The effects of priming on frontal-temporal
Avniel S. Ghuman*†‡, Moshe Bar*, Ian G. Dobbins§, and David M. Schnyer*¶
*Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Massachusetts Institute of Technology/Harvard Medical School,
Boston, MA 02115;†Program in Biophysics, Harvard University, Cambridge, MA 02138;§Department of Psychology and Neuroscience, Duke University,
Durham, NC 27708; and¶Memory Disorders Research Center, Boston Veterans Administration Healthcare System and Boston University School
of Medicine, Boston, MA 02215
Edited by Leslie G. Ungerleider, National Institutes of Health, Bethesda, MD, and approved April 28, 2008 (received for review November 9, 2007)
Repeated exposure to a stimulus facilitates its processing. This is
reflected in faster and more accurate identification, reduced per-
ceptual identification thresholds, and more efficient classifications
for repeated compared with novel items. Here, we test a hypoth-
esis that this experience-based behavioral facilitation is a result of
enhanced communication between distinct cortical regions, which
reduces local processing demands. A magnetoencephalographic
investigation revealed that repeated object classification led to
decreased neural responses in the prefrontal cortex and temporal
cortex. Critically, this decrease in absolute activity was accompa-
nied by greater neural synchrony (a measure of functional con-
nectivity) between these regions with repetition. Additionally, the
onset of the enhanced interregional synchrony predicted the
degree of behavioral facilitation. These findings suggest that
object repetition results in enhanced interactions between brain
regions, which facilitates performance and reduces processing
demands on the regions involved.
functional communication ? learning ? magnetoencepholography ?
synchrony ? memory
termed priming. This experience-based facilitation manifests in
faster and more accurate responses for repeated stimuli relative
to novel stimuli. Because priming is an elementary form of
learning, revealing the cognitive and neural mechanisms that
underlie repetition priming is critical to our complete under-
standing of how experience facilitates behavior.
Previous neuroimaging studies have consistently demon-
strated reduced neural activity particularly in prefrontal cortex
(PFC) and temporal cortex for repeated relative to novel stimuli
during object-recognition and object-decision tasks (1–5). The
regions that show response reductions are those that are critical
to processing a stimulus in the context of the particular exper-
imental paradigm (6). The findings of neural response reduction
coupled with behavioral facilitation in repetition priming have
led to the proposal that these neural reductions reflect more
efficient stimulus processing and/or improvements in stimulus-
related decision processes (3, 6–10). Specifically, repetition is
hypothesized to induce local neural changes that speed access to
relevant object knowledge thereby facilitating performance.
However, recent work has demonstrated the importance of
cross-cortical interactions, particularly top-down influences
originating from the PFC, in object processing (11, 12). Fur-
thermore, these interactions between top-down and bottom-up
processes may be critical to the neural and behavioral manifes-
tations of priming (3, 4, 6, 12, 13). Here, we present results
consistent with a mechanism by which the interactions between
cortical regions involved in identification and object represen-
tations in the temporal cortex (14, 15) and executive and
selection processes in the PFC (16–18) are selectively optimized
The importance of cross-cortical communication in priming
has been suggested by previous research demonstrating that,
revious experience with a stimulus facilitates its subsequent
processing and leads to a fundamental form of learning
during encoding, PFC-temporal interactions were stronger at
stimulus onset for words that showed subsequent behavioral
facilitation (priming) (19). Because the interaction between the
PFC and temporal regions was anticipatory, this result suggests
that attention may modulate PFC–temporal interactions during
encoding (20). Moreover, disrupting PFC activity during encod-
ing attenuates the subsequent behavioral gains and neural
response reductions associated with priming in both the PFC and
temporal cortex (4). These previous studies support the hypoth-
esis that PFC–temporal cortex interactions are critical during the
learning process (6, 9). However, these studies cannot directly
address the mechanisms by which behavior is facilitated on
subsequent exposures when repeated information is retrieved.
To examine the potential relationship between priming and
cross-cortical communication, we examined the functional con-
nectivity between PFC and temporal cortex during repetition.
These regions were chosen based on prior results that showed
that local activation reductions in these regions were associated
with priming (1–3, 5, 21) and that disruption of PFC functioning
has subsequent effects on the neural changes associated with
priming in temporal cortex (4). Finally, we hypothesized that
changes in functional connectivity would correlate with behav-
ioral facilitation if cross-cortical communication were a critical
component of priming.
