Sensitivity of Prefrontal Cortex to Changes in
Target Probability: A Functional MRI Study
B. J. Casey1*, Steven D. Forman2, Peter Franzen2, Aaron Berkowitz2,
Todd S. Braver3, Leigh E. Nystrom4, Kathleen M. Thomas1, and
Douglas C. Noll5
1Sackler Institute for Developmental Psychobiology, Weill Medical College of Cornell University,
New York, New York
2University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
3Washington University, St. Louis, Missouri
4Princeton University, Princeton, New Jersey
5University of Michigan, Ann Arbor, Michigan
Abstract: Electrophysiological studies suggest sensitivity of the prefrontal cortex to changes in the
probability of an event. The purpose of this study was to determine if subregions of the prefrontal cortex
respond differentially to changes in target probabilities using functional magnetic resonance imaging
(fMRI). Ten right-handed adults were scanned using a gradient-echo, echo planar imaging sequence
during performance of an oddball paradigm. Subjects were instructed to respond to any letter but “X”. The
frequency of targets (i.e., any letter but X) varied across trials. The results showed that dorsal prefrontal
regions were active during infrequent events and ventral prefrontal regions were active during frequent
events. Further, we observed an inverse relation between the dorsal and ventral prefrontal regions such
that when activity in dorsal prefrontal regions increased, activity in ventral prefrontal regions decreased,
and vice versa. This finding may index competing cognitive processes or capacity limitations. Most
importantly, these findings taken as a whole suggest that any simple theory of prefrontal cortex function
must take into account the sensitivity of this region to changes in target probability. Hum. Brain Mapping
© 2001 Wiley-Liss, Inc.
Key words: prefrontal cortex; attention, inhibition; neuroimaging; fMRI
Electrophysiological studies suggest the sensitivity
of frontal regions to changes in the frequency or prob-
ability of an event [Sutton et al., 1965; Donchin and
Coles, 1985; McCarthy et al., 1997]. This association
has been argued most extensively in the context of the
P300, an event-related potential (ERP) elicited by in-
frequent events. Neuroimaging studies [Grasby et al.,
1993; Jonides et al., 1993; Cohen et al., 1994; McCarthy
et al., 1994; Casey et al., 1995; Smith et al., 1995; Ka-
washima et al., 1996; Casey et al., 1997] together with
physiological studies in monkeys [Niki, 1974; Mishkin
and Manning, 1978; Goldman-Rakic, 1987; Fuster,
1988; Yajeya and Fuster, 1988] have largely focused on
the involvement of frontal regions in higher cognitive
Contract grant sponsor: NIMH.
*Correspondence to: B. J. Casey, Ph.D., Sackler Institute for Devel-
opmental Psychobiology, Weill Medical College of Cornell Univer-
sity, 1300 York Avenue, Box 140, New York, NY 10021.
Received for publication 12 November 1999; accepted 18 December
? Human Brain Mapping 13:26–33(2001) ?
© 2001 Wiley-Liss, Inc.
processes such as working memory and inhibitory
control. These studies provide better spatial resolution
than do electrophysiology studies and suggest that
different regions within prefrontal cortex are associ-
ated with different types of processing. For example,
dorsolateral prefrontal cortex has been implicated in
working memory [Goldman-Rakic, 1987; Fuster, 1988;
Cohen et al., 1994; McCarthy et al., 1994; Smith et al.,
1995], while more ventral regions of prefrontal cortex
have been implicated in the suppression of prepotent
responses as in the go/no-go task [Kawashima et al.,
1996; Casey et al., 1997; Konishi et al., 1999]. Few
imaging studies have examined the effects of manip-
ulating target probability on these presumed prefron-
tal functions. One example of such an attempt is a
recent study by McCarthy and colleagues . They
showed that increased dorsolateral prefrontal activity
was associated with infrequent events in an oddball
paradigm using both electrophysiology and fMRI. The
current study examined prefrontal activity as a func-
tion of changing target frequency (i.e., frequent and
infrequent) in a modified version of an oddball para-
The goal of this study was to determine if regions of
prefrontal cortex differentially respond to changes in
the probability of an event. Previously, we have ex-
amined prefrontal activity to frequent targets in a
go/no-go version of the oddball paradigm [Casey et
al., 1997]. In that study, either all trials within a block
were targets (100%) or half the trials in a block were
targets (50%). Thus target frequency did not vary from
high to low but was constant within blocks of trials
(i.e., either 50% or 100%). In the current study we
manipulated the frequency of targets across trials al-
lowing for high, moderate, and low frequencies of a
target to occur throughout the experiment. We as-
sumed that this manipulation would allow us to de-
termine the relevance of target frequency to prefrontal
function within a single task design.
