text:
Neural correlates of processing facial identity based on features versus their spacing.
ABSTRACT Adults' expertise in recognizing facial identity involves encoding subtle differences among faces in the shape of individual facial features (featural processing) and in the spacing among features (a type of configural processing called sensitivity to second-order relations). We used fMRI to investigate the neural mechanisms that differentiate these two types of processing. Participants made same/different judgments about pairs of faces that differed only in the shape of the eyes and mouth, with minimal differences in spacing (featural blocks), or pairs of faces that had identical features but differed in the positions of those features (spacing blocks). From a localizer scan with faces, objects, and houses, we identified regions with comparatively more activity for faces, including the fusiform face area (FFA) in the right fusiform gyrus, other extrastriate regions, and prefrontal cortices. Contrasts between the featural and spacing conditions revealed distributed patterns of activity differentiating the two conditions. A region of the right fusiform gyrus (near but not overlapping the localized FFA) showed greater activity during the spacing task, along with multiple areas of right frontal cortex, whereas left prefrontal activity increased for featural processing. These patterns of activity were not related to differences in performance between the two tasks. The results indicate that the processing of facial features is distinct from the processing of second-order relations in faces, and that these functions are mediated by separate and lateralized networks involving the right fusiform gyrus, although the FFA as defined from a localizer scan is not differentially involved.
- Li-Zhuang Yang, Wei Zhang, Bin Shi, Zhiyu Yang, Zhengde Wei, Feng Gu, Jing Zhang, Guanbao Cui, Ying Liu, Yifeng Zhou, Xiaochu Zhang, Hengyi Rao[Show abstract] [Hide abstract]
ABSTRACT: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that can modulate cortical excitability. Although the clinical value of tDCS has been advocated, the potential of tDCS in cognitive rehabilitation of face processing deficits is less understood. Face processing has been associated with the occipito-temporal cortex (OT). The present study investigated whether face processing in healthy adults can be modulated by applying tDCS over the OT. Experiment 1 investigated whether tDCS can affect N170, a face-sensitive ERP component, with a face orientation judgment task. The N170 in the right hemisphere was reduced in active stimulation conditions compared with the sham stimulation condition for both upright faces and inverted faces. Experiment 2 further demonstrated that tDCS can modulate the composite face effect, a type of holistic processing that reflects the obligatory attention to all parts of a face. The composite face effect was reduced in active stimulation conditions compared with the sham stimulation condition. Additionally, the current polarity did not modulate the effect of tDCS in the two experiments. The present study demonstrates that N170 can be causally manipulated by stimulating the OT with weak currents. Furthermore, our study provides evidence that obligatory attention to all parts of a face can be affected by the commonly used tDCS parameter setting.PLoS ONE 12/2014; 9(12):e115772. · 3.53 Impact Factor - SourceAvailable from: Dario BombariSwiss Journal of Psychology 01/2014; 73(4):215-224. · 0.57 Impact Factor
- SourceAvailable from: Christian Wallraven
Article: Do congenital prosopagnosia and the other-race effect affect the same face recognition mechanisms?
[Show abstract] [Hide abstract]
ABSTRACT: Congenital prosopagnosia (CP), an innate impairment in recognizing faces, as well as the other-race effect (ORE), a disadvantage in recognizing faces of foreign races, both affect face recognition abilities. Are the same face processing mechanisms affected in both situations? To investigate this question, we tested three groups of 21 participants: German congenital prosopagnosics, South Korean participants and German controls on three different tasks involving faces and objects. First we tested all participants on the Cambridge Face Memory Test in which they had to recognize Caucasian target faces in a 3-alternative-forced-choice task. German controls performed better than Koreans who performed better than prosopagnosics. In the second experiment, participants rated the similarity of Caucasian faces that differed parametrically in either features or second-order relations (configuration). Prosopagnosics were less sensitive to configuration changes than both other groups. In addition, while all groups were more sensitive to changes in features than in configuration, this difference was smaller in Koreans. In the third experiment, participants had to learn exemplars of artificial objects, natural objects, and faces and recognize them among distractors of the same category. Here prosopagnosics performed worse than participants in the other two groups only when they were tested on face stimuli. In sum, Koreans and prosopagnosic participants differed from German controls in different ways in all tests. This suggests that German congenital prosopagnosics perceive Caucasian faces differently than do Korean participants. Importantly, our results suggest that different processing impairments underlie the ORE and CP.Frontiers in Human Neuroscience 01/2014; 8:759. · 2.90 Impact Factor
Page 1
Neuropsychologia 45 (2007) 1438–1451
Neural correlates of processing facial identity based
on features versus their spacing
D. Maurera,∗, K.M. O'Cravenb, R. Le Grandc, C.J. Mondlochd,
M.V. Springerb, T.L. Lewisa, C.L. Gradyb
aMcMaster University, Hamilton, Ont., Canada
bRotman Research Institute at Baycrest, University of Toronto, Toronto, Ont., Canada
cKwantlen University College, BC, Canada
dBrock University, St. Catharines, Ont., Canada
Received 1 May 2006; received in revised form 12 November 2006; accepted 17 November 2006
Available online 3 January 2007
Adults' expertise in recognizing facial identity involves encoding subtle differences among faces in the shape of individual facial features
(featural processing) and in the spacing among features (a type of configural processing called sensitivity to second-order relations). We used
fMRI to investigate the neural mechanisms that differentiate these two types of processing. Participants made same/different judgments about
pairs of faces that differed only in the shape of the eyes and mouth, with minimal differences in spacing (featural blocks), or pairs of faces that
had identical features but differed in the positions of those features (spacing blocks). From a localizer scan with faces, objects, and houses, we
identified regions with comparatively more activity for faces, including the fusiform face area (FFA) in the right fusiform gyrus, other extrastriate
regions, and prefrontal cortices. Contrasts between the featural and spacing conditions revealed distributed patterns of activity differentiating the
two conditions. A region of the right fusiform gyrus (near but not overlapping the localized FFA) showed greater activity during the spacing
task, along with multiple areas of right frontal cortex, whereas left prefrontal activity increased for featural processing. These patterns of activity
were not related to differences in performance between the two tasks. The results indicate that the processing of facial features is distinct from
the processing of second-order relations in faces, and that these functions are mediated by separate and lateralized networks involving the right
fusiform gyrus, although the FFA as defined from a localizer scan is not differentially involved.
© 2006 Elsevier Ltd. All rights reserved.
Keywords: fMRI; FFA; Face processing; Features; Spacing; Frontal cortex; Configural
1. Introduction
One sign of their expertise is the ability to recognize thousands
of individuals at a glance when faces are seen in their canonical
share the same first-order relations: they have the same arrange-
ment of features with two eyes above a nose, which is above a
∗Corresponding author at: Department of Psychology, Neuroscience &
Behaviour, McMaster University, Hamilton, Ont. L8S 4K1, Canada.
