1Volen Center for Complex Systems, Brandeis University, Waltham 02454; 2Department of Neurosurgery, Children's Hospital; and 3Department of Surgery, Harvard Medical School, Boston, Massachusetts 02115
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ABSTRACT |
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Caplan, Jeremy B., Joseph R. Madsen, Sridhar Raghavachari, and Michael J. Kahana. Distinct Patterns of Brain Oscillations Underlie Two Basic Parameters of Human Maze Learning. J. Neurophysiol. 86: 368-380, 2001. We examine how oscillations in the intracranial electroencephalogram (iEEG) relate to human maze learning. Theta- band activity (4-12 Hz in rodents; 4-8 Hz in humans) plays a significant role in memory function in rodents and in humans. Recording intracranially in humans, we have reported task-related, theta-band rhythmic activity in the raw trace during virtual maze learning and during a nonspatial working memory task. Here we analyze oscillations during virtual maze learning across a much broader range of frequencies and analyze their relationship to two task variables relevant to learning. We describe a new algorithm for detecting oscillatory episodes that takes advantage of the high signal-to-noise ratio and high temporal resolution of the iEEG. Accounting for the background power spectrum of the iEEG, the algorithm allows us to directly compare levels of oscillatory activity across frequencies within the 2- to 45-Hz band. We report that while episodes of oscillatory activity are found at various frequencies, most of the rhythmic activity during virtual maze learning occurs within the theta band. Theta oscillations are more prevalent when the task is made more difficult (manipulation of maze length). However, these oscillations do not tend to covary significantly with decision time, a good index of encoding and retrieval operations. In contrast, lower- and higher-frequency oscillations do covary with this variable. These results suggest that while human cortically recorded theta might play a role in encoding, the overall levels of theta oscillations tell us little about the immediate demands on encoding or retrieval. Finally, different patterns of oscillations may reflect distinct underlying aspects of memory function.
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INTRODUCTION |
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Oscillatory activity is found in
the electroencephalogram (EEG) of many mammals while they perform
numerous cognitive tasks. Rhythmic activity at a variety of frequencies
has been studied extensively, and certain oscillations may be important
for memory function. For example, oscillations in the theta band appear
in the raw, unfiltered signal from field potential recordings
(Bland 1986; Jung and Kornmüller
1938
; Vanderwolf 1969
). Researchers have linked
theta oscillations in rodents and properties of theta to moving within
an environment (Vanderwolf 1969
), orienting
(Gavrilov et al. 1995
), conditioning (Adey et al.
1960
, 1962
; Elazar and Adey 1967a
,b
;
Grastyán et al. 1959
, 1966
), memory performance (Givens and Olton 1990
; Kinney et al.
1999
; Landfield 1977
; Landfield et al.
1972
; Mizumori et al. 1990
; Winson
1978
), speed of learning (Berry and Thompson
1978
), and degree of learning of an environment (Pan and
McNaughton 1997
). The phase within the theta cycle may also be
important for memory function; in addition to findings showing motor
behavior phase-locked to the theta rhythm (Buño and
Velluti 1977
; Forbes and Macrides 1984
;
Komisaruk 1970
; Macrides 1975
),
O'Keefe and Recce (1993)
showed that hippocampal place cells fired in a consistent phase relationship to the theta rhythm and
that this phase varies systematically with the animal's path through
the environment. Givens (1996)
demonstrated reset of the phase of the theta rhythm with stimulus presentation during a rodent
working memory task. These findings suggest that both the presence and
properties of the theta rhythm are intimately involved in both spatial
and nonspatial memory in rodents.
While most extensively studied in the hippocampus, theta oscillations
have also been observed in the cingulate gyrus (Leung and Borst
1987), hypothalamus (Slawinska and Kasicki
1995
), superior colliculus (Routtenberg and Taub
1973
), entorhinal cortex (Blaszcyk et al. 1996
),
and neocortex (Biedenbach 1966
; Nakamura et al. 1992
; Silva et al. 1991
). Thus theta-band
oscillatory activity is a phenomenon that has relevance beyond
hippocampal function.
Theta oscillations also influence long-term potentiation (LTP)
induction, a proposed mechanism of synaptic plasticity, both in vitro
and in vivo (Hölscher et al. 1997; Huerta
and Lisman 1993
; Larson and Lynch 1986
, 1989
;
Larson et al. 1986
; Pavlides et al.
1988
), suggesting that theta acts as a windowing mechanism for
synaptic plasticity. Taken with the behavioral results discussed in the
preceding text, these findings support the notion that theta could be
involved in specific modes of synaptic plasticity that have important
implications for learning and memory at the behavioral level.