Specifically, we assumed that rare targets would
increase interference in the stimulus demands of the
task and hypothesized that this would be associated
with increased activity in dorsolateral prefrontal re-
gions, but not ventral prefrontal regions. Rare target
frequencies result in an increase in the number of
nontarget relative to target stimuli. By frequently pre-
senting the nontarget, “X”, the representation of this
stimulus should become highly salient, relative to the
infrequent presentation of targets. We enhanced the
extent of interference between targets and nontargets
by having the targets consist of a large set of stimuli
(i.e., A–Z, except X) occurring infrequently and having
the nontarget be a single salient stimulus, the letter
“X”. Increasing the salience of the nontarget (X)
should result in increased interference between it and
the set of target stimuli (A–Z, except X). Accordingly,
we hypothesized that this type of interference (i.e.,
among stimuli) would result in increased activity in
the dorsolateral prefrontal cortex. This hypothesis was
based on the assumption that dorsolateral prefrontal
cortex supports relevant stimulus information (e.g.,
any letter but X) against interference from competing
sources over time [Goldman-Rakic, 1987; Cohen and
Servan-Schreiber, 1992] and work by McCarthy et al.
 associating dorsolateral prefrontal cortex activ-
ity with infrequent targets.
Conversely, we assumed that frequent targets
would increase interference in the response demands
of the task (i.e., when to respond or not) and hypoth-
esized that this would activate ventral regions of pre-
frontal cortex but not dorsal prefrontal regions. Fre-
quent targets result in an increase in the number of
responses relative to nonresponses. When a response
is required frequently to a target, the representation of
that response is activated repeatedly, making the re-
sponse more salient. The salience of the response in-
terferes with the task demands of not responding to an
X. If a response is required infrequently, as with rare
targets, the representation of that response is not ac-
tivated repeatedly and therefore does not compete or
interfere with the task demands of not responding to
an X. Accordingly, we hypothesized that this type of
interference (between responses) would result in in-
creased activity in the ventral prefrontal cortex. This
prediction was based in part on the presumed role of
ventral prefrontal cortex in suppression of prepotent
responses as in the go/no-go task [Kawashima et al.,
1996; Casey et al., 1997; Konishi et al., 1999].
MATERIALS AND METHODS
Ten right-handed adult subjects (18–41 years old)
were recruited from the Pittsburgh area and were paid
$50 for their participation in the study. Data from one
male subject was excluded due to excessive in-plane
head motion of more than .5 voxels.
Subjects were presented with a sequence of single
letters, one at a time, at the center of the visual display
and were instructed to respond to any letter except X.
The stimuli were presented for 300 ms with an inter-
stimulus interval of 700 ms (refer to Fig. 1). Target
?Target Frequency and Prefrontal Cortex?
? 27 ?
probability varied between 10% and 60% in 120-sec
oscillations. Each subject performed five runs of two
oscillations and each run lasted 240 sec (i.e., 240 trials).
Stimulus presentation was controlled by a Macintosh
computer using Psyscope [Cohen et al., 1993], a rear
projection screen, and an Infocus projector system.
Subjects responded by pressing a designated button
on a specially constructed handheld fiber-optic re-
sponse box, connected to a transducer via fiber optic
cables to the Macintosh. Both reaction times and ac-
curacy were collected.