Tel.: +1 905 525 9140x23030; fax: +1 905 529 6225.
E-mail address: email@example.com (D. Maurer).
like hair color, adults use three types of information to individu-
ate upright faces: the shape of the external contour (head shape,
chin shape), the shape of individual internal features (shape of
features (distance between the eyes, distance between the eyes
and mouth, etc.) (reviewed in Maurer et al., 2002).
Previous studies have identified a region of occipitotempo-
ral cortex, in the fusiform gyrus, that responds more robustly
to faces than to other types of visual stimuli (Downing, Chan,
Peelen, Dobbs, & Kanwisher, 2006; Grill-Spector, Sayres,
& Ress, 2006; Haxby et al., 1994; Kanwisher, McDermott,
& Chun, 1997; Tong, Nakayama, Moscovitch, Weinrib, &
face area, or FFA, and faces often cause more activation of
the right fusiform than the left. The magnitude of activity in
FFA depends not only on the presence of face stimuli in the
0028-3932/$ – see front matter © 2006 Elsevier Ltd. All rights reserved.
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D. Maurer et al. / Neuropsychologia 45 (2007) 1438–1451
visual field, but also on whether attention is directed to the faces
(O'Craven, Downing, & Kanwisher, 1999) and on the waxing
and waning of conscious perception of a face in compound or
& Blakemore, 2002; Tong, Nakayama, Vaughan, & Kanwisher,
There has been considerable debate as to the precise role of
the FFA in face processing, including whether it is truly selec-
tive for faces or rather mediates the differentiation of objects
with which one has developed an expertise (e.g., Gauthier,
& Kanwisher, 2004; Joseph & Gathers, 2002; Rhodes, Byatt,
Michie, & Puce, 2004). Regardless of whether the processing
in the FFA is specific to faces or generalizes to other objects of
expertise, there remains the question of its specific role in face
processing. Studies of individuals with prosopagnosia suggest
Hoesen, 1982), but not sufficient (Avidan, Hasson, Malach, &
Behrmann, 2005; Hasson, Avidan, Deouell, Bentin, & Malach,
Lindemann, Maeder, Borruat, & Assal, 1997; Evans, Heggs,
Antoun, & Hodges, 1995; Haxby et al., 2001; Ishai, Schmidt,
& Boesiger, 2005). That has led some investigators to conclude
that the FFA is specialized only for face detection (detecting
that a face is present based on its first-order relations of two
eyes above a nose, which is above a mouth), and hence for acti-
its identity. Recent evidence that activation does not attenuate
part is altered on each trial has led others to speculate that neu-
rons in the FFA represent faces holistically (Schiltz & Rossion,
identify an individual face (Mazard, Schiltz, & Rossion, 2006).
One candidate cue for processing identity is the spacing of
internal facial features, a specific type of configural processing
that is called sensitivity to second-order relations (reviewed in
Maurer et al., 2002). Adults use this cue effectively to discrimi-
nate upright but not inverted faces, and, with most stimulus sets,
than for featural changes (Freire, Lee, & Symons, 2000; Leder
& Bruce, 1998; Leder & Carbon, 2006; Malcolm, Leung, &
Barton, 2005; Mondloch, Le Grand, & Maurer, 2002; Rhodes,
Hayward, & Winkler, 2006; but see Riesenhuber, Jarudi, Gilad,
Kanwisher, 2004, 2005). Likewise, adults' ability to recognize
that a face has been distorted decreases significantly as the face
is rotated away from upright when the distortion alters the spac-
ing of features in unnatural ways, but is not impaired when the
distortion alters the features (blackened teeth or white pupils)
(Collishaw & Hole, 2000; Murray, Yong, & Rhodes, 2000).
cesses may underlie the perceptual analysis of a face stimulus
images to remove the low spatial frequencies, on which adults
rely in poor light or at a distance, degrades adults' sensitivity
to differences in the spacing of the eyes but has no impact on
their sensitivity to featural differences (Goffaux, Hault, Michel,
Vuong, & Rossion, 2005). The separability of processing based
of differential rates of development (Mondloch et al., 2002) and
evidence of differential impairment for features versus spacing
in adults with Williams Syndrome, in children with a history of
early visual deprivation from congenital cataract, and in some
2002; de Gelder & Rouw, 2000; Karmiloff-Smith et al., 2004;
Le Grand et al., 2006; Le Grand, Mondloch, Maurer, & Brent,
2001; Le Grand, Mondloch, Maurer, & Brent, 2003).
Little is known about the brain areas involved in the process-
ing of features versus second-order relations. In the only direct
study,1Yovel and Kanwisher (2004) concentrated on the FFA
and contrasted responses to faces and houses with featural or
spacing changes. They found more activity in the fusiform face
area in face blocks than in house blocks, regardless of whether
in the two types of processing.
That conclusion is consistent with the results for inversion
on FFA activity. In contrast to large behavioral effects of face
roimaging studies indicate that, on initial presentation, FFA
activity is similar for upright and inverted photographs of faces
(Aguirre, Singh, & D'Esposito, 1999; Epstein, Higgins, Parker,
Aguirre, & Cooperman, 2006; Haxby et al., 1999; Kanwisher,
Tong, & Nakayama, 1998; Mazard et al., 2006; Schiltz &
Rossion, 2006; but see Yovel & Kanwisher, 2004, 2005). The
lack of a consistent neural inversion effect when a face is first
presented suggests that activity in the FFA is not tied specifi-
cally to the processing of second-order relations. On the other
hand, the fact that brain areas outside the FFA respond differ-
ently to upright and inverted faces suggests that the processing
of second-order relations may be widely distributed. For exam-
for upright than for inverted faces (Leube et al., 2003; Yovel &
Kanwisher, 2005), but activity is greater for inverted than for
upright faces in areas of occipital cortex that typically respond
more to non-face objects, such as houses (Aguirre et al., 1999;
Epstein et al., 2006; Haxby et al., 1999; Kanwisher et al., 1998;
Yovel & Kanwisher, 2005).
In the present study, we used functional magnetic resonance
differences. First, we used stimuli that kept spacing differences
within natural limits. In the study by Yovel and Kanwisher
(2004), the faces for the spacing trials had eyes that had been
1A PET study (Rossion et al., 2000) contrasted two conditions named simi-
larly to ours, but an examination of the methods indicates that the investigators
studied something entirely different. Both conditions (named whole-face and
ing of the features. The conditions differed in whether or not holistic processing
would interfere with correct performance.
