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NeuroImage 28 (2005) 140 – 153
Localization of human supratemporal auditory areas from
intracerebral auditory evoked potentials using distributed
source models
Blaise Yvert,a,* Catherine Fischer,a,b Olivier Bertrand,a and Jacques Perniera
aInserm Unite 280, F-69675, Bron Cedex, FrancebNeurological Hospital, 59 Boulevard Pinel, F-69003 Lyon, France
Received 15 December 2004; revised 21 March 2005; accepted 20 May 2005
Available online 21 July 2005
While source localization methods are increasingly developed to
identify brain areas underlying scalp electro/magnetoencephalographic
data (EEG/MEG), these methods have not yet been used to identify the
sources of intracerebral signals which offer highly detailed information.
Here, we adapted the minimum current estimates method to intra-
cranial data in order to localize supratemporal sources of intracerebral
auditory 1-kHz-tone-evoked potentials occurring within 100 ms after
stimulus onset. After an evaluation of localization method and despite
inter-subject variability, we found a common spatiotemporal pattern of
activities, which involved the first Heschl’s gyrus (H1) and sulcus (HS),
the Planum Temporale (PT), H2/H3 when present, and the superior
temporal gyrus (STG). Four time periods of activity were distinguished,
corresponding to the time range of the scalp components P0, Na, Pa/Pb,
and N100. The sources of the earliest components P0 (16–19 ms) and
Na (20–25 ms) could be identified in the postero-medial portion of HS
or H1. Then, several areas became simultaneously active after 25 ms.
The Pa/Pb time range (30–50 ms) was characterized by a medio-lateral
and postero-anterior propagation of activity over the supratemporal
plane involving successively H1/HS, the Planum Temporale, H2/H3
when present, and the STG. Finally, we found to a large extent that the
N100 (55–100 ms) involved almost the same areas as those active
during the Pa/Pb complex, with a similar propagation of activities.
Reconstructing scalp data from these sources on fictive EEG/MEG
channels reproduced classical auditory evoked waveforms and top-
ographies. In conclusion, the spatiotemporal pattern of activation of
supratemporal auditory areas could be identified on the individual
anatomy using current estimates from intracerebral data. Such detailed
localization approach could also be used prior to epilepsy surgery to
help identify epileptogenic foci and preserve functional cortical areas.
D 2005 Elsevier Inc. All rights reserved.
Keywords: Auditory cortex; Middle-latency components; L1-norm;
Minimum current estimates; EEG; MEG; Stereotactic EEG; Intracranial;
Distributed source method
1053-8119/$ - see front matter D 2005 Elsevier Inc. All rights reserved.
doi:10.1016/j.neuroimage.2005.05.056
* Corresponding author. Present address: LNR-CNRS UMR5816, Bati-
ment B2, Avenue des facultes, F-33405 Talence cedex, France. Fax: +33 5
40 00 25 61.
E-mail address: [email protected] (B. Yvert).
Available online on ScienceDirect (www.sciencedirect.com).
Introduction
The main human auditory areas are housed in the supratemporal
plane (STP) at the level of the first Heschl’s gyrus (H1) and
surrounding Planum Temporale (PT), superior temporal gyrus
(STG), and occasionally other transverse gyri (H2 and/or H3) when
these are present. Anatomical studies (Brodmann, 1909; Galaburda
and Sanides, 1980; Pandya, 1995; Rivier and Clarke, 1997;
Morosan et al., 2001; Hackett et al., 2001) have indeed subdivided
the STP into several distinct areas, with a general agreement,
supported by intracerebral recordings (Celesia, 1976; Liegeois-
Chauvel et al., 1991), that the primary auditory cortex is located
within the most medial portion of H1, sometimes striding postero-
laterally over Heschl’s sulcus (HS) or even H2 when present
(Rademacher et al., 2001; Hackett et al., 2001). The detailed
spatiotemporal pattern of activation of these multiple areas remains
largely unknown.
In humans, different auditory evoked components are com-
monly recorded using electro- and magnetoencephalography
(EEG, MEG). Although several scalp studies have provided
equivalent dipole sources of auditory evoked components in the
supratemporal plane, very few investigations have been focusing
on precise anatomical locations of auditory areas with respect to
detailed individual gyral and sulcal anatomy (Lutkenhoner and
Steinstrater, 1998; Yvert et al., 2001). In a previous EEG/MEG
study, we reported several STP activities underlying the Pa/Pb
complex of the middle-latency components (between 30 and 60
ms) involving H1/HS and secondary areas in the PT and
supratemporal gyrus (STG) (Yvert et al., 2001). However, scalp
EEG/MEG seldom present sufficient spatial precision and signal-
to-noise ratio to probe earliest (and weakest) activities and
disclose sources very close spatially and greatly overlapping in
time.
The increased spatial resolution of fMRI has allowed to identify
several STP areas involved in auditory perception (Lauter et al.,
1985; Binder et al., 1994; see review by Griffiths and Warren,
2002), although not offering enough temporal resolution to observe
their dynamics on a millisecond time scale.
B. Yvert et al. / NeuroImage 28 (2005) 140–153 141
Intracranial auditory evoked potentials (AEPs) obtained in
epileptic patients having electrodes chronically implanted in the
temporal lobe certainly offer great details of STP activities
(Celesia, 1976; Lee et al., 1984; Liegeois-Chauvel et al., 1991;
Howard et al., 1996, 2000). The anatomic origin of intracerebral
data is generally determined by visual inspections of the curves at
each recording site and by assuming that sources are close to
waveform maxima or polarity reversals occurring between
adjacent contacts. In particular, volume conduction effects are
not taken into account. A drawback of the wealth of details of
intracerebral data is also the difficulty to obtain global and
synthetic views of (generally simultaneous) activities, within and
especially across subjects, where the number and locations of the
electrodes generally differ.