Theta oscillations have been far less extensively investigated in
primates than they have been in other mammals. Stewart and Fox
(1991) recorded rhythmic theta-band activity from anesthetized monkeys. This finding suggested that theta oscillations might also be
observable in invasive recordings from the human brain; however,
invasive recording from the human brain is only possible in certain
special cases. Human EEG is normally recorded from the scalp; this
signal is noisier and of lower spatial and temporal resolution than
intracranially recorded signal. Consequently, robust, task-related
theta activity in raw, unfiltered scalp EEG traces has not been
reported. Instead, frequency-domain analyses of human scalp EEG have
focused on measures like theta-band power averaged across trials and
have successfully related these measures to memory function in humans
(Burgess and Gruzelier 1997
; Klimesch 1999
; Klimesch et al. 1994
). It should be noted
that these analyses can proceed even when oscillations cannot be seen
in the raw signal and when the average power spectrum does not show a
peak in the theta band. These results suggest that, while task-related
rhythmic activity may not be apparent in unfiltered human scalp EEG
signal, analyses of average EEG power in the theta band may reflect
underlying rhythmic oscillations that are related to memory function.
In addition to quantitatively analyzing average task-related spectral
power, researchers recording human scalp EEG have extensively characterized evoked potentials and their correlation with
memory function (Rugg 1995; Rugg and Allan
2000
). During a task, if rhythmic oscillations vary in phase
across the cortex, phases might only align for brief periods of time
relative to a measurable task variable (e.g., presentation time of a
stimulus). Coupled with the spatial averaging of the skull and scalp,
this could appear as a single (nonrhythmic) EEG deflection. Certain
evoked potentials might even result from momentary phase resetting of
underlying rhythmic activity (Basar-Eroglu et al. 1992
;
Brankack et al. 1996
; Demiralp and Basar
1992
; Intrilligator and Polich 1994
;
Spencer and Polich 1999
; Yordanova and Kolev
1998
). Direct findings of momentary stimulus-evoked phase
resetting (Givens 1996
; Tesche and Karhu
2000
), like those mentioned in the preceding text lend further
credence to this possibility. This kind of argument further highlights
the utility of obtaining invasive recordings from human participants
while they perform memory tasks.
In previous work, we analyzed invasive recordings from epileptic
patients undergoing long-term monitoring for planning of subsequent
resection surgery. In the unfiltered intracranial
electroencephalographic (iEEG) signal, striking rhythmic activity in
the theta band was seen both during a virtual maze learning task
(Caplan et al. 2000; Kahana et al. 1999
)
and during a verbal working memory task (Raghavachari et al.
1999
). Further, these episodes of oscillatory activity covaried
with task variables, suggesting that, like similar rodent theta, they
may be related to cognitive function. This ability to record, in
humans, rhythmic slow-wave activity that is visually similar to
oscillatory activity recorded in rodents during similar tasks provides
a crucial link between studies of human and nonhuman theta-band
activity. These new iEEG findings provided evidence that theta-band
oscillations are involved in human memory function.
Oscillations outside the theta band are also of interest. For instance,
high-frequency oscillations in the gamma range (above 30 Hz) have been
implicated in perception both in animals (Eckhorn et al.
1988; Gray and Singer 1989
; Gray and
Viana Di Prisco 1997
; Neuenschwander and Singer
1996
) and in humans (Demiralp and Basar 1992
;
Gruber et al. 1999
; Keil et al. 1999
;
König et al. 1995
; Miltner et al.
1999
; Tallon-Baudry et al. 1996
-1998
) and in
motor behavior in rodents (Hamada et al. 1998
).
Researchers have also proposed that high- and low-frequency activity
may work together during certain forms of cognition
(Buzsáki 1996
; Csicsvari et al.
1999
; Jensen and Lisman 1998
; Lisman and
Idiart 1995
). We were therefore interested in examining the
relationship of oscillations at many different frequencies with task
variables. We also sought to determine how theta-band oscillations
compare with oscillations at other frequencies during virtual maze
learning. It is of interest to know whether task-related oscillatory
episodes are a uniquely theta-band phenomenon or an EEG signature that
is also present at other frequencies.
As elaborated in the preceding text, theta bears a relationship to
tasks involving movement within an environment as well as to tasks
involving a memory component. Recently, interest in spatial memory and
way-finding has been revived with numerous behavioral and functional
neuroimaging studies in humans (Aguirre et al. 1996;
Berthoz 1997
; Kahana et al. 1999
;
Maguire et al. 1996
-1998
). We were specifically
interested in multiple T-junction mazes. Extensively studied in animals
(Stone and Nyswander 1927
) and in some cases in humans
(Miles 1928
), T-junction mazes are useful for their
simplicity and uniformity. Additionally, a path through a T-junction
maze is a sequence of left and right turns; hence, it could be treated
purely as a symbolic list-learning task. However, the maze paradigm
additionally includes visual cues. The task, then, can be thought of as
a serial learning task embedded within a spatial navigation task. Given
these properties, it is of interest to study the learning of multiple
T-junction mazes in humans. Recent innovations have made it possible to
design virtual reality experiments in which participants navigate and learn a virtual environment (Gillner and Mallot 1998
;
Tlauka and Wilson 1994
) and to detect changes in human
cortical activity during virtual navigation (Maguire et al.