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D. Maurer et al. / Neuropsychologia 45 (2007) 1438–1451
Fig. 1. The five examplars used for each stimulus set. (A) In the featural set, the sisters differ from Jane (the leftmost face) in the shape of the eyes and mouth. (B)
In the spacing set, the sisters differ from Jane in the relative location of the eyes and mouth. (C) For the scrambled baseline, images were created by cutting the Jane
stimuli into sections and assembling them randomly.
moved beyond natural limits, presumably in order to equate
adults' accuracy in discriminating between upright face pairs
differing in spacing with their accuracy for featural changes.2
ations (S.D.s) from the mean inter-pupillary distance measured
in a representative sample of adult Caucasian faces reported in
Farkas (1981). Changes of this magnitude make the faces look
distorted and unnatural. As well, the mouth was moved not only
up or down but also laterally by a small amount in some of the
stimuli, so that differences in spacing also included differences
in symmetry. Perhaps as a result of the asymmetries and/or the
2Studies keeping spacing changes within natural limits and interchanging
featural differences than the spacing differences (Freire et al., 2000; Gilchrist &
McKone, 2003; Goffaux et al., 2005; Le Grand and Maurer, 2003; Mondloch et
58 different feature swaps indicate that accuracy is usually high (≥90% for 86%
of the pairings) with the swaps used in the current experiment being of a typical
type is needed to determine whether there is a true difference in the efficiency
of differences among individuals.
inversion caused a larger drop in accuracy for their featural set
than for their spacing set, which is the opposite pattern from
what is usually reported.
In contrast, we adapted a task (Mondloch et al., 2002) that
differentiates the processing of face identity based on features
sive behavioral data (Karmiloff-Smith et al., 2004; Le Grand &
Maurer, 2003; Le Grand, Maurer, et al., 2003; Le Grand et al.,
2001; Le Grand, Mondloch, et al., 2003; Mondloch, Dobson,
Parson, & Maurer, 2004; Mondloch et al., 2002). The task
requires the observer to make same/different judgments about
two faces presented sequentially. The two faces in each pair
are identical except for small differences in either the shape of
internal features in some blocks (Fig. 1, Panel A) – a measure
of featural processing – or, in other blocks, differences in the
spacing of the internal features (Fig. 1, Panel B)—a measure of
ified digitally so that the spacing (e.g., distance between eyes,
and distance from eyes to mouth) covers most of the range that
and were titrated to avoid floor and ceiling effects on adults'
differences depend on detecting only featural or only spacing
processing strategies in different blocks. Several pieces of evi-
dence from our previous behavioral research indicate that the
two stimulus sets, when presented in separate blocks, elicit dif-
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D. Maurer et al. / Neuropsychologia 45 (2007) 1438–1451
ferent types of face processing. First, accuracy in detecting a
featural change decreases slightly (and linearly) with increasing
rotation away from the canonical upright position, while accu-
racy in detecting a spacing change drops precipitously between
60◦and 120◦off upright, then asymptotes with further rota-
tion from 120◦to 180◦(Le Grand & Maurer, 2003; Mondloch
et al., 2002). Second, developmental studies show that 6-year-
olds are almost as accurate as adults in discriminating faces in
the featural set (Mondloch et al., 2002) but even 14-year-olds
make more errors than adults in the spacing set (Mondloch,
Le Grand, & Maurer, 2003). Third, LH, an adult with acquired
prosopagnosia from a head injury (Levine, Calvanio, & Wolfe,
1980), is at chance on the spacing set even with simultaneous
and unlimited exposure but performs well above chance on the
featural set (Le Grand, Maurer, et al., 2003). Finally, children
who suffered early visual deprivation from congenital cataracts
that eliminated visual input to both hemispheres (or to mainly
the right hemisphere) perform abnormally on the spacing but
not the featural set (Le Grand et al., 2001; Le Grand, Mondloch,
et al., 2003).
The second important difference between our study and that
the FFA but looked at cortical regions throughout the brain. To
do this, we performed multivariate analyses to determine whole
brain patterns of activity, that is, putative networks that were
differentially recruited by discriminating faces on the basis of
either spacing or features. These multivariate analyses allowed
us to identify the brain regions, including the FFA, with dif-
ferential activity to faces versus objects and houses during a
localizer run, and then to ask whether a similar network, or dif-
ferent areas, characterized spacing and featural blocks. We also
used this multivariate approach to evaluate the influence of any
differences in task difficulty between the spacing and featural
sets (Druzgal & D'Esposito, 2003; Grady et al., 1996, 1998;
Haxby, Ungerleider, Horwitz, Rapoport, & Grady, 1995).
2. Materials and methods
2.1. Participants
Twelve healthy participants (between the ages of 18 and 30 years) were
included in the experiment. One additional participant was unable to complete
right-handed (score >45 on McMaster Handedness Questionnaire (Mondloch et
Visual Acuity chart and normal stereo-acuity as measured by the Titmus test.
None of the participants took medications that might affect brain function and
none had overt brain abnormalities revealed by a structural MRI scan obtained
Science Centre and McMaster University approved this experiment and each
participant gave informed consent.
2.2. Stimuli
2.2.1. Localizer task
For the localizer task, participants viewed 24-s blocks of gray-scale pho-
tographs from each of three categories: faces, houses, and common household
2.2.2. Jane task
The stimuli were identical to those described previously in our behavioral
female faces were taken under standard lighting conditions (Geldart, 2000). A
to create eight new versions ("Jane's sisters"). The four modified faces in the
featural set (Fig. 1, Panel A) were created by replacing the model's eyes and
mouth with the features of the same length from different females. On every
"different" trial, the two faces had different eyes and mouth. The four modified
faces in the spacing set (Fig. 1, Panel B) were created by moving the eyes up
or down from the original, the eyes closer together or farther apart, and the
mouth up or down. We used all possible combinations of the five faces; because
there were only two mouth positions, on 20% of the different trials the mouth
was in the same location in the two faces and only the location of the eyes
differed.3According to anthropomorphic norms (Farkas, 1981), the movements
correspond to shifts in Jane's eyes of 1.3S.D.s up or down, of 2.4S.D.s closer
together, or of 3.2S.D.s farther apart and shifts of Jane's mouth by 0.79S.D.s
up or down. Thus, the differences covered most of the variations in spacing
appeared deformed or unnatural. Control stimuli consisted of scrambled Jane
images created using custom MATLAB software. The original images were cut
digitally into 20×20 pixel sections and reassigned randomly to positions in the
display. Edges between the pieces were smoothed by convolving a Gaussian
filter with the scrambled image (Fig. 1, Panel C).
2.3. Procedure
Prior to the experimental session, participants practiced the task outside the
MRI scanner. They were shown Jane and her sisters and told that they would see
one of the faces flash quickly on the screen followed by another face. They were
face and to press with the middle finger if the two faces were different. Since
the second face appeared for only 1.5s, the speed of responding was empha-
sized in addition to accuracy. Participants also practiced making same/different
judgments about the scrambled images.
After the practice session, participants lay supine on the MRI scanner bed
with their head stabilized with snug foam cushions. They were fitted with
MRI-compatible Silent VisionTMGoggles (Avotec Inc.) which were adjusted
to correct vision (±6D) independently for each eye.
