97:2075-2082, 2007. First published Dec 20, 2006; doi:10.1152/jn.01083.2006
Tervaniemi and Risto Näätänen
Marc Schönwiesner, Nikolai Novitski, Satu Pakarinen, Synnöve Carlson, Mari
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Heschl’s Gyrus, Posterior Superior Temporal Gyrus, and Mid-Ventrolateral
Prefrontal Cortex Have Different Roles in the Detection of Acoustic Changes
Marc Scho ¨nwiesner,1,2,5Nikolai Novitski,1,2Satu Pakarinen,1,2Synno ¨ve Carlson,2,3,4Mari Tervaniemi,1,2
and Risto Na ¨a ¨ta ¨nen1,2
1Cognitive Brain Research Unit, Department of Psychology,2Helsinki Brain Research Centre, and3Neuroscience Unit, Institute of
Biomedicine/Physiology, University of Helsinki, Helsinki, Finland; and4Medical School, University of Tampere, Tampere, Finland; and
5Advanced Magnetic Imaging Centre, Helsinki University of Technology, Finland
Submitted 10 October 2006; accepted in final form 20 December 2006
Scho ¨nwiesner M, Novitski N, Pakarinen S, Carlson S, Tervaniemi
M, Na ¨a ¨ta ¨nen R. Heschl’s gyrus, posterior superior temporal gyrus,
and mid-ventrolateral prefrontal cortex have different roles in the
detection of acoustic changes. J Neurophysiol 97: 2075–2082, 2007.
First published January 3, 2007; doi:10.1152/jn.01083.2006. A part of
the auditory system automatically detects changes in the acoustic
environment. This preattentional process has been studied exten-
sively, yet its cerebral origins have not been determined with suffi-
cient accuracy to allow comparison to established anatomical and
functional parcellations. Here we used event-related functional MRI
and EEG in a parametric experimental design to determine the cortical
areas in individual brains that participate in the detection of acoustic
changes. Our results suggest that automatic change processing con-
sists of at least three stages: initial detection in the primary auditory
cortex, detailed analysis in the posterior superior temporal gyrus and
planum temporale, and judgment of sufficient novelty for the alloca-
tion of attentional resources in the mid-ventrolateral prefrontal cortex.
I N T R O D U C T I O N
Detecting changes in the environment is essential for the
survival of many organisms. Brain mechanisms of acoustic
change detection have been extensively studied in humans
using EEG. The prime experimental model of auditory change
detection is the presentation of infrequent deviant events in a
stream of repeating standard events. The deviant sounds evoke
a frontal negative deflection in the auditory event-related po-
tential, the mismatch negativity (MMN) (Na ¨a ¨ta ¨nen et al. 1978).
The MMN can be recorded in response to any discriminable
change in the stimulus stream, and the response amplitude
correlates with the magnitude of the acoustic change. The
MMN is important in two respects: first as a means to study the
mechanisms of change detection and how these relate to other
cognitive processes such as attention and memory and second
as a widely used tool in diverse areas of research, including
language acquisition, sound localization, and psychiatric and
developmental disorders (Na ¨a ¨ta ¨nen 1995, 2003).
The MMN is often interpreted to imply the existence of a
sensory–memory trace in which the features of the frequently
occurring standard stimuli are represented. Much research has
been dedicated to the translation of this psychological model
into neurobiological mechanisms. The localization of the ce-
rebral origin of the mismatch negativity potential was a major
aim in several functional MRI (fMRI), magneto-encephalo-
graphic, and high-density EEG studies. However, the regional
specificity of the results has remained relatively low. Two
contributions to the change response, one from the temporal
lobes and one from the right frontal lobe, were suggested on
the basis of the current density distribution of evoked potentials
(Giard et al. 1990) and reductions of the MMN amplitude in
patients with lesions in the frontal and temporal lobes (Alain et
al. 1998; Alho et al. 1994). Since then, a number of neuroim-
aging studies have tried to locate the generators of these
components (Doeller et al. 2003; Liebenthal et al. 2003;
Marco-Pallares et al. 2005; Mathiak et al. 2002; Molholm et al.
2005; Muller et al. 2002; Opitz et al. 1999; Rinne et al. 2005;
Schall et al. 2003; Tervaniemi et al. 2006). Results vary
substantially across these experiments. Nevertheless, all of
these studies report activation in the region of the superior
temporal gyrus, sometimes including Heschl’s gyrus, and sev-
eral found activation of the inferior frontal gyrus.
