Cerebral Cortex October 2007;17:2346--2353
doi:10.1093/cercor/bhl143
Advance Access publication December 26, 2006
Event Frequency Modulates the Processing
of Daily Life Activities in Human Medial
Prefrontal Cortex
Event sequence knowledge is necessary to learn, plan, and perform
activities of daily life. Clinical observations suggest that the
prefrontal cortex (PFC) is crucial for goal-directed behavior such
as carrying out plans, controlling a course of actions, or organizing
everyday life routines. Functional neuroimaging studies provide
further evidence that the PFC is involved in processing event
sequence knowledge, with the medial PFC (Brodmann area 10)
primarily engaged in mediating predictable event sequences. However, the exact role of the medial PFC in processing event sequence
knowledge depending on the frequency of corresponding daily life
activities remains obscure. We used event-related functional magnetic resonance imaging while healthy volunteers judged whether
event sequences from high- (HF), moderate- (MF), and lowfrequency (LF) daily life activities were correctly ordered. The
results demonstrated that different medial PFC subregions were
activated depending on frequency. The anterior medial Area 10 was
differentially activated for LF and the posterior medial Area 10 for
HF activities. We conclude that subregions of the medial PFC are
differentially engaged in processing event sequence knowledge
depending on how often the activity was reportedly performed in
daily life.
Keywords: Brodmann area 10, event sequence knowledge, fMRI, scripts
Introduction
Event sequence knowledge contains information about activities of daily life; for example, the activity ‘‘get ready for work’’
consists of a sequence of events such as waking up, getting out
of bed, using the bathroom, taking a shower, getting dressed,
eating breakfast, etc. Retrieving stored event sequences is
essential for planning and performing daily life activities. Clinical observations suggest that the prefrontal cortex (PFC) is
crucial for goal-directed behavior such as carrying out plans,
controlling a course of actions, or organizing everyday life
routines (Shallice 1982; Stuss and Benson 1984; Eslinger and
Damasio 1985; Janowsky et al. 1989; Shallice and Burgess 1991).
For example, when patients with PFC lesions were asked to
evoke a plan of a daily activity, for example, ‘‘organize your
morning routine,’’ the patients generated as many prototypical
events as normals but had difficulties in temporal ordering the
events of the same activity (Sirigu et al. 1995).
The human PFC (Brodmann’s area 10 in particular) is proportionally twice as large in humans as in any of the great apes
(Rilling and Insel 1999; Semendeferi et al. 2002). In addition,
there is evidence that PFC-regulated specialized human behavior must be due to a more sophisticated internal and differentially organized neural architecture (Elston 2000; Elston and
Rosa 2000; Chiavaras et al. 2001). A key principle of neurons in
the human PFC is their ability to sustain firing and to code the
Published by Oxford University Press 2006.
Frank Krueger1, Jorge Moll1, Roland Zahn1, Armin Heinecke2
and Jordan Grafman1
1
Cognitive Neuroscience Section, National Institute of
Neurological Disorders and Stroke, National Institutes of
Health, Bethesda, MD 20892-1440, USA and 2Brain Innovation
B.V., Universiteitssingel 40, 6201 BC Maastricht, The
Netherlands
temporal and sequential properties of ongoing events in the
environment or in mind over longer periods of time (Fuster
et al. 2000; Levy and Goldman-Rakic 2000). Furthermore,
pyramidal cells in the PFC are significantly more spinous, as
compared with pyramidal cells in other cortical areas, making
them capable of handling a larger amount of excitatory inputs
(Elston 2000; Elston and Rosa 2000). These evolutionary
advances from primates to humans in the structure and connectivity of the PFC and the physiological properties of its
neurons enable the human PFC to code, store, and retrieve long
and complex sequences of behavior (Nichelli et al. 1995).