We administered a standard priming task involving repeated
size classifications of line drawings of everyday objects (1, 21) to
test these hypotheses. We measured neural activity using high
temporal-resolution magnetoencephalography (MEG) in 16
subjects while they engaged in study and test phases of the
experiment. During the study phase, each object was repeated
three times, immediately followed by a test phase in which the
studied objects were presented for a fourth time (repeated
objects) interspersed with an equal number of novel objects
(‘‘repeated’’ is used to refer to the fourth presentation of objects
and ‘‘novel’’ is used to refer to the novel objects presented in the
test phase of the experiment). During the entire experiment, the
subject’s task was to make size classifications (e.g., ‘‘is this object
bigger than a shoebox?’’) (1, 21). Repeated size classifications
have been shown to lead to reductions in neural activity in PFC
and temporal regions (1, 2, 22, 23).
research; A.S.G. and M.B. contributed new reagents/analytic tools; A.S.G., M.B., and D.M.S.
analyzed data; and A.S.G., M.B., I.G.D., and D.M.S. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
‡To whom correspondence should be sent at the present address: Laboratory of Brain and
Cognition, National Institute of Mental Health, 10 Center Drive, Room 4C116, MSC 1366,
Bethesda, MD 20892-1366. E-mail: email@example.com.
This article contains supporting information online at www.pnas.org/cgi/content/full/
© 2008 by The National Academy of Sciences of the USA
www.pnas.org?cgi?doi?10.1073?pnas.0710674105 PNAS ?
June 17, 2008 ?
vol. 105 ?
no. 24 ?
To examine the relationship between cross-cortical commu-
nication and priming, we examined synchrony?of the MEG
signal between the PFC and temporal cortex. Previous research
has indicated that this measure can reflect an improvement in
We examined cross-cortical synchrony between the PFC and
temporal cortex for repeated and novel stimuli by calculating
phase-locking values (PLVs) (26, 27). PLVs are a measure of the
trial-by-trial variance in the phase relationship between two
signals at a particular frequency. Larger PLVs reflect smaller
trial-by-trial variance and therefore greater coupling between
the phases of the signals, i.e., greater functional connectivity.
Synchrony analysis was focused on the ? and ? frequency
bands because these frequency bands have shown the most
reliable relationship with learning (19), semantic analysis (28),
and visual attention (29). Additionally, theoretical and experi-
mental work suggests that the lower-frequency bands, such as ?
and ?, are more critical for long-range communication than the
higher-frequency bands, which predominate for local commu-
nication (11, 24, 30, 31).
In addition to examining the cross-cortical interactions result-
ing from priming, it is important to determine the direction of
information flow in the cortex during priming. Recent studies
have suggested that repetition-related reductions in object-
knowledge regions result from top-down modulation from PFC
(4). The question of whether the cortical interactions potentially
underlying priming are mainly from the PFC to the temporal
cortex or from the temporal cortex to the PFC can be examined
by using a method for determining the direction of information
flow between two synchronous signals (32). Specifically, direc-
tion is determined by calculating the directional PLV (dPLV),
which is a measure of how strong relatively earlier time windows
of one signal is phase-locked to relatively later time windows of
a second signal. If the past phase of the signal from one cortical
region is phase-locked to the current phase of the signal in a
second region, then we may infer that the first region directly or
indirectly influences the measured response in the second neural
Priming resulted in reaction times that were 145 ms shorter for
repeated objects than for novel stimuli [repeated (788 ms) ?
