The Journal of Neuroscience, June 1, 2003, 23(11):4726-4736
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Human 
Oscillations Related to Sensorimotor Integration and Spatial Learning
Jeremy B. Caplan,1
Joseph R. Madsen,2,3
Andreas Schulze-Bonhage,4
Richard Aschenbrenner-Scheibe,4
Ehren L. Newman,1 and
Michael J. Kahana1,2
1 Volen Center for Complex Systems, Brandeis University, Waltham, Massachusetts
02454,
2 Department of Neurosurgery, Children's Hospital, Boston, Massachusetts
02115,
3 Department of Surgery, Harvard Medical School, Boston, Massachusetts
02115, and
4 Neurozentrum, Universität Freiburg, 79106 Freiburg, Germany
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Abstract
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oscillations in the rat hippocampus have been implicated in
sensorimotor integration (Bland,
1986
), especially during exploratory and wayfinding behavior. We
propose that human cortical activity coordinates sensory information
with a motor plan to guide wayfinding behavior to known goal locations. To
test this hypothesis, we analyzed invasive recordings from epileptic patients
while they performed a spatially immersive, virtual taxi driver task.
Consistent with this hypothesis, we found oscillations during both
exploratory search and goal-seeking behavior and, in particular, during
virtual movement, when sensory information and motor planning were both in
flux, compared with periods of self-initiated stillness. oscillations
had different topographic and spectral characteristics during searching than
during goal-seeking, suggesting that different cortical networks exhibit
depending on which cognitive functions are driving behavior (spatial
learning during exploration vs orienting to a learned representation during
goal-seeking). In contrast, oscillations in the 
band appeared to be
related to simple motor planning, likely a variant of the Rolandic µ
rhythm. These findings suggest that human cortical oscillations act to
coordinate sensory and motor brain activity in various brain regions to
facilitate exploratory learning and navigational planning.
Key words: oscillations; intracranial electroencephalography; spatial navigation; spatial memory; sensorimotor integration; wayfinding
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Introduction
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Researchers have linked the prominent 3–12 Hz oscillations in rodent
field recordings to conditioning (Seager
et al., 2002), memory (Givens,
1996), path planning (O'Keefe
and Recce, 1993), moving
(Vanderwolf, 1969), orienting
(Gavrilov et al., 1995), and
exploration (Komisaruk, 1970;
Macrides, 1975). Bland and
Oddie proposed the sensorimotor integration hypothesis (i.e., that
oscillations act to coordinate activity in various brain regions to update
motor plans on the basis of sensory input)
(Bland, 1986;
Bland and Oddie, 2001).
We investigated the function of oscillations in the human brain.
Note that traditionally in humans, the band is defined as 4–8 Hz
(Niedermeyer, 1999). We
conservatively define human as 4–8 Hz activity, consistent with
both the human and rodent conventions. Recording intracranial
electroencephalogram (iEEG) from human epileptic patients, bypassing the
filtering of the skull (Sperling,
1997), we could record oscillations visible in the
unfiltered signal (Kahana et al.,
1999; Caplan et al.,
2001). These oscillations appeared while participants
learned to navigate virtual T-junction mazes, and their presence covaried with
maze difficulty, suggesting that human oscillations play a role in
spatial learning. Other evidence suggests that human cortical is
present during active virtual movement (for review, see
Kahana et al., 2001). de
Araújo et al. (2002)
recorded magnetoencephalography (MEG) while healthy participants navigated
virtual environments. They found greater power while participants were
moving than during three control conditions. They did not manipulate spatial
learning. Here, we link these two sets of findings by manipulating both
virtual movement and the type of wayfinding within the same task and test the
hypothesis that human oscillations are the physiological substrate for
sensorimotor integration during exploration and seeking known locations using
a learned representation.
We developed a task, "Yellow Cab," that encourages participants
to find efficient paths from arbitrary locations. Participants were required
to alternately search for passengers placed at random, unknown locations
("searching") and to seek learnable, fixed goal locations,
occupied by stores ("goal-seeking").
If oscillations reflect sensorimotor integration, then we should
see more during active virtual movement, when both sensory information
and the motor program are in states of flux, than during voluntary stillness,
even when those periods of stillness are part of the participant's intentional
wayfinding behavior. The topography and frequencies of oscillations
may depend on whether the participant is navigating to known locations in the
environment (i.e., stores; goal-seeking) versus unknown locations (i.e.,
passengers; searching). Goal-seeking behavior should primarily recruit
networks that are required for orienting to a learned representation relative
to incoming sensory information, whereas searching behavior may in addition
recruit networks involved in updating the spatial representation on the basis
of new sensory information, given its exploratory function. We test these
hypotheses by analyzing interleaved periods of movement versus standing still
and searching versus goal-seeking. We compare these findings with the same
analyses in the band (13–30 Hz), a characteristic component of
the µ rhythm, related to motor planning
(Jasper and Andrews, 1938;
Niedermeyer, 1999;
Klopp et al., 2001). Finally,
we show that the oscillations we observe cannot simply reflect
modulations of a resting posterior 
rhythm.
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Materials and Methods
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Participants and recording
We recorded from 12 patients with medically resistant epilepsy (see
Table 1 for demographics).
Patients had subdural grids and depth electrodes implanted chronically to
localize the seizure focus and to map functional regions to avoid in surgery.
Recordings were acquired over 1–2 weeks. By participating in our
studies, these patients incurred no additional medical or surgical risk, and
informed consent was obtained from the patients and their guardians. The
protocol was approved by the Institutional Review Boards at Children's
Hospital (Boston, MA) and Neurozentrum, Universität Freiburg (Freiburg,
Germany). Signal was sampled at 256 Hz, except for patient 6, who had a 512 Hz
sampling rate. Bandpass filter was 0.3–70 Hz for patients at Children's
Hospital (BioLogic) and 0.015–120 Hz for patients at Universität
Freiburg (DeltaMed). The locations of the electrodes were determined from
coregistered computed tomograms and magnetic resonance images by an indirect
stereotactic technique (Talairach and
Tournoux, 1988) or derived from stereotactic implantation
(Freiburg). Electrodes overlying regions of known lesions or seizure onset
zones or showing interictal spikes or sharp waves were excluded from analysis.