Medial and lateral PFC belong to 2 distinct architectonic
trends within the human PFC (Pandya and Yeterian 1996). The
medial trend is phylogenetically and ontogenetically older than
the lateral trend, which is especially well developed in humans
(Stuss and Benson 1986). There is also strong evidence for a
functional dissociation between medial and lateral PFC (Koechlin
et al. 2000; Burgess et al. 2003; Gilbert, Spengler, Simons, Frith,
et al. 2006; Gilbert, Spengler, Simons, Steele, et al. 2006). In
sequence learning, for example, lateral PFC regions are engaged
in performing sequences contingent upon unpredictable events,
whereas the medial PFC region is engaged in situations when
predictable sequences of stimuli are encountered (Koechlin
et al. 2000). Functional neuroimaging studies provide evidence
that the PFC is involved in mediating event sequence knowledge (Partiot et al. 1996; Crozier et al. 1999; Ruby et al. 2002;
Knutson et al. 2004). In particular, the medial PFC (Brodmann
area [BA] 10, MPFC) is primarily engaged in mediating event
sequence knowledge that has a predictable relationship with
action sequences (Wood et al. 2005). Despite this large body of
evidence, the exact role of the MPFC in processing event
sequence knowledge depending on the frequency of corresponding daily life activities remains obscure.
We have proposed a ‘‘representational’’ account of PFC
function that seeks to establish the format in which contextdependent information is stored in the PFC (Grafman 2002;
Wood and Grafman 2003). According to this approach, storage
and processing are integrally related and dependent on the
same neuronal infrastructure. The PFC processes goal-oriented
event sequence knowledge by encoding and retrieving the sequence of event components. In parallel, the PFC interacts with
knowledge stored in posterior cortical regions (e.g., objects,
faces, and words) to bind these components into the context of
an episode by placing objects, faces, and words into a certain
time, place, and action (via the hippocampus and related
structures) (O’Reilly and Rudy 2000). We argue that the representation of event sequence knowledge is critically dependent
on how frequently a person enacts these sequences. For
example, an activity such as ‘‘getting ready for work’’ includes
a sequence of events that are executed more often than the
events executed in an activity such as ‘‘having your picture
taken.’’ There is neuropsychological evidence that leads to the
hypothesis that low-frequency (LF) and high-frequency (HF)
event sequence knowledge are mediated by different neural
substrates. Patients with anterior PFC damage show frequency
effects in event knowledge tasks, with HF event sequence
knowledge being better preserved than LF event sequence
knowledge (Sirigu et al. 1995).
If frequency is a crucial parameter, then variation in frequency should be closely linked to brain regions recruited during tasks that require event sequence knowledge. In this study,
we used event-related functional magnetic resonance imaging
(fMRI) to investigate the pattern of brain responses when
healthy volunteers were engaged in event order judgments
about the sequential organization of LF (e.g., having your picture taken), moderate frequency (MF) (e.g., doing the laundry),
and HF (e.g., getting ready for work) activities according to
normative data. Based on evidence that the MPFC (BA 10) is
engaged in processing predictable event sequence knowledge
(Wood et al. 2005), we hypothesized that event sequences will
evoke activation in different subregions of the MPFC depending
on how often persons were engaged in such daily life activities.
Material and Methods
Participants
Eighteen healthy volunteers (9 women, mean age 29.4 ± 6.0 years, range
23--40, mean education level 17.2 ± 2.1 years, range 15--22) participated
for financial compensation in the fMRI experiment. All were native
English speakers and strongly right-handed as determined from the
Edinburgh Handedness Inventory (97.1 ± 6.1, range 76--100). They
underwent a neurological examination by a board-certified neurologist
during the previous 12 months and had normal or corrected-to-normal
vision, no history of medical, psychiatric, or neurological diagnoses, and
were not taking medication. Informed consent was obtained according
to procedures approved by the National Institute of Neurological
Disorders and Stroke (NINDS) Institutional Review Board.
Stimuli
Stimuli in the fMRI experiment were derived from 2 normative studies.
Daily life activities were taken from a normative study conducted by
Rosen et al. (2003). In part one of that study, individuals (n = 120)
recorded their daily activities for 7 consecutive days. The activities
ranged along a frequency continuum from activities reported only once
by a single individual during the week (e.g., going to an audition) to
activities reported more than once by most or all of the individuals
during the week (e.g., getting ready for work). The authors selected 5
activities from the beginning, middle, and ending of the frequency continuum. Those activities were grouped into LF, MF, and HF categories. In
part 2 of the study, a new set of individuals (n = 60) was asked to
generate events belonging to the 15 selected activities. The authors
reported the 18 most frequently generated events for each activity.