novel (933 ms), T15? 6.33, P ? 0.001]. This behavioral facili-
tation was accompanied by a significant decrease in the magni-
tude of the event-related, evoked MEG activity (1–5), as mea-
sured by dynamic statistical parameter mapping power, in the
PFC from 333 ms and the temporal cortex from 406 ms after
stimulus onset [see supporting information (SI) Fig. S1]. Criti-
cally, synchrony between the signals from the PFC and temporal
cortex was significantly greater than during 500 ms of prestimu-
lus baseline for repeated stimuli in the low ? (?12–14 Hz)
frequency band and failed to reach significance for novel stimuli
(Fig. 1 and Fig. S2). Synchrony between the PFC and temporal
cortex was significantly stronger for repeated than for novel
stimuli during the time period from 190 ms to 270 ms after
stimulus onset (P ? 0.018 corrected for multiple comparisons)
and peaked at 215 ms (Fig. 2 a and b), a time approximately
coinciding with or preceding the onset of reduced MEG activity
during repetition priming (Fig. S1)(5, 21, 33–35). The regions of
PFC and temporal cortex revealing significant synchronous
in fMRI using the same paradigms used here (1) and other,
similar visual object priming paradigms (2–4). Additionally,
across individual subjects, significantly greater, P ? 0.05, syn-
chrony was seen for repeated compared with novel stimuli in 13
of 16 subjects at some point between 100 and 300 ms, in 9 of 16
subjects at 215 ms, and in 8 of 16 subjects across the entire 190-
to 270-ms time window (with an additional three subjects
showing a P ? 0.1 trend over this time window). The probability
that 9 of 16 subjects show P ? 0.05 is ?0.00001 and for 8 of 16
subjects P ? 0.0001 when a Bernouli distribution, corrected for
multiple comparisons, is used.
The dPLV analysis demonstrated that the phase of the PFC
activity at relatively earlier times was, in fact, predictive of the
later phase of the temporal activity (mean ? 29.5 ms, SE ? 10.7
ms, T15 ? 2.76, P ? 0.015), suggesting that information was
projected from the PFC to the temporal cortex.
We tested the specificity of these synchrony results to inter-
actions between the PFC and temporal cortex by examining
whether phase locking was due to global synchronization. First
examined was the parietal cortex. Inconsistent with the possi-
?Following the convention in cognitive neuroscience, we use the term ‘‘synchrony’’ as a
general term indicating coupling between neural signals. Though the term ‘‘synchrony,’’
strictly speaking, applies only to coupled activity with zero phase difference (i.e. truly
synchronous activity), here it refers to phase locked activity with any phase-lag.
ROI and the entire brain relative to prestimulus baseline synchrony. For a
reference ROI in the temporal cortex, significant synchrony is seen in a
relatively localized region of the PFC for primed, but not novel, objects. For
demonstrate in a reciprocal manner that there is significant synchrony be-
but not novel, objects. Note that the images on the left side of the figure are
lateral views of the left hemisphere, and the images on the right side are
ventral views. Other views across both hemispheres demonstrate that the left
PFC and temporal cortex are the loci of significant synchrony (see Fig. S2 for
other views of the brain).
Peak synchrony (14 Hz, 215 ms, see Fig. 2) between a single reference
prefrontal and temporal cortical regions for repeated vs. novel visual objects.
repeated (fourth presentation) vs. novel trials, demonstrating significant
differential phase synchrony concentrated in the low ? frequency band
(?12–14 Hz). (b) PLV time course at 14 Hz, indicating significantly greater
phase locking for repeated vs. novel trials 190–270 ms after stimulus onset.
Phase-locking analysis, demonstrating greater synchrony between
www.pnas.org?cgi?doi?10.1073?pnas.0710674105 Ghuman et al.
bility of global synchronization, PLVs failed to reach statistical
significance between the PFC and parietal cortex (approxi-
mately corresponding to the left intraparietal region) (T15 ?
0.85, P ? 0.41) and between the temporal and parietal cortices
(T15 ? 1.14, P ? 0.27) for the frequency and time period of
significant PFC–temporal synchrony (190–270 ms, ?12–14 Hz).
parietal cortex or between the temporal and parietal cortices
across all time windows from 0 to 500 ms. Additionally, a whole
brain analysis of the synchrony from the temporal cortex to the
rest of the brain and from the PFC to the rest of the brain also
supported the idea that the cross-cortical interaction is specific
to these two regions (Fig. 1 and Fig. S2).
To rule out the possibility that the increased synchrony could
be explained by coincidental phase-locking to the stimulus onset,
a trial-shuffle method (26, 27) was used. The results of this
analysis demonstrate that synchrony was not due to coincidental
phase locking to the stimulus onset both within subjects (15 of
point between 190 and 270 ms). This trial-shuffled result con-
firms that the synchrony increase was due to the PFC phase-
locking to the temporal cortex and not due to each region being
independently phase-locked to the stimulus onset.