Recordings at Children's Hospital were referred to a physically linked set of
scalp electrodes. Recordings at Universität Freiburg were referred to an
intracranial contact during acquisition and analyzed relative to a reference
signal computed from an average of intracranial electrodes, excluding sites
overlying seizure-onset zones or showing interictal artifacts.
Figure 1 shows the locations
of all recording sites sampled, both included and excluded from analyses.
Additionally, while participants 4, 6, and 8–11 had hippocampal as well
as cortical recordings, the number of such sites was not large enough to
enable us to make statements about oscillatory activity in the hippocampus.
Therefore, we report findings from cortical recording sites only.
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Figure 1. Locations of recordings sites across all 12 participants. These topographic
maps show electrode locations on four views of a standard brain. Top left,
Right lateral view. Top right, Left lateral view. Bottom left, Inferior view.
Bottom right, Mid-axial/hippocampal view. Different shapes denote locations in
different participants. Unfilled sites were excluded from our analyses; filled
sites were included (see Materials and Methods).
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Task and procedure
Construction of virtual towns. The virtual towns were laid out on
a 3 x 3 regular grid of roads, surrounded by an outer wall. The
regularity of the layout facilitated automatic generation of the towns,
randomizing the sets of stores and buildings and their locations in each town.
Defining the width of a road as one unit (IU) in the virtual world, the size
of the entire environment was 10 x 10 U. Of the 100 total square units,
36 square units consisted of nine equally sized blocks, each containing one
structure (building or store). Blocks were separated from each other and the
outer wall by roads. Environments had six buildings and three stores
(Fig. 2b).
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Figure 2. a, Sample screen shot from a first-person perspective.
Participants viewed the environment in color. The road is textured gray, and
grass is textured green, elevated from the road with a curb. A store (the
"Java Zone") is visible on the left, and other (non-store)
buildings are also visible. The stone-textured wall that surrounds the town is
visible in the distance. The participant's goal is indicated in the top left
corner, and the score is indicated in the top right corner. b, A blue
print of a sample environment layout (never seen by the participants). Note
that there are three store blocks (light squares) and six building blocks
(dark, unlabeled squares). The dark outline denotes the city wall.
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Each building occupied 
1 square unit centered within a block.
Buildings could vary slightly in the area of their base and substantially in
their height. Each building had a unique façade mapped onto all four
walls and a lawn separating the building from the road
(Fig. 2a). These
nontarget structures provided a rich visual context for the virtual towns. The
stores were of uniform shape and size, with a single storefront image mapped
onto all four sides. Each store occupied a 0.7 x 0.7 x 0.35 U
rectangular cube centered on each block. Unlike buildings, stores were not
surrounded by a lawn but by paved roads that allowed participants to drive up
to the storefronts.
The outer boundary of the environment had the image of a stone wall mapped
along its length. No other heterogeneous visual information could be seen
beyond this boundary.
Navigation. The virtual taxi allowed participants to navigate from
a first-person point of view at an eye height of 0.1 U. The field of view was
set to 56 x 44° in 640 x 480 mode. Participants used the four
arrow keys on a standard keyboard to navigate. They could not make arcing
turns; if more than one key was pressed, only the most recent key press would
apply, and a key had to be released before it could be used again. The turning
rate was 20°/sec such that it would take 18 sec to complete a full
rotation. The walking speed was set to 1.17 U/sec. The view was refreshed
every 30 msec. Pick up or delivery of passengers occurred when the participant
came within 0.20 U of the passenger or the storefront, respectively.
During searching, a single would-be passenger was placed in the
environment. On each delivery, the location of the next passenger was chosen
randomly. When the passenger was picked up (by driving up to the passenger), a
text screen appeared, instructing the participant to find a specific target
store for a virtual fare. Participants pressed the "Enter" key;
this was followed by a timed text screen reiterating the instructions to seek
the target store. The text screen was displayed for 2 sec, after which
participants were returned to the virtual town. The timed text screen was
included to have a consistent, interleaved control task (reading control
phase) to contrast with navigation. Because the forms of the text screens were
constant throughout the task, the reading task may be more akin to a rest
condition than to an actual reading task.
As soon as the passenger was delivered, another text screen was displayed,
indicating success and instructing the participant to look for another
passenger. Again, when participants pressed the Enter key, a 2 sec, timed text
screen appeared, reiterating the instructions to search for a passenger,
representing more reading control time. Participants were rewarded $50 virtual
cash for each delivery and were docked $1 for every 10 sec spent moving,
turning, or standing still, but there was a restriction that a maximum of $1
could be docked for any continuous period of standing still. Their earnings
were continuously displayed in the top right corner of the screen. At the top
left, a short description of their current goal was shown (e.g., "Find a
passenger" or "Find the Java Zone"). This prevented
participants from forgetting their current goal.
Procedure. Participants navigated in two distinct virtual towns.
Before beginning the test environments, participants completed two different
practice tasks. In the first task, participants delivered four passengers, one
to each of four stores, in an open-field, practice environment. This practice
environment was a 3 x 3 grid with four stores, one in each corner block.
These stores were not used in the main task. The other blocks were covered
with grass, which restricted movement to the paved areas without obstructing
the participants' view. Navigating this practice environment familiarized
participants with the controls of the taxi and with the method for picking up
and delivering passengers.