For our fMRI experiment, we used the same 15 daily life activities (5
activities per category): HF activities (e.g., getting ready for work), MF
activities (e.g., doing laundry), and LF activities (e.g., having your picture
taken). Each activity was composed of a header and 16 sequentially
structured events (supplementary appendix). Note that each of these
activities represents a predictable event sequence that stores both the
goals and boundaries of events. However, each of these event sequences
varies in terms of frequency depending on how often the event
sequence was reportedly performed in daily life. Eight event pairs, adjacent in sequence, were created for each activity, which were either
ordered in chronological sequence (e.g., wake up—get out of bed) or
inverse sequence (e.g., get in car—leave house). Each frequency group
consisted of 40 event pairs balanced for order of sequence. Headers
contained 2--5 words (mean ± standard error of the mean [SEM] 3.47 ±
0.46), and event pairs contained 2--7 words (mean ± SEM 3.02 ± 0.36).
In another normative study we conducted, 40 participants (20
women, mean age 31.5 ± 8.6 years, range 22--48, mean education level
17.5 ± 3.4 years, range 14--23) rated the emotion and scene-likeness for
all the events from each activity to control for emotion processing
(positive and negative valence) and autobiographic retrieval (episodic
and semantic), respectively. Participants rated the emotion and scenelikeness for the events of each activity while seeing both the header and
the event of the activity on a 7-point Likert-scale: 1) Emotion: We would
like you to rate each event according to whether it has an intense
emotional association for you (positive and negative valence scale: 1 =
not at all and 7 = extremely intense); and 2) Scene-likeness: We would
like you to rate how intensely are you reminded of a specific episode or
scene that you have experienced during your life while reading about
the event (1 = not at all and 7 = extremely intense). Emotion (positive
valence) and scene-likeness ratings were equal among frequency
categories (emotion [positive valence]: F2,78 = 2.06, P = 0.134; scenelikeness: F2,78 = 2.10, P = 0.145), whereas emotion ratings (negative
valence) decreased significantly from the LF to HF category (emotion
[negative valence]: F2,78 = 30.95, P < 0.001).
In addition, participants rated the frequency and familiarity for the
events of each activity on a 7-point Likert-scale: 1) Frequency: We would
like you to rate how often you are usually involved in each of the
following events (1 = never and 7 = daily); and 2) Familiarity: We would
like you to rate how familiar you are with each of the following events,
that is, how often you have performed or observed these events (1 =
extremely unfamiliar and 7 = extremely familiar). Because frequency
and familiarity ratings were highly correlated for each frequency
category (LF: r = 0.93, P < 0.001; MF: r = 0.94, P < 0.001; HF: r = 0.89,
P < 0.001), stimuli were not further analyzed separately for the
frequency and familiarity dimensions.
Presentation Conditions and Procedure
An experimental condition (sequence order judgment task) and
a control condition (font discrimination task) were employed. The
latter condition served as a baseline that controlled for display, motor
responses, and attentional mechanisms, thought to be common to the
sequence order judgment task across frequency conditions. At the start
of each trial an instruction indicating the type of task (order task or font
task) was presented for 1600 ms on the screen. After a blank screen of
400 ms, a header for an activity (e.g., get ready for work) appeared. After
a delay of 1000 ms the header disappeared. For the sequence order task,
a pair of events (e.g., wake up—get out of bed) belonging to this activity
was displayed vertically in the middle of the screen. For the font task,
a pair of events (e.g., enter a store—brush teeth) belonging to different
activities was displayed on the screen. Within a fixed time of 5000 ms,
participants had to make a decision on a hand-response pad with their
right index (left button) or middle finger (right button). Stimulus
presentation was event-related and trials were separated by a randomly
assigned jittered interstimulus interval of 4 s (range: 2--6 s) (Fig. 1).
Instructions for the experiment were as follows. For the sequence
order task, participants had to decide which of the events occurs first in
the chronological sequence of the activity. Half of the participants were
instructed to press the left button if the event on the top occurred first
(e.g., wake up—get out of bed) or to press the right button if the event
on the bottom occurred first (e.g., get in car—leave house). The other
half of the participants was instructed to do the converse. For the font
discrimination task, participants had to decide which event had the
same font (Swiss or Helvetica Font) as the header. If the event on the top
had the same font, of the participants had to press the left button or if
the event on the bottom had the same font as the header the right
button had to be pressed. For the remaining participants, the converse
was requested. Participants were asked to perform the tasks to the best
of their ability and to respond as quickly and accurately as possible. After
their response, participants saw a blank screen for the rest of the
5000 ms.