All subjects were screened carefully for any metal
implants or contraindications for a MRI and then ac-
climated to the scanner environment in a simulator
that mimics the scanner in appearance and sound.
Images were acquired on a 1.5T GE scanner modified
by Advanced NMR (Wilmington, MA) and a head
coil. A set of T1-weighted sagittal images was ac-
quired using a spoiled gradient sequence (spin echo,
TE 18, TR 500) for localization and prescription of
coronal slices. A second set of T1-weighted coronal
images (spin echo, TE 18, TR 500, 4-mm skip 0) was
acquired covering the whole brain. Next, five sets of
T2*-weighted coronal images (4-mm skip 0) were ac-
quired using an echo planar imaging (EPI) gradient
echo sequence (EPI-ISGR) with TE 40, TR 5000, and a
flip angle of 90°. Each of five sets of images consisted
of 52 repetitions of 23 slice locations (Fig. 2).
Each subject’s images were realigned in 3D space
using Wood’s automated image registration (AIR) al-
gorithm [Woods et al., 1992]. All subjects’ data were
then registered to one representative subject’s brain
and pooled. Each 5-sec scan consisted of five 1-sec
behavioral trials. Scans were divided into those with
high (e.g., ? two targets out of five trials), moderate
(two targets out of five trials), and low (e.g., ? two
targets out of five trials) target probabilities. The first
two 5-sec scans of each of five runs were excluded
from the analysis to allow the hemodynamic response
to peak. Further we collected two 5 sec scans at the
end of each run to assess the signal change for the last
10 trials (i.e., 52 repetitions per run). Thus we analyzed
80 scans per probability condition (high, moderate,
and low), and the mean percentage of targets in each
An illustration of the behavioral task with the stimulus parameters
and timing of events.
Sagittal view of acquired slice locations.
Bold lines indicate the slice locations
used for the analysis of the current
?Casey et al.?
? 28 ?
probability condition was approximately 60%, 30%,
and 10%, respectively. A 9 (subjects) ? 3 (high, mod-
erate, and low target probabilities) ANOVA was per-
formed on the pooled data with subject as a random
factor, and areas of significant activation were identi-
fied that satisfied a contiguity threshold of three con-
tiguous pixels with P ? .005 [Forman et al., 1995]. Post
hoc t-tests were performed on the condition (high,
moderate, and low target probabilities) means to de-
termine the direction of change in MR signal intensity.
Images were warped into stereotaxic space using
AFNI [Cox, 1996]. The time series for significant re-
gions of activation were calculated by averaging
across subjects (9) and runs (5). For the purpose of this
study, activity was examined in the prefrontal cortex
from the genu of the corpus callosum to the frontal
pole to exclude motor areas in the analysis (refer to
Fig. 2). This area represents prefrontal regions of 17
mm ? Y ? 55 mm in Talairach stereotaxic space.
Localization of activity to specific gyri (i.e., superior,
middle, inferior, and orbital) in the prefrontal cortex
was based on the coordinate system of the Talairach
atlas [Talairach and Tournoux, 1988] and confirmed
by two independent raters using a standard brain atlas
[Duvernoy, 1991]. Inter-rater reliability was .98. The
results are presented by individual gyri and by region
(i.e., ventral and dorsal prefrontal cortex). Ventral pre-
frontal activation was defined as activity of the infe-
rior frontal gyrus and/or orbital frontal gyrus and
dorsal prefrontal activation was defined as activity of
the superior frontal gyrus and/or middle frontal gy-
rus. The conditional means of MR signal intensity for
each significant cluster (identifed by gyrus) was aver-
aged within the dorsal prefrontal region and likewise
within the ventral prefrontal region. Percent change in
MR signal intensity for each cluster was calculated as
the difference of the condition mean (high, moderate,
or low target probabilities) from the overall mean and
then divided by the overall mean. These percent dif-
ferences were then averaged across clusters falling in
dorsal or ventral prefrontal regions.
Behaviorally, mean accuracy rate and mean reaction
time did not differ significantly for rare and frequent
target probabilities. Mean accuracy rate across the en-
tire experiment was 92% or greater.1The imaging
results are presented in Figures 3 and 4 and Table I.