Here, we have adapted to intracerebral data the minimum
current estimates distributed source method (weighted MCE)
originally introduced for scalp data (Matsuura and Okabe, 1995,
1997; Uutela et al., 1999). This method was then used to
localize sources of intracerebral AEPs before 100 ms with
respect to the individual gyral and sulcal STP anatomy in three
patients having several deep multicontact electrodes surrounding
the STP.
Methods
Patients
Signals were recorded using deep multicontact intracerebral
electrodes chronically implanted in the right hemisphere of three
right-handed patients (FY, DC, NG) undergoing presurgical
evaluation of a pharmaco-resistant temporo-mesial epilepsy (aged
23–40, 2 women) and having at least 3 electrode tracks
surrounding H1, PT, and STG. The relatively high number of
electrodes that circumscribed the STP was required to obtain good
localization results. This perquisite was however very rarely
satisfied for most patients recorded in clinical routine at the
hospital had only 1 or 2 deep electrodes exploring the STP, and it
was very seldom that 3 electrodes were implanted in this region. In
the present study, the number of tracks was 7 for subject FY, 7 for
DC, and 6 for NG (Fig. 1). Electrodes were 2-mm-long cylinders
0.8 mm in diameter separated by 1.5 mm of insulation. The three
patients reported no auditory complaints and gave informed
consent to undergo these routine clinical recordings serving as
pre-operative cortical functional mapping and approved by the
ethical committee of the hospital.
Experimental paradigm
Stimuli consisted of 1000 short 1000-Hz pure tones (3-ms rise/
fall times, 50-ms plateau) delivered at 60 dB above hearing
threshold to the ear contralateral to the implanted hemisphere every
200–400 ms (random stimulus onset asynchrony). The three
patients were comfortably seated with the instruction to keep their
eyes open. One patient watched a silenced self-selected movie
during the recordings.
Signals were acquired continuously, bandpass filtered (1–500
Hz), digitized at 2 kHz, and averaged off-line after periods of
artifacts were discarded by careful visual inspection of the raw
data. Contacts showing high level of noise or epileptic spikes were
also discarded so that 59 (FY), 47 (DC), and 32 (NG) channels
were finally considered. In two patients (FY and DC), ground and
reference were taken at intracerebral contacts distant from the
STP. In patient NG, ground and reference were taken on the scalp.
In all cases, signals were average-referenced prior to source
estimation.
Anatomical registration of the electrodes
For each patient, T1-weighted 3D MRIs (voxel size 1 � 1 �1.27 mm) were acquired prior to implantation. Angiography was
used to determine the target electrode positions in Talairach space
as described by Musolino et al. (1990). Because our depth
multicontact electrodes were not visible on MRIs, their final
anatomic positions (which could slightly differ from their target
ones) were determined a posteriori using 2 orthogonal sagittal and
coronal X-ray radiographs obtained at the end of the implantation.
This registration method was described previously (Yvert et al.,
2002): first, the bone contour of the mid-sagittal MRI was matched
to that of the sagittal X-ray view, providing antero-posterior and
inferior–superior coordinates. Then, medio-lateral coordinates
were obtained on the coronal X-ray view. The registration
precision was evaluated to be ¨2 mm in one patient (not included
in this study) for whom MRIs, acquired less than 24 h after
electrode removal, still showed electrode tracks.
The minimum current estimates (MCE) method
The MCE method, as all distributed source methods, makes no
assumption on the number of active regions. A set of candidate
sources with fixed positions and orientations (source domain) is
given a priori, and the strength of each source is estimated so as to
explain the data under some constraints. Let N be the number of
candidate sources, M (<N) the number of electrode contacts, and L
the lead-field matrix, the (i, j) element of which being the potential
created by the jth dipole source with strength 1 onto the ith channel.
Let D be the M-vector of potential values at a given latency, and J
the N-vector of unknown source strengths. The weighted MCE
seeks for the particular solution J* verifying
jjWJ4jj1 ¼ MinJ
jjWJ jj1=LJ ¼ D��; ð1Þ
where ||&||1 denotes the L1-norm, and W is an N*N diagonal matrix
of weighting factors precluding possible bias favoring sources
close to the sensors:
Wj ¼ffiffiffiffiffiffiffiffiffiffi~i
L2ij
r
By writing the strength of the jth source
Jj ¼ Jþj � J�j ;
where J j+ and J j
� are positive values, and using the singular value
decomposition of L = USVt, Eq. (1) can be written as the following
linear programming (LP) problem with 2*N unknowns Jj+ and Jj
�:
Minimize :~j
Wj Jþj þ J�j
� �;
subject to the M constraints:
SVt Jþ � J�ð Þ ¼ UtD: ð2ÞIt should be noted that the number of constraints actually
determines the maximum number of non-zero solution values in
Fig. 1. Visualization of the source domain and position of electrode contacts in each subject. (A) Subject FY. (A1) Sagittal view of the right-hemisphere
midgray surface showing the position of the electrode tracks. (A2) The extracted STP region is surrounded by several multicontact electrodes, 3 of which
contained most of the signal: electrodes H and T located just below the posterior and anterior part of the STP, respectively, and electrode N above the STP. (A3)
Distribution of candidate dipole sources with respect to electrode positions. (B and C) Source domain and electrode contacts for subject DC and NG,
respectively. For subject NG, two contacts of an orbito-frontal electrode track (FO_) were included even though these contacts showed no AEPs. Electrodes
labeled in color corresponded to those showing most signal and were used to build the spatiotemporal maps in Fig. 3. The orientation of the source domain is
the same for the 3 subjects: m, medial; s, superior; a, anterior. Scale: electrode contacts are 2-mm-long cylinders separated by a gap of 1.5 mm.
