Assessing the spatiotemporal evolution of neuronal
activation with single-trial event-related potentials
and functional MRI
Tom Eichele*†‡, Karsten Specht*†, Matthias Moosmann*§, Marijtje L. A. Jongsma¶, Rodrigo Quian Quiroga?,
Helge Nordby*, and Kenneth Hugdahl*,**
*Department of Biological and Medical Psychology, University of Bergen, 5009 Bergen, Norway;§Berlin Neuroimaging Center–Charite ´, Campus Mitte, 10117
Berlin, Germany;¶Nijmegen Institute for Cognition and Information, Department of Biological Psychology, Radboud University of Nijmegen, P.O. Box 9104,
6500 HE, Nijmegen, The Netherlands;?Department of Engineering, University of Leicester, LE1 7RH Leicester, United Kingdom; and **Division of Psychiatry,
Haukeland University Hospital, 5009 Bergen, Norway
Edited by Marcus E. Raichle, Washington University School of Medicine, St. Louis, MO, and approved October 17, 2005 (received for review June 30, 2005)
The brain acts as an integrated information processing system,
which methods in cognitive neuroscience have so far depicted in a
fragmented fashion. Here, we propose a simple and robust way to
integrate functional MRI (fMRI) with single trial event-related
potentials (ERP) to provide a more complete spatiotemporal char-
acterization of evoked responses in the human brain. The idea
behind the approach is to find brain regions whose fMRI responses
can be predicted by paradigm-induced amplitude modulations of
simultaneously acquired single trial ERPs. The method was used to
study a variant of a two-stimulus auditory target detection (odd-
ball) paradigm that manipulated predictability through alterna-
tions of stimulus sequences with random or regular target-to-target
responses to auditory targets per se, single-trial modulations were
expressed during the latencies of the P2 (170-ms), N2 (200-ms),
and P3 (320-ms) components and predicted spatially separated
fMRI activation patterns. These spatiotemporal matches, i.e., the
prediction of hemodynamic activation by time-variant informa-
tion from single trial ERPs, permit inferences about regional re-
sponses using fMRI with the temporal resolution provided by
multimodal imaging ? P3 pattern learning ? target detection
changes in brain hemodynamics associated with a cognitive process
noninvasively with a high spatial resolution. However, an unsolved
BOLD response. In contrast to the spatial resolution of BOLD-
synaptic activity instantaneously, with an effective temporal reso-
lution on the order of tens to hundreds of milliseconds in case of
generators cannot be inferred with certainty. In combination, these
two complementary noninvasive methods would allow for joint
under investigation and add to a more complete understanding of
this integrated spatial and temporal precision could so far be
obtained only in direct intracranial recordings, usually performed
in patients receiving brain surgery for treatment of epilepsy (4–7).
There are basically three approaches to multimodal integration:
or generative model that can explain both the electroencephalo-
gram (EEG) and fMRI data (8, 9); (ii) through constraints, where
spatial information from the fMRI is used for a (spatiotemporal)
source reconstruction of the EEG (10–12); and (iii) through
prediction, where the fMRI signal is modeled as some measure of
the EEG convolved with a hemodynamic response function, a
principle used in our study.
unctional MRI (fMRI) of the blood oxygenation level-
dependent (BOLD) response (BOLD-fMRI) measures local
Invasive recordings in animals have shown that the BOLD
response is approximately linearly related to local changes in the
underlying neuronal activity. The relationship appears to be stron-
ger for the afferent pre- and postsynaptic processing, which pro-
duces the local field potential (LFP), than it is for the output from
the neuron, i.e., spike rate or multiunit activity (13–16). The LFP
is the basis for the scalp EEG and ERP when coherent at a more
macroscopic scale (17), implying that spatiotemporal data integra-
tion can be achieved by investigating correlations between BOLD
and scalp EEG?ERP. This can be done either continuously over
time, as in the study of background rhythms (18–20) and epileptic
discharges (21, 22) in the EEG, or in the context of inducing
variation in a given cognitive operation (23–25). When a consistent
remote generators (18–25). However, the temporal evolution of
used the trial-to-trial variability of single-trial ERPs (26, 27) re-
corded simultaneously with the fMRI as predictors for hemody-
namic responses to a variant of an auditory target detection
(oddball) paradigm. In this design, infrequent targets were inter-
spersed with frequent standard stimuli at random or regular
intervals in an alternating way (see also Fig. 5, which is published
as supporting information on the PNAS web site). Sequences of
regularly spaced targets, i.e., patterns embedded in this design,
affect the subjective predictability?expectancy (28, 29), and pilot
experiments indicated that several components, at different laten-
cies in the ERP, are modulated according to a sigmoid function of
the number of times an interval is repeated, and learned. These
amplitude modulations (AMs) develop across trials, on a timescale
slow enough to be sampled with fMRI, and should be consistently
the observation time, assuming temporally and spatially indepen-
dent neuronal generators (Fig. 1). fMRI responses that can be
at the time of the AMs. The approach thus allows inferences about
This work was presented in part in poster form at the Helsinki School in Cognitive
Neuroscience, March 2–11, 2005, Lammi, Finland, and at the Annual Meeting of the
Organization for Human Brain Mapping, June 12–16, 2005, Toronto, ON, Canada.