Seven clusters of significant activation were identified.
Each cluster is identified by location (e.g., gyrus), max-
imum F ratio, size in voxels, and percent change in
signal in Table I. As predicted, dorsal prefrontal activ-
ity increased when the target frequency was low and
ventral prefrontal activity increased when target fre-
quency was high. There was an inverse relation be-
tween dorsal and ventral prefrontal activity whereby
when activity in dorsolateral prefrontal cortex in-
creased, activity in ventral prefrontal cortex decreased
and vice versa. This pattern of results is depicted in
Figure 3. To illustrate that the change in MR signal
intensity corresponded to the experimental manipula-
tion, the time course for the change in MR signal as a
function of target frequency is depicted in Figure 4 for
all seven clusters separately.
The overall goal of this study was to determine if
regions of prefrontal cortex differentially respond to
changes in target probability. Specifically, we as-
sumed that rare targets would increase interference in
the stimulus demands of the task and that this would
be associated with increased activity in dorsolateral
prefrontal regions. Conversely, we assumed that fre-
quent targets would increase interference in the re-
1The mean reaction times and mean accuracies across subjects for
the high, middle, and low target probability conditions were 427,
449, and 456 msec and 96%, 94%, and 93%, respectively.
Percent change in MR signal as a function of high, moderate, and
low target frequency in ventral and dorsal prefrontal cortex (PFC).
?Target Frequency and Prefrontal Cortex?
? 29 ?
sponse demands of the task (i.e., when to respond)
and would activate ventral regions of prefrontal cor-
tex. Our results appear to be consistent with our pre-
dictions in that rare targets were associated with dor-
solateral prefrontal cortex activity and frequent targets
were associated with activity in ventral regions of
The most robust activation was observed during
frequent targets in ventral prefrontal cortex. One con-
found of the current task design is the greater number
of responses in the frequent versus rare target condi-
tions. Accordingly, one may interpret the observed
ventral prefrontal activity as merely reflecting the in-
crease in number of motor responses. However, our
previous study using a similar paradigm controlled
for the number of motor responses (by slowing the
stimulus presentation rate) and the same ventral pre-
frontal areas were activated [Casey et al., 1997]. Fur-
ther, in our previous study, an increase in number of
false alarms (i.e., more motor responses) was related
to a decrease in overall ventral prefrontal activity,
suggesting that those subjects who performed worse
and made extra motor responses activated ventral
prefrontal cortex less. Finally, in an event-related fMRI
study using a go/no-go task, Konishi et al. 
showed ventral prefrontal activity during the no-go
trials. Taken together, our current results and previous
findings are consistent with the idea that the ventral
prefrontal cortex is involved in successful representa-
tion of the response demands of the task. Alterna-
tively, the results may be interpreted as the ventral
prefrontal region being recruited simply to inhibit a
response [Diamond, 1990; Fuster, 1997; Roberts et al.,
1998]. If this were the case, one would expect this
region to be activated during the low target probabil-
ity when a number of responses are being inhibited,
Time course of change in MR signal intensity averaged across subjects (n) as a function of target
frequency for (a) dorsal prefrontal cortex and (b) ventral prefrontal cortex. MR signal change is
plotted in black and target probability is plotted in red. [Color figure can be viewed in the online
issue, which is available at www.interscience.wiley.com.]
?Casey et al.?
? 30 ?
but in that condition we show a decrease in ventral
prefrontal activity. Accordingly, our data appear more
consistent with Cohen and Servan-Schreiber’s 
theory of prefrontal cortex being involved in support-
ing the representation of relevant task demands.
When response competition is high, ventral prefrontal
cortex helps maintain the relevant response demands
of the task thereby suppressing the representation of
the competing response.