B. Yvert et al. / NeuroImage 28 (2005) 140–153142
vector J*. In practice, a regularization variant of this formulation is
required to obtain solutions more stable with respect to noise
(Matsuura and Okabe, 1997; Uutela et al., 1999; Fuchs et al.,
1999). Here, we used the strategy proposed by Uutela et al. (1999),
which consists in reducing the number of constraints in Eq. (2).
The lowest rows of the linear system (Eq. (2)) indeed correspond to
the smallest singular values and require high coefficients of J to
meet the non-zero right-hand side. Hence, removing these rows
stabilize the solutions. In practice, we chose a number of
constraints between M/4 and M/2: 26 for FY, 15 for DC, and 16
for NG. The number of constraints was chosen so as to obtain a
tradeoff between the number of degrees of freedom of the solution
(number high enough to allow several possible simultaneous
sources) and the stability of the solution (number small enough to
obtain a solution stable with respect to noise). The LP problems
were solved using the CPLEX package (ILOG, Homburg,
Germany).
Construction of the source domain
For each patient, the white matter and pial surfaces of the
implanted hemisphere were segmented using the Freesurfer tool set
freely available at http://surfer.nmr.mgh.harvard.edu/. The inter-
mediate Fmidgray_ surface located midway between white and pial
surfaces was then computed (Fig. 1A1). The midgray STP region
including Heschl’s gyri, PT, and STG was extracted, and the
corresponding mesh was simplified to contain approximately 80
triangles/cm2 (Figs. 1A2, B1, C1). This portion of cortical surface
constituted the source domain: one source was positioned on each
mesh node with a fixed orientation perpendicular to the local
surface (Figs. 1A3, B2, C2). The number of nodes of the source
domain was 1128 for FY, 1013 for DC, and 1535 for NG. The
average distance between neighboring sources was 1.7 mm. The
source domain was chosen on the basis of anatomical data
available from the literature (Brodmann, 1909; Galaburda and
B. Yvert et al. / NeuroImage 28 (2005) 140–153 143
Sanides, 1980; Rademacher et al., 1993, 2001; Pandya, 1995;
Rivier and Clarke, 1997; Morosan et al., 2001; Hackett et al., 2001)
so as to include the regions known to house the main human
auditory areas within the superior temporal plane and supra-
temporal gyrus. This domain was thus limited medially by the
bottom of the insula, laterally by the upper bank of the superior
temporal sulcus, caudally by the superior end of the Planum
Temporale, and rostrally by the anterior limit of Heschl’s gyrus. In
patient NG, a more anterior part of the supratemporal gyrus was
considered because several more anterior electrodes were present
among which electrode FT_ showed significant signal on its
contacts. By contrast, subject FY had only one anterior electrode
track (FJ_) which showed no signal, and subject DC had no anterior
electrodes. Figs. 1A–C show the source domain with respect to the
electrode contacts for all 3 subjects. Each electrode track was
labeled by a letter (e.g., FH_ for the electrode track close to Heschl’sgyrus), and each contact on the track was numbered starting from
F1_ for the most medial contact. As seen in Fig. 1, several tracks
surrounded the supratemporal plane for each subject.
Lead-field computation
The intracerebral lead-fields were calculated with a single-layer
homogeneous spherical model (conductivity r = 0.45 S/m) best
fitting the inner skull surface lateral to the STP. For a given source,
(r,h,/) denotes spherical coordinates with origin at the center of
the model and z axis passing through the source, and Q and r0stand for the dipole moment and position, respectively. The
potential V(r) created at any intracerebral location r = (r,h,/)
within the model is given by (Bertrand et al., 1991):
V ¼ U þ Vs; ð3Þ
where
Vs ¼1
4prr� r0ð Þ IQjr� r0j3
is the solution that would be created by the source in an infinite
medium of conductivity r, and
U r; h;/ð Þ ¼ 1
4pr
XVn ¼ 1
nþ 1ð Þ
� rn � 10
R2n þ 1rn QrPn coshð Þ þ QtP
1n coshð Þcos/
�ð4Þ
In Eq. (4), R is the radius of the spherical model, Qr and Qt are
the radial and tangential components of the source moment, and Pn
and P1n denote Legendre and associated Legendre polynomials,
respectively.
Solution stabilization using bootstrap reaveraging of the single
trials
In order to check whether a solution was stable with respect to
background noise, we repeated the source estimation on 30 different
average responses obtained by bootstrap reaveraging of the initial
single trials. This method was described previously (Yvert et al.,
2002): let NS be the number of available single trials. Thirty sets of
NS single trials were randomly drawn with replacement from the
original set of NS single trials (NS = 1000). Averaging trials within
each set lead to 30 bootstrap average responses spanning the
intrinsic variability of the average data, without assumption on the
noise structure. At each time sample between 0 and 100 ms, STP
sources were then estimated using the weighted MCE method for
each bootstrap average, leading to 30 bootstrap solutions. These 30
solutions were then averaged to obtain a first solution J1. Next, only
nodes displaying stable solution values across bootstraps were
considered as reliably active, although allowing that the activity
could locally jump a few nodes across bootstraps: to do so, a node
value of J1 was considered unstable and thus zeroed if there was at
least one bootstrap for which no node within a geodesic distance of
7.5 mm was active (i.e. non-zero). Then, a threshold was applied so
that nodes with values below 15% of the maximum value of the
map were zeroed. This lead to the modified solution J1m. Finally,
J1m was globally rescaled by a scalar k in order to minimize the
quadratic error between original data D (mean of all bootstrap
averages) and reconstructed data: Jfinal = k J1m with k = (D, LJ1m) /
(LJ1m, LJ1m) and (,) being the dot product.