1998
). As in previous work (Kahana et al. 1999
;
Kirschen et al. 2000
), our participants learned to
navigate three-dimensional rendered, virtual, multiple T-junction mazes
(Fig. 2). This task has been shown to involve the processing of spatial
cues and has a significant memory component. It was shown that the
presence of optic flow, a visual cue, helps participants learn
T-junction mazes, even though optic flow in itself contains no
information about the maze path (Kirschen et al. 2000
).
This suggested that the spatial cues interact with memory function in a
complex way. We sought to determine how oscillations at various
frequencies relate to virtual maze learning.
In this paper, we present a new method for detecting episodes of EEG
oscillations. This method enables us to ask how bouts of rhythmic
activity at a given frequency are related to cognitively relevant
variables. This oscillatory episode detection algorithm takes advantage
of the high signal-to-noise ratio and good spatial and temporal
resolution of intracranially recorded signal. It is designed to pick
out rhythmic features in the raw signal. One advantage of the algorithm
is that it is applicable across a broad range of frequencies. Combined
with the fact that intracranial recordings bypass the low-pass
filtering of the skull and scalp, this allows us to apply the same
analyses over the 2- to 45-Hz range. We first take advantage of the
method by comparing the incidence of oscillatory episodes at different
frequencies. We show that electrodes tend to have a specific
"characteristic" frequency (sometimes more than one; sometimes
none), but most of the oscillations during a virtual maze learning task
occur within the theta band. We improve on the maze-length analysis reported in earlier work (Kahana et al. 1999). In these
studies, we confined ourselves to the 4- to 8-Hz theta band, and our
analyses could not be used to compare across frequency. With the new
oscillatory episode detection algorithm, we are able to apply the
maze-length analysis similarly across a broad range of frequencies and
show that the maze-length effect occurs primarily within the theta band.
A limitation of the maze-length analysis is that maze length was an
experimental manipulation of task difficulty. It is
desirable to examine the electrophysiological correlates of behavioral
measures that relate to learning. Therefore we examined participants'
response times as they navigated through the mazes. This measure of
response time, mean decision time per trial, has already been shown to correlate with learning (Kirschen et al. 2000). Mean
decision time has also been shown to be a better correlate of learning than the number of errors committed (Kirschen et al.
2000
) (also see RESULTS). We show that the patterns
of oscillations that correlate with decision time are different from
those that correlate with maze length.
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METHODS |
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Participants and recording
We recorded from five patients with drug-resistant epilepsy.
When the location of the seizure focus is unclear, patients undergo invasive monitoring to localize the focus. Intracranial platinum electrodes (3-mm diam) are placed subdurally in many brain regions to
test hypotheses about the localization of the seizure focus as well as
mapping functional regions to be avoided in surgery. 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
Board at Children's Hospital, Boston. We sampled a total of
345 electrodes across five participants. iEEG signal was sampled at 200 Hz (Telefactor apparatus, band-pass filter: 0.5-100 Hz) for
participants 1-4 and at 256 Hz (BioLogic apparatus,
band-pass filter: 0.3-70 Hz) for participant 5. The locations of the electrodes were determined from co-registered computed
tomograms and magnetic resonance images by an indirect stereotactic
technique (Talairach and Tournoux 1988). Electrodes overlying regions of known lesions or seizure onset zones were excluded
from analysis (a total of 91 such electrodes were excluded).
All recording sites are plotted in Fig. 1, and further information about our patients is given in Table 1.
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Task and procedure
Participants learned to navigate multiple T-junction mazes consisting of T junctions linked by corridors (Fig. 2). All corridors were of equal length. The sequences of successive turns were constrained to prevent the path from crossing itself (this happens with 3 successive left turns or right turns). We also eliminated paths that contained the subsequence right-left-right-left-right-left (or its complement) to avoid especially easy paths. Invisible walls prevented participants from moving down incorrect corridors.
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Participants navigated the maze in a highly constrained fashionone
keystroke (up arrow) moved the participant's view down a corridor (the
participant's virtual position was always on the midline of the
corridor) and one keystroke (left or right arrow, respectively) turned
the view 90° to the left or right. The keyboard buffer was cleared
following each movement so that holding down a key had the same effect
as pressing it once briefly. Turns immediately followed the keystroke,
while forward movements occurred over 280 ms. In some cases, this delay
was filled with optic flow (the view was redrawn multiple times as the
participant's view moved along the corridor, simulating virtual
movement); in others, there was no illusion of motion and the
participant's view moved to the T junction following the delay.
Participants traversed a maze in two distinct cueing modes: initially, four consecutive trials were presented in study mode, in which arrows were placed on the walls to indicate the correct path. Participants then traversed the same maze in test mode, with the arrow cues removed, forcing them to rely on a learned representation of the maze. Participants navigated a maze repeatedly until they reached a performance criterion: namely, three consecutive errorless traversals. Task difficulty was manipulated by varying path length, which we term maze length. After a practice block of four eight-junction mazes (we excluded these mazes from our analyses), maze length was either short (sequences of 6 left and right turns) or long (sequences of 12 left and right turns) mazes.