Conflict of interest statement: No conflicts declared.
This paper was submitted directly (Track II) to the PNAS office.
Freely available online through the PNAS open access option.
Data deposition: The neuroimaging data have been deposited with the fMRI Data Center,
www.fmridc.org (accession no. 2-2005-120AE).
Abbreviations: ERP, event-related potential; fMRI, functional MRI; BOLD, blood oxygen-
ation level-dependent; EEG, electroencephalogram; AM, amplitude modulation; TTI,
†T.E. and K.S. equally contributed to this work.
‡To whom correspondence should be addressed. E-mail: firstname.lastname@example.org.
© 2005 by The National Academy of Sciences of the USA
December 6, 2005 ?
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no. 49 www.pnas.org?cgi?doi?10.1073?pnas.0505508102
regional responses using fMRI with the effective temporal resolu-
tion afforded by the ERP.
Subjects. Fifteen healthy right-handed participants (21–28 years,
seven female and eight male) took part in the experiment after
providing a written statement of informed consent.
of 50-ms chords presented continuously during the sparse sampling
fMRI acquisition in an eyes-closed condition via headphones (?80
dB) with an onset asynchrony of 2 s. Infrequent ‘‘targets’’ (500 Hz,
25% probability) were interspersed with frequent ‘‘standards’’ (250
Hz, 75% probability). For a total of 216 targets, alternating
sequences of six consecutive targets were presented either with a
random target-to-target interval (TTI) ranging from 4 to 22 s or
with a regular 8-s TTI (Fig. 5). Each of these 12-target cycles lasted
on the average 96 s. When detecting a target, participants were
instructed to press a response button in the middle of the interval
between the target and the next standard stimulus. The delayed-
response mode was chosen to focus on stimulus-related perceptual
and cognitive effects associated with predictability. The instruction
hampers the expected behavioral effect, i.e., response-time speed-
ing, and thus minimizes the confounding effect of motor-related
potentials on the auditory evoked potential. Participants received a
training session with random targets and were not informed about
the presence of regularity beforehand.
fMRI Data Acquisition and Preprocessing.Imagingwasperformedon
a 1.5-T Siemens (Erlangen, Germany) scanner. Scanning of anat-
omy was done with a T1-weighted MPRAGE sequence. Thereaf-
ter, 300 BOLD-sensitive echo planar images (EPI) were collected
were anterior–posterior comissure line aligned and consisted of 18
axial slices with 5.5-mm thickness including a 0.5-mm interslice gap
64 ? 64 voxel). We used a sparse-sampling acquisition design (30)
with 8 s repetition time (TR) and 2 s acquisition time, leaving a 6
end of each session, nullevents were defined as EPI volumes with
only standard stimuli during the TR. Preprocessing and statistical
analyses were carried out by using SPM2 (Wellcome Department of
Imaging Neuroscience, University College London, London) run-
ning in MATLAB (Mathworks, Natick, MA). All images were re-
aligned to the first image in the time series to correct for head
movement and normalized to the Montreal Neurological Institute
reference space. Normalized data were resliced to a voxel size of 3
mm3, smoothed with an 8-mm full-width half-maximum Gaussian
kernel, and high-pass-filtered (256 s).