Evaluation of the inverse procedure
Simulations were used to test the inverse procedure on 100
pairs of two simultaneous sources located randomly in the source
domain and separated by at least 15 mm. For each source pair, the
corresponding intracerebral data created by the 2 sources (with
amplitude 1 nA) were computed at the intracranial electrodes using
Eq. (3), and the inverse procedure described above was used to
retrieve the sources. Four situations were tested with increasing
perturbations on the data: (1) data were left unchanged, (2) thirty
different random Gaussian noises (signal-to-noise ratio SNR = 25,
which corresponded to typical values encountered in our record-
ings) were added to these data to create 30 different data sets,
mimicking 30 bootstrap averages, (3) the two sources were shifted
by 1.7 mm (i.e. 1 internode distance) and tilted by 5- prior to
computing the data, and random Gaussian noise was added as
before, (4) the two sources were shifted by 3.5 mm and tilted by
10- prior to computing the data, and random Gaussian noise was
added. Source shifts mimicked possible registration error of the
electrodes, and source tilts mimicked possible errors in the
estimation of the source orientations on the cortical surface. For
each solution, patches of activity were defined as clusters of active
neighboring nodes. Smoothed patches were also defined as clusters
of active neighboring nodes after the solution was spatially
smoothed with a Gaussian filter of radius 7.5 mm (r = 2.5 mm,
coefficients zeroed beyond 3*r).
Results
Evaluation of the localization method
Results of the simulations are shown in Fig. 2, which can be
read from left to right to see the effect of increasing perturbations.
Fig. 2A shows three examples of source reconstructions for 3
different pairs of dipoles. When data were not perturbed and the
source domain contained the sources used to generate the data, the
inverse procedure reconstructed perfectly these two sources in
96% of the cases. It should be noted that 100% perfect
reconstruction was achieved in absence of regularization when
no constraints were removed in Eq. (2), but then these solutions
became very unstable with respect to noise so that this approach
could not be used in practice. As data were increasingly perturbed,
Fig. 2. Evaluation of the inverse procedure. One hundred pairs of dipoles distant by at least 15 mm were chosen randomly on the source domain. As explained
in the Methods section, 4 types of perturbation were applied to the data before the sources were reconstructed using the MCE method. From left to right, the
amount of perturbation increases. (A) Examples of source reconstruction for 3 dipole pairs and the 4 perturbations (original source positions are marked by red
dots). Orientation as in Fig. 1: m, medial; s, superior; a, anterior. (B–E) Histograms showing the number of active patches reconstructed (B), the distance
between each reconstructed patch and the nearest actual source position (C), the number of active patch after spatial smoothing (D), and the number of
smoothed patches close to each dipole source (E). Overall, these results show that, as perturbations increased, the method did not tend to generate multiple
spurious phantom sources. Rather, sources were either correctly reconstructed (usually by several neighboring patches) or not reconstructed at all. It should be
noted that the position of the sources with respect to the electrodes was random and sources could be reconstructed even at remote sites with respect to the
electrodes (e.g., source in the Planum Temporale in A2 or A3).
B. Yvert et al. / NeuroImage 28 (2005) 140–153144
B. Yvert et al. / NeuroImage 28 (2005) 140–153 145
the number of patches of activity increased (Figs. 2A and B).
However, these patches remained close to each other, meaning that
each source became reconstructed by several small neighboring
patches (e.g., see Figs. 2A2 and A3). This is further assessed in
two ways. First, the distances between each patch and the nearest
dipole remained acceptable even for the strongest perturbations
(Fig. 2C). Second, after spatially smoothing the solutions, most
reconstructions (88%) showed actually only 1 or 2 smoothed
patches of activity for the strongest perturbation (Fig. 2D), and
either 1 or zero patch was reconstructed next to each dipole (Fig.
2E). This important result shows that the method was robust in the
sense that, as data became more and more perturbed, the method
did not tend to generate multiple spurious phantom sources.
Rather, sources were either correctly reconstructed (usually by
several neighboring patches) or not reconstructed at all. It should
be noted that the position of the sources with respect to the
electrodes was random and sources could be reconstructed even at
remote sites with respect to the electrodes (e.g., source in the
Planum Temporale in Figs. 2A2 or A3).
