Hemispheric shifts of sound representation
in auditory cortex with conceptual listening
Andre ´ Brechmann and Henning Scheich
Leibniz-Institute for Neurobiology, Brenneckestr. 6,
39118 Magdeburg, Germany
The weak field specificity and the heterogeneity of neuronal filters
found in any given auditory cortex field does not substantiate the
view that such fields are merely descriptive maps of sound
features. But field mechanisms were previously shown to support
behaviourally relevant classification of sounds. Here the prediction
was tested in human auditory cortex (AC) that classification-tasks
rather than the stimulus class per se determine which auditory
cortex area is recruited. By presenting the same set of frequency-
modulations we found that categorization of their pitch direction
(rising versus falling) increased functional magnetic resonance
imaging activation in right posterior AC compared with stimulus
exposure and in contrast to left posterior AC dominance during
categorization of their duration (short versus long). Thus, top-down
influences appear to select not only auditory cortex areas but also
the hemisphere for specific processing.
Keywords: cognition, laterality, magnetic resonance imaging, prosody, task
Specializations of auditory cortex (AC) for sounds including
speech and music have been viewed as analytical dispositions
for physical properties of stimuli, namely that right and left
hemispheres are recruited by stimuli with high demand on
spectral respectively temporal processing (Zatorre et al., 2002).
However, any sound contains spectral and temporal informa-
tion. The significance of one or the other for a receiver may
change with conceptual listening involving e.g. discrimination
or categorization. This could modify the representation of the
physical properties of sounds. In animal auditory cortex (AC)
electrophysiological correlates of categorization have recently
emerged in right AC from experiments with frequency modu-
lations (FM) (Ohl et al., 2001, 2003a,b). There it was shown that
after an initial state of descriptive (tonotopic) FM representa-
tion AC can switch to a state in which the two categories rising
and falling FM are captured independent of frequency content.
This approach was adapted to the present fMRI study with
the aim of distinguishing areas of human AC in the two hemi-
spheres that are related to such categorizations. The design in
experiment I was to present the same set of linearly frequency
modulated tones initially for uninformed listening and then
for the task of categorizing FM direction (rising versus falling).
In experiment II categorization of direction was compared
with categorization of duration (long--short) of stimuli of the
same set. This addressed the questions of feature-selectivity of
category-dependent activation. The design with the chosen
FM stimuli has two further implications, related to (i) the
generally uncertain functional differences between AC areas for
FM processing and (ii) the unsettled question of hemispheric
specialization for FM as features of speech prosodies.
Single unit mapping results in animal AC have left an
explanatory gap as to functions of different fields. Fields contain
types of neuronal filters tuned to various spectral and temporal
sound features. The specialization of any member of a type
covers only a restricted range of a feature dimension. The
prevailing (bottom up) concept derived from such properties
views fields as descriptive maps of stimulus parameters suffi-
ciently challenged experimentally at the neuronal level by
simple exposure to sounds and even in an anesthetized state
(Clarey et al., 1992; Suga, 1994; Ehret, 1997; Eggermont, 1998;
Rauschecker, 1998; Kaas et al., 1999; Schreiner et al., 2000;
Nelken, 2002; Read et al., 2002). But except for highly special-
ized animals (Stiebler, 1987; Suga, 1994) to date a given neur-
onal selectivity described by receptive field organization and
stimulus response transfer functions has turned out to be only
one of many heterogeneous selectivities in a field and the same
selectivities may be found across various fields with merely
quantitative differences. This has previously led to the synopsis
that ‘similarities of AC fields outweigh differences’ (Eggermont,
Frequency modulations (FM), which are addressed in this
study, are a particularly salient case as they activate large
populations of neurons in primary and secondary AC fields.
Comparisons of a few fields have revealed differences of spatial
distribution of FM responsive clusters of neurons but only small
differences of field selectivity for features like direction and
steepness of modulation (Whitfield and Evans, 1965; Mendelson
and Cynader, 1985; Phillips et al., 1985; Heil et al., 1992a,b;
Mendelson and Grasse, 1992; Mendelson et al., 1993; Shamma
et al., 1993; Tian and Rauschecker, 1994, 1998; Gaese and
Ostwald, 1995; Kowalski et al., 1995; Schulze et al., 1997; Heil
and Irvine, 1998; Horikawa et al., 1998; Ricketts et al., 1998;
Nelken and Versnel, 2000; Ohl et al., 2000; Zhang et al., 2003).