In the second practice task, participants viewed the images of all six
storefronts that would be encountered later in both towns. Below each image,
the name of the store was displayed; these names were later used by passengers
to indicate where they wanted to go. Participants looked at each picture and
read its name aloud. The list was presented once in a random order and read at
a self-paced rate. Then, the list was presented 10 times, each time in a new
random order, at 2 sec per storefront and a 1 sec interstimulus interval. This
practice task was designed to familiarize participants with the appearance of
the stores before entering the towns. This task was repeated, with a new
random presentation order, before navigating each environment.
Two towns were generated automatically for each participant, randomizing
the sets of stores and buildings used, and their locations. To complete each
environment, participants delivered 21 passengers, seven to each of the three
stores. Before each test environment, participants repeated the second
practice task (viewing and naming store fronts), but only performed the
practice environment once, at the beginning of testing. Participants performed
21 deliveries in the first town, 21 deliveries in the second town, and,
finally, were returned to the first town for 21 new randomized deliveries.
We recorded time-stamps for all key presses and stimulus presentation
times, allowing us to recreate the sequence of events within the testing
session.
Measuring learning
Learning was assessed by computing excess path length as a function of
number of deliveries within a novel town. Excess path length was defined as
the traveled path length minus the actual distance from start to finish, where
the actual distance is approximated as the city-block distance
(
X + Y), and should decrease as participants
find more efficient routes to goal locations.
Detecting oscillatory episodes
We used the oscillatory episode detection technique developed in previous
work (Caplan et al., 2001).
This method enables us to identify epochs of iEEG signal that exhibit
high-power rhythmic activity at a particular frequency, lasting a few cycles,
while excluding the estimated noise spectrum. An oscillatory episode at a
particular frequency, f * is defined as an epoch longer than a
duration threshold, DT (in number of cycles), during which
power at frequency f * exceeded a power threshold,
PT. The two threshold parameters were chosen as follows:
(1) We wavelet transformed the raw traces [Morlet wavelet, window = six cycles
(Grossmann and Morlet, 1985)]
at 24 logarithmically spaced frequencies in the range of 1–54 Hz. This
gave us wavelet power as a function of time at each frequency of interest. (2)
We selected PT separately for each frequency at each
recording site. We assumed that the background spectrum was "colored
noise" with the form Af-. We
estimated this background spectrum by fitting the observed spectrum for each
electrode by computing a linear regression in log–log units. Because the
wavelet power values are expected to be distributed like 
2(2)
(Percival and Walden, 1993),
the estimated background power at frequency f * would be the mean of
that 2(2) distribution. We extrapolated to the 95th percentile
of the fit distribution, and this was used as the threshold,
PT(f *) function, excluding 95% of the
estimated background signal. (3) DT at frequency
f * was set to three cycles (i.e., 31 f *) to eliminate
artifacts and physiological signatures that were nonrhythmic. Both threshold
values (95th percentile power threshold and three cycle duration threshold)
were equal to those used in previous work, in which we also found the
qualitative nature of our results to be relatively insensitive to the precise
choice of thresholds (Caplan et al.,
2001). Finally, Pepisode(f), or
percentage of time in episodes, was defined as the total amount of a time
segment during which episodes occurred at frequency f divided by the
total time in the segment of interest.
In subsequent analyses, we considered the 2–45 Hz range, excluding
the frequencies at the ends of the spectrum. This was done to keep clear of
the bandpass filtering of the amplifiers at the low-frequency end and to avoid
contamination by the line noise artifact at the high end (60 Hz for Boston
recordings; 50 Hz for Freiburg recordings).
To determine whether the sets of electrodes/frequencies in two analyses
were similar or different, we correlated z-transformed test
statistics (z-transformed Mann–Whitney U or t
values) across electrodes and frequencies showing at least one effect to a
liberal significance criterion. The significance criterion was selected to fix
the type I error level across participants and to yield enough data to compute
a reliable correlation. Therefore, the p value threshold was set to
give a type I error of 0.5 comparison. If the correlation is significantly
positive, this indicates that the two effects show the same
topographic/spectral characteristics. If the correlation is significantly
negative, this indicates that the two effects are anticorrelated or are
significant at distinct sites and frequencies. If the correlation is
nonsignificant, then we fail to reject the hypothesis that the two effects are
independent.
Mapping the rhythm
To induce resting posterior activity, a computer-driven program
played the recorded instructions to the participants to close their eyes for 5
sec and then open them for 5 sec, alternately for five repetitions.
Power at each frequency was computed using the same set of wavelets used in
the task analyses. At each frequency, the sets of power values during
eyes-closed periods and eyes-open periods were log-transformed and then
compared using a two-tailed t test, accounting for the reduced
degrees of freedom because of overlapping wavelets
(Plett, 2000). Oscillatory
episode detection was not used because that would yield only 5
Pepisode(f) values for each condition, too weak
to perform a reasonable t test.
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Results
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Oscillations related to virtual movement
We asked whether any sites exhibited significantly heightened levels of
oscillations during navigation (moving forward, turning left, and
turning right) relative to the reading control phase (p < 0.00001;
two-tailed Mann–Whitney U test). The total time analyzed during
reading, moving while searching, and moving while goal-seeking was 535
± 23, 2072 ± 101, and 1060 ± 76 sec, respectively (mean
± SEM across participants). The average time spent continuously in
these three types of behavior was 2.00 ± 0.00, 2.02 ± 1.16, and
1.92 ± 0.15 sec, respectively. Indeed, 276 of the 584 sites had
significant increases in Pepisode(f) in at least
one sampled frequency within the band during searching. During
goal-seeking, 338 sites exhibited movement-related . In contrast, far
fewer showed the reverse effect (21 sites for searching vs reading; 19 sites
for goal-seeking vs reading). This confirmed that oscillations were
more prevalent during virtual movement than during a non-navigation control.
However, these oscillations could have been related to being in the virtual
town, or to virtual movement in particular, as predicted by the sensorimotor
integration hypothesis.