Correlating the synchrony results with behavioral facilitation
in object classification revealed a connection between PFC–
temporal synchrony and improved behavioral performance in
repetition priming. Specifically, subjects that showed greater
behavioral priming also had a trend for an earlier peak in
learning-related synchrony (defined as the time point of maxi-
mum repeated PLV ? novel PLV), between the PFC and
temporal cortex (r ? ?.49, P ? 0.052; Fig. 3). Because the
difference between repeated and novel PLV reflects how learn-
ing affects neural interactions, this result demonstrates that the
earlier that learning-related neural synchrony manifests, then
the greater is the facilitation of performance. The increase of
synchrony for repeated relative to novel stimuli, as well as the
correlation between the latency of synchrony and behavior,
supports the hypothesis that optimized cortical communication
induced by learning plays a critical role in priming-related
This study examined the critical importance of experience-
induced changes in communication across neural regions for
priming and improved performance. The findings we report here
demonstrate that there is increased cross-cortical synchrony for
repeated images between the PFC and temporal cortex, two
central cortical areas where decreases in activation are typically
observed. Additionally, the timing of the increased cross-cortical
synchrony correlated with behavioral facilitation, demonstrating
the importance of synchrony as a neural correlate of priming.
This correlation suggests that earlier cross-cortical communica-
tion leads to greater facilitation in behavioral performance
during priming. These results support a mechanism for the
performance improvement associated with repetition priming
whereby repeated experience optimizes communication be-
tween the PFC and temporal cortex and may have broader
implications for neural basis of other forms of learning as well.
The proposed hypothesis, that improved performance in
priming results from optimized communication between the
PFC and temporal cortex, leads to a unique prediction. Specif-
ically, disrupting more abstract processing in the PFC will
attenuate priming not only in the PFC, but also in the ‘‘lower-
level,’’ stimulus-driven temporal cortex because these two re-
gions act cooperatively to facilitate behavior. In contrast, most
previous hypotheses regarding the neural mechanisms of behav-
ioral facilitation and neural-response reduction in repetition
priming have suggested that processing efficiency is enhanced
through local changes in neural populations (8). These local
models do not predict that a higher-level disruption would effect
lower-level processing. Previous findings have demonstrated
attenuation of the priming induced neural response reductions
in the prefrontal and temporal cortices and attenuation of the
behavioral facilitation in priming when PFC activity was dis-
rupted by transcranial magnetic stimulation (TMS) during en-
coding (4). If activity reductions in temporal cortex were the
result of exclusively local stimulus-dependent mechanisms, then
these previous results would not be possible. Instead, our current
results and these prior findings (4) are predicted by a mechanism
in which priming takes place as a result of facilitated interactions
between cortical regions.
The results of the directionality analysis suggest that object
information required for the classification task is accessed by the
influence of selection and control processes in the PFC acting on
object processing in the temporal cortex. The hypothesis that
priming behavior depends heavily on feedback PFC processes is
supported by MEG findings demonstrating that repetition-
induced neural-response changes generally occur earlier in PFC
than temporal cortex regions (5, 11, 21, 33, 34) and by the TMS
results described previously (4). Finally, several studies demon-
strate that PFC-activity reductions are a more reliable predictor
of behavioral facilitation during priming paradigms than neural-
response reductions in posterior temporal and inferotemporal
regions (1, 36).
One hypothesis that arises from the present result is that
increased synchrony may reflect a process whereby neural and
computational efficiency are enhanced by selectively promoting
only the cross-cortical interactions critical to individuals’ cogni-
tive objectives. This optimized communication is based on the
idea that the interactions that successfully integrate information
reflected in synchronous, phase-locked electrophysiological ac-
tivity (24). Establishment of these synchronous interactions that
optimize goal-directed behavior could reflect a Hebbian-like
learning process (37), whereby synchronous activity perseveres,
and asynchronous activity falls away, resulting in selective rein-
forcement of the cortical interactions critical to successful task
completion. Alternatively, these interactions could reflect the
development of stimulus–decision associations mediated by the
medial temporal lobe (described in refs. 1, 38, and 39). In either
case, by selectively optimizing only critical neural interactions,
top-down and bottom-up processes more readily converge on an
interpretation of the input stimulus (e.g., the object decision),
which, in turn, improves behavioral performance (1, 3, 6, 12, 13,
38, 39). Indeed, recent modeling work demonstrates computa-
tionally how reduced activity with increased synchrony can result
peak synchrony increase across subjects. The peak of synchrony increase was
defined as the time point at which synchrony for repeated stimuli minus
synchrony for novel stimuli was the greatest. These results demonstrate that
those subjects whose peak synchrony increase for repeated items occurred
earlier in time also demonstrated greater performance advantages for these
items relative to novel items.