The fMRI experiment consisted of 3, 12-min runs counterbalanced
across participants. Each run included both trials for the sequence order
task (n = 40) and trials for the font discrimination task (n = 20)
randomized across each run. The experiment consisted of 180 trials
Cerebral Cortex October 2007, V 17 N 10 2347
Figure 1. Experimental design. Each 8-s trial began with an instruction indicating the type of task (sequence order task [SOT] or font discrimination task [FDT]) for 1600 ms,
followed by a 400-ms blank interval. Afterward the task began with the presentation of the activity header. After 1000 ms, the header disappeared and a pair of events was
displayed. For the SOT (experimental condition), participants had to decide which one of the events occurs first in the chronological sequence of this activity by pressing an assigned
key. For the FDT (control condition), participants had to decide which of the events had the same font (Swiss or Helvetica) as the header. The same headers and event pairs were
used for both tasks but for the FDT, event pairs from one activity were assigned to headers of other activities. Within a fixed time of 5000 ms, participants had to make a decision on
a hand-response pad with their right index or middle finger. After their response, participants saw a blank screen for the rest of the 5000 ms. Trials were separated by randomly
assigned jittered intervals (mean 4 s, range 2--6 s). RT and ER were recorded for each trial.
(font discrimination task: 60 trials and sequence order task: 120 trials).
Response time (RT) and error rate (ER) were recorded for each trial. All
stimuli were carefully matched for word length across frequency
conditions. The same headers and event pairs were used for both the
experimental and control conditions, but for the control condition,
event pairs from one activity were assigned to headers of other activities.
Data Acquisition
Before being scanned, participants were first trained on a separate set of
stimuli to familiarize them with the experiment. Stimulus presentation
was synchronized with the scanner pulses using the Experimental Run
Time System (Berisoft Cooperation, Germany, http://www.erts.de)
software package running on an IBM computer. With a magnetically
shielded LCD video projector, stimuli (18-point font type) were backprojected onto a translucent screen placed at the feet of the participant.
During scanning, participants had to lie flat on their back in the MRI
scanner and viewed the screen by a mirror system attached to the head
coil. Head motion was restricted using foam pads placed around the
participants’ head.
Imaging was performed on a 3-T General Electric whole-body scanner
equipped with a standard circularly polarized head coil. Anatomical (T1weighted 3D Magnetization-Prepared Rapid Acquisition Gradient Echo
sequence: time repetition [TR], 9.7 ms; time echo [TE], 4.0 ms; flip angle,
12°; field of view, 240 mm; matrix size, 256 3 256; thickness, 1.2 mm; inplane resolution, 0.8594 3 0.8594 mm2) and functional images (T2*weighted 2D gradient echo-planar image [EPI] sequence: TR, 2 s; TE, 30
ms; flip angle, 90°; thickness, 6 mm; number of slices, 22; field of view,
240 mm; in-plane resolution, 3.75 3 3.75 mm2) were acquired. Per run
365 volume images were taken parallel to the anterior commissure-posterior commissure line. The first 5 volumes were discarded to allow
for T1 equilibration effects.
Data Analysis
Behavioral data analysis was carried out using SPSS (SPSS Inc., Chicago,
IL, http://www.spss.com). Alpha was set to P < 0.05 for all behavioral
analyses. Image analyses were performed using Brain Voyager 2000 and
Brain Voyager QX (Brain Innovation, Maastricht, The Netherlands,
http://www.BrainVoyager.com). The following data preprocessing steps
2348 Medial PFC Mediates Daily Life Activities
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Krueger et al.
were applied: slice scan time correction (using sinc interpolation), linear
trend removal, temporal high-pass filtering to remove low-frequency
nonlinear drifts of 3 or fewer cycles per time course, spatial smoothing
(8-mm full width at half maximum), and 3D motion correction to detect
and correct for small head movements by spatial alignment of all
participants to the first volume by rigid body transformation. Estimated
translation and rotation parameters were inspected and never exceeded
2 mm or 2°. Functional slices were coregistered to the anatomical
volume using position parameters from the scanner and manual
adjustment to obtain optimal fit and transformed into Talairach space
(3 3 3 3 3 mm3) (Talairach and Tournoux 1988).