A more precise localization that allows reliable comparison
to known anatomical areas or functional parcellations of the
superior temporal and inferior frontal gyri would allow signif-
icant progress in the understanding of preattentive change
detection. For instance, the part of the inferior frontal gyrus in
which most of the reported MMN-related activations fall can
be subdivided into three areas (Brodmann areas 44 and 45 and
the deep frontal operculum), with different connection patterns
in individual subjects using diffusion tensor imaging (Anwan-
der et al. 2007). A second example is the question of whether
the change-detection mechanism is co-localized with primary
feature processing (i.e., does the detection of small pitch
changes happen in areas that extract pitch). This is a tacit, but
unproven, assumption in studies that use the MMN as a tool to
locate the processing of acoustic features (Pulvermu ¨ller et al.
2006; Shestakova et al. 2004; Tervaniemi et al. 2006).
Several factors have considerably hindered pinpointing the
location of the change-related effects with higher regional
specificity using fMRI: 1) the response to a subtle change in a
stream of acoustic stimuli is much smaller in magnitude than
the response to an isolated sound; 2) the dynamic range of the
response is further decreased by the MRI scanner noise if
images are acquired continuously; 3) in block-design experi-
ments, the number of standard stimuli between deviants is
necessarily low, decreasing the amplitude of responses to the
Address for reprint requests and other correspondence: M. Scho ¨nwiesner,
Dept. of Neurology and Neurosurgery, Montreal Neurological Inst., McGill
Univ., 3801 rue University, Montreal, Quebec H3A 2B4, Canada (E-mail:
The costs of publication of this article were defrayed in part by the payment
of page charges. The article must therefore be hereby marked “advertisement”
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
J Neurophysiol 97: 2075–2082, 2007.
First published January 3, 2007; doi:10.1152/jn.01083.2006.
20750022-3077/07 $8.00 Copyright © 2007 The American Physiological Society www.jn.org
on July 9, 2008
deviants (Haenschel et al. 2005); 4) responses to sounds
deviating in frequency (the most commonly used deviant type)
are confounded, because infrequent stimulation with a different
frequency will activate “fresh” neural populations in tonotopi-
cally organized cortical areas—this is not the case for other
sound features that are not represented topologically, such as
violations of temporal order (complex MMN; see Paavilainen
et al. 2001), and perhaps sound duration (but see Pantev et al.
1989); 5) studies using duration deviants to avoid adaptation-
related confounds have not accounted for the decreased stim-
ulus energy of deviants shorter than the standard, further
diminishing the amplitude of the response. Some of the previ-
ous studies address one or more of these points, but none
Here we surmount those problems with a parametric event-
related fMRI and EEG experiment, using sparse imaging to
eliminate the effects of scanner noise. This procedure permits
localization of the responses in individual brains, as well as
individual comparison of EEG and fMRI results. We measure
responses to several deviant magnitudes to separate different
parts of the change detection mechanism.
M E T H O D S
Thirteen volunteers (between 20 and 30 yr, 4 male, 10 right-
handed) took part in this experiment after giving written informed
consent. The participants had no history of audiological or neurolog-
ical disease. The experimental procedures conformed with the Code of
Ethics of the World Medical Association (Declaration of Helsinki)
and were approved by the Ethics Committee for Ophthalmology,
Otolaryngology, Neurology, and Neurosurgery of the University Hos-
pital and by the Ethics Committees of the Department of Psychology
of the University of Helsinki.
All sounds were click trains with a rate of 500 Hz, low-pass filtered
at 8,000 Hz. At this rate, the clicks are not perceived individually but
as a complex tone with a pitch of 500 Hz, including all harmonics of
this frequency up to the low-pass filter cut-off. The number of clicks
was varied to generate stimuli of different durations: 100, 74, 52, and
30 ms. The 100-ms click train is referred to as “standard,” whereas the
shorter click trains are referred to as “deviants,” specifically as small
(74 ms), medium (52 ms), and large deviants (30 ms), according to the
acoustical difference from the standard sound. The sound durations
were chosen to elicit an approximately linear increase in the magni-
tude of the deviance response (Na ¨a ¨ta ¨nen et al. 2004). The stimuli were
equalized for root-mean-square energy, so that the energy contour of
a sequence of stimuli was constant over time. Changes in the sound
duration were thus the only salient feature in the stimulus stream to
elicit responses. No attempt was made to control for effects of the
physical differences between standard and deviant sounds on brain
activation, such as the shorter delay of the neural offset response to
deviants of slightly shorter duration than standard sounds. Such
effects can be controlled by reversing the role of standards and
deviants between blocks (Kujala et al. 2007). Note, however, that
because of its low temporal resolution, fMRI is relatively insensitive
to differences in the temporal layout of the response, such as caused
by the slightly different delay of the sound offset. It is therefore
unlikely that this particular confound of sound duration changes is a
large factor in the observed responses.