To answer this question, we examined whether oscillations were more
prevalent while the participant was moving in the virtual town relative to
remaining "stationary." Periods of stillness, like periods of
virtual movement, were under the participant's voluntary control. The total
time analyzed during stillness while searching and goal-seeking was 277
± 82 and 187 ± 55 sec, respectively (mean ± SEM). Mean
time spent continuously stationary during searching and goal-seeking was 0.74
± 0.08 and 0.87 ± 0.10 sec, respectively.
Figure 3a shows a
sample raw trace suggesting that oscillations are related to virtual
movement. The average wavelet power spectrum
(Fig. 3b) at this
example site does not show an obvious peak in the band but does show
differences between epochs of moving versus stillness that would be consistent
with movement-related . When we apply the oscillatory episode detection
algorithm and plot the percentage of time occupied by oscillatory episodes,
Pepisode(f) as a function of behavior
(Fig. 3c), the effect
is much more apparent. The colored noise spectrum [P(f) =
Af-], in which lower frequencies tend to
have higher amplitudes than higher frequencies, has been accounted for,
putting all frequencies on an equal footing. In addition to removing the bias
across frequency, clear peaks can now be seen in the band (4–8
Hz) and band (13–30 Hz). This suggests that the power spectrum
was sensitive to signal other than contiguous runs of heightened power at a
given frequency (e.g., nonoscillatory evoked potentials, spikes, and sharp
waves, etc.). By removing a large portion of this nonoscillatory signal, one
can analyze the oscillations more directly. Third, the
Pepisode(f) plots show a clear difference between
moving and stillness at this site. We next tested for this pattern at all
recording sites.
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Figure 3. a, A sample intracranial EEG trace. Letters indicate the
participant's keystrokes (F, up arrow; L, left arrow;|, released a key).
b, Wavelet power spectrum averaged across searching paths while
virtually moving (solid plot) or standing still (dashed plot). c,
Pepisode(f) for searching paths while moving and
standing still. Error bars denote SEM. All three plots are taken from
participant 2, left inferior temporal gyrus [Talairach coordinates
(left–right, anterior–posterior, inferior–superior) =-22,
40, -16 mm].
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We computed Pepisode(f) for each searching and
goal-seeking path (from starting point to destination) across all continuous
epochs of a particular type of
behaviora
(moving forward, turning left, turning right, or standing still), including
only epochs of duration 0.5–30 sec, and compared the
Pepisode(f) for movement versus standing
still.
Many recording sites showed more oscillations related to virtual
movement, during both searching (Fig.
4a; 278 sites) and goal-seeking
(Fig. 4b; 87 sites)
phases (two-tailed Mann–Whitney U test; p <
0.0001). The effect is widespread, including bilaterally in the peri-Rolandic
region and the temporal lobes. In the case of goal-seeking, the effect is
asymmetric, with more movement-related appearing in the right
hemisphere, and notably less movement-related in dorsal regions. The
movement-related effect in both searching and goal-seeking is
strikingly unidirectional, with only one site showing more
oscillations during standing still.
In addition to this effect being present in the band, it also
showed significance at other frequencies, especially in the band.
Figure 5 shows the locations of
sites showing movement-related oscillations during searching
(a; 290 sites) and goal-seeking (b; 123 sites), as well as
the Pepisode(f) plot for an example electrode
showing the effect in the band (c). Movement-related
oscillations are found in widespread regions, but the effect is strongest in
the peri-Rolandic region, supporting the notion that this signal related to
the Rolandic motor µ rhythm (Jasper and
Andrews, 1938; Niedermeyer,
1999; Klopp et al.,
2001). Its presence at ventral sites, however, suggests that
movement-related activity may not be exclusively related to Rolandic
µ rhythm.
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Figure 5. Movement-related oscillations during searching (a) and
goal-seeking (b). Dark-filled shapes denote sites showing more
oscillations in the 13–30 Hz band during movement than when standing
still (two-tailed Mann–Whitney U test; p < 0.0001).
Light-filled shapes denote sites showing the opposite pattern. Unfilled shapes
denote sites that did not show a significant effect. Estimated type I error
rate = 0.29 sites. c, Pepisode(f) as a function
of behavior during goal-seeking for an example site showing the effect
[participant 6, Talairach coordinates (left–right,
anterior–posterior, inferior–superior) = 52, -21, 52 mm]. Error
bars denote SEM.
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Figure 6 shows the same for
the 
band (searching, 54 sites; goal-seeking, 20 sites). The
activity dominates neither the average spectrum nor the
Pepisode(f) plots. Although the overall
percentage of task time occupied by oscillations is small, these scarce
oscillations nonetheless are significantly modulated by movement versus
standing still (Fig. 6), which
is reminiscent of the low levels of activity found during virtual maze
navigation that nonetheless correlated significantly with a measure of degree
of learning [Caplan et al.
(2001), their Fig. 12].
Movement-related appeared primarily in the peri-Rolandic region. This
may be a higher-frequency component of the Rolandic µ rhythm
(Jasper and Andrews, 1938;
Niedermeyer, 1999). However,
its presence at ventral temporal and frontal sites suggests that
oscillations could be involved in the memory-related aspects of the
task, perhaps in activating object representations (Tallon-Baudry et al.,
1996,
1999;
Rodriguez et al., 1999;
Howard et al., 2003). At all
frequencies, it was rare for a site to show more oscillations during standing
still than during virtual movement.
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Figure 6. Movement-related oscillations during searching (a) and
goal-seeking (b). Dark-filled shapes denote sites showing more
oscillations in the 31–45 Hz band during movement than when standing
still (two-tailed Mann–Whitney U test; p < 0.0001).