Detecting oscillatory episodes
We designed the oscillatory episode detection algorithm to identify times within the signal that exhibited high-power rhythmic activity at a particular frequency, lasting a few cycles. We therefore defined an oscillatory episode at a particular frequency, f*, as a duration longer than a time threshold, DT, during which power at frequency f* exceeded a power threshold, PT. We selected the two parameters, PT and DT, as follows.
We moved to the frequency domain by wavelet transforming the raw traces
[Morlet wavelet, window = 6 cycles (Grossmann and Morlet
1985)]. Frequency was sampled logarithmically because with wavelets, the relative bandwidth is preserved; 24 frequency steps were
used in the range 1-54 Hz. The wavelet transform gave us wavelet power
as a function of time at each frequency of interest.
To select the threshold, PT,
separately for each frequency at each recording site, we first assumed
that the background spectrum was "colored noise," with the form
Af, a
general property of natural, autocorrelated signals (Schlesinger and West 1988
). We wanted to set
PT in such a way as to exclude this
background signal. We therefore fit the theoretical
Power(f) = Af
function
to the actual power spectrum over the 1- to 54-Hz range at each
electrode. The curve fitting was done by log-transforming the average
wavelet spectrum and then computing a linear regression on these
values. The intercept is then equal to log (A) and the slope is equal to the exponent,
. The fit value at frequency f* was taken to be the mean of the
2(2) distribution of wavelet power at that
frequency (Percival and Walden 1993
). The parameter,
PT, at a given frequency, was set to
the 95th percentile of the cumulative distribution of this fit
2(2) function to exclude 95% of the
background signal.
The duration threshold, DT, at frequency f* was set to three cycles [i.e., 3(1/f*)] to eliminate artifacts and physiological signatures that were nonrhythmic.
Finally, we introduce the measure:
Pepisode(f),
percent trial time in episodes.
Pepisode(f)
was defined as the total amount of time during which episodes occurred
at frequency f divided by the total time in trial.
P was defined as the union of all
Pepisode(f)
time in the 4- to 8-Hz band (5 logarithmically spaced frequency values).
In subsequent analyses, we excluded frequencies at the ends of the 1- to 54-Hz range; our range of interest was 2-45 Hz. This was done to keep clear of the band-pass filtering of the amplifiers at the low-frequency end and to avoid contamination by the 60-Hz line noise artifact at the high end.
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RESULTS |
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In all analyses, significance thresholds were chosen to obtain a low estimated Type I error (false positive) rate. Between-subject variability in electrode placement and possible reorganization of function due to the pathology of the subject pool make it meaningless to average over anatomical regions with only five participants. Therefore it is not possible to generalize the localization of the task dependencies; however, at this stage, we are primarily interested in the overall pattern of results; hence, we ensured that the hit rate exceeded by far a conservative estimate of false positive rate.
Behavioral data
Participants learned to navigate virtual, multiple T-junction
mazes. Participants were first presented with four
study-mode trials, where an arrow (a visual cue) appeared at
each junction, denoting the correct path. Then participants were
required to navigate the maze in test mode without the aid
of the arrow cues. They continued until they reached a criterion of
three perfectly navigated consecutive test trials. We manipulated the
number of junctions in the maze, a variable that has a profound effect
on task difficulty (Kirschen et al. 2000). Across
participants, mean number of test trials required to reach the learning
criterion (not including the three criterion trials) was 8.66 ± 3.31 (mean ± SE) for long (12 junction) mazes and 0.58 ± 0.10 for short (6 junction) mazes.
To test the hypothesis that oscillations are related to learning, we
wanted a sensitive measure of degree of learning. The first candidate
is the number of erroneous turns committed. However, in a T-junction
maze-learning task, or any binary sequence-learning task, the guess
rate for each element of the list/path is 50%. Furthermore, once a
wrong turn is made, the participant either corrects the error by making
two successive turns in the opposite direction or turns around and
becomes disoriented in the maze and makes many more errors. Thus, after
the first error it is hard to evaluate the quality of memory for the
subsequent choice points (Kirschen et al. 2000). For
two-alternative forced choice tasks, measures of inter-response times
(IRTs) are especially useful for tapping performance levels even when
response accuracy is perfect, and have been applied extensively in the
literature (Kahana and Loftus 1999
). We therefore chose
to use the mean response time at choice points, decision
time, as a measure of performance (Fig.
3). Consistent with the foregoing
reasoning, earlier work demonstrated decision time to be a more
sensitive measure of learning than the number of errors committed;
learning curves that were significant in decision time were
nonsignificant in number of error turns (Kirschen et al.
2000
). Finally, the decision time measure allows us to compare
perfect traversals of mazes, which, while they vary in demands on
encoding, are matched for number of keystrokes.
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Oscillatory episodes
As reported previously (Kahana et al. 1999), theta
oscillations appear in the raw, unfiltered human iEEG signal. Figure
4 shows sample traces that show
theta-band oscillations in selected electrodes from each of the five
participants. We developed an algorithm to detect such episodes of
oscillatory activity with two significant objectives in mind. First, we
wanted the algorithm to tag signals that looked like rhythmic activity
in the raw trace (as does theta activity in rodents). Second, we
developed our method to be unbiased across frequency to be able to
compare oscillatory activity at different frequencies. Previous
analyses of oscillatory activity in humans and in rodents have tended
to focus on measures of average power (Benington et al.