The observed increase in dorsolateral prefrontal cor-
tex with the low target probability presumably reflects
the importance of maintaining relevant stimulus in-
formation against interference from competing non-
target stimuli. The dorsolateral activity is consistent
with ERP and fMRI findings reported by McCarthy et
al.  using infrequent targets in a similar oddball
paradigm. In the current study, the percent change in
MR signal for the dorsal areas, although significant,
was modest. The modest increase in signal in more
dorsal prefrontal regions may be due to the lack of
demand in maintaining stimulus information on-line
since the only stimulus of real significance was “X”.
Other studies [e.g., Braver et al., 1997] have shown
that the percent change in MR signal in dorsolateral
prefrontal cortex increases monotonically as a function
of increasing memory load from one to four items. In
our current study, we have the equivalent of a mem-
ory load of one item in terms of the relevant item of
information (X), but the target set actually includes 25
letters (all letters of the alphabet except X).
The decrease in dorsal prefrontal activity and in-
crease in ventral prefrontal activity is suggestive of
competing cognitive processes. A similar inverse rela-
tionship has been reported in at least one other neu-
roimaging study to date using positron emission to-
mography [Drevets and Raichle, 1998]. In that study
the authors report decreases in areas implicated in
emotional processing (e.g., amygdala and orbitofron-
tal cortex) during demanding cognitive tasks and con-
versely, decreases in areas implicated in cognitive pro-
cessing (e.g., dorsolateral prefrontal cortex) during
emotion related tasks. Given the similarities of the
prefrontal regions activated in that study relative to
the current one, one may argue that the ventral pre-
frontal activity observed in the current study reflects
an emotional component to the task. Clearly, the word
“no” has social relevance and so it might seem plau-
sible that an emotional response would occur when an
individual did something they were asked not to do
(i.e., do not respond to X) as in the case of making a
false alarm. However, in the current study, there was
no significant difference in the number of false alarms
during conditions of rare (93% accuracy) and frequent
targets (96%) and so this interpretation appears insuf-
ficient to explain the inverse relation between dorsal
and ventral prefrontal activity. Alternatively, these
data may be suggestive of competing cognitive pro-
cesses or bottlenecks in the processing stream of cog-
nition associated with capacity limitations.
A limitation of the current study is the confound
introduced by the use of a blocked design over an
event-related design. As such, each condition includes
blocks of trials with targets and nontargets and thus
any change observed in the MR signal intensity cannot
be linked to a specific event (no-go vs. go trials).
Accordingly, our interpretation of increases and de-
creases in ventral and dorsal prefrontal regions, re-
spectively, are confounded. Clearly, event-related
fMRI studies that take into consideration the previous
context of trials (e.g., number of targets preceding a
nontarget or vice versa) will more adequately address
how regions of the prefrontal cortex respond differen-
tially to changes in target probabilities.
TABLE I. Location and magnitude of prefrontal activation during performance of the go no-go task
Regions of interest
Dorsal Prefrontal Regions
R. Superior Frontal Gyrus
L. Superior Frontal Gyrus
R. Middle Frontal Gyrus
L. Middle Frontal Gyrus
Ventral Prefrontal Regions
R. Inferior Frontal Gyrus
R. Orbitofrontal Gyrus
L. Orbitofrontal Gyrus
?Target Frequency and Prefrontal Cortex?
? 31 ?
The purpose of this study was to determine if re-
gions of prefrontal cortex respond differentially to
changes in target probabilities. We showed that even
within the same task, the manipulation of target prob-
ability changes the neural systems involved in per-
forming the task. Any theory of prefrontal cortex func-
tion must therefore account for the sensitivity of the
prefrontal cortex to changes in target probability.