Light-filled shapes denote sites showing the opposite pattern. Unfilled shapes
denote sites that did not show a significant effect. Estimated type I error
rate = 0.18 sites. c, Pepisode(f) as a function
of behavior during searching for an example site showing the effect
[participant 1, Talairach coordinates (left–right,
anterior–posterior, inferior–superior) =-38.2, 52.9, -20.3 mm].
Error bars denote SEM.
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Comparing movement-related oscillations in searching and
goal-seeking
The previous analyses revealed movement-related oscillation,
supporting the sensorimotor integration hypothesis. A more complete analysis
of the data revealed movement-related oscillations at a broad range of
frequencies, including fast (>30 Hz) oscillations. Do these effects
found during searching and goal-seeking reflect the same underlying phenomenon
or distinct oscillating networks? Although the effect is stronger during
searching, as evidenced by the greater number of sites showing the effect, it
is still possible that oscillations at those frequencies and sites that
correlated with movement during searching were generally the same as those
that correlated with movement during goal-seeking, but with slightly different
significance values.
First, we tested whether searching and goal-seeking represented different
modes of learning. We tested whether the 12 participants could find shorter
paths with increasing experience, reducing their excess path length (see
Materials and Methods). During goal-seeking, excess path length decreased
significantly from the first set of three deliveries to the last nine
deliveries within town B and the first session in town A
(t(11) = 4.51; p < 0.001), indicating that
participants learned more efficient paths with experience in the town. In
contrast, during searching, participants' excess path length did not change
significantly from the first three searches to that last nine
(t(11) = -0.41; NS). This pattern did not change when we
used Euclidean distance instead of city-block distance as the measure of
actual distance (goalseeking: t(11) = 4.66, p
< 0.001; searching: t(11) = -0.42; NS). This suggests
that our participants learned a spatial representation of the layouts of the
towns and were able to use this information to find more efficient paths in
goal-seeking but not in searching. Do these different types of wayfinding
recruit different patterns of oscillations?
It is important to distinguish two hypotheses. The first hypothesis is that
these oscillations are simply involved in virtual movement or key pressing.
According to this hypothesis, the effect sizes should covary significantly
positively across brain regions and frequencies between the searching and
goal-seeking. The second hypothesis is that these oscillations are involved in
spatial learning during exploration on the one hand (primarily during
searching) and navigational planning on the other (primarily during
goal-seeking). Thus, different sites and frequencies should be implicated in
searching versus goal-seeking. In this case, the effect sizes should be
independent or even anticorrelated between the searching and goal-seeking
behavior.
We hypothesized that oscillations should be modulated by type of
search, if they are involved in both spatial learning and navigational
planning. For comparison, we hypothesized that oscillations in the band
are involved in simple motor planning of the participants' key presses
(Niedermeyer, 1999;
Klopp et al., 2001). In this
case, although the overall amount of motor planning may vary between searching
and goal-seeking, we expect that the topographic and spectral characteristics
should be the same, supporting the first hypothesis in the band.
Visually comparing topography of movement-related in searching
(Fig. 4a) and in
goal-seeking (Fig. 4b)
suggests that the two patterns differ substantially, especially in the
peri-Rolandic region. We wanted to test for this difference quantitatively.
Caplan et al. (2000)
demonstrated that when analyzing significant thresholded data, a thresholding
artifact could produce apparent differences in the patterns. They found that
with a conservative significance threshold, a standard 2 test
found the patterns of oscillations to be significantly different
between study and test trials of a maze navigation task, but when thresholding
more liberally, the patterns overlapped considerably. A conservative threshold
reveals only the very most significant values, so which specific statistical
comparisons pass a conservative threshold may be primarily determined by
noise. To avoid such a thresholding artifact, we noted that if the patterns
are the same but experience some noise, then the statistical tests will be
highly correlated across recording sites and frequencies; if the test for
movement-related at a given frequency and electrode is strong during
searching, we predict that it should also be strong during goal-seeking (at
that same frequency and electrode). If the patterns are distinct, then knowing
the strength of the effect at an electrode/frequency during searching should
give no information about the significance during goal-seeking. In this case
of independence, the correlation between the two patterns should be
nonsignificant.
Table 2 shows the
correlation values that test these hypotheses. As predicted, the set of
sites/frequencies showing movement-related oscillations during
searching was highly similar to the set of sites/frequencies showing the
effect in goal-seeking, for 10 of the 12 participants. Collapsing across all
participants confirmed this result; the correlation is positive and highly
significant: r(1428) = 0.39; p < 0.0001.
However, in the band, we obtained mixed results. For participant 1,
the two effects were anticorrelated. Because the movement-related was
predominantly unidirectional for both searching and goal-seeking, this finding
indicates that movement-related oscillations appeared at distinct
sites/frequencies in searching rather than in goal-seeking. For participants
3, 5, 7, and 9, the effects covaried significantly in the positive direction,
suggesting that the same sites/frequencies were showing movement-related
oscillations during searching as during goal-seeking. For participants 2, 4,
8, 11, and 12 the effects failed to show a significant correlation, despite
having substantial data, suggesting that for these participants, the effects
were independent. Participant 6 did not have enough data to compute a reliable
correlation. This variability could be partly attributable to differences in
brain region sampling and partly caused by differences in the participants'
strategies. Collapsing across all sites from all participants, the correlation
in the band is quite small: r(1504) = 0.07;
p < 0.01. This suggests that at least a good portion of human
cortical oscillations during virtual movement are not simply related
to movement per se but may be present during specific aspects of
search/seeking behavior, depending on brain region and frequency.
Although the topographic/spectral pattern characterizing movement-related
during searching is distinct from the pattern obtained during
goal-seeking, some sites do show both effects within the band. We
asked whether oscillations at these sites were more prevalent during searching
or goal-seeking. If oscillations are related to learning during
exploratory behavior, then we would expect more of the time during
searching; if is related to retrieving information from a learned
spatial representation or planning paths to known locations, then we would
expect more of the time during goal-seeking.