Spatiotemporal mapping of intracranial AEPs
Intracranial AEPs were characterized by a complex succession
of multiple components at several electrode contacts. This is
illustrated in Fig. 3 for all subjects, where the 30 bootstrap
averages are superimposed to show the intrinsic variability of the
data. As shown in Figs. 3B, D, and F, more synthetic visualization
of the data recorded simultaneously on several channels was
achieved by displaying spatiotemporal maps (Badier and Chauvel,
1995). Only channels showing most activity were included in these
maps. These representations showed a succession of activities,
either as peaks occurring simultaneously on several contacts or as
polarity reversals (PRs) between adjacent contacts of the same
track. Based on these maps, we divided the activity into four
distinct periods for each subject (gray bars below each map): (1) a
weak initial activity occurring at 16–19 ms on medial H contacts
and characterized by a PR in subjects FY and DC; (2) then, a PR at
20–25 ms involving slightly different medial H contacts and
spreading monopolarly on other tracks (e.g., tracks N and T in
subject FY and NG); (3) the 30–50-ms time range then showed a
more complex pattern of evoked activity, which remains relatively
stable in this range and showed several simultaneous PRs in
subjects FY (between H5 and H6 and between H6 and H7) and DC
(between H2 and H3 and between H3 and H4), suggesting several
simultaneous sources; (4) between 50–60 ms and 80–100 ms,
these complex activities were still present although with a reversed
polarity on most recording contacts. It is interesting to note that
these 4 time periods of activity actually correspond fairly well to
the time ranges of known scalp auditory evoked components: (1)
P0, (2) Na, (3) Pa/Pb, and (4) N100. In an attempt to link our
results with classical scalp data, we used a parallel terminology to
that of scalp components with the ?iX subscript standing for
?intracerebralX: P0i, Nai, Pai/Pbi, and N100i.
Estimation of underlying sources
The sources of the intracerebral AEPs were estimated at all
latencies between 0 and 100 ms in each subject, using the weighted
MCE method described above. Results are presented in Figs. 4–6
for each time periods separately. For sake of clarity, the sources are
shown at key latencies corresponding to either the maxima of the
evoked components (Fig. 4 for P0i and Nai) or at several latencies
in order to describe the detailed evolution of the activity in the
supratemporal plane in a movie-like fashion (Fig. 5 for Pai/Pbi and
Fig. 6 for N100i). These latencies are indicated by vertical dashed
lines on the corresponding spatiotemporal maps in Fig. 3. Then, in
order to allow the correspondence between localization maps and
electrode data, AEPs were color-coded at each contact next to the
localization results. Although individual STP anatomy and source
localization results were variable across subjects, we found a
common spatiotemporal pattern of activity.
As seen in Fig. 4, the earliest activity (P0i) occurred between 16
and 19 ms, with a single source localized in the postero-medial
portion of HS in 2 subjects (FY, DC) and of H1 in subject NG and
pointing outward of the midgray surface. Then, the Nai (20–25
ms) stemmed from an inward source in H1/HS at a different
location than P0i in all three subjects.
Next, after 25 ms, we observed the progressive activation of
several sources in the supratemporal plane, having outward
orientations. As seen in Fig. 5, activity propagated medio-laterally
and postero-anteriorly as latency increased during the Pai/Pbi time
range. For subject FY, sources tended to overlap in time. At 28.5
ms, activity could be found in medial HS, PT, and also already in
the STG. Then, the STG tended to become silent while activity left
HS and involved only the PT around 42.5 ms, before propagating
to a more anterior part of HS and to the STG again. It can be noted
that this scheme of activity was very consistent with the activities
recorded on the electrodes (see maps in Fig. 3B and color-coded
data next to the source reconstructions). At 49 ms, these two last
regions remained active, while the PT has become silent. An
inwardly oriented source then started to be active in the posterior
part of H1, indicating the beginning of the N100i. For subject NG,
an early activation of the STG was also found at 28.5 ms. Then, at
32 ms, activity was located in the postero-medial part of H2 and
then propagated laterally along H2. At about 40 ms, the anterior
part of H2 was solely active as was the PT for subject FY. Finally,
the STG became active around 45 ms at the level of the anterior
end of HS. The same type of spatiotemporal scheme was also
observed in subject DC between 30 and 41 ms, with activity first
involving HS and H2 at 30 ms and then also the anterior part of HS
and H3 at 35 ms. At 40 ms, activity was predominant in lateral
areas (H2 and H3). It can be noted that this lateral progression of
activity could also be seen on the spatiotemporal map presented in
Fig. 3F. By 46–50 ms, the lateral activity at the anterior part of H3
remained, while activity in medial H1 started again characterizing
the beginning of the N100i. For this subject, some polarity
reversals, such as that at medial H contacts at the beginning of
the N100i, were reconstructed as a few neighboring inward and
outward sources and not as single patches. This was due to the fact
that electrode H actually crossed the supratemporal plane in several
places (see Fig. 1B1) generating very strong lead-fields in these
places (note also the high signal-to-noise ratio of the data for this
subject). Despite the presence of these singularities, the weighted
MCE method actually managed to reconstruct the sources fairly
well.
Finally, as seen in Fig. 6, the supratemporal sources of the
N100i showed comparable activation patterns in subjects FY and
DC (Fig. 6, left and right columns). This activity was characterized
by sources oriented inwardly, beginning around 52–55 ms in the
postero-medial part of H1/HS. Then, activity propagated to the PT
in subject FY and in H2 for subject DC around 58–60 ms before
reaching more lateral regions (STG for FY and H3 for DC) around
g. 3. Bootstrap averages (top row) and spatiotemporal maps (bottom row). (A, C, E) Superimposed bootstrap averages for 5 recording contacts of H, N, a electrodes in subject FY, NG, and DC, respectively.
bject DC showed a very high signal-to-noise ratio. (B, D, F) Spatiotemporal maps showing the evoked AEPs on all contacts of the 3 electrodes showing st signals for subjects FY, NG, and DC, respectively.
he 4 time ranges considered are represented by horizontal gray bars below each map. Vertical lines indicate the latencies for which the localization res are shown in Figs. 4–6.