It does not necessarily follow from these electrophysiological
results that imaging studies of human AC using sound exposure
without tasks also fail to identify area-specific activations. They
measure population responses of neurons in both hemispheres
and especially with variation of sound features they may detect
differences in stimulus responsiveness not obvious from the
statistics of single unit mapping. Several corresponding studies
on differential stimulus responsiveness of AC areas have been
published (e.g. Baumgart et al., 1999; Belin et al., 2000; Binder
et al., 2000; Wessinger et al., 2001; Zatorre and Belin, 2001; Hall
et al., 2002; Harms and Melcher, 2002; Hart et al., 2002;
Patterson et al., 2002; Warren et al., 2002), and one found
right--left AC differences for level-dependent processing of FM
sweeps (Brechmann et al., 2002). However, these results do not
Cerebral Cortex V 15 N 5 ? Oxford University Press 2004; all rights reserved
Cerebral Cortex May 2005;15:578--587
Advance Access publication August 18, 2004
by guest on December 25, 2015
allow a functional interpretation in terms of conceptual listen-
ing tasks which may change the representations obtained with
sound exposure. The present experiment was designed to
clarify this issue.
An important aspect of the selected stimulus class of FM
sweeps may be its relevance for human communication. Voice
fundamental frequency (F0) changes independent of individual
voice is a key to discrimination of prosodies in speech and of
melody contours in song (Frick, 1985; Scherer, 1995; Banse and
Scherer, 1996). Even though linear FM are highly impoverished
models of these voiced sounds, the direction of pitch change
may represent a crucial parameter. This view is supported by
a study (Divenyi and Robinson, 1989) that showed that patients
with right brain damage not only had prosodic deficits but also
showed impairments in directional discrimination of linear FM
stimuli. Furthermore, animal studies showed that directional
discrimination of FM is dependent on an intact auditory cortex
(Ohl et al., 1999; Harrington et al., 2001) and relies pre-
dominantly on the right AC (Wetzel et al., 1998). Therefore
we expected right AC regions to be specifically involved in
directional categorization of FM.
Materials and Methods
In experiment I 10 females and eight males (20--36 years old, mean age
24.7) and in experiment II eight females and eight males (20--39 years
old,mean age25.5)werescanned.One subjectof experiment Irepeated
the task six times over a period of two month. None of the subjects of
experiment II participated in experiment I. All subjects were right
handed (Edinburgh Handedness Inventory) with normal hearing, and
had given written informed consent to the study which was approved
by the ethical committee of the University of Magdeburg.
For experiment I two sets of 40 linearly frequency modulated (FM)
tones were used as stimuli. Each covered half an octave in 0.5 s of either
low frequency range (0.1--0.15 kHz, 0.2--0.3 kHz in steps of 100 Hz up
to 2--3 kHz and the reverse) covering frequency changes in speech or
high frequency range (5.0--7.5 kHz, 5.1--7.65 kHz in steps of100 Hz up to
6.9--10.35 kHz and the reverse) (Fig. 1). Each block consisted of
45 randomized FM (repetition rate 1 Hz) of either the low or high
frequency range. One experimental session consisted of 16 alternating
‘silence’ and stimulus blocks of 45 s, resulting in 12 min total duration.
During the first part of the session, subjects were instructed to attend to
the stimuli with no specific task (uninformed listening). During the
second part, immediately following the first, subjects had to categorize
upward and downward FM presented with a target rate of 50%. A group
of nine subjects had to press a mouse button indicating downward FM
and a group of eight subjects indicating upward FM. For a correct
response, subjects had to press within 0.5 s after FM offset. In two
subjects these behavioural responses were not recorded due to
For experiment II a set of 32 FM of either 0.4 or 0.6 s duration served
as stimuli. The centre-frequency (Fc) varied between 1 and 3.2 kHz in
steps of 100 Hz. To achieve the same speed of modulation for 0.4 ms and
0.6 s FM of the same centre-frequency the starting and end frequencies
were calculated by Fc ± Fc/2 3 FM duration. Each block of 46
randomized FM contained 23 upward FM and 23 downward FM, half
of which had 0.4 s duration. The interval between two FM was 500 ms.