EEG Data Acquisition and Preprocessing. EEGs were recorded at
5-kHz sampling frequency with a MR-compatible amplifier (Brain
Products, Munich) placed inside the MR scanner. Subjects were
Herrsching, Germany) containing 28 Ag?AgCl electrodes (FP1,
FP2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8, O1,
OZ, O2, FC5, FC1, FC2, FC6, CP5, CP1, CP2, and CP6). Vertical
eye movement was acquired from below the right eye; the electro-
cardiogram was recorded from the subject’s back. Channels were
referenced to FCz, with a forehead ground and impedances kept
from 1 to 45 Hz. Target epochs from ?312 to 712 ms around
(ICA), implemented in EEGLAB (31) (Institute for Neural Compu-
tation, University of California, San Diego) running in MATLAB.
Components related to pulse and eye-movement artifacts were
removed from the data. After recalculation to average reference,
single trials were wavelet-denoised (26). Coefficients were selected
on the basis of ICA-corrected ERPs and were the same for all
participants and electrodes. For targets (9%) that were presented
within the echo planar image volume acquisition, the ERP was
estimated as the mean of two surrounding targets. The data were
then downsampled to 125 Hz, smoothed to account for intra- and
intersubject latency variability, and high-pass-filtered across trials
(216 s). For all these 8-ms frames from ?100 to 600 ms (n ? 88)
around stimulus onset, separate single-trial amplitude vectors were
extracted and entered into the joint ERP-fMRI analysis.
Joint ERP-fMRI Analysis. The fMRI time series were modeled with
a design that was deployed sequentially for all frames of the ERP
time series and replicated for four frontal-central electrodes (Fz,
FC1, FC2, and Cz), i.e., those electrode sites where paradigm-
induced amplitude modulations were maximally expressed. For
each of these designs, two regressors were formed by convolving
stimulus functions with a canonical hemodynamic response func-
tion. The first stimulus function encoded a generic obligatory
response to target stimuli of constant amplitude, applicable to
areas sensitive to the manipulation and are detectable in both ERP and fMRI. Consecutive correlation analysis between the fMRI time series and the multiple
ERP time series yields complementary information regarding the spatial location and timing of these processes. Neuroelectric source acitivities need not
necessarily propagate to the scalp directly but can modulate or be modulated by remote sources (indicated by arrows).
Illustration of how ERP AM can achieve high temporal resolution in fMRI. A suitable paradigm in a simultaneous ERP-fMRI recording can be used to
Eichele et al. PNAS ?
December 6, 2005 ?
vol. 102 ?
no. 49 ?
regional fMRI responses associated with ‘‘exogenous’’ features of
the auditory evoked response and the motor task. The second
stimulus function encoded the amplitude of the single-trial ERPs
be predicted by paradigm-induced amplitude modulations at that
ing. This function was decorrelated (Schmidt–Gram orthogonal-
some general feature in the evoked response to targets. The
regressors were entered into single-subject fixed-effects regression
analyses; on the group level, random effects analyses were per-
formed by entering the contrast images of each subject into
one-sample t tests. fMRI activation to targets (first regressor) is
significant at P ? 0.05, family-wise error corrected, extent 10 voxel.
AM-related activations (second regressor) are significant at P ?
0.001 on the voxel level, cluster extent threshold P ? 0.01, unless
otherwise stated uncorrected for multiple comparisons. This
threshold appears adequate in this experiment, because we were
interested in the profile of responses and their colocalization with
the auditory evoked responses per se, with maximal sensitivity. To
minimize the risk of reporting Type I false-positive activation, we
applied a descriptive criterion: results were considered reliable and
reported only when same?similar activation patterns are replicated
in adjacent time points and in two or more of the electrodes. A
schematic of the analysis procedure is given in Fig. 2.
Curve Fitting. To illustrate the principal ERP amplitude modula-
P2, N2, and P3 amplitudes in the frontocentral region of interest
(i.e., the average of Fz, FC1, FC2, and Cz) and the response times.