A general linear model (GLM) corrected for first-order serial
correlation was applied. Multisubject analyses were performed by multiple linear regressions of the blood oxygen level--dependent (BOLD)-RT
course in each voxel using 4 predictors: LF, MF, HF, and control (C).
Predictor time courses were adjusted for the hemodynamic response
delay by convolution with a hemodynamic response function (delta 2.5,
tau 1.25) (Boynton et al. 1996). Random-effect analyses were performed
on the group data (n = 18), enabling generalization of the statistical
inferences to the population level. Due to a priori hypothesis regarding
the role of the MPFC (BA 10) in mediating daily life activities (Wood
et al. 2005) an uncorrected P value adjusted to a per-voxel false-positive
probability of P < 0.0005 with a cluster size threshold of 300 mm3 was
applied (Forman et al. 1995). Due to a lack of a priori hypothesis for
other areas of the brain a corrected P < 0.05 was used for whole-brain
analysis.
Time courses of each frequency category were extracted from each
ROI to compute their respective percentage signal changes with the
condition-per-file-based series as baseline. Statistical images were overlaid
onto Brain Voyager’s single subject canonical T1 image in Talairach space.
BAs were determined by using the Talairach Daemon Client software
(Research Imaging Center, San Antonio, TX, http://ric.uthscsa.edu/).
Results
Behavioral Data
The RTs (mean ± SEM) and ERs (mean ± SEM) from the control (C)
condition differed significantly from those in the experimental
conditions (LF, MF, and HF) (RT: t(17) = 13.57, P < 0.001; ER: t(17) =
9.63, P < 0.001) (Table 1). The RTs and ERs of the experimental
conditions were submitted to a one-way analysis of variance
(ANOVA) with frequency as a within-subject factor. The ANOVA
Table 1
RTs (mean ± SEM in ms) and ERs (mean ± SEM in %) for control and experimental conditions
RT (ms)
ER (%)
Control condition
Control
1060 ± 362
2.6 ± 3.9
Experimental condition
LF
MF
HF
2369 ± 458
2380 ± 453
2334 ± 461
9.3 ± 7.5
10.3 ± 7.5
12.2 ± 4.4
revealed no significant main effect for the factor frequency
(RT: F2,34 = 0.83, P = 0.445, ER: F2,34 = 2.14, P = 0.133); that is,
RTs and ERs were equal across frequency conditions.
Imaging Data
To identify brain regions commonly activated by processing
event sequence knowledge independently of the factor frequency, we first contrasted the control (C) from the experimental conditions (LF, MF, and HF). The task contrast (LF + MF +
HF > C) yielded activation in the medial frontal gyrus (MFG) (BA
10; x = 3, y = 54, z = –5), supporting our hypothesis that the
MPFC is involved in processing of event sequence knowledge
(Fig. 2A). Additional activations were found in the left inferior
frontal gyrus (BA 9) and the left supplementary motor area
Figure 2. Brain activation for task contrast. (A) When contrasting control from experimental condition activation was observed in the MPFC (BA 10; x 5 3, y 5 54, z 5 5). Brain
activation for frequency contrasts. (B) When each frequency condition was contrasted against the other 2 frequency conditions activation was observed in the anterior MPFC (BA
10; x 5 3, y 5 68, z 5 5) for the LF contrast, in the middle MPFC (BA 10; x 5 8, y 5 53, z 5 5) for the MF contrast, and in the posterior MPFC (BA 10; x 5 5, y 5 43, z 5 9)
for the HF contrast. Time courses of activation profiles for each frequency condition. x axis, poststimulus onset times measured from header onsets. y axis, adjusted BOLD signal
changes expressed in relative percentage of the mean.
Cerebral Cortex October 2007, V 17 N 10 2349
(BA 6) (Table 2). The activation of the supplementary motor
area is consistent with findings of other neuroimaging studies
dealing with sequencing information (Crozier et al. 1999; Ruby
et al. 2002), whereas the activation of the inferior frontal gyrus
is consistent with findings dealing with semantic judgment
(Demb et al. 1995; Poldrack et al. 1999) and response selection
(Rowe et al. 2000; Badre and Wagner 2004) relevant for the
experimental task.