During the EEG session, scanner noise recorded from the sequence
used in the fMRI experiment was played back to the participants,
simulating the acoustical environment in the MRI scanner.
All participants took part in an EEG recording and in a subsequent
fMRI session. FMRI scanning was done at the Advanced Magnetic
Imaging Centre, Helsinki. During the fMRI session, participants wore
pneumatic headphones (which provided sufficient playback quality
for the relatively simple stimuli), and looked at a screen though a
mirror attached to the head coil. Auditory stimulus presentation was
organized in 9-s trials. Each trial started with the 1.2-s sound of the
fMRI image acquisition, played back through headphones (in the EEG
session) or produced by the scanner (in the fMRI session). Starting 50
ms past trial onset and continuing during the whole duration of the
trial, 27 100-ms click trains were presented repetitively with a stim-
ulus onset asynchrony of 333 ms. Either 2, 3, 4, 5, or 7 s before trial
offset, one of the standard stimuli was replaced by a deviant sound.
While irrelevant in the EEG session, this timing allowed estimation
of the hemodynamic response to the deviants in the fMRI session
We weighted the sampling of the hemodynamic response function
according to their potential contribution in locating the responses, i.e.,
we acquired most repetitions from time-points close to the expected
peak of the hemodynamic response, thus trading some of the response
function estimation power for response detection power. For each
deviant, we acquired 15, 20, 20, 20, and 10 repetitions for time-points
2, 3, 4, 5, and 7 s, respectively (85 repetitions in total). Additionally,
25 trials containing only standard sounds served as a baseline, making
up a total of 16 experimental conditions (3 deviant types ? 5 possible
onset times within the trial ? baseline). Altogether 280 trials (85 trials
per deviant ? 3 deviant types ? 25 baseline trials) were presented in
pseudorandom order with equalized transition probabilities. Total
experimental time in the fMRI session was 42 min, which was split in
To control attention and direct it away from the acoustic stimuli and
to reduce eye movements, participants were asked to fixate a cross at
the center of the screen and perform a visual control task. The task
was to press a button with the left or right index finger on each
occurrence of a capital letter in a sequence of random digits that were
shown at the center of the screen. The digits were presented for 80 ms
at irregular intervals with an average of four digits per trial. The target
occurred on average once every two trials. After the fMRI session,
participants were asked to rate their level of alertness during scanning,
the subjective sound level, and the difficulty of the task in comparison
to the EEG session.
During the EEG session, participants were seated in a comfortable
chair in a sound attenuated room. The presentation of the experiment
and the task were the same as in the fMRI session. Because the EEG
analysis required a higher number of repetitions per deviant, the
experiment was run twice during the EEG session.
EEG recording and analysis
An EEG was recorded with 128 active sintered Ag-AgCl electrodes
(BioSemi, Amsterdam, The Netherlands), positioned radially equidis-
acquisition (gray bars) left 7.8 s of silence for stimulus presentation (row of
black lines) and decay of response to scanner noise (dashed line). Deviant
stimuli (black arrows) were presented a different time-points in relation to
image acquisition. This allowed sampling of different time-points (white
arrows) of hemodynamic response (black curve) at 2, 3, 4, 5, and 7 s (from
right to left) after deviant onset.
Experimental design in functional MRI (fMRI) session. Image
2076 SCHO ¨NWIESNER ET AL.
J Neurophysiol • VOL 97 • MARCH 2007 • www.jn.org
on July 9, 2008
tant from the vertex across the scalp (BioSemi ABC layout). Addi-
tional electrodes were placed at the left and right mastoid, at the outer
canthi of each eye, at the right eye supra- und infraorbitally, and on
the nose tip. The setup does not use a conventional recording refer-
ence but instead actively clamps the average potential of the subject
by a feedback loop between two dedicated electrodes to the A/D
conversion reference voltage. The data were recorded direct-current-
coupled and digitized with 512-Hz sampling rate. Low-pass filtering
to avoid aliasing was performed by the decimation filter of the A/D
converter (5th order sinc response, ?3 dB point at 102 Hz). The
resulting data files were transformed into the Neuroscan continuous
data format (PolyRex software, http://psychophysiology.cpmc.columbia.
edu/Software/PolyRex; constant gain across all data sets). Signals
from the scalp electrodes were rereferenced to the nose tip potential.