B.Yvert
etal./NeuroIm
age28(2005)140–153
146
Fi
Su
T
nd T
mo
ults
Fig. 4. Sources of the earliest components P0i and Nai. Source reconstruction are color mapped for all three subjects at the latency corresponding to the peak of the
P0i and Nai components as seen on the spatiotemporal map (see vertical lines in Fig. 3). Below each source map, the AEPs recorded at the corresponding latency
are represented on each electrode contact as a color scale (dark colors correspond to small potential values). The position of each source of the domain is
represented as a dot. Scale: electrode contacts are 2-mm-long cylinders separated by a gap of 1.5 mm. Orientation as in Fig. 1: m, medial; s, superior; a, anterior.
B. Yvert et al. / NeuroImage 28 (2005) 140–153 147
70–100 ms. For these two subjects, the areas involved in the
N100i were very similar to those involved in the Pai/Pbi complex.
This postero-anterior and medio-lateral propagation of activity was
also clearly seen in subject NG between 65 and 95 ms, where it
successively stemmed from medial HS, anterior HS, and the STG
at the level of the tip of H1 (Fig. 6, middle column). A more
posterior region of the STG was also active between 65–75 ms. In
this subject, areas involved in the N100i were generally more
anterior than those involved in the Pai/Pbi complex.
Reconstruction of fictitious scalp AEPs and AEFs
Comparing intracerebral and scalp AEPs has shown to be very
difficult for two main reasons. First, for practical reasons, it is rare
that both types of signals can be recorded simultaneously (Godey
et al., 2001). Second, intracerebral AEPs are characterized by
multiple local features highly dependent on the location of the
recording contacts and are thus difficult to relate to the relatively
simple succession of well-known scalp components. Although
scalp data were not available for our patients, we checked that the
sources reconstructed from intracerebral data were compatible with
classical scalp components. For that purpose, we reconstructed
fictitious scalp components produced by the sources estimated
from intracerebral data. For each subject, fictitious scalp AEPs
were computed at 32 electrodes extending the 10–20 system using
a classical 3-shell spherical model. For subject FY, we also
reconstructed fictive AEFs on the BTi 37-channel gradiometer
configuration covering the right hemisphere. When reconstructing
AEPs, the source configurations obtained in the right hemisphere
using the weighted MCE were symmetrized in the other hemi-
sphere to account for the activity of both hemispheres. Because
MEG is more focal than EEG, this was not done for AEFs. As
shown in Fig. 7, these reconstructed data resembled classical scalp
evoked responses (Na, Pa, Pb, N100).
Discussion
Up to now, source analysis methods were exclusively used to
analyze scalp data. Although previous simulations have shown that
source position and strength can be estimated from intracerebral
data by computing the mean electric field and center of energy
(Lemieux and Leduc, 1992), such method can only be considered
in the case of a single active source, which is rarely the case. The
first purpose of our study was to illustrate that scalp methods can
also be used to estimate sources of intracerebral data, despite the
fact that dipole approximation might not be optimal in this case
where sources are close to the recording contacts (Church et al.,
Fig. 5. Sources of the components Pai/Pbi. Source reconstructions are color mapped for all three subjects at several latencies spanning the Pai/Pbi time range
(see vertical lines in Fig. 3). On the right of each source map, the AEPs recorded at the corresponding latency are represented on each electrode contact as a
color scale. The position of each source of the domain is represented as a dot. Scale: electrode contacts are 2-mm-long cylinders separated by a gap of 1.5 mm.
Orientation as in Fig. 1: m, medial; s, superior; a, anterior.
B. Yvert et al. / NeuroImage 28 (2005) 140–153148
1985). The second goal of this paper was to use this method in
order to localize the supratemporal sources of intracerebral AEPs.
We found that the weighted MCE method could be used to
reconstruct sources from intracerebral data on the individual gyral
and sulcal anatomy and that this method was quite robust when
data were increasingly perturbed: sources tended to be either well
reconstructed or not reconstructed at all. We also tested the effect
of removing several electrodes on the accuracy of the solution. We
found that the solutions were still reliable after electrode removal.
Although the precision of the localization decreased as perturbation
increased, solutions remained robust even when electrodes show-
ing the maximum signals were removed.
We compared the MCE method with other distributed source
methods such as the weighted L2-norm (Hamalainen and
Ilmoniemi, 1994) and the LORETA (Pascual-Marqui et al., 1994)
methods combined with Tikhonov regularization (and determina-
tion of optimal lambda with the L-curve, see Hansen and O’Leary,
1993). In particular, we tested how these different methods
performed when 2 patches of increasing size were reconstructed
(Fig. 8). In theory, the MCE method is expected to perform less
reliably in a situation with large distributed sources since this
method seeks to reconstruct focal sources (Uutela et al., 1999).
Two patches of dipoles were considered having amplitudes
following a Gaussian distribution in space (in the geodesic sense)
of radius equal to 3*r (r = 0, 1, 2, 3, and 4 mm). No perturbations
were introduced. As the size of the patch increased, we found that
the L1 method tended to reconstruct several nearby focal sources,
the strongest of which being located near the center of the patches.
Fig. 6. Sources of the component N100i. Source reconstructions are color mapped for all three subjects at several latencies spanning the N100i time range (see
vertical lines in Fig. 3). On the right of each source map, the AEPs recorded at the corresponding latency are represented on each electrode contact as a color
scale. The position of each source of the domain is represented as a dot. Scale: electrode contacts are 2-mm-long cylinders separated by a gap of 1.5 mm.
Orientation as in Fig. 1: m, medial; s, superior; a, anterior.
B. Yvert et al. / NeuroImage 28 (2005) 140–153 149
We found that both L2-based methods lead to less reliable
solutions, with ample fantom sources distant from the actual patch
positions.