1994
; Mendelson et al. 1980
; Pradhan et
al. 1993
; Roncagliolo and Vivaldi 1991
;
Smith et al. 1979
). These methods have been applied
without controlling for the levels of oscillatory activity that one
would expect by chance. We present an oscillatory-episode detection
algorithm that exploits the fact that theta episodes, runs of several
cycles of theta-band activity, appear visibly in the unfiltered signal
(see METHODS). By taking advantage of the known properties
of background EEG and using these to tailor the method in the same way
at each frequency, the method allowed us to fairly compare oscillatory
episodes across frequencies. First, this allowed us to demonstrate that
oscillatory episodes within the theta band are a prominent feature of
our signal at many electrode locations. Second, we were able to compare incidences of the identified oscillatory episodes with task variables.
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We designed our oscillatory episode detection algorithm to be able to identify times within the iEEG record that exhibited high-power rhythmic activity lasting a few cycles. We wanted the algorithm to be relatively insensitive to sharp transients as found in epileptic EEG. The method requires two parameters. An oscillatory episode at a particular frequency, f*, was defined as a duration longer than DT cycles during which power at frequency f* exceeded PT (see METHODS).
We selected the threshold, PT,
separately for each frequency at each recording site to make the method
comparable across frequency, brain region, and subject. This selection
of PT relied on the ubiquitous
property of background EEGthat the background power spectrum is
colored noise, with the form
Af
, where
f is frequency and A and
vary. The exponent
generally takes on values between 1 and 2; in our case, mean value,
compiled across participants, was 1.69 ± 0.31 (mean ±
).
Colored noise is a common property of a wide range of natural
(including biological) signals (Schlesinger and West
1988
). Power spectra indeed show this kind of background (Fig.
5A,
), and sometimes have
superimposed peaks (Fig. 5B,
). These peaks likely occur
because oscillations are present in the signal. Therefore we fit the
function Af
to the wavelet power spectrum (e.g., Fig. 5, - - -). The duration threshold, DT, at frequency
f* was set to three cycles [i.e.,
3(1/f*)]. This biased the algorithm to tag
oscillations consisting of sustained runs of rhythmic activity.
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By choosing the thresholds in this manner, we could estimate the effect
of the episode detection algorithm on "background EEG." A power
threshold PT = 95th percentile should,
on average, eliminate 95% of the background signal. This would
set an upper limit (on average) on the value that
Pepisode(f*)
could takenamely, 5%. However, by adding the duration threshold,
DT = 3 cycles, this 5% high power not
only has to be present, but it has to cluster in time to be
able to surpass the DT. Therefore a
good benchmark for identifying a significantly high amount of
oscillatory episodes at a given frequency is
Pepisode(f*) > 5%.
The oscillatory episode detection algorithm agreed well with visual inspection. Figure 4 also illustrates the performance of the algorithm. Note that the algorithm picked out oscillatory episodes with the parameters used in this example (PT = 95th percentile, DT = 3 cycles).
Frequency tuning of oscillatory episodes
If oscillations are present at particular "characteristic" frequencies, this should be evident as a local peak in the incidence of oscillatory episodes relative to nearby frequencies. In the episode-detection method we used, the parameters are selected for each wavelet frequency and electrode location individually, in a manner that is minimally biased with respect to the actual (experimental signal) distribution of wavelet coefficients. This makes it possible to compare the amount of oscillatory activity at one frequency to nearby frequencies.
By plotting the function
Pepisode(f)
(Figs. 6 and
7), one can see that there is
indeed a significant peak at certain electrode locations, in the theta
band (Figs. 6 and 7, A-C) as well as outside the theta band
(Fig. 7, D-F). In some cases, multiple peaks can be seen
(Fig. 7, G and H). While it is possible that
multiple peaks are evidence of the fine spectral structure of a complex waveform, this is not always the case. For instance,
Pepisode at the two peak frequencies
at the site in Fig. 7G shows statistical independence over
maze trials [r(114) ± 0.022, NS]. In still other
sites, there is no pronounced peak (Fig. 7I). Figure 6 also illustrates the robustness of the frequency dependence to the choice of
the two analysis parameters (PT and
DT).
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We next addressed the question of whether oscillatory episodes occur
predominantly within the theta band or comparably across the broad band
examined. We simply evaluated the mean
Pepisode(f*) against the value 5% (the estimated maximum
Pepisode value for background signal).
Figure 8 shows how many electrodes show
significantly high
Pepisodes(f*) at
each frequency (2-tailed t-test, P < 0.001). It is evident that oscillatory episodes are most prevalent
within the theta band. The nonuniformity of the distribution of
Pepisode by frequency is significant
for all participants [2(22) = 223, 103, 588, 162, and 520 for participants 1-5, respectively; P < 10
14].