Signals from the face electrodes were used to compute the horizontal
and vertical bipolar electro-oculogram. The data were filtered with a
digital band-pass filter between 1 and 15 Hz with slopes of 24
dB/octave. Data epochs from 100 ms before to 350 ms after stimulus
onset with samples exceeding ?75 ?V were rejected from the
subsequent analysis. The data were visually inspected for residual
artifacts. Responses to standards (excluding standards directly after
deviants) and each of the deviants were averaged separately. The
responses to standards were subtracted from those to deviants. In such
difference waveforms, the MMN is a negative-going potential at Fz
and a positive-going potential at the right and left mastoid in the range
of 150–250 ms after deviant onset. To assess statistical significance of
MMN responses, the distribution across participants of peak ampli-
tudes in the baseline interval was compared with the distribution of
peak amplitudes across participants in an interval of equal length
around the latency of the grand average MMN. This method was
chosen instead of the usual comparison of individual peak amplitudes
in the MMN latency range to zero, which is slightly biased, because
the expected value of amplitude maxima in a certain time range in the
absence of a signal is not zero. If this value reflects noise in the signal
and the experimental conditions are presented in a balanced pseudo-
random sequence, the distribution of baseline amplitude maxima
across conditions should be nearly identical. A significant difference
in the baseline power between deviant conditions might bias the
results of a test for significance of the MMN responses. The peak
amplitudes in the root mean square across all channels of the EEG
data in both intervals for all participants and deviant conditions were
entered into a one-tailed paired t-test. We also tested for equal
baseline peak amplitude using a two-way ANOVA with factors
interval and deviance. If the baseline power is caused by noise, a
significant interaction between interval and deviant conditions is
expected (effect of deviant in the MMN but not baseline interval). The
peak amplitudes of the MMN responses at Fzwere additionally
subjected to a one-way ANOVA with factor deviance and at right and
left mastoids with two-way ANOVA with factors deviance and
hemisphere. Greenhouse-Geisser correction was applied when neces-
To obtain a data-driven estimate of the number of components in
the MMN response, we performed a spatial principal component
analysis of the individual evoked potentials, and visualized the prob-
able cerebral origin of the first two principal components as the
average of the electrode locations weighted by the contribution of
each electrode to the principal component analysis (PCA) component.
This estimate takes into account that PCA components are often not
dipolar. These location estimates were used solely to seed a subse-
quent dipole model, and all further analyses and conclusions are based
on the dipole analysis.
The sources of the responses to the three different deviants were
analyzed with three regional sources and a four-shell ellipsoidal
volume conductor as a head model using version 5.1 of the Brain
Electrical Source Analysis software (BESA, Gra ¨felfing, Germany).
The locations of two of the regional sources were constrained to be
symmetric about the midsagittal plane, and fitting was performed
within a 50-ms time window centered on the peak of the response.
Within the fit window, the residual variance of the model amounted to
only 1.4% for the small, 0.7% for the medium, and 1.1% for the large
The model was used as a spatial filter to derive the activation
time-course of each regional source (source waveform) for the three
deviant conditions in the grand-average and in each individual sepa-
rately. The orientations of the regional sources were adjusted in each
individual so that one of the components captured the maximum of the
global field power in the fit window. The peak amplitudes of these
components across individuals were analyzed in a two-way ANOVA
with factors deviance and location. Greenhouse-Geisser correction
was applied when necessary. Post hoc analyses were performed with
Tukey’s test for honestly significant differences.
fMRI and analysis
Blood-oxygen level dependent contrast images were acquired at 3
T (SIGNA EXCITE, General Electric) using gradient echo planar
imaging (TR/TE 9,000 ms/32 ms) with a head quadrature receiver/
transmitter coil. The functional images consisted of 19 ascending
slices with an in-plane resolution of 3 ? 3 mm (matrix size 642), a
slice thickness of 3 mm, and an interslice gap of 1 mm. The seventh
slice followed the line connecting the anterior and posterior commis-
sures. The slices were acquired in direct temporal succession in the
first 1,200 ms of the TR, followed by 7,800 ms of stimulus presen-
tation without acquisition noise. This clustering of the slice acquisi-
tion at the beginning of a long TR (sparse imaging) reduces the effect
of scanner noise on the recorded response to the stimuli (Edmister et
al. 1999; Hall et al. 1999).