In order to explore brain regions that differed in their
activation according to frequency, we performed 3 frequency
contrasts in which each frequency category was contrasted
against the other 2 frequency categories (LF contrast: LF > MF +
HF; MF contrast: MF > LF + HF, and HF contrast: HF > LF + MF).
We refer to these effects as content dependent because they
reflect the content of the event sequence knowledge about daily
activities without the interference of task contingencies (which
were balanced across conditions). These contrasts revealed 3
distinct regions that varied along an anterior-to-posterior axis of
the MPFC dependent on frequency: the right anterior MPFC
(MFG; BA 10; x = 3, y = 68, z = 5) for the LF contrast (LF > MF +
HF), the right middle MPFC (MFG; BA 10; x = 8, y = 53, z = –5) for
the MF contrast (MF > LF + HF), and the left posterior MPFC
(MFG; BA 10; x = –5, y = 43, z = –9) for the HF contrast (HF > LF +
MF) (Table 2, Fig. 2B). The whole-brain analysis gave no
additional regions of activation.
To further confirm the specificity of the anterior-to-posterior
axis within the MPFC subregions we employed a region of
interest (ROI) analysis. For each participant, parameter estimates (mean beta weights) for each frequency condition were
derived from each MPFC location after identifying the peak of
activation and surrounding voxels encompassing 50 mm3 (Fig.
3A). A 2-way ANOVA with parameter estimates was performed
to explore the interaction between the factor frequency (low
and high) and ROI (anterior MPFC and posterior MPFC). The
main effects of frequency and ROI were not significant (frequency: F1,17 = 0.62, P = 0.439; ROI: F1,17 = 0.43, P = 0.523).
Importantly, the frequency by ROI interaction was significant
(F1,17 = 18.33, P < 0.001). Planned follow-up paired t-tests
indicated a significantly higher mean activation level for the LF
compared with HF condition in the anterior MPFC (t (17) = 2.35,
Table 2
Brain areas activated during the task and frequency contrasts
Regions of activation
Cluster size (mm3) Laterality Talairach coordinates
Task contrast
LF þ MF þ HF [ control
MFG (10)
1686
Supplementary motor area (6) 620
Inferior frontal gyrus (9)
1332
Frequency contrasts
LF contrast: LF [ MF þ HF
MFG (10) (ant MPFC)
347
MF contrast: MF [ LF þ HF
MFG (10) (mid MPFC)
486
HF contrast: HF [ LF þ MF
MFG (10) (post MPFC)
315
t-score
x
y
z
R
L
L
3
5
45
54
4
15
5
53
20
R
3
68
5
3.79*
R
8
53
5
3.58*
L
5
43
9
3.59*
3
3.43*
8.06**
7.40**
Note: Brodmann’s areas are depicted in parentheses. Cluster size (volume in mm ), laterality
(right and left hemisphere), and t-score are also given. The stereotaxic coordinates of the peak of
the activation are given to Talairach space. Abbreviations: ant, anterior; mid, middle; post,
posterior
*P \ 0.0005, cluster size of minima 300 mm3.
**Pcor \ 0.05, whole-brain analysis.
2350 Medial PFC Mediates Daily Life Activities
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Krueger et al.
P < 0.05) and a significantly higher mean activation level for the
HF compared with LF condition in the posterior MPFC (t (17) =
–3.79, P < 0.001); that is, the anterior MPFC is more engaged in
LF activities and the posterior MPFC more in HF activities (Fig.
3B). On the subject-by-subject level, parameter estimates in 14
of 18 participants were higher for LF than MF and HF condition
for the anterior MPFC, whereas parameter estimates in 15 of 18
participants were higher for HF than LF and MF condition for
the posterior MPFC (Fisher’s Exact test: v2(1) = 13.11, P <
0.001) (Fig. 3C).
Finally, to control our findings for possible confounds with
emotion processing and autobiographic retrieval, we adopted
a parametric approach by defining additional predictors for the
GLM using ratings from the dimensions emotion (positive and
negative) and scene-likeness as modulation parameters. We
tested for linear relationships between changes in emotion and
scene-likeness ratings and changes in the BOLD signals. Analyses for both emotion and scene-likeness ratings did not modulate activations in any of the previous identified MPFC regions.