H0 assumed that the measure is insensitive to patterning, repre-
sented by a straight horizontal line; H1 assumed that the measure
best described as a sigmoid function.
appeared rhythmically, indicating that they explicitly apprehended
was able to recollect whether these regular patterns were of
constant length, or whether regular patterns alternated with ran-
dom target sequences in succession, suggesting that the overall
order of the experiment remained either unrecognized or was
Average ERPs. The sequence of cerebral processes leading to dis-
crimination of a target stimulus in an active oddball condition may
be indexed by a number of generic ERP components: N1, P2,
mismatch negativity (MMN), N2b, P3a, and P3b (32). The extent
to which components are detectable in the waveforms depends on
experimental parameters. N1 and P2 typically tend to be enhanced
under ‘‘attend’’ compared with ‘‘ignore’’ conditions (33). MMN,
being an automatic response to changes in auditory stimulation,
the N2b, which is elicited by infrequent events in attended input, or
when the difference between standard and deviant stimuli is
relatively large, as in a standard oddball paradigm (32). N2b is
usually followed by P3a, indicating a passive shift of attention, and
the P3b (also labeled P3 or P300), which is particularly sensitive to
task relevance, target probability, sequence, and TTI (23, 28, 29,
34–36). Fig. 3 displays the grand-average ERPs to standards,
regular and random TTI target categories, along with results from
P1 (70–80 ms), a broad centrally distributed N1 (100–120 ms)
emerges, followed by a more central-parietal P2 (160–180 ms). The
dominant feature in the ERPs to both target categories in com-
parison with the standard is a frontal-central N2 (200–220 ms),
followed by a double-peaked P3 (270–360 ms). The earlier peak at
270 ms is more prominent frontocentrally, the later peak is prom-
inent at parietal sites. TTI regularity in the averaged waveforms
most strongly affects N2 and P3 amplitudes but is also seen as a P2
decrement and reduced N1 enhancement.
Curve Fitting. Response times were on average 905 ms (SD 200)
and remained unaffected by the presence of patterns (F ? 0.22,
not significant), indicating that participants followed the de-
N1 amplitudes were also insensitive to regularity (F ? 0.31, not
significant). All three subsequent components were found to be
with sigmoid curves: P2 (F ? 5.60, P ? 0.005), N2 (F ? 15.64, P ?
0.0001), and P3 (F ? 29.89, P ? 0.0001). The estimated turning
points of these functions were all between the second and third
target presentation in regular sequences. The effect strength grad-
and intersubject consistency at later timepoints or more stable
single-trial estimates. Although the global effect could be well
unique shape variations (Fig. 4 Left).
fMRI–Target Processing. Areas constantly contributing to target
formation, the inferior parietal lobe, anterior cingulate gyrus,
supplementary motor area, pre- and postcentral gyri (left?right),
cuneus, and the middle and superior frontal gyri (right?left), (Fig.
4 and Table 1, which is published as supporting information on the
PNAS web site).
all four electrodes and timepoints, along with plots of the average
scalp topography, waveform, and AM, were compiled into Movies
1 and 2, which are published as supporting information on the
PNAS web site.
Here, we focus on the maxima of the three most consistent
ms), and P3 (?320 ms).
Inverse relations between BOLD and AM on P2 were seen in
posterior cingulate, precuneus, supramarginal gyri, left parietal,
and frontal areas (Fig. 4; see also Table 2, which is published as
supporting information on the PNAS web site). Inverse relations
were also seen for N2, with the most consistent region across
electrodes being located in the right medial frontal gyrus. Addi-
tional clusters were in the right and left superior frontal gyri, left
also Table 3, which is published as supporting information on the
PNAS web site). Note that these latter results stem from the Cz
at FC1 and FC2 at a lower cluster extent threshold. We observed
positive linear relations between BOLD responses and the P3 AM
(?320 ms), mainly in the middle and inferior frontal gyri, inferior
parietal lobule, and middle temporal gyri in the right hemisphere.
right postcentral gyrus, left supramarginal, and middle frontal gyri
(Fig. 4; see also Table 4, which is published as supporting infor-
mation on the PNAS web site). There was no consistent amplitude
modulation of N1 (100 ms), such that it did not differ from the
stimulus function and thus did not support a significant regression.
Except for a close spatial relationship between P2- and P3-
y, 9; and z, 8), there was no considerable overlap among the AM
and target?response-related local maxima. Note that the delayed-
response instruction used in this experiment effectively pruned the
salient speeding of response times induced by target predictability.
www.pnas.org?cgi?doi?10.1073?pnas.0505508102Eichele et al.