A high-resolution structural image was acquired from each
subject using a T1-weighted spoiled grass gradient-recalled three-
dimensional (3D) sequence with a resolution of 1 mm3(matrix
size, 256 ? 256 ? 150).
Data were corrected for head motion [1 participant’s data were
excluded from further analysis because of excessive (?2 mm) head
translation], spatially smoothed with a 6- (individual participant anal-
ysis) or 10-mm (group analysis) full-width-at-half-maximum Gauss-
ian kernel, and transformed into the stereotaxic space of the interna-
tional consortium for brain mapping 152 atlas (MNI space) using
MINC 1.4 software. Statistical analysis was done with Matlab (The
MathWorks, Natick, MA) and the FMRISTAT toolbox (Worsley et al.
2002). All deviant conditions were contrasted with the baseline
condition, and regions of interest (ROIs) were defined as nine-voxel
neighborhood of local maxima in the resulting statistical parameter
map. Hemodynamic response functions in those ROIs were estimated
by contrasting responses to each of the three deviants at each of the
five time-points separately with the baseline. Because of the high
number of modeled conditions, this is the statistically least powerful
contrast, and only group level effects are reported. The average
hemodynamic response function was used to model the responses to
all three deviants in a single contrast against the baseline in individual
participants. This is the statistically most powerful contrast, and
resulting statistical parameter maps were used to identify activated
areas in individual brains. The individual results were combined in a
random effects analysis to allow inferences on population level
(Worsley et al. 2002) and identify activated areas in the group. Signal
changes in response to the three deviants were extracted from the
ROIs to check correlation with EEG results.
R E S U L T S
Significant mismatch negativity (MMN; Fig. 2) responses to
the deviant sounds were observed in EEG recordings (compar-
ison of RMS maxima in baseline and MMN intervals: P ?
0.0001; t12? 7 for large and medium deviants; P ? 0.0087; t12?
2077CEREBRAL PATHWAY FOR AUDITORY CHANGE DETECTION
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on July 9, 2008
2.7 for small deviant; ANOVA of RMS maxima in baseline
and MMN intervals: effect of interval P ? 0.0001, F1,11?
43.9, effect of deviant P ? 0.001, F2,10? 17.4, interaction
interval ? deviant P ? 0.0001, F2,10? 22.6, no significant
differences in baseline RMS maxima across deviant condi-
tions). MMN amplitudes at Fzand at the mastoids increased
with deviance magnitude (ANOVA of voltage maxima: P ?
0.001; Fz: F2,24? 15.8, mastoids: F2,26? 30.6). There was no
significant effect of the factor hemisphere. The latency of the
MMN decreased with deviance magnitude (P ? 0.05; Fz: F2,24?
4.3, mastoids: F2,26? 4.3).
No differences were observed between the reaction times
and detection rates for the visual target detection task
between the fMRI and EEG sessions. Most participants
reported a similar subjective difficulty of the task in both
sessions, but three subjects reported that the novel environ-
ment of the MR scanner made it initially more difficult to
We performed a random effects analysis of the groups’
fMRI data to localize cerebral origins of these responses.
Clusters of significant (P ? 0.05 corrected) activations were
found bilaterally in the temporal lobes and in the right inferior
frontal lobe (Fig. 3).
The anatomical loci of the activation maxima were as
follows (see Table 1 for coordinates): medial and lateral
Heschl’s gyri (HG), antero-lateral and medial portions of the
left planum temporale (PT), portions of the superior temporal
gyrus (STG) and sulcus (STS) inferior and posterior to HG,
and the mid-ventrolateral prefrontal cortex, bracketed by the
horizontal and ascending rami of the inferior frontal sulcus
(Brodmann area 45). Hemodynamic responses in those areas
peaked ?3–4 s after deviant onset and returned to baseline ?7
s after deviant onset (Fig. 4, top).
A hemodynamic response function was fitted to the average
of all responses and used to model the response magnitude for
each deviant across the five time-points. In activated areas of
the superior temporal lobe, but not in the prefrontal cortex, the
response magnitude increased with the deviance magnitude
(Fig. 4, bottom).
Results of individual participants
To check the consistency of the group results across indi-
viduals, statistical parameter maps were computed for each
participant. Activated areas were compared with individual
structural images, and effect sizes were extracted from local
maxima in the maps. The constant stream of standard stimuli
and small deviants at different locations across scalp (nomenclature of loca-
tions according to extended 10–20 system; Jasper 1958; Sharbrough et al.
1991). Amplitude difference between responses to standard and deviants is
plotted over time relative to stimulus onset.