We confined this analysis to sites showing movement-related oscillations at
a given frequency during both searching and goalseeking to a liberal
significance criterion (p = 0.1); 443 sites met this initial
criterion in at least one sampled frequency. We then tested whether
sites showed significant differences in
Pepisode(f) between searching and goal-seeking
during epochs of virtual movement (p < 0.01).
Figure 7 shows that a small
subset of sites that showed movement-related (4–8 Hz) in both
searching and goal-seeking showed a significant difference in
Pepisode(f) between the two types of search.
Thirty-eight sites showed more during searching than goal-seeking; six
showed the reverse effect. However, of the 443 sites showing movement-related
in both types of search, 268 failed to show a significant difference
in levels of oscillations between searching and goal-seeking even when
we used a very liberal significance criterion (p = 0.1). In summary,
movement-related oscillations are either only found in searching or
goal-seeking or tend to show comparable levels of during each type of
search. However, when a difference is found, there tends to be more
during searching than during goal-seeking.
Ruling out posterior resting
To determine whether our findings could have resulted from
modulations of the resting posterior rhythm
(Berger, 1929;
Niedermeyer, 1999), we
separately mapped the rhythm (see Materials and Methods). The
dark-filled sites in Figure 8
showed the resting rhythm pattern (more oscillations during
eyes-closed periods than eyes-open periods; p < 0.001). Many of
these are in the posterior regions, also consistent with resting posterior
. Other sites show the opposite pattern (light-filled sites). These
tend to be more anterior and ventral, consistent with the classic
scalp-recorded pattern.
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Figure 8. Resting posterior rhythm. a, Sites that exhibit more
oscillations (8–12 Hz band) during eyes-closed periods
(dark-filled sites) or during eyes-open periods (light-filled sites;
two-tailed t test; p < 0.001). Estimated type I error
rate = 0.12 sites. b, Example of a site showing this effect
[participant 2, left medial temporal gyrus, Talairach coordinates
(left–right, anterior–posterior, inferior–superior) =-62,
-45, -2 mm]. Note a trend toward the opposite effect in the band, as
commonly found in scalp recordings
(Klimesch et al., 1993). Error
bars denote SEM, with degrees of freedom corrected for the autocorrelation of
the continuous wavelet transform. Power was used as the dependent measure
instead of Pepisode(f), because there would have
been too few observations to perform a statistically robust comparison.
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In relation to our task analyses, first note the bipolar nature of the
results. shows primarily one effect in posterior sites and primarily
the opposite effect elsewhere. If movement-related was a modulation of
resting posterior , it should also show this bipolar pattern. In all
comparisons, however, the task dependency was almost exclusively in one
direction (either positive or negative).
Next, participants 1, 3, and 11 had no sites showing significantly more
oscillations during eyes-closed periods than eyes-open periods. This
is likely attributable to the fact that we were not recording from regions
that generate resting posterior . For these participants, it is
implausible that we are mistaking modulations in resting posterior for
movement-related .
For the remaining nine participants, we followed up by correlating the sets
of sites and frequencies (within a more inclusive, 6–13 Hz band; the
results do not change when using a narrower, 8–12 Hz band) in the
analysis with the pattern obtained in the task analyses. Here, sites
were included in the analysis if they showed the movement-related oscillations
and more oscillations during eyes-closed periods than during eyes-open
periods. None of the nine participants showed a significant positive
correlation (p < 0.05) between the pattern and
movement-related oscillations during searching or goal-seeking
(Fig. 4), with the exception of
participant 10, who showed a significant positive correlation between
movement-related oscillations during searching with the pattern of resting
posterior (r(38) = 0.48; p < 0.005).
Therefore, we can safely rule out as an alternate account of the bulk
of our task-dependent findings.
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Discussion
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|---|
Virtual navigation induced cortical activity. oscillations
appeared more of the time during virtual movement than during interleaved
periods of voluntary stillness, consistent with the MEG findings of de
Araújo et al. (2002).
These effects were found during two types of wayfinding: exploratory
searching, in which the target locations were unknown, and goal-seeking to
fixed targets, allowing participants to make use of learned knowledge about
the layout of the town. The topographic and spectral characteristics of
movement-related , however, differed between searching and
goal-seeking, especially in peri-Rolandic regions. This suggests that the
network of brain regions exhibiting oscillations depends on which
regions are involved in a particular kind of behavior and, in particular,
which regions are required for sensorimotor integration. The pattern of
movement-related found during goal-seeking may primarily act to
coordinate brain regions during wayfinding. The pattern found during searching
may be primarily involved in learning that spatial layout during exploratory
behavior. Movement-related and oscillations were also observed.
The activity, which had topographic and spectral characteristics during
searching that were very similar to those seen during goal-seeking behavior,
may be involved in motor planning and might be related to the Rolandic µ
rhythm (Jasper and Andrews,
1938; Niedermeyer,
1999; Klopp et al.,
2001). This buttresses the finding that oscillations in the
band are modulated by searching versus goal-seeking.
The most well characterized human EEG rhythm is the resting posterior
rhythm, typically 8–12 Hz
(Berger, 1929;
Niedermeyer, 1999); this
rhythm is routinely observed in the raw, scalprecorded EEG during light sleep
and resting wakefulness. It originates in the posterior portion of the
neocortex and tends to be anticorrelated with power
(Klimesch, 1999). Could our
findings be modulations of the resting posterior rhythm, especially
given that our patient population may have abnormal frequencies? Our
task-related oscillations might reflect the degree of drowsiness or
inattentiveness (Gevins et al.,
1997) rather than cognitive function. We ruled out this
possibility by directly comparing our virtual navigation findings with the
empirically obtained pattern of and found that it had different
topographic and spectral properties than movement-related .