Following anatomical scans, two 5-min functional localizer scans were per-
formed during which subjects were instructed to simply look at the screen. Each
picture in the 24-s block was presented for 1900ms, with an ISI of 100ms.
Blocks with only a fixation cross provided a low level baseline at the beginning,
task." The instructions given during the practice session were repeated before
the first run. Each trial consisted of the presentation of the first (model) face
for 200ms, an inter-stimulus interval of 300ms, followed by the second face
for 1500ms. On half the trials, the correct response was "different." Blocks of
22 trials with the same type (spacing, featural, or scrambled) were presented
during 45-s intervals in a fixed order during each run (scrambled, spacing, fea-
ture, spacing, feature, scrambled, spacing, feature, spacing, feature, scrambled).
but the subjects were not explicitly informed about the distinctions. Each run
lasted for 8min and 15s, and participants performed three to five runs, resulting
in a total of 540–900s of data from each face task across the runs. Responses
the location of only the eyes differ as on the majority of trials with differences
in the location of both the eyes and mouth (Mondloch, Le Grand, & Maurer,
unpublished data). This is not surprising since adults process faces holistically
even in tasks where they are instructed not to and where it is disadvantageous
to do so (e.g., Hole, 1994; Young, Hellawell, & Hay, 1987), making it unlikely
that they relied only on eye position in the current study. Accuracy was also
similar when different trials included pairings with the subtle makeup cues visi-
ble in Fig. 1 (89%) versus being restricted to pairings without those cues (92%)
(Mondloch, Dobson, & Maurer, unpublished data).
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D. Maurer et al. / Neuropsychologia 45 (2007) 1438–1451
were collected through a RURB (Rowland Universal Response Box, Rowland
Institute for Science, Cambridge, MA). As our primary question here was the
difference between the spacing and feature conditions, we included only these
conditions in the analyses of the Jane task described below.
2.4. Scanning procedure
a standard head coil (CV/i hardware, LX8.3 software; General Electric Medical
Systems, Waukesha, WI). During the functional scans, the blood oxygenation
level dependent (BOLD) MRI signal was measured in 26 5mm slices (no gap).
The imaging sequence was a single shot T2*-weighted pulse sequence with
spiral readout, offline gridding, and reconstruction (TR=2000ms, TE=40ms,
a 3D T1-weighted pulse sequence (TR=35.0ms, TE=6.0ms, flip angle 35◦,
the in-plane resolution was 3mm×3mm and the slice thickness was 5mm.
2.5. Data analysis
Motion correction and linear de-trending of the images were performed
using Analysis of Functional NeuroImages (AFNI, Cox & Hyde, 1997). Each
volume in the time series was aligned to an early fiducial volume from the first
imaging run in the scanning session, such that the amount of motion across
all functional runs was at a minimum. Motion corrected images were spatially
normalized to an fMRI spiral scan template in the brain space of the Montreal
Neurological Institute (MNI) and smoothed with a Gaussian filter of 8mm full-
Neuroscience, http://www.fil.ion.ucl.ac.uk/spm).
Partial least squares, or PLS (McIntosh, 1999), was used to assess whole-
brain patterns of activity during the localizer runs and during the spacing and
were related to brain activity (i.e., to assess the effects of task difficulty). This
multivariate approach is similar to a principal component analysis (e.g., Fristen,
Frith, Liddle, & Frackowiak, 1993) and assumes that brain function reflects
the coordinated activity of groups of brain regions rather than the independent
activity of any single brain region. As such, it is ideal for assessing patterns of
activity in all voxels covaried across the conditions, thus identifying a new set
of variables (so-called latent variables or LVs, similar to principal components)
that characterized this covariance and to determine how much covariance each
LV explained. Each LV from such an analysis identifies a pattern of differences
in brain activity across the experimental conditions and specifies which brain
voxels show this effect. Each brain voxel has a weight on each LV, known as a
salience, which indicates how strongly that voxel is related to the LV. A salience
can be positive or negative, depending on whether the voxel shows a positive
or negative relation with the pattern of task differences identified by the LV.
We calculated a "brain score" for each condition in the LV for each subject by
multiplying the salience of each voxel on that LV by its signal intensity in the
subject's brain, and then summing across all voxels. The brain score indicates
the degree to which each subject expresses the pattern of brain activity seen on
the LV. Examination of these scores indicates how brain activity varies across
conditions, as greater activity in brain areas with positive (or negative) weights
on an LV correspond to positive (or negative) mean brain scores for a given
the conditions in the localizer scan (i.e., faces, houses, objects, and fixation
blocks). This analysis was carried out on the mean activity for each condition
across the 12 participants. The second LV from this analysis identified regions
with more activity for faces than the other conditions, and we report the results
of this LV here. In the second analysis, we used PLS to identify brain areas
whose activity differentiated the spacing and feature task conditions. In order
to gain power and evaluate consistency across the runs, the data from each run
were entered into the analysis separately, rather than averaging across the runs
as we did for the localizer. Because 11 of the 12 participants had at least four
runs of the tasks, we restricted the analyses to those 11 subjects and 4 runs (one
female subject omitted). We calculated an average fMRI signal map for each of
in a total of eight images for each subject. To examine task-related activity, an
analysis was conducted on the images for all eight "conditions" (spacing in runs
1–4 and featural for runs 1–4).
To assess whether the differential activity between featural and spacing runs
could be attributed to task difficulty, we did a similar analysis based on perfor-
mance accuracy. Specifically, we used PLS to calculate the correlation between
the fMRI signal in each brain voxel and accuracy within each of the eight task
conditions (four runs of the featural task and four runs of the spacing task), and
then contrasted these correlations across tasks (McIntosh, 1999). The logic of
this analysis is that if activity were increased in any brain region because of
greater difficulty in performing the tasks, the increase would be greatest in those
participants who experienced the greatest difficulty with the task as indicated by
the poorest performance. One male participant was omitted from this analysis
because a technical problem caused a loss of behavioral data.
Two independent statistical measures were obtained for each PLS analysis.
The reliability of each voxel's contribution to each LV was determined through
bootstrap resampling (Efron & Tibshirani, 1986; Sampson, Streissguth, Barr, &
of its salience to the standard error of the salience greater than or equal to 3.0,
which approximates p<0.005 (Sampson et al., 1989). The bootstrap also gives
confidence intervals for each correlation between performance accuracy and the
brain scores for each LV, which provide a measure of how strongly the pattern
of activity seen on the LV is correlated with performance. The 95% confidence
to these bootstrap measures, we assessed the statistical significance of each LV
by means of a permutation test (Edgington, 1980; McIntosh, Bookstein, Haxby,
& Grady, 1996). In both the permutation and bootstrap procedures all saliences
are calculated in a single analytical step; hence there is no need for correction
for multiple comparisons (McIntosh et al., 1996). The clusters reported here
contained at least five voxels (i.e., 320?l).