Grand-average mismatch potentials in response to large, medium,
maps showing significant responses (P ? 0.05 corrected) shown on sections
through mean structural image of group (A–E: radiological orientation; inset:
slice locations) and on an individual gray matter surface [international con-
sortium for brain mapping single subject anatomical template; right (F) and left
hemispheres (G)]. HG, Heschl’s gyrus; STG, superior temporal gyrus; PT,
planum temporale; STS, superior temporal sulcus; PFC, mid-ventrolateral
Activations in response to duration changes. Statistical parameter
acoustic stimulus stream
Brain areas activated by duration changes in the
Region SideCoordinates (x,y,z)
?44, ?25, 7
?52, ?12, ?1
42, ?20, 5
46, ?10, 1
?50, ?35, 16
?63, ?28, 10
65, ?36, 6
?64, ?16, ?2
?62, ?2, ?8
60, ?23, ?4
46, 23, 11
49, 32, 2
Coordinates are in MNI space (lookup available at www.bic.mni.mcgill.ca/
cgi/icbm_view). P values are corrected for multiple comparisons. HG,
Heschl’s gyrus; PT, planum temporale; STG, superior temporal gyrus; STS,
superior temporal sulcus; IFG, inferior frontal gyrus.
2078SCHO ¨NWIESNER ET AL.
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on July 9, 2008
reduces the dynamic range of responses to deviant stimuli
compared with typical sound versus silence contrasts. Re-
sponses were nevertheless clear in all participants, albeit with
a high degree of interindividual variability (Fig. 5).
Note that while the locations of the suprathreshold local
maxima were variable, the regions that show responses just-
below threshold were similar in all participants (leading to
significant activations in the random effects analysis). The
majority of the activation foci in the superior parts of the
temporal lobes were in HG, STG, STS, and PT. The small foci
in the frontal lobes were consistently located in parts of the
mid-ventrolateral prefrontal cortex across subjects.
To compare the responses from temporal and frontal sites
between fMRI and EEG sessions, we separated the individual
ERPs into temporal and frontal components using source
analysis. To check whether this separation is in fact possible in
a data-driven manner without knowledge of the fMRI activa-
tion sites, we performed a spatial PCA of the individual ERPs.
In all individuals, the first component was localized between
the superior parts of the left and right temporal lobes, consis-
tent with a superposition of bilateral responses from the audi-
tory cortices. The second component was located in the inferior
part of the frontal lobe in the majority of individuals, with a
slight average lateralization to the right. The locations of these
two components indeed suggested temporal and frontal contri-
butions to the overall response, consistent with the hemody-
namic response pattern.
To obtain a reliable estimate of the response of the temporal
and frontal contributions to the different deviant sounds, we
analyzed the sources of the evoked potentials with equivalent
current dipole modeling, using the location estimates of the
principal components as seeds. The group average responses
were modeled with three regional sources, two of them with a
symmetry constraint to account for bilateral responses from the
auditory cortices. The resulting sources were located bilaterally
in the vicinity of HG and in the right frontal lobe. The spatial
resolution of this model is relatively low, but the general
locations agree with the fMRI data. The model was used as a
spatial filter to derive the activation time-course of each re-
gional source (source waveform) for the three deviant condi-
tions in the grand average (Fig. 6) and the peak amplitudes in
each individual separately (Fig. 7, left).
In both EEG and fMRI data, the response magnitudes from
sites in the temporal lobes increased with deviance magnitude,
whereas the responses from frontal sites were not modulated by
the deviance magnitude across participants (F2,11? 30, P ?
0.0001 for effects of response origin, deviance magnitude, and
their interaction; post hoc tests for deviance dependence of
temporal sites P ? 0.001, and frontal sites P ? 0.89; Fig. 7).
Activity of the frontal sites showed in some individuals a trend
toward increasing or decreasing responses with deviance mag-
medium, and large deviants (arbitrary units). IFG a.r./h.r., ascending and
horizontal rami of inferior frontal gyrus, respectively.
Hemodynamic responses from different cerebral locations to small,
and extent of slice portions indicated at bottom). Responses in superior
temporal lobes showed considerable variation across the 12 participants.
Responses in frontal lobes (arrows) clustered consistently around the
horizontal and ascending rami of the IFG.
Individual activation patterns (radiological orientation, position,
model of grand-average EEG responses to 3 deviants. Responses from bilateral
temporal sources strongly depend on deviance magnitude. They are followed
?50 ms later by response from right frontal source that is independent of
Activation time-courses obtained from equivalent current dipole
2079 CEREBRAL PATHWAY FOR AUDITORY CHANGE DETECTION
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on July 9, 2008
nitude. Of the responsive sites in the temporal lobes, in 9 of 12
participants, the posterior STG and neighboring lateral PT
showed the clearest modulation by deviance magnitude.