Cortical in rodents
Unlike most rodent studies that have focused on hippocampal , we
recorded task-dependent in the human neocortex. Physiologically,
cortical in rodents may be closely related to hippocampal .
Lukatch and MacIver (1997)
analyzed carbachol-induced in cortical slices, finding that these
oscillations exhibited similar responses to various drugs, including
bicuculline, atropine, CNQX, and APV, as has been shown in hippocampal slices.
Bland and Colom (1993) reviewed
the studies of hippocampal, septal, cingulate, and entorhinal
oscillations and suggested that the limbic cortex contains multiple
synchronizing systems. Manns et al.
(2002) provided evidence that
different populations of medial septal cells control in different
cortical regions. These dynamics could provide a means for lower brain regions
to selectively induce in patterns of hippocampus and various cortical
regions, depending on task demands. This selective control could in turn
produce coherence among oscillations in multiple cortical regions
(Manns et al., 2002). However,
there is no a priori reason to assume that navigational strategies
are the same in the human as in the rodent brain.
Human cortical may represent a local physiological state conducive
to local learning for synapses in the vicinity of the field oscillation.
Alternatively, cortical may act to synchronize activity between the
hippocampus and cortical regions or between pairs of cortical regions. In the
following sections, we consider the evidence for each function of cortical
in turn, and then discuss how they may be brought together in our
task.
Virtual versus real navigation
We chose virtual navigation rather than real navigation for practical
considerations: its ease of use in a hospital setting, control over the visual
properties of the task, and obtaining precise timing of the participants'
responses. Virtual reality has been used extensively in recent years to study
human spatial behavior (Klatzky et al.,
1998; Redlick et al.,
2001; Warren et al.,
2001; Lambrey et al.,
2002) (E. L. Newman, J. B. Caplan, M. P. Kirschen, R. Sekuler, and
M. J. Kahana, unpublished observations) and physiology
(Kahana et al., 1999;
Burgess et al., 2002;
de Araújo et al., 2002;
Pine et al., 2002). The
principal difference between real and virtual navigation is that in the
latter, participants lack vestibular cues. Human participants can path
integrate using visual cues alone (Redlick
et al., 2001; Warren et al.,
2001), and Witmer et al.
(1996) found that participants
could transfer route knowledge they learned in a virtual setting to its
real-world equivalent. Thus, what is learned may be similar with or without
vestibular input. Lambrey et al.
(2002) found that when
vestibular and visual input conflict, participants appear to learn both
conflicting sets of information, favoring the visual source when visual cues
are available and favoring the vestibular when blindfolded, highlighting the
flexibility of navigation behavior. However, Klatzky et al.
(1998) found that the
participants made fewer turning errors when they walked along a path than when
they experienced optic flow in a virtual setting. Thus, vestibular input
certainly appears to enhance spatial updating.
The physiological place representation appears to be intact with or without
vestibular input. Matsumura et al.
(1999) found place cells in
monkey hippocampus while monkeys were performing real or virtual movement.
Furthermore, Dees et al.
(2001) found comparable levels
of rat hippocampal during navigation with or without vestibular cues.
It is quite possible that, with vestibular input, human oscillations
would show similar task dependencies in additional, vestibular sensory regions
in the neocortex. Thus, the general function of oscillations may be
preserved across sensory modalities, whereas the localization of this activity
could vary with modality. A real-movement condition would be useful in future
designs to address this question directly.
and coordinated activity of brain regions
One class of theories implicates oscillations in coordinating the
activity of multiple brain regions. It has been proposed in two papers
(Bland, 1986;
Bland and Oddie, 2001) that
rodent oscillations coordinate sensory and motor brain regions during
types of behavior that require the animal to update its motor plan on the
basis of incoming sensory information. This hypothesis would predict more
during virtual movement than periods of stillness, as reported here
and in a MEG study by de Araújo et al.
(2002). It would also account
for the finding of during wayfinding to known targets, as found during
goal-seeking.
Other researchers have proposed that information is encoded in the phase
within an oscillation (McLardy,
1959; Adey et al.,
1962; Landfield,
1977; Jensen et al.,
1996) (D. S. Rizzuto, J. R. Madsen, E. Bromfield, A.
Schulze-Bonhage, D. Seelig, R. Aschenbrenner-Scheibe, and Kahana, unpublished
observations). Jensen (2001)
also demonstrated that oscillations could act like carrier waves for
information transfer between any pair of regions, via synchronized
oscillations at the same frequency. These approaches are all consistent with
the notion that oscillations are critical for coordinating activity
during wayfinding, as well as during exploratory (virtual) movement. Cortical
sites exhibiting could be acting simultaneously, but not interacting,
in which case might represent a specific kind of physiologically
"active" state. Alternatively, sites exhibiting could be
interacting with the hippocampus via oscillations at distinct, rather than the
same, frequencies (for example, accessing a hippocampal "cognitive
map") (O'Keefe and Nadel,
1978) or interacting with other cortical (or hippocampal) regions
via oscillations at the same frequency
(Bland and Oddie, 2001;
Jensen, 2001).
and learning
In contrast to theories that implicate in coordinating activity in
disparate brain regions, another class of theories implicates in local
synaptic plasticity and learning at the behavioral level.
oscillations have been linked to the induction of long-term
potentiation (LTP) and long-term depotentiation, and stimulation that mimics
the rhythm is effective in inducing LTP (Larson and Lynch,
1986,
1989;
Larson et al., 1986).
Furthermore, the phase of the rhythm determines whether synapses will
be potentiated or depotentiated (Pavlides
et al., 1988; Huerta and
Lisman, 1995; Hölscher et
al., 1997; Orr et al.,
2001). Manipulations that suppress oscillations tend to
impair learning (Landfield et al.,
1972; Landfield,
1977; Winson,
1978; Givens and Olton,
1990; Mizumori et al.,
1990). Several drugs known to improve memory function in rats were
shown to enhance amplitude (Kinney
et al., 1999). Finally, both power and frequency show a
systematic shift during the course of conditioning in the cat
(Grastyán et al., 1959;
Adey et al., 1960), and
frequency is predictive of spatial learning rate in the rat
(Berry and Thompson, 1978),
suggesting that amplitude and frequency as well as phase are critical for
learning.