3. Results
3.1. Behavioral results
Participants performed well on both the spacing and fea-
ture tasks. Although the "Jane" stimulus set was constructed
originally with the goal of equating difficulty on the featural
and spacing tasks as much as possible without going outside
the bounds of normal variation of face configurations, partici-
pants performed somewhat better on the featural set during the
scanning session, consistent with previous behavioral studies
(Mondloch et al., 2002). Accuracy was significantly higher for
featural (91% correct) than for spacing (80% correct) blocks
(t=6.79, p<0.0001). The difference in reaction times between
the two face sets was also significant (featural=723ms; spac-
ing=752ms; t=2.68, p<0.05).
3.1.1. fMRI results: localizer scans
set of regions (p=0.02) with more activity for faces than for
houses, objects, and baseline (Fig. 2; Table 1). These included
tified in other studies using more traditional univariate analyses
(Downing et al., 2006; Grill-Spector et al., 2006; Haxby et al.,
1994; Kanwisher et al., 1997). More activity for faces also was
seen in the left middle frontal gyrus, bilateral middle occipital
gyri, and left parahippocampal gyrus. Interestingly, more activ-
ity for faces also was found in the right amygdala, consistent
with the proposed role of the amygdala in identifying emotional
expressions in faces (e.g., Adolphs, 2002; Anderson, Christoff,
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D. Maurer et al. / Neuropsychologia 45 (2007) 1438–1451
Fig. 2. The results of the PLS analysis of the localizer scans is shown on an average MRI (Panel a). The axial levels relative to the AC-PC line are provided under
each image. The graph (Panel b) indicates how the pattern of activity seen in the images differentiated the three stimulus conditions and fixation. Brain scores are
a summary measure of activity across all voxels. Positive brain scores for faces, and to a lesser extent for objects (red bars), indicate that activity was increased in
regions shown in red, including the right fusiform gyrus (indicated by arrow). Negative brain scores (blue bars) indicate that activity increased for houses and fixation
in the brain areas shown in blue. Error bars represent the standard error. In this figure, and all subsequent figures, the right hemisphere is shown on the right side of
the images.
Table 1
Brain areas with activity differentiating faces from houses
Region HemBA
(a) More activity for faces
Inferior Frontal gyrus
Middle Frontal gyrus
Middle Frontal gyrus
Medial Frontal gyrus
Middle Temporal gyrus
Middle Occipital gyrus
Middle Occipital gyrus
Fusiform gyrus
Fusiform gyrus
Parahippocampal gyrus
4 5.7
(b) More activity for houses
Calcarine sulcus
Fusiform gyrus
Fusiform gyrus
ratios correspond to regions with negative salience on the LVs (seen in blue in Fig. 2). X (right/left): Negative values are in the left hemisphere; Y (anterior/posterior):
negative values are posterior to the zero point (located at the anterior commissure); Z (superior/inferior): negative values are inferior to the plane defined by the
anterior and posterior commissures. Abbreviations: Hem, hemisphere; R, right; L, left; BA, Brodmann's area; ratio, salience/S.E. ratio from the bootstrap analysis,
which is a measure of each voxel's reliability.
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D. Maurer et al. / Neuropsychologia 45 (2007) 1438–1451
Fig. 3. Brain areas with task-related modulation of activity across the spacing and feature runs are shown on an average MRI (Panel a). The axial levels relative to
the AC-PC line are provided under each image. The graph (Panel b) indicates how the pattern of activity seen in the images differentiated the spacing and feature
tasks across the four runs of each task. Positive brain scores in the spacing task (red bars) indicate that activity was increased in regions shown in red, including the
right fusiform region indicated by the arrow. Negative brain scores in the feature task (blue bars) indicate that activity increased during this task in the brain areas
shown in blue. Error bars represent the standard error.
Panitz, De Rosa, & Gabrieli, 2003; Glascher & Adolphs, 2003;
Morris et al., 1998). Less activity for faces compared to houses
studies reporting medial occipital or parahippocampal activity
for houses (Aguirre, Zarahn, & D'Esposito, 1998; Epstein &
Kanwisher, 1998; Haxby et al., 1999).
3.1.2. fMRI results: spacing versus feature blocks
A significant pattern of activity (p<0.02) differentiated the
spacing and featural tasks across all four runs, although there
was some variability in the strength of this dissociation across
the feature runs (Fig. 3). There were several regions of right
prefrontal cortex, where activity was greater during the spac-
ing task compared to the feature task (Table 2a). The area that
maximally differentiated the two tasks was in the right middle
frontal gyrus. In addition to frontal regions, the right fusiform
gyrus showed greater activation during the spacing task in a
region adjacent to, but not overlapping, the FFA defined from
the localizer runs (Fig. 4; see Fig. 5 for a conventional univari-
ate analysis that shows a similar result: a region in the right
fusiform that is sensitive to faces, when compared to scram-
bled faces, but no difference between spacing and feature in this
region). Inspection of the time course indicates that this region
in the right fusiform gyrus shows increased activation over the
course of a block for spacing blocks but decreased activation
(and perhaps adaptation) for feature blocks (Fig. 6). There also
was greater activation during the spacing task in two additional
posterior regions, in the left posterior fusiform gyrus and right
inferior parietal cortex. In contrast, the areas with more activity
including the middle temporal gyrus, middle frontal gyrus, and
the posterior cingulate gyrus (Table 2b).
The analysis correlating task performance with brain activity
identified a set of brain regions (p<0.002) where activity was
Fig. 4. Images at Z=−24mm illustrating the overlap (b) of activity in the right
fusiform gyrus during the localizer scan (a) and the Jane tasks (c). The orange
areas from the localizer scan are those where there was more activity for faces
than for houses and objects. The orange regions from the Jane task are those
where there was more activity for the spacing condition than the feature condi-
in all images) indicates the overlap between the localizer and Jane clusters. The
overlap in the right fusiform is limited to a single voxel.
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D. Maurer et al. / Neuropsychologia 45 (2007) 1438–1451
Table 2
Brain areas with activity differentiating spacing from feature conditions
Region Hem
(a) Spacing>featural
Middle Frontal gyrus
Middle Frontal gyrus
Inferior Frontal gyrus
Inferior Frontal gyrus
Inferior Frontal gyrus
Medial Frontal gyrus
Fusiform gyrus
Fusiform gyrus
Inferior Parietal
(b) Featural>spacing
Middle Frontal gyrus
Medial Frontal gyrus
Middle Temporal gyrus
Posterior Cingulate
Lingual gyrus
−28 12
ratios correspond to regions with negative salience on the LVs (seen in blue in Fig. 3). X (right/left): Negative values are in the left hemisphere; Y (anterior/posterior):
negative values are posterior to the zero point (located at the anterior commissure); Z (superior/inferior): negative values are inferior to the plane defined by the
anterior and posterior commissures. Abbreviations: Hem, hemisphere; R, right; L, left; BA, Brodmann's area; ratio, salience/S.E. ratio from the bootstrap analysis,
which is a measure of each voxel's reliability.
runs of the feature task, was associated with increased activity
in bilateral temporal regions and the right thalamus (Table 3a).