Clearly the notion that prefrontal regions are special-
ized according to spatial vs. object working memory
[Goldman, 1994] or for complex information manipu-
lation versus simple maintenance and retrieval [Pe-
trides, 1994; Owen, 1997] do not adequately address
the findings of the current study. The same type of
stimuli (letters) and task instructions were provided
regardless of whether the target was rare or frequent
and task difficulty was not different between these
conditions. What appeared to change over the course
of the study was the type of interference (i.e., the
stimulus or response demands of the task). Thus our
results appear most consistent with a role of the pre-
frontal cortex in supporting task-relevant information
from interference similar to that proposed by Cohen
and Servan-Schrieber . Specifically, the dorsolat-
eral prefrontal cortex appears more involved when
there is interference in stimulus demands, while the
ventral prefrontal cortex appears more involved when
there is interference in the response demands of the
task. However, our results are not conclusive as an
event related fMRI study would more directly link
changes in MR signal to a specific type of event (target
In sum, we show two interesting results with the
simple manipulation of target probability. First, this
study demonstrates that even within the same task
using the same stimulus type and instructions, the
manipulation of target probability changes the cogni-
tive and neural systems involved in performing the
task. Dorsal prefrontal activity increases as a function
of low target frequency and ventral prefrontal cortex
increases as a function of high target frequency. Ac-
cordingly, any simple theory of prefrontal cortex func-
tion must therefore account for the sensitivity of the
prefrontal cortex to changes in target probability. Fur-
ther, we showed an inverse relation between activity
in the dorsal and ventral prefrontal regions with de-
creases in one corresponding to increases in the other.
This pattern of activity may provide evidence for com-
peting cognitive processes and insight to biological
mechanisms underlying bottlenecks in the processing
stream of cognition or capacity limitations.
This work was supported in part by a NIMH K01
award to the first author. The authors thank Drs.
Michael Posner, Jonathan Cohen, Bruce McCandliss,
and an anonymous reviewer for their helpful com-
ments on an earlier draft of this manuscript and Clay-
ton Eccard for his help in preparation of this manu-
Braver TS, Cohen JD, Nystrom LE, Jonides J, Smith EE, Noll DC.
1997. A parametric study of prefrontal cortex involvement in
human working memory. Neuroimage 5:49–62.
Casey BJ, Cohen JD, Jezzard P, Turner R, Noll DC, Trainor RJ, Giedd
J, Kaysen D, Hertz-Pannier L, Rapoport JL. 1995. Activation of
prefrontal cortex in children during a nonspatial working mem-
ory task with functional MRI. Neuroimage 2:221–229.
Casey BJ, Trainor RJ, Orendi JL, Schubert AB, Nystrom LN, Giedd
JN, Castellanos FX, Haxby JV, Noll DC, Cohen JD, Forman SD,
Dahl RE, Rapoport JL. 1997. A developmental functional MRI
study of prefrontal activation during performance of a go-nogo
task. J Cogn Neurosci 9:835–847.
Cohen JD, Forman SD, Braver TS, Casey BJ, Servan-Schreiber D,
Noll DC. 1994. Activation of prefrontal cortex in a non-spatial
working memory task with functional MRI. Hum Brain Mapp
Cohen JD, MacWhinney B, Flatt MR, Provost J. 1993. Psyscope: a
new graphic interactive environment for designing psychology
experiments. Behav Res Meth Instr Comp 25:257–271.
Cohen JD, Servan-Schreiber D. 1992. Context, cortex and dopamine:
a connectionist approach to behavior and biology in schizophre-
nia. Psychol Rev 99:45–77.
Cox RW. 1996. AFNI: software for analysis and visualization of
functional magnetic resonance neuroimages. Comput Biomed
Diamond A. 1990. Developmental time course in human infants and
infant monkeys, and the neural bases of higher cognitive func-
tions. Ann NY Acad Sci 608:637–676.
Donchin E, Coles MGH. 1988. Is the P300 component a manifesta-
tion of context updating? Behav Brain Sci 11:357–374.
Drevets WC, Raichle ME. 1998. Reciprocal suppression of regional
cerebral blood flow during emotional versus higher cognitive
processes: implications for interactions between emotion and
cognition. Cogn Emot 12:353–385.
Duvernoy HM. 1991. The human brain: surface, three-dimensional
sectional anatomy and MRI. New York: Springer-Verlag.
Forman SD, Cohen JD, Fitzgerald M, Eddy WF, Mintun MA, Noll
DC. 1995. Improved assessment of significant activation in func-
tional magnetic resonance imaging (fMRI): use of a cluster-size
threshold. Magn Reson Med 33:636–647.