We also tested whether the localization results obtained at the
different latencies of the auditory evoked responses (Figs. 4–6)
could be summarized using a set of fixed dipole sources located in
the center of the main patches of activity, the amplitude of which
being estimated by a least-square fit. We found that the accuracy of
amplitude estimation strongly depended on the level of colinearity
between the lead-fields of each fixed source. Accurate reconstruc-
tion could be achieved only when all lead-fields were nearly
orthogonal two-by-two. However, when a pair of lead-fields had an
angle smaller than 60-, then the amplitude of the corresponding
sources tended to become strongly correlated with opposite polar-
ities. This problem, which has been raised previously (Lutkenhoner,
2003), could not be alleviated using a regularized version of the
least-square fit by removing the smaller singular values.
For all these reasons, we used the weighted MCE method and
found that individual spatiotemporal functional mapping of supra-
temporal auditory areas could be achieved in subjects having
several multicontact electrodes surrounding the STP.
Our localization results may be paralleled to the anatomical
organization of the supratemporal auditory areas. Detailed parcel-
lation of the supratemporal plane has been achieved in monkeys,
where up to 13 areas organized in 3 major regions have been
identified: the core line region, containing the primary auditory
cortex (PAC) and two more rostral regions, is surrounded by a belt
of 8 distinct areas, which in turn is boarded laterally by two areas
forming the parabelt region in the STG (Pandya, 1995; Rau-
schecker, 1998; Kaas and Hackett, 2000; Kaas et al., 1999). A
similar organization has been described in humans. Recent data
indicate that the human PAC is also contained within a core region
composed of 3 areas along H1, the denser one being located in the
first postero-medial third of H1 (Galaburda and Sanides, 1980;
Rivier and Clarke, 1997; Morosan et al., 2001), often straddling
over H2 when several Heschl’s gyri are present (Rademacher et al.,
1993, 2001; Hackett et al., 2001). Like in monkeys, this core
region is surrounded by a belt of secondary areas medially in the
circular sulcus and laterally over additional Heschl’s gyri (when
present), the PT, and the STG (Galaburda and Sanides, 1980;
Rivier and Clarke, 1997). Functional imaging studies have also
reported several sites of activation in the supratemporal plane
(depending on the stimulus types), mostly involving Heschl’s gyri,
the PT, and the STG, which are the regions we found active here
(Lauter et al., 1985; Binder et al., 1994; Scheich et al., 1998; Belin
et al., 1999; Lockwood et al., 1999; Hashimoto et al., 2000;
Fig. 7. Fictive scalp data reconstructed from intracerebral sources. Topographies of reconstructed scalp Na, Pa, and N100 components are shown on the left
panel: average EEG across all subjects (top row), as well as individual EEG (middle row) and MEG (bottom row) of subject FY. Panels on the right show
reconstructed evoked responses on both electrodes and both MEG sensors circled on the maps (red: averaged EEG, black: individual EEG and MEG). Classical
evoked responses Na, Pa, Pb, and N100 can be recognized. For EEG, average reference was used.
B. Yvert et al. / NeuroImage 28 (2005) 140–153150
Talavage et al., 2000, 2004; Wessinger et al., 2001; Griffiths and
Warren, 2002; Hall et al., 2002; Schonwiesner et al., 2002). These
studies often report activation of the full length of H1 from its
postero-medial end to its antero-lateral tip. Here, we mostly found
activity in the postero-medial part of H1. Activity in the antero-
lateral was observed only in subject NG. More anterior source
locations have been found for more cognitive components tasks
such as the MMN (Csepe et al., 1992). Furthermore, fMRI studies
also report activities on the medial side of H1 in the circular sulcus.
Here, we did not find sources in this region near the insula possibly
because this secondary area of the belt is not activated by simple
tone stimuli. It should be noted that most dipole localization studies
using MEG data also report source activity in H1 and the PT but
not in the circular sulcus (e.g., Pantev et al., 1995; Lutkenhoner
and Steinstrater, 1998; Gutschalk et al., 1999; Herdman et al.,
2003; Yvert et al., 2001). These differences in the localization of
active areas between fMRI studies and our study could stem from
the following facts: (1) we used a very simple tone-evoked
protocol, while most fMRI studies use different paradigms and
stimuli; (2) while fMRI measures variations of blood parameters in
the microvascularization, AEPs directly reflect the neuronal
activity; (3) fMRI integrates activation over several seconds, while
the present localization results reflect activity on a millisecond time
scale; and (4) fMRI signal is not necessarily related to transient
evoked responses only but also to more sustained oscillatory
activities not strictly phase-locked to the stimuli (Logothetis et al.,
2001). Moreover, human fMRI studies and animal multi-unit
recordings using pure tones do not show as focal activation as
those found in the present study, but rather activation over more
extended patches (even on the single subject level without spatial
smoothing). The more focal activity patches found in our study
should be attributed to the MCE method which seeks to reconstruct
focal sources. Indeed, as illustrated in Fig. 8, using the MCE
method, larger patches are reconstructed as several nearby focal
sources, the strongest of which being located near the center of the
patches.
Here, despite an important inter-subject variability, a common
scheme of spatiotemporal activity could be highlighted. Four time
ranges were considered, corresponding to the classical P0, Na, Pa/
Pb, and N100 scalp components.