Fig. 5. Results of a typical univariate analysis (t-tests carried out using AFNI)
are shown on coronal slices (averaged MRI from the study participants). An
area of right fusiform was activated for both the featural (a) and spacing (b)
conditions, relative to scrambled faces. The bottom images show that there is
no differential activation within the right fusiform (c), even at a very lenient
threshold (d).
was associated with activity in the right middle frontal gyrus,
left parietal cortex, cerebellum, and caudate nuclei (Table 3b).
This pattern of correlations was reversed for the last run, so that
the activity correlated with worse performance in the early runs,
tasks (see Fig. 7).
It is important to note that the regions identified by the
analysis of spacing versus feature blocks and the analysis of
correlates of task performance are largely independent: that is,
there is almost no overlap in the regions that showed modula-
and regions where activity indexed task difficulty. There was
one exception, in right frontal cortex. The region of right
prefrontal cortex that maximally differentiated the spacing from
Fig.6. Timecourses(averagedoverallspacingandfeatureblocks)ofactivityin
by arrow in Fig. 3a). The signal is normalized to the first time point (0) in each
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D. Maurer et al. / Neuropsychologia 45 (2007) 1438–1451
Fig. 7. Brain areas where activity was correlated with task performance (proportion correct) are shown on an average MRI (Panel a). The axial levels relative to the
AC-PC line are provided under each image. The graph (Panel b) shows the values of the correlations between brain scores and performance accuracy for the four
runs of the spacing and feature tasks. All correlations are reliable at the 95% confidence interval except for run 3 of the feature task. A positive correlation in the
earlier runs indicates that as activity increases in red areas (and the brain scores become more positive), accuracy of performance improves. Conversely, as activity
in the blue areas increases (and the brain scores become more negative), accuracy decreases. In both tasks, the pattern changes for the fourth run, in which more
activity in the areas shown in blue is associated with better performance.
the featural task (see Fig. 8c) was a relatively large cluster of
where activity was associated with poorer performance on the
tasks (see Fig. 8a). However, the maxima of these two regions
differed by more than 12mm. In addition, there was no overlap
with any of the other right frontal regions with more activity
for spacing than feature blocks. Overall, the behavioral PLS
supports the idea that the brain activity distinguishing the pro-
cessing of second-order relations from that of facial features is
largely due to differences in the type of processing necessary to
Table 3
Brain areas with activity related to task performance in early runs of the spacing and feature tasks
RegionHem BA
(a) Correlations with better task performance
Middle temporal gyrus
Middle temporal gyrus
Thalamus (Pulvinar)
(b) Correlations with worse task performance
Middle frontal gyrus
Inferior frontal gyrus
Inferior parietal
Parahippocampal gyrus
Lingual gyrus
Cingulate gyrus
Cingulate gyrus
Caudate nucleus
Caudate nucleus
36 56
ratios correspond to regions with negative salience on the LV (seen in blue in Fig. 7). X (right/left): Negative values are in the left hemisphere; Y (anterior/posterior):
negative values are posterior to the zero point (located at the anterior commissure); Z (superior/inferior): negative values are inferior to the plane defined by the
anterior and posterior commissures. Abbreviations: Hem, hemisphere; R, right; L, left; BA, Brodmann's area; ratio, salience/S.E. ratio from the bootstrap analysis,
which is a measure of each voxel's reliability.
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D. Maurer et al. / Neuropsychologia 45 (2007) 1438–1451
Fig. 8. A single slice is shown (Z=16mm) to illustrate the overlap of activity
in the right frontal cortex between the behavior and task PLS analyses. The
blue areas from the behavioral analysis are those where increased activity was
correlated with worse performance on the spacing and feature tasks. The orange
regions from the Jane task are those where there was more activity during the
(location indicated by green cross hairs in all images) indicates the overlap.
Although there were several voxels where these two clusters overlapped, the
maxima for these two regions were different (behavior: X=36, Y=56, Z=12;
spacing>feature: X=32, Y=44, Z=24).
carry out these two tasks and not the result of differences in task
4. Discussion
The results reveal three novel findings: (1) increased activ-
ity in the right fusiform gyrus adjacent to, but not overlapping
the classic FFA, is involved in the processing of facial identity
based on second-order relations; (2) the right fusiform activity
for second-order relations is part of a distributed set of regions
extending into the right frontal cortex; and (3) frontal cortex
activity correlated with task difficulty is largely distinct from
frontal activity involved in the processing of facial identity.
4.1. Specialization of the fusiform face area
Using a localizer scan, we were able to identify a region of
2006; Grill-Spector et al., 2006; Haxby et al., 1994; Kanwisher
et al., 1997; Tong et al., 2000). In addition, our results showed
ditions in right fusiform activity, but not in the FFA per se. The
fusiform area with more activity for spacing than features was
adjacent to the FFA defined by the localizer runs, but slightly
superior and posterior to it. Our findings are consistent with
the findings of Yovel and Kanwisher (2004) for the FFA, as
both indicate that FFA activity does not distinguish between
face processing strategies. Our results further suggest that there
are multiple regions in the right fusiform gyrus that respond
to faces, one (the FFA) responding selectively to faces—and
perhaps other objects of expertise, and another specialized for
a specific type of face processing, namely the processing of
clusters containing at least five voxels, as is typical in the litera-
of activation within the FFA that differentiate the processing of
features versus spacing that are equal in overall signal strength
and hence disappear at larger cluster sizes. That possibility is
suggested by a recent report by Grill-Spector et al. (2006) that
identified different small clusters in the FFA highly selective for
faces, for cars, for animals, and for sculptures, with the differ-
number of clusters favoring faces. In any event, our results indi-
cate that with the unit of analysis typically used to study the
selectivity of responding in the FFA, like Yovel and Kanwisher
(2004) we found no differential activation for the processing of
facial identity based on features versus second-order relations.