Correlation between behavioral priming levels and the latency of
Ghuman et al.
June 17, 2008 ?
vol. 105 ?
no. 24 ?
in more efficient processing (40).** Therefore, this hypothesis
would account not only for reduced neural activity through
asynchronous activity falling away but also for increased syn-
chrony between the PFC and temporal cortex through the
selective reinforcement of critical cross-cortical interactions.
This study reveals that repetition priming is associated with
increased synchrony between frontal and temporal cortical
regions and that the earlier subjects exhibit this increased
synchrony, then the greater is the benefit to behavioral perfor-
mance. Given these and earlier findings, we have described an
interactions-based mechanism in which optimized communica-
tion between neural regions, rather than purely locally confined
neural mechanisms, facilitates both neural processing and be-
havioral performance. These results underscore the critical role
of further examining the dynamics of the interactions across
large-scale brain networks in learning and cognition.
See SI Methods for more details on the methods.
Subjects. Sixteen young native speakers of English (2 male, 14 female), with
normal or corrected vision took part in the experiment.
Recording. MEG signals were recorded by using a 306-channel neuromagne-
tometer (Vectorview; Neuromag) system in a three-layer magnetically
shielded room (Imedco) that contained 102 identical sensor triplets, two
head of the subject.
Behavioral Task. Stimuli. Four hundred eight colored line drawings of common
animate and inanimate objects were selected from commercially available
clip-art collections [CD ROM from Corel Mega Gallery (1997); Corel Corpora-
tion]. Pictures reflected varying orientations and visual size. The stimuli were
presented through a back-projection system into the MEG chamber, and
objects were presented centrally on a screen ?3 feet from the MEG recording
Procedure. The data for this analysis were a subset of a larger study examining
specificity effects in object priming (21). The common repetition trials used
here were counterbalanced across subjects for the order in which they were
the same as what is described below (i.e., a size judgment). The experiment
spanned two runs, each of which consisted of a study and test phase. There
across the two test runs.
presented in pseudorandom order, consisting of 25 items repeated three times.
Participants were asked to make a size judgment by deciding whether the
real-life object depicted in the picture was ‘‘bigger than a shoe box’’ and to
ordered pictures. Twenty-five of these were repeated from the study phase and
25 were novel objects being seen for the first time.
The behavioral task was identical in the study and test phases of the
experiment. Pictures were presented every 2 sec. Approximately 8 min sepa-
rated the first repetition of an image at study and the fourth repetition in the
test phase of the experiment.
All analyses and results reported were performed on the data gathered in
the stimuli and ‘‘novel’’ to the new stimuli presented in the test phase.
Phase-Locking Analysis. To determine the trial-by-trial phase locking between
neural regions, we used the spectral dynamic statistical parametric mapping
method (27), a method to measure phase synchrony between signals pro-
jected onto the cortical surface. This method employs the anatomically con-
strained minimum-norm estimate (MNE) inverse solution (5, 41) to determine
PLVs (26) between regions on the cortical surface.
width-five Morlet wavelet transform at each frequency of interest. The wave-
let representation of each trial was then mapped from the sensors onto the
cortex by using the MNE inverse solution. The phase was then extracted from
trial about each time point and at each frequency of interest. The a priori
frequencies of interest were in the ? and ? frequency bands (8–20 Hz) based
on previous studies (19, 29, 31), although all even frequencies from 8 to 40 Hz
using the following formula:
exp ? j??1?t, n??? ? ??2?t, n???
where ?(t, n) is the phase of the signals from the two ROIs at time t and trial
0 indicates that the phase relationship is completely random.
Statistical Testing. The cumulative distribution function was estimated for the
by using a t test across subjects both by determining significance of the PLVs
compared with 500 ms of prestimulus baseline and between experimental
for multiple comparisons (42). All of the time-frequency points that were P ?
0.05 were determined and clustered on the basis of temporal and frequency
adjacency. Cluster-level statistics were calculated by determining the sum of
the t values within each cluster (cluster mass), and the maximum cluster mass
collected trials were placed into subset one, and the remaining were placed
into subset two. The maximum cluster mass was then determined for all
possible permutations of the data (216partitions for 16 subjects). The propor-
tion of these permutations that had a smaller maximum cluster mass than the
nonpermuted data were the P value calculated by using a complete permu-
this method inherently controls for multiple comparisons.