D I S C U S S I O N
Using an experimental design that overcomes previous me-
thodical difficulties, we were able to localize the cerebral sites
involved in the detection of duration changes in a constant
stream of acoustic stimuli with greater precision than previ-
ously achieved. We characterized the degree of interindividual
variability and showed, at the level of individual brains, dif-
ferent response patterns of temporal and frontal sites in the
high-density EEG and fMRI data.
Responses in the temporal lobes were found in lateral and
medial portions of HG, in the medial and lateral PT bordering
HG, and along STG and STS, mostly posterior to HG.
In earlier brain imaging studies on preattentive auditory
deviance detection, activation of the STG has been the most
consistent finding (Doeller et al. 2003; Liebenthal et al. 2003;
Mathiak et al. 2002; Molholm et al. 2005; Muller et al. 2002;
Opitz et al. 1999, 2002; Rinne et al. 2005; Schall et al. 2003;
Tervaniemi et al. 2000, 2006). The region responsive to devi-
ants in our study encroached the PT, mostly adjacent to STG,
STS, and the medial and lateral parts of HG. The posterior STG
and adjacent parts of the planum temporale showed an increase
in activity with increasing deviance magnitude. Several ana-
tomical areas have recently been delimited on the STG using
observer-independent measures of differences in cyto- and
receptoachitecture (Morosan et al. 2005; Schleicher et al.
2005). According to this schema, area Te3 covers the posterior
two thirds of the outer convexity of the STG (a posterior
portion of Brodmann area 22). Its location fits well with the
activated regions of STG that showed the highest dependence
on deviance magnitude. Homolog regions in the primate audi-
tory cortex belong to the parabelt, a tertiary region in the
processing hierarchy of the auditory cortex (Kaas and Hackett
2000) that receives indirect connections from the primary
auditory cortex through adjacent secondary (belt) areas (Kaas
and Hackett 1998). In humans, the belt region would occupy
the lateral HG and parts of the PT and planum polare adjacent
to HG (Galaburda and Sanides 1980). We indeed found re-
sponses to the deviant sounds in the lateral HG and the planum
Medial portions of HG were found active in 6 (9 if responses
slightly below statistical significance are included) of 12 par-
ticipants. This suggests a contribution of the primary auditory
cortex to deviance detection, which is in agreement with the
demonstration of stimulus-specific adaptation of responses in
the primary auditory cortex—a candidate neural correlate for
some of the change responses observed in humans (Ulanovsky
et al. 2003). Recordings of duration–MMN responses from
depth electrodes in human HG also implicate the primary
auditory cortex in change detection (unpublished observa-
tions). Opitz et al. (2005) showed that, during detection of
frequency deviants, secondary areas on lateral HG seem to
mediate a memory-trace based mismatch response, whereas
activity on medial HG is related to a sensory mechanism of
change detection, the stimulation of nonrefractory portions of
tonotopic auditory cortex. The authors concluded that both of
these mechanisms contribute to the MMN evoked by frequency
deviants. It is unclear whether a similar sensory mechanism
can account for the activation of primary auditory cortex
during the detection of duration deviants. In mammals, dura-
tion-selective neurons have been found in the mouse inferior
colliculus (Brand et al. 2000) and cat auditory cortex (He et al.
1997), but there is no indication of a large-scale topographic
representation of sound duration in the human primary auditory
Based on these findings, we suggest that changes in the
acoustic environment are initially detected at or below the level
of the primary auditory cortex. Because the responses from the
posterior STG and lateral PT follow the deviance magnitude
most closely, these structures might extract the details of the
acoustic change. The STS may be involved in a secondary
process that is only loosely dependent on deviance magnitude
and relies on input from the STG. There is indeed evidence that
activity in the STS is more dependent on involuntary shifts of
attention toward the deviant sound than on passive detection of
the deviant (Sabri et al. 2006). Interestingly, the hemispheric
distribution of the responses to duration changes might depend
on the nature of the stimulus. In this study, we found no
significant systematic differences in the responses from the left
lobe (white bars) responses between EEG and fMRI sessions. For clarity, only
fMRI activity of local maximum that showed closest correlation with EEG
responses is shown. r/l, right/left side structure.