4.2. Involvement of right frontal cortex
In addition to the difference in the right fusiform gyrus, there
was increased activity in several areas of right frontal cortex
when the discrimination was based on spacing rather than on
features. This finding echoes a previous report of right frontal
activity for comparing whole faces versus comparing just noses
2000) and recent evidence for a role of right frontal cortex in
recognizing one's own face (Platek et al., 2006; Uddin, Kaplan,
Molnar-Szakacs, Zaidel, & Iacoboni, 2005). Our results indi-
cate that there is a lateralized network of activity in the right
One concern is that the increased right frontal activation
might reflect increased difficulty or effort during the spacing
accuracy and reaction time. Since increased difficulty is known
to produce increased activity in frontal cortex (Braver et al.,
1997; Grady et al., 1996), it is important to evaluate this alterna-
whether any of the areas that formed part of the network active
during the spacing task also showed a correlation between this
activity and difficulty in performing the task, as measured by
accuracy. The set of regions where activity was correlated with
greater task difficulty, as indexed by worse accuracy, differed
from the regions where activity differentiated the two face pro-
frontal cortex with activity related to the spacing blocks. Only
one of these regions, in BA 10, showed any overlap with the
feature tasks (see Fig. 8; Table 3). This region of BA 10 is simi-
lar in location to a region that has been shown to be more active
during difficult face discrimination tasks (Grady et al., 1996),
so activity in this region during the spacing task may be partly
related to the greater difficulty of the task. On the other hand,
the distance between the maximum of this region, and that of
the frontal area correlated with worse performance in the early
of frontal cortex.
The other right frontal regions that responded differently
during spacing compared to feature blocks did not show any
correlation with performance. One of these regions, BA 44 in
the inferior frontal gyrus (see Table 3), has been reported to
differentiate faces from non-face stimuli (Henson et al., 2003;
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D. Maurer et al. / Neuropsychologia 45 (2007) 1438–1451
Kelley et al., 1998). Another, in the ventral and anterior portion
of the right frontal lobe (see Table 3), is active when individ-
uals identify their own face (Uddin et al., 2005), and when
they discriminate famous faces from one another (Rotshtein,
Henson, Treves, Driver, & Dolan, 2005) or from non-famous
faces (Leveroni et al., 2000). This region also responds to a
specific target face among other equally familiar faces (Jiang,
Haxby, Martin, Ungerleider, & Parasuraman, 2000). Collec-
tively, these results suggest that this region in the ventral frontal
cortex is involved specifically in identifying particular individ-
uals and/or retrieving person-specific semantic information. All
second-order relations in a face, that is, the spacing of features;
indeed, it may be that these right frontal areas work together,
along with activity in the right fusiform gyrus, to mediate this
aspect of face processing. The activity in the right inferior pari-
etal lobe for spacing is consistent with the role of this area in
spatial attention, since the spacing task requires more attention
to the relations among spatial locations than the feature task
(Corbetta, Miezin, Shulman, & Petersen, 1993; Losier & Klein,
2001; Nobre et al., 1997).
Because second-order relations are by definition distributed
across the face, our findings of greater activation in the right
fusiform, and other right hemisphere regions, during spacing
than during featural blocks is consistent with the well known
finding that global processing of visual information relies more
on the right hemisphere (e.g., Fink et al., 1997; Martinez et al.,
1997; Robertson & Delis, 1986), It is also consistent with the
finding that children for whom a unilateral left cataract reduced
visual input during infancy mainly to the right hemisphere later
have deficits in the processing of the Jane spacing set but not the
Jane feature set (Le Grand, Mondloch, et al., 2003).
4.3. Featural processing
visual cortex and a number of other areas in the left hemisphere.
The activation of posterior areas of visual cortex is not surpris-
stimulus changes to which early visual cortex would be selec-
tive. In addition, the activity in left frontal and temporal regions
that was greater during the feature task was not correlated with
task performance, a pattern suggesting that these regions medi-
ate the discrimination of facial features independently of the
difficulty of the discrimination. The left lateralization of areas
more active during the featural than during spacing blocks fits
well with evidence for left lateralization of local as opposed to
global processing (e.g., Fink et al., 1997; Hellige, 1996; Ivry
& Robertson, 1998; Martinez et al., 1997). It is also interest-
ing in light of a recent demonstration of the importance of high
spatial frequencies for featural processing: subjects were much
more accurate in discriminating featural differences in a Jane-
high spatial frequencies than when the faces contained only low
spatial frequencies (Goffaux et al., 2005). The same manipula-
tions had little impact on subjects' accuracy in discriminating
spacing differences, although there was a small but significant
difference favoring low spatial frequencies. The importance of
4.4. Are these networks face-specific?
The networks identified here for the processing of facial fea-
tures versus second-order relations may play similar roles for
the processing of non-face objects. That likelihood is suggested
by the fact that most of the areas identified as preferring faces
in the localizer runs (Table 1) were not identified in the contrast
between spacing and feature blocks (Table 2). The networks
identified for processing features versus second-order relations
could be involved in the processing of stimuli, including non-
frequencies or emphasizing more local versus more global pro-
cessing. The processing of features requires the decoding of
The processing of second-order relations, in contrast, involves
the processing of low spatial frequency information about the
a larger region of the face than that occupied by any single fea-
represent general object processing networks that are recruited,
as needed, for different aspects of face processing.
oncile with this view. Adults are unable to attend to just the
features in one part of a face, ignoring incongruent information
which they do not have expertise (Gauthier & Tarr, 2002; Hole,
1994; Robbins & McKone, 2003, Robbins & McKone, 2006;
Tanaka & Farah, 1993; Young et al., 1987). Moreover, adults'
reliance on second-order relations appears to be restricted to
upright human faces: they are unable to use it effectively to
identify monkey faces or inverted human faces and the absence
of an inversion effect suggests they do not rely on it for rec-
ognizing objects (e.g., Mondloch, Maurer, & Ahola, 2006; Yin,
1969). Thus, it is unlikely that the networks identified here are
identical to those used for object processing.
4.5. Implications for understanding the nature of face
Because they found no difference in FFA activity for trials in
which subjects made same/different judgments about faces dif-
Yovel and Kanwisher (2004) argued "against a differential role
to configuration and parts in face perception" (see Riesenhuber
et al., 2004, for a similar argument based on similar inversion
cost for their faces with spacing and featural differences). Our
results are consistent with the idea that there is no differen-
tial FFA activity during the processing of features versus their
spacing. However, our whole brain analysis, like the behavioral
evidence summarized in Section 1, suggests that adults do use
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D. Maurer et al. / Neuropsychologia 45 (2007) 1438–1451
differentiable types of face processing involving second-order
relations versus features when decoding facial identity that are
the brain.