Consequently, we did not observe ERP-related fMRI activation in
areas in the motor system(s) that were found sensitive to sequence
learning elsewhere (37, 38).
When studying the neuronal substrates of cognitive processes,
the researcher typically considers both their spatial and temporal
properties. There is, however, a disparity between the major
methods in human cognitive neuroimaging, focusing either on
the ‘‘where’’ (e.g., fMRI) or ‘‘when’’ (e.g., ERPs), thus providing
only a limited window into the neuronal correlates. We propose
here that a key to merging both methods is to exploit the
functional resolution, that is, how signatures of an experimental
manipulation are correlated. The crucial aspect of this approach
for spatiotemporal integration is to make effective use of single-
trial variability in the entire ERP time series to predict regional
fMRI activations, i.e., using time-variant effects induced by a
manipulation as a vehicle to achieve a temporal expansion of the
fMRI. The prospect of this conjunction is that it allows appli-
cation of an electrophysiologically derived temporal order to
fMRI activation that aids in determining the hierarchy and
‘‘serial’’ functional connectivity of brain regions associated with
(cardioballistic, eye movement) are removed. Effects of component removal on the ERPs are shown in a representative subject (Upper Center). Subsequently,
wavelet denoising (b) is applied to the single trials. AM vectors are derived separately for each time point and electrode. To ensure specificity, shared variance
between target presentation and AM is removed by orthogonalization. The regressors are convolved with canonical hemodynamic response functions (HRF) to
account for the neurovascular coupling before voxelwise correlations with the fMRI signal (c).
Flowchart of the single-trial ERP and fMRI analysis. Data are decomposed with independent components analysis (a), and artifact topographies
Eichele et al. PNAS ?
December 6, 2005 ?
vol. 102 ?
no. 49 ?
a process, in this case recognition of temporal patterns in the
Later components in the ERPs are often attributed to ‘‘endog-
enous’’ or ‘‘top-down’’ processing (32). Recent models of brain
function in the context of perceptual inference and learning focus
on the hierarchical nature of cortical systems and suggest that these
components derive from high levels of processing (ref. 39; for an
overview, see ref. 40). We therefore expected that the regionally
specific correlates of target predictability would most likely be
located outside the sensory regions, in multimodal higher-order
components (32), insensitive to the manipulation, would be local-
ized in the vicinity of sensory regions. For this reason, we did not
constrain our search for AM-related effects to the main effects of
auditory stimulation but used a whole-brain search for the latency-
specific correlates. The regional deployment of our activations
were coherent with metabolic or synaptic activity in higher cortical
Activation associated with auditory target processing, insensitive
to predictability, was seen maximally expressed in the superior
temporal gyri and in further areas associated with auditory and
visual target detection (12, 41, 42).
Three independent stages separated from peak to peak by 30 ms
(P2-N2), 120 ms (N2-P3), and 150 ms (P2-P3) were additionally
identified, where the amplitude modulations of single-trial ERP
sequences selectively predicted fMRI activation.
The first stage reached maximum intensity during P2 (?170 ms)
after target onset. It is worth noting that the regions mediating the
P2 effect overlap with those being associated with ‘‘default mode’’
brain activation (18, 43). P2 hosts processing negativities that
indicate matching processes between the sensory input and a
neuronal representation of stimuli selected for further processing
and as such are markers of sensory memory and selective attention
temporal and frontal lobes (32). It is, however, conceivable that
activated brain regions have a modulating effect on these compo-
nents, allowing for optimization of resource allocation when target
occurrence is predictable. This interpretation would also be con-
sistent with the role appointed to the ‘‘default mode’’ (18, 43). In
addition, fMRI?positron-emission tomography results of spatial
and temporal attention, and sequencing are overlapping with the
sites seen here (37, 38). The fMRI activation in the supramarginal
The second spatiotemporal stage during the N2 (?200 ms) was
located in the anterior frontomedian cortex and parahippocampal
regions. Portions of the N2 reflect the attentive detection of a
mismatch between stimulus features and an actively generated
memory template. fMRI correlates of this memory process are
?100 to 600 ms around stimulus onset for all targets at the third to sixth
of all regular TTI cycles (orange), and all standards not immediately before or
after a target (gray dotted). Effects of target predictability appear most
prominently as amplitude reductions of N2 and P3 and, to a lesser degree, P1,
0.05) from a pointwise t statistic are plotted as blue rectangles for random
target vs. standard comparison and in orange for the regular target vs.
standard comparison. Black rectangles below the waveform indicate signifi-
cant differences between the random and regular target categories.