For individual subject analyses, a similar statistical test was used, with
permutations created across trials instead of across subjects. The time-
frequency points that showed P ? 0.05 in each individual were determined. A
Bernouli distribution was used to find the probability that the proportion of
A Bonferroni adjustment was then applied to this probability to correct for
Directional Phase-Locking. Directional phase-locking is based on the theory
that if information from ‘‘X’’ can predict future information about ‘‘Y,’’ it is
likely that X is driving Y. Phase-locking values measure how predictive the
phase of X in a particular time window is of the phase of Y in the same time
are determined between all possible time shifts of the two signals. This
measure determines how predictive the past phase of X is of the future phase
of Y and vice versa. We infer that the direction of information flow is from X
phase of X and that X is more predictive of Y than chance (chance distribution
taken from the prestimulus baseline) (32).
a particular time window
dPLV?t? ? ?
exp ?j??1?t ? ?, n??? ? ??2?t ? ?, n???
that the first signal should be shuffled into the past for maximum predict-
ability of the future phase of the second signal. To minimize the possibility of
finding spurious directional interactions (i.e., false-positives), dPLVs are cal-
culated only for time-windows where the underlying PLVs are significant
relative to baseline. Statistical values are established by calculating t values
across subjects of the dPLV relative to the expected value of the dPLV (i.e.,
**The computational work described in (40) concentrated on reduced activity with in-
creased local synchrony and would require modification to account for the cross-cortical
synchrony seen in the present work.
www.pnas.org?cgi?doi?10.1073?pnas.0710674105 Ghuman et al.
Region of Interest (ROI) Selection. For the whole-brain analyses (Fig. 1), Download full-text
functional ROIs were created by first determining the region in the PFC that
demonstrated the strongest power for the third presentation of the stimuli
(43). This region was used as a reference region, and the phase-locking
between this region and the entire brain was determined for the fourth
presentation. For novel objects (in Fig. 1), the reference region was deter-
mined by finding the portion of the PFC that demonstrated the strongest
power for these novel objects. This ROI selection was used because the use of
the response for novel objects to determine the reference ROI gives the
greatest chance of seeing synchrony in this condition, if there is any. There-
fore, this is an extremely conservative ROI selection criterion, biased against
our effects of interest. The same process was repeated for the reference ROIs
in the temporal cortex.
For the ROI analysis (Figs. 2 and 3), two regions were needed, a reference
region and a target region. The reference region was found by locating the
Fig. 1). We then determined the target region in the temporal cortex that
cortex that have been previously demonstrated to show response reductions
(1–5). These ROIs were then used to compare the phase-locking for novel
versus repeated (fourth presentation) stimuli (Fig. 2) and for the correlation
analysis (Fig. 3). This method for determining ROIs for individual subjects
(i.e., the third presentation) was collected independently of the data used in
the analysis (i.e., the fourth presentation and novel stimuli). This method of
ROI selection can be compared favorably with the independent cross-
validation method used in fMRI analysis (44). It is important to note that this
ROI selection is also completely independent of the timing of the synchrony,
i.e., a reference ROI in the temporal cortex and a target ROI in the PFC were
determined, and the results hold for this reversal as well. The frontal region
included the anterior and posterior inferior frontal gyri, the inferior frontal
sulcus, and the anterior portion of the precentral sulcus. The temporal region
included the fusiform gyrus, the occipitotemporal sulcus, and the collateral
ACKNOWLEDGMENTS. We thank L. Nichols for data collection; M. S.
Ha ¨ma ¨la ¨inen, D. Handwerker, and F.-H. Lin for assistance with analysis; and E.
Aminoff, N. Gronau, K. S. Kassam, K. Kverega, A. Martin, W. K. Simmons, and
G. Wig for critical readings of the manuscript. This work was supported by
National Institutes of Health Grant K23MH64004 (to D.M.S.), National Insti-
tute of Mental Health Grant R01 MH073982–01A1 (to I.G.D.), National Insti-
tute of Neurological Disorders and Stroke Grants R01 NS44319–01 and R01
NS050615 (to M.B.), National Center for Research Resources Regional Re-
source Grant P41RR14075, and the M.I.N.D. Institute.
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