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Because we controlled for the background colored noise spectrum, the predominance of oscillatory activity in the theta band cannot be explained by the general tendency for lower frequencies to exhibit larger amplitudes. The episode-detection algorithm successfully identifies episodes of oscillatory activity at different frequencies and is robust to the choice of parameters. Different brain regions show different dominant frequencies, and in some cases show multiple "active" frequencies. Still, at this stage, the theta band is the most well represented band at the electrode locations sampled.
Dependence of theta episodes on task parameters
We have found significantly elevated levels of both theta and non-theta oscillations during this task (Figs. 4-8). If it can be shown that these oscillations are correlated with variables known to have a strong influence on behavior, this would suggest that theta oscillations reflect an important feature of human cognition. We therefore examined the dependence of Pepisode on two important task variables, maze length and decision time (see behavioral results in the preceding text).
MAZE-LENGTH EFFECT. We examined how Pepisode varied with maze length (number of junctions) during perfect traversals of mazes, in both the training phase and test phase. Trials containing errors were excluded from this analysis because these trials were virtually absent in the short mazes, and thus, any difference observed between the short and long mazes could be carried by these error trials. Furthermore, on a significant number of error trials, participants became lost in the maze, and rather than trying recall or learn the sequence spent their time trying re-orient themselves.
As shown in Fig. 9, many sites showed significantly greater Pepisode during long mazes than during short mazes (P < 0.0005 by a 2-tailed Mann-Whitney U test). This maze-length effect is seen for study trials as well as test trials1 and at a broad range of frequencies; however, the effect is predominantly found in the theta and alpha bands. It is striking that only one electrode exhibited more theta-band oscillatory activity in short mazes than in long mazes.
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DECISION-TIME EFFECT.
The average amount of time a participant takes at a maze junction,
decision time, is a good measure of learning at a given maze
length and is a more sensitive probe of learning than the number of
wrong turns a participant makes (Kirschen et al. 2000) (also see Behavioral results). We sought to determine
whether this variable was related to oscillatory activity, independent of maze length. If the maze-length dependent theta we observed were
directly related to the amount of encoding or retrieval difficulty during a trial, we should see an increase in theta per unit time with
increasing decision time.
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DISCUSSION |
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Distinct patterns of task-related oscillations
We have shown that oscillatory episodes occur in relation to two
task variablesmaze length, a manipulation of task complexity, and
decision time, a measure of encoding/retrieval demands. We found both
effects at many different frequencies and at a variety of brain
regions. The maze-length effect was found primarily within the theta
and alpha bands, while the decision-time effect was primarily found in
the delta, beta, and gamma bands, with relatively little effect found
in the theta band. The different frequency characteristics of the two
effects suggests that the two analyses are functionally distinct
that
they tap quite different cognitive operations.
Possible roles of theta oscillations
Our findings rule out a number of competing accounts of the role
of theta in our maze-learning task. According to one hypothesis, increases in the rate of key presses produce increased theta activity. Because the key-press rate and associated virtual movement is faster in
short mazes than in long mazes, this would produce the opposite
maze-length effectnamely, more theta in short mazes than in long
mazes. Furthermore this account would predict a negative correlation
between decision time and theta activity, and again, little support was
found for this effect.
A follow-up account is that theta is related not to key presses or virtual movement but to time spent in encoding and retrieval, specifically when the participant is not moving through the maze. This account would predict the strong positive maze-length effect which we observed. However, it would also predict an even stronger positive decision-time effect, which was not found.
Further supporting evidence comes from analyses of oscillatory activity
during a symbolic working memory task that failed to relate theta-band
oscillatory activity to keystrokes or stimulus presentation alone
(Raghavachari et al. 2001).
The finding of a robust theta-band maze-length effect led us to
hypothesize that the presence of theta oscillations were related to the
encoding or retrieval demands on the participant. However, the notable
absence of a decision-time effect (both in
Pepisode and in average power)
contradicted this hypothesis. While it is possible that properties of
theta that we have not analyzed (e.g., frequency, phase-locking of
behavior) might covary with demands on encoding or retrieval, the
results still present a problem. We propose the following resolution.
We suggest that theta oscillations are conducive to specific
types of encoding (Hölscher et al. 1997;
Huerta and Lisman 1993
; Larson and Lynch
1989
; Larson et al. 1986
; Pavlides et al.
1988
). However, theta oscillations are invoked not by the
immediate demands of the task but by the overall demands of
the task. It is plausible that the brain cannot anticipate the
cognitive demands at the time scale of hundreds of milliseconds. We
suggest that instead, theta oscillations are invoked by the general
demands of the task at hand, on the time scale of learning a maze. This
would account for the presence of a maze-length effect combined with
the relative lack of a decision-time effect in theta. This account is
also consistent with theta being present in rodents during diverse
types of exploratory behavior; the presence of theta tells us only that
encoding and retrieval are facilitated, but tells us little of
how much of these cognitive operations is achieved.