More directly linking naturally occurring and learning, Seager et
al. (2002) measured the rate
of conditioning in rabbits while recording from their hippocampus. They
presented conditionings pairs when power was either high or low.
Animals in the high- group took fewer trials to learn the response than
their yoked controls, whereas those in the low- group required more
trials than their controls. This suggests that naturally varying is
critical for learning, perhaps because synaptic plasticity is modulated by the
phase of naturally varying (Orr et
al., 2001).
Our previous finding of increased during navigation of more complex
mazes (Kahana et al., 1999;
Caplan et al., 2001) and our
present findings of increased during movement and searching are
consistent with being involved in spatial learning.
Learning coordinated brain activity
One possibility is that may coordinate brain activity in different
regions. In contrast, may be a physiological state underlying learning
at the local network level. These two classes of theories may be linked. In
particular, the type of learning that oscillations facilitate may
necessarily involve learning coordinated interactions among different brain
regions. For instance, Buzsáki
(1996) proposed that learning
is consolidated by transferring information from the hippocampus to the
neocortex, mediated by cortical oscillations.
Bland and Oddie (2001)
pointed out that the paradigms used to implicate in memory involved
learning an association between a motor response and a sensory stimulus. Some
models of human navigation propose that the learned representation is
comprised of associations between local views (sensory information) and
movements (motor planning) (Schölkopf
and Mallot, 1995; Mallot et
al., 1997). Thus, the models learn to do sensorimotor integration.
The notion that accompanies learning of associations between sensory
stimuli and motor behavior could account for previous findings of
oscillations in the human cortex during virtual maze learning
(Kahana et al., 1999;
Caplan et al., 2001).
Conclusions
We propose that the patterns of cortical found during virtual taxi
driving reflect two different, but related, functions. First, during
goal-seeking, underlies updating motor plans in response to incoming
sensory information during wayfinding. Second, during exploratory searching
behavior, facilitates the encoding of coordinated activity in multiple
brain regions and that this is the information that comprises the
participant's cognitive map.
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Footnotes
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Received Oct. 28, 2002;
revised Feb. 21, 2003;
accepted Mar. 5, 2003.
This work was supported by National Institute of Mental Health Grants
MH-12860 and MH-61975. We thank Arne Ekstrom, Dan Rizzuto, Marc Howard, Kelly
Addis, Per Sederberg, and Dan Kimball for helpful discussions and feedback on
this manuscript. We are grateful to David Seelig and Emily Dolan for data
collection and processing.
Correspondence should be addressed to Dr. Jeremy B. Caplan, The Rotman
Research Institute–Baycrest Centre for Geriatric Care, Toronto, Ontario,
Canada M6A 2E1. E-mail:
jcaplan{at}rotman-baycrest.on.ca.
Copyright © 2003 Society for Neuroscience
0270-6474/03/234726-11$15.00/0
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References
|
|---|
Adey WR, Dunlop CW, Hendrix CE (1960) Hippocampal slow
waves: distribution and phase relationships in the course of approach
learning. Arch Neurol 3:
96/74–112/90.
Adey WR, Walter DO, Lindsley DF (1962) Subthalamic
lesions: effects on learned behavior and correlated hippocampal and
subcortical slow-wave activity. Arch Neurol
6: 34–47.
Berger H (1929) Über das Elektroenkephalogramm
des Menschen. Archiv für Psychiatrie und Nervenkrank
87: 527–570.
Berry SD, Thompson RF (1978) Prediction of learning
rate from the hippocampal electroencephalogram. Science
200:
1298–1300.[Abstract/Free Full Text]
Bland BH (1986) The physiology and pharmacology of
hippocampal formation theta rhythms. Prog Neurobiol
26: 1–54.[ISI][Medline]
Bland BH, Colom LV (1993) Extrinsic and intrinsic
properties underlying oscillation and synchrony in limbic cortex. Prog
Neurobiol 41:
157–208.[ISI][Medline]
Bland BH, Oddie SD (2001) theta band oscillation and
synchrony in the hippocampal formation and associated structures: the case for
its role in sensorimotor integration. Behav Brain Res
127:
119–136.[ISI][Medline]
Burgess N, Maguire EA, O'Keefe J (2002) The human
hippocampus and spatial and episodic memory. Neuron
35: 625–641.[ISI][Medline]
Buzsáki G (1996) The hippocampo-neocortical
dialogue. Cereb Cortex 6:
81–92.[Abstract/Free Full Text]
Caplan JB, Kahana MJ, Sekuler R, Kirschen MP, Madsen JR
(2000) Task dependence of human theta: the case for multiple
cognitive functions. Neurocomputing
32–33:
659–665.
Caplan JB, Kahana MJ, Raghavachari S, Madsen JR (2001)
Distinct patterns of brain oscillations underlie two basic parameters of human
maze learning. J Neurophysiol 86:
368–380.[Abstract/Free Full Text]
de Araújo DB, Baffa O, Wakai RT (2002) theta
oscillations and human navigation: a magnetoencephalography study. J
Cognit Neurosci 14:
70–78.[Abstract/Free Full Text]
Dees JA, Terrazas A, Bohne KM, Krause M, McNaughton BL, Barnes CA
(2001) Presence of hippocampal theta during navigation without
actual movement. Soc Neurosci Abstr 27:
643.13.
Gavrilov VV, Wiener SI, Berthoz A (1995) Enhanced
hippocampal theta E
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Abstract
Introduction