(blue) correlations with the respective AM. Each correlation map shows for each voxel the maximum t value from the four electrodes (FZ, FC1, FC2, and Cz). To
the left of each rendering of the AM-correlated fMRI, the average AM (empty circles ? SEM) and the fitted sigmoid curves are shown. Top row, target-related
activation, P ? 0.05 (FWE), cluster size ?10; second row, P2 (170 ms); third row, N2 (200 ms); and fourth row, P3 (320 ms). All AM-related activations were
thresholded at P ? 0.001 (uncorrected), cluster extent threshold P ? 0.01.
AM-correlated fMRI results. Render views and maximum-intensity projections of the general target related activation and positive (red) and negative
www.pnas.org?cgi?doi?10.1073?pnas.0505508102Eichele et al.
observed in the same brain regions as activated in the present study
(38, 42). Moreover, intracranial recordings in the vicinity of these
(6, 7). Further, the scalp N2 to auditory targets is strongly reduced
AM is consistent with recent fMRI findings showing rapid pre-
frontal and hippocampal habituation to novel events (45).
The overall strongest and most extensive spatiotemporal stage
was related to the P3 (?320 ms) and yielded activations in frontal,
temporal, and parietal regions most prominent in the right hemi-
sphere. For all these regions, intracranial recordings have evi-
denced depth P3s with about the same peak latency (5–7). P3 has
been suggested to index a mechanism that is elicited when a
memory representation of the recent stimulus context is updated
upon detection of deviance from it (36, 46, 47). The effects of a
variety of manipulations (e.g., task relevance, information content,
probability, and sequence) have been delineated in support of this
view (36, 46, 47). fMRI activation in the P3-related regions is seen
in a variety of related cognitive operations, including target pro-
cessing (12, 23, 41, 42, 48), attention, working memory (38, 49) and
hemodynamic activity to auditory target?novel stimuli has been
right hemisphere (42, 50). Also, our data co-localize with fMRI
studies reporting right-lateralized attentional mechanisms that
would host much of the functionality that is probed by target
detection in general (49, 51–53) and, specifically, by manipulating
target predictability (54, 55). One should note, however, that a
portion of the lateralization might also be attributable to the
left-lateralized activation around the central sulcus induced by the
motor task, which could have minimized the relative contribution
of the predictability effect in adjacent areas to the total variance of
the fMRI signal.
The common feature in all three sequential spatiotemporal
stages was the sigmoid-shaped response amplitude modulation
coherently expressed in the ERP and fMRI, because the target-
psychophysiological example for such behavior is the orienting
response?reflex, which displays rapid habituation to regularly pre-
sented stimuli and dishabituation to deviants from a pattern of
ing is overlapping with that of the mismatch negativity (32) and P3
components (36, 42, 46, 58). At the core, all these neuronal
processes encompass detection of a salient change in the environ-
ment, comparison against a stored representation, and the elicita-
tion of an adequate response. Models accounting for these effects,
however, to an extent are conceptually incomplete in the sense that
they focus more on why and how the brain responds to unexpected
events than on how the representation, i.e., a prediction, is estab-
lished in the first place. This aspect, however, can be accounted for
by linking these orienting response?reflex-type responses with a
Bayesian scheme that defines neuronal systems as reciprocally
connected hierarchical generative models that construct context-
dependent expectancies (39). The amplitude behavior of ERP
components (and, correspondingly, the fMRI signal) would here
represent the state of prediction error in the model, indicating to
to detect the presence (or absence) of patterns in the environment
means to extract contingency rules with highly salient predictive
value for the anticipation of future events (39, 55, 59). In this
context, our data capture the spatiotemporal dynamics associated
with such perceptual inference and learning.
We thank Roger Barndon for his invaluable help with MRI data
acquisition, Christine Holen for her help with subject preparation, and
Jody C. Culham and Anthony Singhal for helpful comments on an earlier
draft. M.M. was supported by the Berlin Neuroimaging Center, Berlin
(BMBF). The present study was financially supported by grants from the
Research Council of Norway (to K.H.).
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