Interpreting task-related oscillations in patients with epilepsy
Recording intracranially from epileptic patients affords a number
of significant advantages over scalp-recorded EEG in normal participants (Sperling 1997), including freedom from
eye-movement artifacts, improved spatial resolution, and absence of the
filtering of the skull and scalp as well as access to ventral brain
regions. Other groups have successfully used intracranial recordings
from human participants to investigate cognitive function including memory (Fernandez et al. 1999
; Guillem et al.
1996
) and language (McCarthy et al. 1995
) as
well as mapping the topography of the oddball (P300) potential
(Halgren et al. 1980
; Smith et al. 1990
). However, there are some important caveats to iEEG research.
First, in addressing the issue of localization, it is important to keep in mind that the placement of electrodes is determined by the clinical team in order to identify the locus of seizure onset. When the seizure focus is unclear, large numbers of subdural electrodes, placed directly on the cortical surface, can be especially informative. Because the focus is often in temporal lobe, there is a sampling bias toward temporal regions. The necessary clinical bias in electrode placement means that the data obtained from any given patient give limited information about the locations associated with a cognitive function. To obtain a complete picture of the topography of brain activity associated with cognitive function, one must assess patterns across a large number of participants with varying electrode coverage. For the results presented here, therefore, it is premature to draw conclusions as to the general loci of human task-related oscillations.
It is also crucial to ask whether our results might be a direct result of the pathology of the subject pool. This is especially pertinent given that epilepsy is a disorder of rhythmic activity, and it is precisely rhythmic activity that we focus on in this paper.
There are several arguments against the possibility that our findings
are related to epilepsy. First, we excluded sites involved in seizure
onsets as well as electrodes placed over known lesions. Second, at a
large number of sites, the incidence of oscillations covaried with the
task, and these sites showed a high degree of spatial localization, a
pattern quite the opposite of what one would expect from an
uncontrolled process like epilepsy. Third, participants performed the
task at levels similar to university undergraduates, indicating that
the brain regions responsible for performing the maze learning task
were functioning well. Fourth, sleep spindles, a different kind of
theta-band oscillatory activity, have been recorded intracranially in
epileptic patients and shown to be both similar to spindles in normals
and unrelated to epilepsy (Malow et al. 1999),
demonstrating that it is possible to observe rhythmic activity in
epileptic participants that is not a result of their pathology.
Finally, some evidence suggests that theta oscillations may in fact act
to inhibit seizure onset in vivo (Miller et al.
1994
) and that theta in vitro is insensitive to osmolality, a
variable that affects patients' susceptibility to seizures
(Osehobo and Andrew 1993
). This kind of evidence
dissociating theta oscillations and seizure activity supports the
notion that the task-related rhythmic activity observed in our patients
is not a consequence of their pathology.
Episode detection algorithm
Previous algorithms have been developed for analyzing oscillatory
activity in rodents or theta-band power in humans as well as for
analyzing sleep (both human and nonhuman), where rhythmic activity can
be seen in raw scalp EEG signal. These algorithms have focused on
measures of average power (Basar-Eroglu et al. 1992;
Benington et al. 1994
; Gavrilov et at.
1995
; Intrilligator and Polich 1994
;
Klimesch et al. 1994
; Yordanova and Kolev
1998
) or zero-crossing algorithms (Berry and Thompson
1978
; Roncagliolo and Vivaldi 1991
) or measures
not directly related to the rhythmic nature of the signal
or else have
assumed that an underlying oscillation is present and proceeded to
analyze its phase relative to task variables (Burgess and
Gruzelier 1997
; Givens 1996
; O'Keefe and Recce 1993
; Tesche and Karhu 2000
;
Yordanova and Kolev 1998
). Instead, we developed an
algorithm that would allow us to ask whether a site expressed
significant levels of oscillatory episodes at a given frequency,
relative to a reference spectrum. In this way, we could analyze
oscillatory episodes in a manner that was not biased across frequency,
electrode location, or subject.
The algorithm presented here makes use of known properties of the
background EEG to identify significantly elevated runs of oscillatory
activity. It has the desired properties and, importantly, agrees with
visual inspection (Fig. 4) and is robust to choice of analysis
parameters (Fig. 6). Our method takes advantage of the relative clarity
of the signal obtained from invasive recordings in humans and the fact
that the rhythmic activity can be seen in the unfiltered trace.
Additionally, the algorithm is simple in that it is an extension of
average power analyses but overcomes some important biases of power
spectrum analyses, including estimating background levels (Plett
2000) and requiring a sustained run of elevated power. The
algorithm allows us to compare oscillatory activity across frequencies
and favors frequencies that show spectral peaks.