First, the earliest cortical activity P0i occurring around 16–19
ms, previously described by several authors (Celesia, 1976, Lee et
al., 1984; Liegeois-Chauvel et al., 1991; Howard et al., 1996),
could be localized in the medial portion of HS for two subjects and
H1 in subject NG. This area likely corresponds to the PAC for it is
the first one active. Although an earlier cortical activity has been
previously described around 10 ms using click stimuli (Celesia,
1976; Steinschneider et al., 1999), we did not observe this response
in any of our patient, likely because we used tone burst stimuli
which elicit weaker responses than clicks. Second, another area
also located in the medial H1/HS region was responsible for the
Nai component until about 25 ms. It is possible that this area also
belongs to the PAC, although it was found to be different than the
area involved in the P0i. Indeed, like in monkeys, three areas have
been described to form the PAC in humans (Morosan et al., 2001).
Third, during the Pai/Pbi complex, up to 4 areas became active
with a postero-anterior and medio-lateral propagation of activity in
H1/HS, PT or H2, and then more anterior part of HS and STG or
H3. Given the anatomical description of auditory cortical areas in
monkeys and humans summarized above and given that the belt
region receives projections from the PAC (Brugge et al., 2003), it is
possible that the Pai/Pbi components reflect the activation of
several areas of the lateral belt region and possibly also of the
parabelt in the STG. These regions correspond to areas PaAi,
Fig. 8. Comparison between the MCE method and L2-based methods (weighted MNE and LORETA regularized using the Tikhonov approach and the L-curve)
for activity patches of increasing sizes. Two patches of dipoles were considered, each having amplitudes following a Gaussian distribution in space (in the
geodesic sense) of radius equal to 3*r (r = 0, 1, 2, 3, and 4 mm). The centers of the patches are indicated by red crosses. No perturbations are introduced. The
MCE method tends to reconstruct several neighboring focal sources, the strongest of which being close to the centers of the patches. The L2-based methods
tend to generate fantom sources a remote distance from the patches.
B. Yvert et al. / NeuroImage 28 (2005) 140–153 151
PaAe, and Tpt described by Galaburda and Sanides (1980, Fig. 1)
and to areas PA, LA/STA, and AA identified by Rivier and Clarke
(1997, Fig. 10). Activities in the PT for subject FY corresponded to
activities in H2 in the two other subjects, suggesting a similar
functional role of these structures. Finally, during the N100i time
range, the same areas were active in two subjects, whereas in
subject NG, the sources were found slightly more anteriorly.
The present results confirm and extend in more details previous
EEG/MEG findings reporting 3 different active areas underlying the
Pa/Pb complex (Yvert et al., 2001). However, in this previous scalp
study, the earliest components (P0 and Na) could not be detected
and the corresponding source locations not estimated. Actually,
achieving precise anatomical localization of the sources of the
earliest auditory evoked cortical components occurring before 30
ms has turned to be very difficult using scalp data. A recent MEG
study, using a very high number of click stimuli (8500) to obtain a
sufficient signal-to-noise ratio, suggested that the postero-medial
part of H1 was solely responsible for the earliest ascending phase of
the Pa component already at 20 ms (Lutkenhoner et al., 2003). Here,
using pure tones, we found that the Pa component started after 25
ms. This latency difference may likely be due to the fact that clicks
are more broadband stimuli recruiting a larger amount of cells and
leading to smaller latencies than single-frequency pure tone stimuli
(e.g., see Fig. 1 of Woods et al. (1995) showing latency differences
of several ms). To our knowledge, however, detailed source
localization of the P0 and Na components elicited by pure tones
has never been presented. Mapping analysis of click-evoked AEPs
has suggested that Na and Pa likely stemmed from different
generators (Deiber et al., 1988) and that Na sources were localized
deeper in the brain than Pa sources, possibly even from diencephalic
structures. Here, we propose that the Na component reflects the
activation of a single source from the postero-medial part of H1 and
that, by contrast, the Pa already stems from the simultaneous
activation of several sources including lateral areas such as the PT
and the STG. Thus, a single dipole model is a good approximation
of the auditory activity in the STP only at the earliest stage of the
evoked response (P0/Na). In particular, after 25 ms, several sources
become simultaneously active and would not be perfectly disclosed
with this simplified model.
A pioneer localization study by Scherg and Von Cramon (1986)
showed that middle-latency (19–40 ms) and long-latency (45–200
ms) AEPs recorded on a near-coronal arrangement of electrodes
B. Yvert et al. / NeuroImage 28 (2005) 140–153152
could be well explained by the activity, in each hemisphere, of a
radial and a tangential stationary sources that were maximally
active at different latencies: N19, P30, and N100 for the tangential
source and N27, P39, and P100 for the radial source. It was
suggested that the tangential source could mimic the activity of
Heschl’s gyrus and that the radial source could reflect the activity
of the STG. This hypothesis was further supported by an MEG
analysis of steady-state and middle-latency components reporting
two underlying sources: a tangential source in postero-medial H1
and another source in the PT (Gutschalk et al., 1999). Our
localization results extend these studies by showing that the
number of active sources was actually greater than 2 thereafter 25
ms and that 3 to 4 areas (including H1, PT, and STG) then
overlapped their activities during both the Pa/Pb and the N100 time
ranges. These findings support that source localization from
intracerebral EEG data using distributed source methods, which
do not make any assumption on the number of sources, can help
distinguish between several simultaneously active regions which
may be difficult to separate using MEG/EEG (see also Lutkenh-
oner (2003) on the limit of MEG to separate close sources).
In conclusion, the spatiotemporal pattern of activation of
supratemporal auditory areas could be identified on the individual
anatomy using current estimates from intracerebral auditory
evoked potentials. The localization approach described in this
study also offers a new way to map functional areas prior to
cortical resections considered to cure pharmaco-resistant epilep-
sies. Furthermore, such source localization approach could also be
useful for precise sulcal and gyral identification of epileptic foci
prior to surgery.
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