One experimental session consisted of 20 alternating ‘silence’ and
stimulus blocks of 46 s, resulting in 15 min 20 s total duration. During
one half of the session subjects had to press a mouse button indicating
0.4 ms FM and during the other half they had to indicate downward FM.
For a correct response, subjects had to press within 0.5 s after FM-offset.
In one subject these behavioural responses could not be recorded due
to technical failure. Task sequence was counterbalanced between
subjects and the subjects were not informed about a change of
instruction. The digitized instructions (8 s each) were inserted at the
beginning of the first and second half of the stimulus file.
The FM of both experiments had linear ramps of 10 ms and were
presented with a sound pressure level of 70 ± 5 dB via fMRI-compatible
electrodynamic headphones equipped with ear-muffs for further re-
duction of residual background noise (Baumgart et al., 1998).
Low-noise-fMRIexperimentswerecarriedout ona BRUKER3T/60head
scanner equipped with a quadrupolar birdcage head-coil. Pilot scans
were used for orientation of contiguous slices covering the superior
temporal plane in both hemispheres by following the course of the
noise to 54 dB (A) peak amplitude (for details of scanner noise-
measurement see Brechmann et al., 2002). The beginning of each
stimulus and ‘silence’ block coincided with the acquisition of a volume.
during each repetition, the gradient echo sequence acquires one line in
obtain anatomical landmarks and immediately followed the fMRI. The
containing the fMRI compatible headphones.
In experiment I 80 functional volumes each consisting of four 8 mm
slices were collected in two 12 min runs (TE= 30.7 ms; TR= 167 ms; flip
angle = 15?; FOV = 18 cm). One slice was positioned above the sylvian
fissure.In experimentII 120functional volumes each consistingof three
6 mm slices were collected in 15 min 20 s (TE= 30.7 ms; TR= 127.9 ms;
flip angle = 15?; FOV = 18 cm).
Each functional dataset was subjected to a quality check: first, subjects
head movement was monitored using the AIR package (Woods et al.,
1998). In case of a continuous movement of >1 mm or rotation of >1? in
any direction, the whole dataset is discarded (this was not the case in
the two experiments). Second, the mean grey value of all functional
volumes (obtained in the temporal lobe of two slices) was analysed
because transient head movements can lead to large deviations in the
mean grey value. Single images with grey-valuedeviations of >2.5% were
excluded from further analysis. In case of exclusion of more than two
images of one stimulus- or silence condition, the whole functional
datasetisdiscarded(this was not thecase inthe twoexperiments). After
motion detection, image matrix size was increased to 128 3 128 by pixel
replication followed by smoothing with a Gaussian filter [full-width half-
maximum = 2 voxel (2.8 mm), Kernel = 5 voxel]. Functional activation
was assessed by linear vector-space analysis (Bandettini et al., 1993). A
simple trapezoid function served as correlation vector, roughly model-
ling the expected BOLD response. The first image of each stimulus and
‘silence’ block was set to half-maximum values. This takes into account
that the full development of the BOLD response and the return to
baseline takes a few seconds. The remaining images acquired during
silence were set to minimum values and the remaining images acquired
during stimulus periods were set to maximum values. In the second
experiment each two images during which the instructions were
presented were excluded from analysis.
The activation of auditory cortex was scrutinized with Brain-
Voyager2000TMin a three-dimensional analysis of both hemispheres of
individual brains in relation to the prominent anatomical landmarks
insular sulcus, first transverse sulcus, Heschl’s sulcus and superior
temporal sulcus (Fig. 1). This served to determine the continuity of
activation patterns across slices and the regional parcellation of AC
activation. Generally, activation patterns appeared as more or less
separate clusters of activated voxels in different regions of AC (Figs 1
and 3). In both experiments, activated voxels (P < 0.05) in each slice
were attributed to one of the four territories TA, T1, T2 and T3 defined
as previouslydescribed (Brechmann et al., 2002).In short, territoryT1is
located on the antero-medial rim of Heschl’s gyrus (red activation) and
presumably covers core fields including primary AC. Due to a lack of
further reliable anatomical landmarks or functional borders as demon-
strated in a 7 T fMRI study (Formisano et al., 2003) we could not
subdivide T1 into a medial and a lateral aspect. T2 was centred on
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