Oscillatory episodes at different frequencies
When interpreting analyses of oscillatory episodes at different frequencies, it is important to consider the following. The iEEG is dominated by a colored noise background spectrum, which means that activity at lower frequencies will tend to be of higher amplitude than activity at higher frequencies and will therefore dominate the unfiltered trace. We applied the analysis methodology at each frequency using an estimate of the background spectrum. This is similar to "prewhitening" the signal or weighting the analyses equally across frequency. Therefore, results obtained at higher frequencies may be just as statistically significant as those obtained at lower frequencies; however, the higher-frequency oscillations will not tend to dominate the raw signal. Depending on the theoretical questions being addressed, one may wish to favor either oscillations of large amplitude or oscillatory activity that shows a highly significant relationship to the experimental paradigm. At present we are more interested in how oscillatory activity might be related to cognition, rather than the physiological mechanisms by which it might be generated; therefore we focused on oscillations that covary significantly with important task variables. Nonetheless, we found that oscillations occurred more in the theta band than at other bands in general during the task (Fig. 8). Further, the theta band appears to be the predominant band at which oscillations covary with maze length (Fig. 9) but not decision tune (Fig. 11). Hence, oscillations that do in fact tend to be large in amplitude are also the most prevalent and dominate one of the task dependencies analyzed here.
Summary and conclusions
We analyzed oscillatory episodes in human iEEG signal recorded during a virtual maze-learning task. We asked whether the oscillations recorded intracranially in humans bore a consistent relationship to two basic task variables, maze length and decision time. We presented an algorithm to detect episodes of oscillatory activity. This algorithm and the clarity of the iEEG recordings made it possible to demonstrate that oscillatory episodes in our experiment occurred predominantly in the theta band (Figs. 6 and 8), tying our observations more closely to studies of both rodent and human theta-band results.
At a large subset of recording sites, the incidence of theta episodes increased with maze length (Fig. 10). However, inconsistent with the notion that these theta oscillations reflect the amount of encoding or retrieval, theta oscillations did not tend to covary with decision time, a measure of the immediate demands on encoding and retrieval. This suggested to us that theta oscillations might be a physiological state that is conducive to certain types of encoding but that theta is invoked to differing degrees based on the general demands of the task (e.g., how difficult the task is) but not by the immediate cognitive demands. Gamma and delta oscillations did covary with decision time. These oscillations may reflect specific cognitive operations involved in integrating new information into memory.
The precise functions of patterns of oscillations during virtual maze
learning remain unknown. Based on the rodent literature, one might
expect that theta-band oscillations in particular are related to
movement within the virtual space. As human theta oscillations have
been observed in a symbolic working memory task using intracranial recordings (Raghavachari et al. 1999) and theta has been
shown to be related to a similar task using MEG recordings
(Tesche and Karhu 2000
), this activity could not be
exclusively related to the virtual navigation aspects of the
task; however, it is still possible that theta plays an additional,
unique role in tasks that rely on spatial cognition.
In the rat, theta has been shown to act as a dynamic window for the
induction of long-term potentiation and depression at the synaptic
level (Hölscher et al. 1997; Huerta and
Lisman 1993
, 1996
; Larson and Lynch 1989
;
Pavlides et al. 1988
). This raises the possibility that
oscillations, especially in the theta band, are related to encoding
and/or retrieval operations. However, in any memory task, including our
maze-learning task, these operations are impossible to separate;
learning strategies may in fact actively confound encoding
and retrieval as in the case of rehearsal. Therefore we cannot
distinguish oscillations related to encoding versus retrieval.
Oscillations at high frequencies (beta and gamma bands) are thought to
play a role in perception (Demiralp and Basar 1992; Eckhorn et al. 1988
; Gray and Singer
1989
; Gray and Viana Di Prisco 1997
;
Gruber et al. 1999
; Keil et al. 1999
;
König et al. 1995
; Miltner et al.
1999
; Neuenschwander and Singer 1996
;
Tallon-Baudry et al. 1996
-1998
), motor behavior
(Hamada et al. 1998
), and memory (Buzsáki
1996
; Csicsvari et al. 1999
; Jensen and
Lisman 1998
; Lisman and Idiart 1995
). The
high-frequency oscillations we observed might be related to perception
or to memory operations or possibly to complex motor programming
(although we eliminated the hypothesis that these oscillations are
simply related to key pressing).
Finally, oscillations of varying frequencies may act together to perform multiple complex cognitive functions.
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ACKNOWLEDGMENTS |
---|
Thanks are due to the members of the Computational Memory Lab, A. Kepecs and J. Lisman, for helpful comments. We thank S. Sternberg for suggesting that we look for independent involvement of oscillations in behavior. We are most grateful to the patients and their families for their participation and support.
This research was funded by National Institute of Mental Health Grant MH-55687.
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FOOTNOTES |
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Address for reprint requests: M. J. Kahana, Volen Center for Complex Systems, MS 013, Brandeis University, Waltham, MA 02254-9110 (E-mail: kahana{at}brandeis.edu).
1 Because the task is self-paced and participants are directed through the maze during the first four trials, the task blurs the distinction between study and test. On each maze traversal after the very first, participants are engaged in both learning the maze sequence and attempting to retrieve the information learned on previous traversals from memory. We nonetheless analyzed these trials separately and found no systematic differences. Therefore, we do not plot analyses of study and test trials separately in the figures.
Received 20 September 2000; accepted in final form 6 March 2001.
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