Comparison of individual temporal lobe (black bars) and frontal
2080 SCHO ¨NWIESNER ET AL.
J Neurophysiol • VOL 97 • MARCH 2007 • www.jn.org
on July 9, 2008
and right superior temporal plane, whereas Tervaniemi et al.
(2006) showed that complex speech- and music-like stimuli
might evoke stronger responses in the left and right STG/STS,
In the majority of the participants, the responses in the IFG
were found between the ascending and horizontal ramus. This
region of the mid-ventrolateral prefrontal cortex corresponds to
Brodmann area 45 (Amunts et al. 1999) and is connected with
the STG and STS through the arcuate and superior longitudinal
fascicle (Geschwind 1970; Petrides and Pandya 2002). There
was a latency difference of ?50 ms between the peak in
activity of the frontal and temporal sources in our equivalent
current dipole model of the EEG responses. Tse et al. (2006)
found a similar latency difference of ?60 ms with optical
imaging in humans between activity in the superior temporal
and inferior frontal lobe. These differences in the latency of
temporal and frontal responses suggest that change-related
activity in the IFG relies on afferent projections from the
perisylvian region of the temporal lobes. Moreover, the latency
difference was almost an order of magnitude higher that the
passive conduction time between the two sites, suggesting that
the activity observed in the mid-ventrolateral prefrontal cortex
indicates cerebral processing rather than passive conduction.
This processing is thought to relate to a possible switch of
attention to the deviant sound (Giard et al. 1990). The P3a
(Squires et al. 1975) is an event-related potential, probably
generated in the prefrontal cortex (Knight 1984) and thought to
indicate the allocation of attentional resources to novel events
(Daffner et al. 2000; Escera et al. 1998). The latency of the
frontal source (just between MMN and P3a) and its indepen-
dence of deviance magnitude (whereas MMN and P3a increase
in amplitude with deviance magnitude (Na ¨a ¨ta ¨nen et al. 2004;
Schro ¨ger and Wolff 1998) suggest that activity of the frontal
source might indicate an intermediate step, perhaps a decision
whether the stimulus is sufficiently novel to require attentional
resources. Our deviant sounds are presented 85 times each and
probably cease to be novel to the participants after the first few
presentations. Because novelty is not an attribute of the stim-
ulus, but an evaluation that arises in the brain of the listener, it
must have a neural correlate. While the detection of a deviating
sound can be based on sensory memory (which decays within
a few seconds; Ma ¨ntysalo and Na ¨a ¨ta ¨nen 1987; Sams et al.
1993), the decision of whether this sound is in fact novel (was
not heard previously during the experiment) would have to be
based on a process with a longer lasting memory span than the
MMN system. An involvement of the right mid-ventrolateral
prefrontal cortex (Brodmann areas 45 and 47/12) in such
memory-based decisions has been shown in humans and ma-
caque monkeys (Petrides 2005). Petrides et al. (2002) found
activation of the mid-ventrolateral prefrontal cortex when hu-
man participants had to find a novel stimulus in pairs of
familiar and novel visual stimuli. Activation in the perisylvian
areas of the temporal lobe related to the detection of a deviant
stimulus may trigger activity in the mid-ventrolateral prefrontal
cortex to quickly determine whether the deviant stimulus
requires additional attentional resources (call for attention;
O¨hman 1979). However, at least one study found frontal
activity preceding activation in the temporal lobes by ?20 ms
(Yago et al. 2001). According to these authors, the early frontal
activity might reflect either a genuine MMN component, a
frontal subcomponent of the N1 involved in the processing of
the frequency deviants presented, or an artifact of the proce-
dure. Nevertheless, because activity in temporal and frontal
regions largely overlaps in time, it is possible that one response
is not merely triggered by the other but that information is
flowing back and fourth between these areas.
In summary, our results suggest that at least three regions of
the cerebral cortex are involved in the automatic processing of
acoustic changes: the primary auditory cortex, the posterior
superior temporal gyrus and planum temporale, and the mid-
ventrolateral prefrontal cortex. Analysis of the timing of activ-
ity in the EEG data and comparison with previous results
support a hierarchical model in which these three regions are
involved in the initial detection of an acoustical change, a
detailed analysis of the change, and judgment of sufficient
novelty for the allocation of attentional resources, respectively.
A C K N O W L E D G M E N T S
We thank E. Brattico for helpful discussions throughout the study, K. Alho
for comments on the manuscript, and A. Tarkiainen and M. Kattelus for
technical assistance with the scanner.
G R A N T S
This work was supported by Academy of Finland, National Centre of
Excellence Program Grants 211486, 211487, 211488, and 213933.
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