysis is
required. This makes regression analysis more specific
because the lack of sensory or cognitive equivalence
between neutral and emotional conditions easily hampers
the specificity of subtraction analysis (see, e.g., [34]). A
special advantage of the regression method is the possibility
to study the different nuances of an emotional experience.
An emotional experience is typically a multidimensional
construct containing elements from the emotion-eliciting
appraisal processes and the following action tendencies and
feelings [9,38]. With an appropriate rating questionnaire and
separate regression analyses, it would be possible to locate
brain areas associated with the different dimensions of
emotional experience. This novel approach also seems to be
applicable when utilizing an objective variable, e.g., in
research on the association between rCBF and the EEG
entropy index of the depth of anesthesia [25].
Subject-specific regression analysis utilizing subjective
evaluation of emotional experience turned out a promising
method of analysis in imaging studies of emotions. Our
results give support to the idea that, in several regions, the
neural responses related to amusement and sadness associate
linearly with the self-evaluated intensity of emotional
experience. The most significant benefit of SSRA using
subjective ratings is the possibility to explore the neuro-
biological correlates of actual and individual emotional
experience. Using a random-effects analysis, the results of a
multi-subject study can be generalized at the population
level, enabling reliable comparisons between the results of
different studies. The regression approach seems more
S. Aalto et al. / Brain Research Protocols 15 (2005) 142–154152
suitable than the traditional subtraction methodology in
studies focusing on subjective and experiential aspects of
emotions and might reveal completely new aspects in
studies on emotion-related phenomena.
7. Quick procedure
7.1 Measurement of emotional experience;
7.2 Manipulation check;
7.3 Evaluation of rating data;
7.4 PET data acquisition;
7.5 Preprocessing of image data;
7.6 Voxel-based statistical analyses;
(i) Exploratory regression analysis;
(ii) Exploratory subtraction analysis;
(iii) VOI-based analysis;
(iv) Localization and calculation of correlation
coefficients.
8. Essential literature references
[5–7,10,16,18,27,30,33,43]
Acknowledgments
The study has been funded by Signe and Ane Gyllenberg
Foundation. E.W. was funded by the Academy of Finland
and Tampere Graduate School of Information Science and
Engineering (TISE). The authors thank the following
persons for their valuable comments: Dr. Jyrki Mottonen,
Institute of Signal Processing, Tampere University of
Technology, Finland, and Virpi Ahola, M.Sc., Department
of Statistics, University of Turku, Finland.
Appendix A. Theory of subject-specific regression and
random effects analysis
A.1. General linear model
The subject-specific regression model was presented by
Friston et al. [10] in the framework of a general linear model
with the following matrix equation:
Y ¼ Gcb c þ Hlcl þ Hccc þ e; ð1Þ
where Y is the response matrix consisting of the voxels j =
1,. . .,J as columns and the observations (e.g. scans) i = 1,. . ., I
as rows. The columns of Gc contain the covariates of interest
(e.g. subject-by-covariate interaction), Hl is a matrix con-
taining the effects of no interest (e.g. subject or block effects),
Hc contains the nuisance variables (e.g. global activity),
matrix bc includes the regression coefficients of interest,
matrix cl contains the effects of no interest, matrix cc includes
the regression coefficients for the nuisance variables, and ( isthe residual matrix. It is assumed that the residuals are
independent and normally distributed with zero mean and
variance rj2 [N(0, rj
2)]. In the regression model, it is possible
to test statistically the distinct contribution of the covariate of
interest (e.g. subjective rating) in Gc to the variance of the
response (rCBF) while simultaneously controlling for the
effect of the nuisance covariates in Hc. However, one should
be careful in choosing the covariates, since the use of
correlated covariates may confound the results (see [3]).
A.2. Application to multi-subject repeated-measures data
SSRA is based on the subject-specific regression model
(SSRM), where a parametric regressor is fitted at a single-
subject level for each subject of the study. It differs
fundamentally from the group-level regression model
known as ‘‘simple regression’’ in Statistical Parametric
Mapping (SPM) or, more generally, as ‘‘correlation analy-
sis’’. The group-level model is inappropriate in multi-subject
repeated-measures designs with multiple pairs of observa-
tions (scans and corresponding ratings) per subject because
it leads to a within-subject covariance structure, which
violates the fundamental independence assumptions under-
lying the analyses. SSRM models the within-subject
covariance structure by decomposing the observations into
blocks for each subject, and the model therefore includes
only within-subject variation, so that the independence of
residuals can be maintained. SSRM also allows subject-by-
covariate interactions, i.e., individual variation in the slopes
of the regression lines, which may be considerable,
especially in the context of study on emotions.
A.3. Types of inference and generalizability of results
All first-level SPM analyses are fixed-effects analyses
(FFX), but subject-specific models enable random-effects
analysis (RFX) [18] as a second-level analysis. The type of
statistical inference and the generalizability of the results
differ essentially between FFX and RFX (see [12,13] for a
review). In FFX, the error variance is estimated on a scan-
to-scan basis, whereas in RFX, it consists of both scan-to-
scan error variance and a between-subject component of
variance. FFX tests statistically the subjects’ mean effect,
which, however, might be due to a large effect in some
subjects, but no effect in the others. Whenever there is a lot
of between-subject variation in the effects, which is typical
in emotional responses, the within-subject variance in FFX
is inappropriate for between-subject (group-level) inference
[18]. Thus, FFX is principally a case study [18] allowing
only inference for the subjects studied.
RFX or, to be exact, mixed-effects analysis enables
population level inference. This is achieved by testing
statistically the significance of the mean effect in the within-
subject parameter estimates (h:s) for the sum of scan-to-scan
error variance and a between-subject component of var-
S. Aalto et al. / Brain Research Protocols 15 (2005) 142–154 153
iance. RFX is the only method that yields generalizable
results and reliable inter-study comparisons. However, this
is accomplished at the cost of degrees of freedom (df),
which collapse from N (scans)-rank(X) to N (subjects)-1,
where N (scans) is the number of scans, N (subjects) is the
number of subjects, and rank (X) is the rank, i.e., the
number of linearly independent columns of the design
matrix X, which consists of Gc, Hl, and Hl in (1).
A two-phase RFX can be made using SPM as well as the
first-level analysis. However, the study sample should be
fairly large to yield a reliable estimate of the between-
subject variance needed in RFX [13]. Moreover, with low df
or low smoothness, the multiple-comparison correction in
SPM based on the theory of random fields [47] has been
found too rigorous for voxel-level inference [17,40],
resulting in insensitivity of analysis. Thus, in studies using
typical samples (n < 15), the recently developed Statistical
non-Parametric Mapping 99 software (SnPM99) [27] is a
promising tool for RFX. In SnPM, the multiple comparison
correction is based on permutations, which enables analyses
with small samples [19,27]. Moreover, it is possible to use
non-parametric pseudo-t statistics [27,29] with local
smoothing of noisy variance images. This increases the
statistical power of the analysis, making SnPM even more
appropriate for RFXs with a modest number of subjects.
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