Instead, ratings with positive emotional valence were associated
with activations in the amygdala (x = 21, y = –3, z = –21) and
superior frontal gyrus (BA 10; x = 11, y = 60, z = 18), ratings with
negative emotional valence were associated with activations in
the anterior insula (BA 13; x = 30, y = 20, z = 15), lateral middle
frontal gyrus (BA 10; x = 41, y = 52, z = 12), and retrosplenial
cortex (BA 31; x = 2, y = –57, z = 26), and scene-likeness ratings
were associated with activations in the retrosplenial cortex (BA
31; x = –5, y = –63, z = 21) (supplementary Fig. S1).
Discussion
We used event-related fMRI to investigate the pattern of brain
responses when healthy volunteers were engaged in event
order judgments about the sequential organization of LF, MF,
and HF activities according to normative data. The analyses
revealed a frequency gradient along the anterior-to-posterior
axis of the MPFC, in which the anterior medial Area 10 is
engaged in LF activities, and the posterior medial Area 10 is
engaged in HF activities.
But were the identified MPFC subregions selectively related
to the variation in frequency? First, it could be argued that the
MPFC regions were responding to task difficulty associated with
higher cognitive control or conflict (Carter et al. 1998). However, because RTs and ERs did not differ among frequency conditions, this is unlikely. Second, it could be argued that the
results reflect activation of a multipurpose working memory system rather than being specific to event sequence knowledge.
Against this interpretation is the fact that working memory load
was kept constant across conditions. Moreover, according to
accumulated evidence from neuroimaging studies, brain activation in working memory tasks is primarily associated with
dorsolateral regions of the PFC (Smith and Jonides 1999; Cabeza
and Nyberg 2000). Third, it could be argued that differences in
emotions could lead to different MPFC activations because the
MPFC is also engaged in emotion processing (Lacroix et al.
2000; Kalisch et al. 2005). The regression analysis with the
parameter emotion did not modulate activation in any of the
previously identified frequency-dependent MPFC regions. Instead, emotion ratings showed associations with brain regions
such as amygdala and anterior insula, in agreement with the
crucial role of these regions in emotion processing (Zalla et al.
2000; Calder et al. 2001; Cardinal et al. 2002). Finally, it could
Figure 3. ROI analysis. (A) Parameter estimates (mean beta weights) for each frequency condition (low, moderate, and high) derived from each functional ROI (anterior MPFC,
middle MPFC, and posterior MPFC). (B) On the group level, a 2-way ANOVA with parameter estimates showed a significant interaction between the factor frequency (low and high)
and ROI (anterior MPFC and posterior MPFC). (C) On the subject-by-subject level, participants’ parameter estimates were plotted separately for the factor frequency (low and high)
and ROI (anterior MPFC and posterior MPFC). For the anterior MPFC parameter estimates in 14 of 18 participants were higher for LF than MF and HF condition, whereas for the
posterior MPFC parameter estimates in 15 of 18 participants were higher for HF than LF and MF condition. Both the group and subject-by-subject analysis provide support for
a frequency gradient along the anterior-to-posterior axis of the MPFC, in which the anterior MPFC is more engaged in LF activities and the posterior MPFC in HF activities.
be argued that judgments about LF event sequences primarily
require retrieval of episodic autobiographical memories, whereas
judgments about HF event sequences require primarily semantic autobiographical memory (Levine et al. 2004). Therefore,
activation in the identified MPFC regions would reflect the
monitoring of information retrieved from episodic or semantic
autobiographical memory. We think this is unlikely because
scene-likeness ratings were equal among frequency conditions.
In addition, the regression analysis with scene-likeness ratings
did not modulate activation in the frequency-dependent MPFC
regions. Instead activation was found in the retrosplenial cortex
in agreement with the role of this region in retrieval of
autobiographical memories (Fink et al. 1996; Piefke et al. 2003).
Altogether, the results support the idea that the MPFC
modulates the processing of event sequence knowledge with
an anterior--posterior gradient according to the frequency of
use in real life. Note that during the experimental task
participants were not actually involved in carrying out these
activities. However, to solve the task participants had to access
the stored representations about the sequential organization of
LF, MF, and HF activities. Therefore, it appears reasonable to
assume that performing the task taps into the same sorts of
representations that are activated during planning, monitoring,
and executing those daily life activities.