5. Conclusions
We found evidence of a cortical network that is active during
the processing of facial identity based on second-order rela-
tions but not on features. The network includes a region in the
fusiform gryus close to, but not overlapping, the classic right
absence of differential activity in the right FFA for featural and
spacing blocks confirms the findings of Yovel and Kanwisher
(2004) with a different stimulus set and is consistent with evi-
presentation of faces, whether they are upright or inverted, and
whether they are intact or have features missing (Aguirre et al.,
1999; Epstein et al., 2006; Haxby et al., 1999; Kanwisher et al.,
1998; Mazard et al., 2006; Schiltz & Rossion, 2006; Tong et al.,
2000; but see Yovel & Kanwisher, 2004, 2005 for a different
fMRI pattern for inversion). The FFA may act as a rapid "face
detector" that forms an overall representation of the whole face
(Schiltz & Rossion, 2006) and then sends information to the
appropriate network for further processing. When the further
processing involves determining the second-order relations in
order to decode the identity of the face, our results indicate that
the network includes nearby regions in the fusiform gyrus and
an extensive set of regions in the right frontal cortex. Feedback
from that network may minimize the adaptation of neurons in
the FFA if the identity of the face changes during a block, but
only if it is presented under conditions that allow adults to use
second-order relations to decode identity, i.e., upright and with
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Direct and indirect effects of amino acids on hepatic glucose metabolism in humans
- M. Krebs,
- A. Brehm,
- M. Krssak,
- C. Anderwald,
- E. Bernroider,
- P. Nowotny,
- E. Roth,
- V. Chandramouli,
- B. R. Landau,
- W. Waldhäusl,
- Dr. M. Roden
- … show all 11 hide
The study was designed to examine the contribution of direct (substrate-mediated) and indirect (hormone-mediated) effects of amino acids on hepatic glucose metabolism in healthy men.
The protocols were: (i) CON+S (n=7): control conditions with somatostatin to inhibit endogenous hormone release resulting in fasting plasma concentrations of amino acids, insulin (~28 pmol/l) and glucagon (~65 ng/l), (ii) AA+S (n=7): amino acid infusion-fasting insulinaemia-fasting glucagonaemia, (iii) GLUC+S (n=6): fasting amino acids-fasting insulinaemia-hyperglucagonaemia (~99 ng/l) and (iv) AA-S (n=5): amino acid infusion without somatostatin resulting in amino acid-induced hyperinsulinaemia (~61 pmol/l)-hyperglucagonaemia (~147 ng/l). Net glycogenolysis was calculated from liver glycogen concentrations using 13C nuclear magnetic resonance spectroscopy. Total gluconeogenesis (GNG) was calculated by subtracting net glycogenolysis from endogenous glucose production (EGP) which was measured with [6,6-2H2]glucose. Net GNG was assessed with the 2H2O method.
During AA+S and GLUC+S, plasma glucose increased by about 50% (p<0.01) due to a comparable rise in EGP. This was associated with a 53-% (p<0.05) and a 65% increase (p<0.01) of total and net GNG during AA+S, whereas net glycogenolysis rose by 70% (p<0.001) during GLUC+S. During AA-S, plasma glucose remained unchanged despite nearly-doubled (p<0.01) total GNG.
Conditions of postprandial amino acid elevation stimulate secretion of insulin and glucagon without affecting glycaemia despite markedly increased gluconeogenesis. Impaired insulin secretion unmasks the direct gluconeogenic effect of amino acids and increases plasma glucose.
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- Title
- Direct and indirect effects of amino acids on hepatic glucose metabolism in humans
- Journal
Volume 46, Issue 7 , pp 917-925
- Cover Date
- 2003-07-01
- 10.1007/s00125-003-1129-1
- Print ISSN
- Online ISSN
- 1432-0428
- Publisher
- Springer-Verlag
- Additional Links
- Keywords
- Amino acids
- endogenous glucose production
- gluconeogenesis
- glycogenolysis
- liver glycogen
- glucagon
- Industry Sectors
- Authors
- M. Krebs (A1)
- A. Brehm (A1)
- M. Krssak (A1)
- C. Anderwald (A1)
- E. Bernroider (A1)
- P. Nowotny (A1)
- E. Roth (A2)
- V. Chandramouli (A3)
- B. R. Landau (A3)
- W. Waldhäusl (A1)
- Dr. M. Roden (A1)
- Author Affiliations
- A1. Division of Endocrinology and Metabolism, Department of Internal Medicine III, University of Vienna Medical School, Währinger Gürtel 18–20, 1090, Vienna, Austria
- A2. Department of Surgery, University of Vienna Medical School, Vienna, Austria
- A3. Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA

subset: commoncrawl


text:
New to Typophile? Accounts are free, and easy to set up.
Hi everyone.
In creating my first personal identity, I've also ventured into creating my first letters. I'm very new at this so once again I'm asking for advice. I've posted information on this project a while ago and have since been heading in a different direction. Here is the original post.
If there are any ways to improve the wordmark, please share your thoughts.
I also need to choose a primary font to go with this, and so far I've been looking at large x-heights like Axel, Info, Unit, Meta. Any suggestions?
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text:
Mon, Oct 4, 10 at 12:54
Snapped a pic of this gorgeous Skipper nectaring on the blossoms of common basil, Ocimum basilicum. Anybody recognize the species? I'm in eastern North Carolina, about 10 miles from the Great Dismal Swamp, and two miles from the bay. Regards,
Here is a link that might be useful: Who can it be now?
Follow-Up Postings:
- Posted by butterflymomok 7 (My Page) on Mon, Oct 4, 10 at 13:17
Looks like a very nice picture of a Sachem. |
- Posted by seedmoney 8A (email@example.com) on Mon, Oct 4, 10 at 14:12
Wow! You are good, butterflymomok---your efforts and quick response are appreciated. Applause Seedmoney |
It's a male Sachem, you usually see them chasing females, which are a bit different looking. These are having an irruption this year, too. This year I raised the blade on my mower to 3 inches. I think this has helped my yard raise more Sachems. They are migrants. They do not winter over in the north. |
Here is a link that might be useful: Sachem, Atalopedes campestris
What a nice shot! I love the little Skippers. They have been in abundance here this season. |
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September 4, 2008 | News
Better Homes & Gardens showed the summer season polishes and mentioned a few favorite professional brands.
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September 1, 2008 | Article
Having trouble choosing the right music to set the tone for your salon? Real-life salon owners share the sounds that work for them in a variety of environments.
The president isn't the only thing you'll be voting on this fall. Log on to www.nailsmag.com NOW and tell us what your favorite products are.
CND's Team Creative has brought a whole new level to respect for nails in the world of high fashion. Catch the full story, including a video and podcast behind the scenes of the work of your fellow nail professionals. You'll feel so proud!
Solving the problem of dried-out nail art paint.
August 17, 2008 | News
In a "green guide" to makeup, Lucky likes Zoya's nail polish.
Feathered Friends7 photos
Frozen: Icicle Nail Art Ideas20 photos
Benign growths that occur on the sole (plantar surface), heel, or ball of the foot. Learn More
Should I have a TV in my salon?
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How big is the U.S. nail business? $7.3 billion. What's the average service price for a manicure? Dig into our decades' deep research archives.
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The U.S. Vietnamese salon industry represents more than half of all salons. VietSALON serves this market with a bi-monthly Vietnamese language print magazine and a community-oriented website.
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