Fuster JM. 1988. The prefrontal cortex: anatomy, physiology and
neurophysiology of the frontal lobe. New York: Raven Press.
Fuster JM. 1997. The prefrontal cortex: anatomy, physiology and
neuropsychology of the frontal lobe. New York: Raven Press.
?Casey et al.?
? 32 ?
Goldman-Rakic PS. 1987. Circuitry of primate prefrontal cortex and
regulation of behavior by representational memory. Handbook
Physiol Nerv Syst 5:373–417.
Goldman-Rakic PS. 1994. The issue of memory in the study of
prefrontal functions. In: Theiry AM, Glowinski J, Goldman-
Rakic PS, Christen Y, editors. Motor and cognitive functions of
the prefrontal cortex. New York: Springer-Verlag.
Grasby PM, Frith CD, Friston KJ, Bench C, Frackowiak RS, Dolan RJ.
1993. Functional mapping of brain areas implicated in auditory-
verbal memory function. Brain 116(Pt.1):1–20.
Jonides J, Smith EE, Koeppe RA, Awh E, Minoshima S, Mintun MA.
1993. Spatial working memory in humans as revealed by PET.
Kawashima R, Satoh K, Itoh H, Ono S, Furumoto S, Grotoh R,
Koyama M, Yoshioka S, Takahashi T, Takahashi K, Yangagisawa
T, Fukuda H. 1996. Functional anatomy of GO/NO-GO discrim-
ination and response selection—a PET study in man. Brain Res
Konishi S, Nakajima K, Uchida I, Kikyo H, Kameyama M, Miyashita
Y. 1999. Common inhibitory mechanism in human inferior pre-
frontal cortex revealed by event-related functional MRI. Brain
McCarthy G, Blamire AM, Puce A, Nobre AC, Bloch G, Hyder F,
Goldman-Rakic P, Shulman RG. 1994. Functional magnetic res-
onance imaging of human prefrontal cortex during a spatial
working memory task. Proc Natl Acad Sci USA 91:8690–8694.
McCarthy G, Luby M, Gore J, Goldman-Rakic P. 1997. Infrequent
events transiently activate human prefrontal and parietal cortex
as measured by functional MRI. J Neurophys 77:1630–1634.
Mishkin M, Manning FJ. 1978. Nonspatial memory after selective
prefrontal lesions in monkeys. Brain Res 143:313–323.
Niki H. 1974. Prefrontal unit activity during delayed alternation in
the monkey: I. relation to direction of response. Brain Res 68:
Owen AM. 1997. The functional organization of working memory
processes within human lateral frontal cortex: the contribution of
functional neuroimaging. Eur J Neurosci 9:1329–1339.
Petrides M. 1994. Frontal lobes and working memory: evidence
from investigations of the effects of cortical excisions in nonhu-
man prmates. In: Boller F, Grafman J, editors. Handbook of
neuropsychology. Vol 9. Elsevier: Amsterdam, pp. 59–82.
Roberts AC, Robbins T, Weiskrantz L. 1998. The prefrontal cortex:
executive and cognitive functions. Oxford: Oxford University
Smith EE, Jonides J, Koeppe RA, Awh E, Schumacher EH,
Minoshima S. 1995. Spatial vs. object working memory: PET
investigations. J Cogn Neurosci 7:337–356.
Sutton S, Braren M, Zubin J, John ER. 1965. Evoked-potential cor-
relates of stimulus uncertainity. Science 150:1187–1188.
Talairach J, Tournoux P. 1988. Co-planar stereotaxic atlas of the
human brain. New York: Thieme.
Woods RP, Cherry SR, Mazziotta, JC. 1992. Rapid automated algo-
rithm for aligning and reslicing PET images. J Comput Assist
Yajeya J, Quintana J, Fuster JM. 1988. Prefrontal representation of
stimulus attributes during delay tasks: II. The role of behavioral
significance. Brain Res 474:222–230.
?Target Frequency and Prefrontal Cortex?
? 33 ?