A previous functional imaging study corroborates our findings. In another parametric event-related fMRI study, multidimensional scaling was used to establish the psychological
structure of event sequence knowledge (Wood et al. 2005).
The same normative data sample was employed as in the
present study with several activities from the MF and HF
condition. Using a different group of participants, experimental
paradigm, and analysis procedure a selective association between activation in the middle MPFC (BA 10; x = 4, y = 54, z = –9)
and a psychological dimension termed ‘‘experience’’ was
Cerebral Cortex October 2007, V 17 N 10 2351
demonstrated. This dimension was best described by the
variables of rule knowledge, commonality, and frequency. In
our experiment, we found an almost identical location in the
middle MPFC (BA 10; x = 8, y = 53, z = –5) for the MF contrast.
Our results replicate the earlier finding but they provide
additional evidence that the MPFC modulates the processing
of event sequence knowledge according to its frequency of
execution in real life.
How can the frequency gradient along the anterior-toposterior axis of the MPFC be explained? According to our
representational account of PFC function, the PFC processes
goal-oriented event sequence knowledge by encoding and
retrieving the sequence of event components (Grafman 2002;
Wood and Grafman 2003). The MPFC represents event sequences that have a predictable relationship with action sequences.
We argue that the anterior MPFC codes more complex
cognitive information about an event sequence. As an event
sequence becomes more frequently used an economy of
representation develops, in which the posterior MPFC, activated in parallel with the anterior MPFC, codes sparser cognitive
information about the same event sequence. This coding format
leads to different profiles of MPFC activation depending upon
the frequency of the event sequence the person executes. It
would allow simpler representational codes to rapidly instruct
lower level systems (e.g., motor) to implement action sequences. In other words, the same activity can be performed quickly
(e.g., by using a heuristic based on sparser coding) or slowly
(e.g., by using deliberate reflection based on detailed coding)
depending upon situational demands on the stored event
sequence knowledge.
Interestingly, each of the frequency-dependent MPFC regions
falls onto one of the 3 architectonic subdivisions of human BA
10 proposed by Ongur et al. (2003) (anterior MPFC: polar area
10p, middle MPFC: rostral area 10r, and posterior MPFC: medial
area 10 m). Area 10 is probably the single largest cytoarchitectonic area of the PFC (Ramnani and Owen 2004). The
subregions have a similar cellular pattern but vary in the degree
of granularity and the development of cortical layer III (and
layer IV), with the most prominent and well-developed layer III
located in the polar area, which is not observed in nonhuman
primates. Each subregion has a module-like organization with
specific input--output relations. Because layers III and IV contain
intracortical association fibers (Creutzfeldt 1995), it is reasonable to assume that integration of increasingly large neuronal
assemblies is supported as a result of increasing numbers of
association fibers. This architectural complexity increase along
the medial axis toward the frontopolar cortex may be an
indication of the underlying frequency-dependent knowledge
represented in each of the medial subregions.
In conclusion, our findings demonstrate that subregions of
the MPFC are differentially engaged in processing event sequence knowledge depending on how often the corresponding
daily life activities are performed. The anterior medial Area 10
was differentially activated for LF and the posterior medial Area
10 for HF daily life activities. Because BA 10 is one of the few
brain regions in humans that shows a proportional increase in
size compared with other primates (Semendeferi et al. 2001,
2002), one of the driving features of this size increase may be its
special capability to store a large number of experiences composed of many events, enabling humans to have access to event
sequences that span the past, present, and future. Being able to
represent this kind of knowledge would confer humans with
2352 Medial PFC Mediates Daily Life Activities
d
Krueger et al.
a great advantage in carrying out plans, controlling a course of
actions, or organizing everyday life routines.
Supplementary Data
Supplementary material can be found at: http://www.cercor.
oxfordjournals.org/.
Notes
This research was supported by the Intramural Research Program of
the National Institutes of Health/National Institute of Neurological
Disorders and Stroke/Cognitive Neuroscience Section. The authors are
grateful to Eric Wassermann for his help with performing the
neurological exams. Conflict of Interest: None declared.
Address correspondence to Jordan Grafman, PhD, Cognitive Neuroscience Section, National Institute of Neurological Disorders and Stroke,
National Institutes of Health, Bldg. 10, Room 5C205, MSC 1440,
Bethesda, MD 20892-1440, USA. Email: [email protected].
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